This paper aims to identify and compare changes in trends and research interests in soccer articles from before and during the COVID-19 pandemic.We compared research interests and trends in soccer-related journal articles published before COVID-19 (2018–2020) and during the COVID-19 pandemic (2021–2022) using Bidirectional Encoder Representations from Transformers (BERT) topic modeling.In both periods, we categorized the social sciences into psychology, sociology, business, and technology, with some interdisciplinary research topics identified, and we identified changes during the COVID-19 pandemic period, including a new approach to home advantage. Furthermore, Sports science and sports medicine had a vast array of subject areas and topics, but some similar themes emerged in both periods and found changes before and during COVID-19. These changes can be broadly categorized into (a) Social Sciences and Technology; (b) Performance training approaches; (c) injury part of body. With training topics being more prominent than match performance during the pandemic; and changes within injuries, with the lower limbs becoming more prominent than the head during the pandemic.Now that the pandemic has ended, soccer environments and routines have returned to pre-pandemic levels, but the environment that have changed during the pandemic provide an opportunity for researchers and practitioners in the field of soccer to detect post-pandemic changes and identify trends and future directions for research. Furthermore, the repercussions and revenues generated by soccer worldwide not only affect sports but also have social, economic, and even cultural impacts . Asken aHowever, on March 11, 2020, the World Health Organization (WHO) declared the highest pandemic alert for COVID-19, specifically categorizing it as a “pandemic”  and warnThe COVID-19 pandemic has majorly impacted professional soccer around the world. In Europe, most leagues temporarily suspended play in 2020–2021 to help contain the spread of the virus . For a tFor example, since the temporary lockdown, leagues have had to play more games than usual in a short period of time to finish on time, and there have been reports of a noticeable increase in player variability and injuries due to long breaks and a lack of training . StudiesA careful examination of the arguments so far suggests that the COVID-19 pandemic has caused many changes in the soccer landscape and that soccer-related research has also been affected. This observation implies that we can detect some changes in soccer research before and after the pandemic, but we need specific solutions to analyze them more objectively and quantitatively. The development of soccer is growing quantitatively and qualitatively with an exponential increase in research and a multidisciplinary approach. A significant amount of research and empirical reporting is published in academic articles, but extracting this extensive and heterogeneous of knowledge is a complex task. While analyses such as Delphi and meta-analyses are essential, they do not fully capture the scale, scope, and complexity of soccer research. Therefore, it is necessary to consider data science-based approaches to organize the domain knowledge of a large and specific field . KeywordUsing topic modeling to study research trends can provide useful information along with new ideas for future research , 2022. TIn this study, we use topic modeling with journal articles to analyze the knowledge structure of soccer-related research published before (2019–2020) and during (2021–2022) the COVID-19 pandemic. By comparing the content of the research agendas for the two periods, we aim to determine if there are differences in academic and research interests in soccer before and during the COVID-19 pandemic. In other words, we contrast the trends in soccer-related research topics during the two periods to provide insights into changes, future predictions, and implications.Therefore, this study aims to comprehensively identifying and comparing the changes in research interests, topics, and trends from before COVID-19 to during the COVID-19 pandemic.To address the research aims, the research question was set as follows.Q1. What are the topic areas of soccer research before, during, and after the COVID-19 pandemic?Q2. What are the topical areas of soccer research organized from the extracted topics?Q3. How have the topics and topic areas of soccer research changed before, during the COVID 19 pandemic?2.2.1.In topic modeling, document embedding is used to convert the raw text of a document into a mathematical representation that can be compared semantically, i.e., documents containing the same topic are semantically similar. This technique is commonly used in topic modeling, which is the process of identifying latent topics in a corpus of text documents. To perform the embedding step in this study, we used BERTopic’s Sentence-BERT (SBERT) , which i2.2.N is the number of documents, One weighting method for representing text is term frequency–inverse document frequency (TF–IDF). The basic idea is to create a vector for each document in the corpus, where each element of the vector represents a specific feature or term of the document. The value of the element is typically a weight that reflects the importance of the feature in the document. A TF–IDF value is determined according to the relative frequency of a word in a specific document based on its frequency in the document and its rarity across all documents in the corpus. The TF–IDF is given by  word i.2.3.Topic modeling is an analytical, unsupervised learning model that discovers topics in a corpus. In this sense, a topic can be defined as a repeated pattern of terms . RecentlInitially, BERTopic works by generating dense vector representations of each document in a corpus using BERT or SBERT; we take the sentence embedding from our documents. These embeddings, however, are primarily used to cluster semantically similar documents and are not directly used in generating the topics.Primarily, the BERTopic technique utilizes two algorithms: uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) . UMAP reThe second algorithm, HDBSCAN, is a powerful clustering algorithm that can automatically determine the number of clusters in the data, making it a valuable tool for unsupervised text analysis. Additionally, HDBSCAN can handle noise and outliers, which is particularly important when dealing with large and complex datasets. HDBSCAN provides a robust and efficient method for unsupervised topic modeling in social science research. By leveraging advanced NLP techniques and clustering algorithms, BERTopic can capture the nuances and subtleties of natural language and produce highly interpretable topics that can provide deep insights into the underlying structure and meaning of textual data.c is A is the average number of words per class, and The standard TF–IDF equation is used to calculate the importance of each term within each topic rather than across the entire corpus. In contrast, c-TF–IDF defines the importance of a word within a class (topic)  and treaFinally, by calculating the c-TF–IDF score for each term within each topic, BERTopic can identify the most important terms for each topic rather than across the entire corpus. These important terms can then be used to label and interpret the resulting topics, providing a more accurate and specific representation of the underlying themes and trends within the data.3.3.1.Soccer studies before and during the COVID-19 pandemic were collected by searching the Web of Science (WOS). Articles were collected from the following indexes: Science Citation Index, Science Citation Index Expanded, Social Sciences Citation Index, and Arts & Humanities Citation Index. Keywords were used in the searches, including “soccer” and “football.” The research data from before COVID-19 included 3,956 studies from 2019 to July 2020, and the research data from the period during the COVID-19 pandemic included 3,423 studies from 2021 to July 2022. Irrelevant articles were excluded from the collected data.First, we excluded papers regarding “football” that focused on the National Football League (NFL) in the United States and the Australian Football League (AFL) in Australia. However, articles that presented a comparative approach to soccer were allowed. Articles that were not journal articles , did not have an abstract, or were written in a language other than English were excluded. The research flow and procedures for this study are shown in 3.2.This paper proposes a two-step approach for topic modeling in soccer data. The first step involves pre-processing the data to extract relevant information and enhance the original data representation, while the second step uses BERTopic to generate topics that preserve the semantics of the original data representation. The BERTopic algorithm is based on word-embedding models and is capable of identifying relevant topics in the data . SpecifiData Cleaning: We extracted only English text from the research data using regular expressions to minimize noise in the dataset that might impact topic extraction. We converted all textual contents within the dataset into lowercase to ensure consistency.Stemming: We applied the Snowball Stemmer technique to reduce the word variation in the data and reduce the number of unique tokens in the dataset . The remStop-Word Removal: We removed common stop words such as “the,” “and,” and “in,” which do not carry significant meaning. Likewise, we filtered out generic words  that were frequently observed in the articles but did not contribute to the creation of semantically consistent topics. To edit the stop-word list, we utilized NetCorp’s library to modify the stop words. In this way, we ensured that only the words with a single meaning were included in the dataset.Vectorization: Next, we converted the pre-processed text into a numerical format that can be used for analysis, which is embedding. Each document was embedded using the implemented BERT model. We used SBERT to manufacture high-quality contextual word and sentence vector representations.Topic Modeling: Finally, we used BERTopic to perform topic modeling on the pre-processed and vectorized soccer data. BERTopic is a state-of-the-art topic modeling algorithm that utilizes transformer-based language models such as BERT or Sentence-BERT to generate document embeddings, which are then clustered using hierarchical clustering algorithms such as HDBSCAN. This process results in a set of topics, each with a list of important terms and their corresponding weights, which can be interpreted and labeled by the researcher.In summary, the methodology used in this study consisted of data cleaning, stemming, stop-word removal, vectorization using BERT, and topic modeling using BERTopic. This method enabled us to perform topic modeling on the soccer data systematically and efficiently, while also accounting for potential noise and variations in the data. We implemented the pre-processing steps because we believed they would be beneficial for reducing noise and variation in the data while also having a negligible impact on the semantics of the soccer data. The resulting topics and insights can be used to inform decision-making and analysis in the field of sports and medicine.As a result of the above method, 47 topics were generated in 8 Topic Areas from the pre-COVID-19 period, and 51 topics were generated in 8 Topic Areas from the period during the COVID-19 pandemic. Clusters of keywords with unclear identities were deleted from the generated topics . TherefoFrom the topic modeling results, we first examined the topic areas and keyword clusters of soccer-related articles published before the COVID-19 pandemic; second, we examined the topic areas and keyword clusters of soccer-related articles published during the COVID-19 pandemic. Then, we defined the research topics using the topic areas and clusters mapping the research areas in the two periods and deduced the changes in topic approaches and future directions through comparison.4.4.1.The soccer research Topic Areas for the pre-COVID-19 period (2018–2020) and the keyword composition for each Topic Area are shown below. The results of topic modeling for soccer research in the pre-COVID-19 period are shown in Topic Area-1 was labeled “Social Sciences-1: Psychology & Sociology” related to soccer and includes the following Topics. Topics 7, 15, and 24 were identified as psychological topic areas. Specifically, Topic 7 was clustered with keywords about team or athlete efficacy based on the coach’s leadership style. Topic 15 was clustered with keywords that explored various variables related to parents that affect youth and children soccer players. Topic 24 was clustered from straightforward keywords like psychology to keywords about soccer player motivation and burnout. Topic 38 was clustered around validated interventions for soccer players’ mental health, with keywords ranging from players’ social skills to mental-healthcare-related smartphone applications .On the other hand, Topics 10 and 46 were found to be clustered around soccer-related sociology topics. Topic 10 clustered keywords related to identifying the social and environmental determinants  of the relative age effect (REA) on soccer players. Topic 46 clustered research topics related to soccer player talent identification and development; we also found one soccer business and one technology topic in this Topic Area. Topic 37 was clustered with keywords related to identifying changes in soccer game decision-making following the introduction of video assistant referee (VAR) systems, and Topic 41 was clustered with keywords related to exploring market value determinants (such as foreign players) of each club in professional soccer leagues.Next, Topic Area-2 has the domain name “Social Sciences-2: Business & Sociocultural” as related to soccer and includes the following Topics. Topics 8, 23, and 29 were identified as detailed research topics related to the soccer business. Topic 8 was clustered with keywords exploring the determinants of stadium attendance in soccer leagues. Topic 23 was clustered with keywords related to evaluating the financial performance of a soccer league or club. Topic 29 was clustered with keywords representing topics such as sponsor, sponsorship, and loyalty that affect the formation of a fan’s brand image of a club or league. Topics 0 and 22 were identified as sociocultural themes. Topic 0 was clustered into thematic keywords dealing with soccer fans’ culture and political ideology, and Topic 22 was clustered under topic keywords dealing with sociological issues, including the perceptions, stereotypes, gender roles, and identities of female soccer players.Topic Area-3 was labeled “Sport Science & Technology” as related to soccer and includes the following Topics. Topic 5 was clustered with keywords related to data analysis, which is related to team performance topics such as ball possession, shots, and score. Topic 9 was clustered with keywords representing research related to power analysis using a team’s pass network and prediction model. Topic 12 was clustered with keywords related to research on the structural response of stadium roofs to wind, temperature, etc., in terms of structural engineering. Topic 12 was clustered with keywords that propose, apply, and validate algorithms for image-processing tracking systems that recognize opposing robots and balls for soccer robot functions. Topic 45 was clustered with keywords related to the technique of exploring the determinants of how to import variables that generate revenue from soccer matches.Topic Area-4 has the domain name “Sport Medicine-1: - Injury” as related to soccer and includes the following Topics. First, Topics 1, 20, and 44 were clustered with research keywords related to injuries in the “lower limb” area, such as “knee,” “hamstring,” “pelvic,” “groin,” and “adductor,” and dealt with the factors and symptoms of such injuries. In addition, “nfl” appeared in Topic 19, which is a keyword for the American NFL, and was identified as a topic generated in the process of comparing injuries according to the sport characteristics of American football and soccer.Research on head-related injuries was also identified. Topic 25 was clustered under the subject keywords “acceleration” and “impact magnitude,” which explored acceleration and impact magnitude depending on the head region studied. Topic 42 was clustered under topic keywords related to monitoring impact exposure and measurement based on athlete recall through Headcounter, an athlete self-report questionnaire on headers. Topic 36 was clustered with keywords representing topics that explored differences in bone mineral content and bone mineral density according to demographic characteristics, such as sport and gender, and the types of injuries they cause. Topic 16 consists of keyword clusters representing topics related to myocardium injuries in soccer players.Topics 3, 26, 28, and 32 consist of topics related to lifestyle and environmental factors that lead to injury. Topics 3 and 28 were clustered with topic keywords that explore the relationship between injury risk, injury-triggering factors, and injury incidence in elite soccer players and school elite players. Topic 26 was clustered with keywords describing topics related to energy expenditure, nutrient intake, and weight control in soccer players and their relationship to performance and injury. Topic 32 was clustered with keywords describing topics related to infiltration for safe and effective turf surface stability, including topics comparing injury rates and player fatigue on natural and artificial turf.Topic Area-5 was labeled “Sport Medicine-2: Brain Injury” as related to soccer and includes the following Topics. Topic 11 is centered on chronic traumatic encephalopathy in soccer players and is clustered with topic keywords covering dementia, sequelae such as Alzheimer’s, and delaying and preventing tau protein through immunization. Topic 43 was clustered under the topic keyword “concussion,” which explores the relationship between soccer players’ cognitive abilities, history of ADHD, etc.Topic Area-6 was labeled “Sport Science-1: Exercise Performance” as related to soccer. In this Topic Area, Topic 17 was clustered into topic keywords that explored the relationship between soccer performance and complex body composition, such as player weight, body fat percentage, and body mass index (BMI), from various perspectives. Topic 39 was clustered with topic keywords that explored the relationship between maturity and soccer performance from various perspectives, including players’ basic physical fitness factors such as strength, jump, and sprint. Topic 47 was clustered with keywords that addressed topics related to soccer players’ athletic performance, such as change of direction (COD) and reactive agility.Topic Area-7 was labeled “Sport Science-2: Biology & Performance & Nutrition” and includes the following Topics. Topic 4 was clustered with keywords related to nutrition and performance, including nutritional intake, eating habits, and blood measurements for validation. Topic 6 was clustered with keywords representing research related to the role of the heart, such as heart rate and cardiac output in athletes’ performance during competition and training. Topic 13 was clustered with keywords representing the demands of athletic performance for effective performance in a match, such as distance, speed, and acceleration. Topic 18 was clustered with keywords that explored the relationship between an athlete’s training load during the season and their performance. Topic 21 was clustered into topics that explored biological studies such as sweat production, cortisol and testosterone secretion, and mood during athletic performance such as competition and training. Topic 31 is clustered with keywords that refer to performance measurement research topics using digital devices such as GPS, such as distance markers and player tracking, which are used to measure performance in athletic and training situations. Topic 34 is clustered with keywords that explore the performance and physiological responses of youth or adult players to soccer field dimensions, such as exercise volume and exercise characteristics. Topic 35 was clustered with keywords related to analyzing sleep habits such as insomnia in soccer players or exploring the effects of interventions such as effective sleep and NAP to improve performance.Topic Area-8 was labeled “Sport Science-3: Visual Skill in Performance.” In this Topic Area, Topic 30 was clustered under the following research topic keywords: “cognitive control,” “cognitive state,” and “psyche in soccer-specific creative expression.” Topic 27 was clustered under the topic keywords exploring the level of soccer players’ saccade eye movements and its relevance to tasks and performance in different settings and interventions. Topic 14 was clustered under topic keywords related to factors that contribute to an effective kick, such as kick speed, angular velocity, and foot (right or left) use. Topic 2 was clustered under research keywords related to jump training interventions for various performance enhancements, including sprinting, one of the most important motor skills in soccer. The pre-COVID (2018–2020) Topic Areas and Topics are shown in 4.2.The soccer research Topic Areas for the period during the COVID-19 pandemic (2021–2022) and each Topic’s keyword organization are as follows, and the topic modeling results for soccer research from this period are shown in Topic Area-1 is a soccer-related domain named “Social Sciences-1: Business & Sociology with Covid-19” and includes the following Topics. Topic 1 is clustered with research topic keywords related to Corporate Social Responsibility (CSR) and Customer Relationship Management (CRM), which are common in the sports business field. Topic 8 clustered together topics related to player transfers in European soccer leagues and exploring league and club financial factors. Topic 31 was clustered under the keyword “socio-business,” which refers to soccer club branding; franchise culture, such as fan and club communication; and socio-business research that explores the social identity of clubs for business and, in the process, community club activities and fan identities. Topic 37 is clustered under the keyword “socio-business,” which refers to studies exploring the effects of home advantage, focusing on unattended professional soccer matches in the context of COVID-19. Topic 0 was clustered with topic keywords that explored the socio-scientific factors affecting performance from various perspectives, including match-play analyses of players during matches.Topic Area-2 was labeled “Social Science-2: Psychology & Sociocultural with COVID-19” as related to soccer and includes the following Topics. Topic 23 was clustered into keywords that represented general psychological research topics such as soccer players’ perfectionism, self-efficacy, and coping strategies for various negative psychological factors. Topic 3 was clustered under keywords of research topics that explored coach leadership, parenting styles, and educational environments as they impact soccer player development. Topic 28 was clustered with keywords representing studies that explored specific causal relationships between mental health issues experienced by soccer players during COVID-19, such as anxiety and depression. Topic 38 was clustered under the keyword “sociological issues in soccer related to sex and gender,” including gender roles in men’s and women’s soccer, LGBTQ fan attitudes and their impact on professional soccer as a whole, and LGBTQ players. Topic 14 was clustered under the keyword “sociological issues in soccer,” referring to topics on issues related to discrimination against female athletes in sports from a sociological perspective, using women’s soccer as a case study. Topic 11 was clustered under the keywords “social media” and “social communities” and keywords that related to other social activity opportunities for clubs to build and maintain a fanbase, as well as methods and effects of club–fan interactions. Topic 5 was clustered under the keyword “political and social issues,” including national branding through soccer, the media’s role in image-making, and soccer fans’ national identity. Topic 9 was identical to one of the topics in the “Sport Science-3: Visual Skill” Topic Area from the pre-COVID-19 period. This can be attributed to the inclusion of the psychology keyword “cognitive” in this Topic Area, as the “Sport Science-3: Visual Skill” Topic Area was not formed in the COVID-19 period.Topic Area-3 was renamed to the soccer-related domain “Sport Science & Technology.” Topic 15 was clustered under the research topic keywords related to the development and validation of artificial intelligence (AI) algorithms for automatically tracking player movements from soccer videos. Topic 17 shows a similar keyword cluster to Topic 12 in the pre-COVID-19 period and was clustered with topic keywords related to enhancing humanoid functions through machine learning for AI soccer robots.Topic Area-4 was labeled “Sport Medicine-1: Lower Limb Injury-1,” a topic similar to Lower Limb Injury in Topic Area-4 (“Sport Medicine-1: - Injury”) from the pre-COVID-19 period. Topic 47 was clustered with keywords of research topics related to athlete thresholds for minimizing lower extremity injuries through hip and hip outcome scores. Topic 26 clustered keywords for topics that explored the relationship between chronic ankle instability, which has various causes such as playing soccer and landing after jumps during training. Topic 7 was clustered with topic keywords that explored the relationship between the level of knee muscle extension and injury in soccer players, applying various subjects and external factors.Topic Area-5 was labeled “Sport Medicine & Science-1” related to soccer and includes the following Topics. Topic 44 was clustered with keywords that explored the physiological phenomena of female soccer players, such as hormonal contraception, pregnancy, menstruation, and breastfeeding, and factors related to athletic activities, such as performance and injury. Topic 20 was clustered with keywords describing topics related to energy expenditure, nutritional intake, and weight control in soccer players, similar to Topic 26 in the pre-COVID-19 period, as well as performance and injury. Topics 21 and 24 can be seen as clusters of topics that address the impact on various soccer stakeholders arising from the fear of infection due to COVID-19 and the medical issues of lockdown. Topic 30 consists of a cluster of keywords describing topics related to myocardium injuries in soccer players, similar to Topic 16 in the pre-COVID-19 period.Topic Area-6 is soccer-related, and the domain is named “Sport Medicine & Science-2.” Because this Topic Area includes many topics, it was divided into subtopic areas. Topics 6, 12, and 41 have subtopic domains named “Sport Medicine-3: Biology & Nutrition in Performance.” Topic 6 was similar to Topic 4 in the pre-COVID-19 period and was clustered with nutrition and performance-related keywords, including nutrition as a factor in athletic performance, the relationship between diet and immunity, and blood measurements for validation. Topic 12 was clustered with research topics that explored the relationship between physiological measures of aerobic fitness levels and performance ability. Topic 41 was clustered under the keyword “measurement and validation,” which refers to research on hormones, including cortisol, and serum tests for creatine kinase to explore the effects of cold-water immersion on soccer players’ recovery process.In addition, Topics 27, 39, 42, 46, and 49 include a subtopic domain named “Sport Science-1: Exercise Performance.” In the pre-COVID-19 period, these domains were categorized as independent Topic Areas, but during the COVID-19 period, they were included in Topic Area 6. Topic 27 was clustered with research keywords that explored the relationship between sleep quality and training, performance, and injury in soccer players. Topic 39 was similar to Topic 14 in the pre-COVID-19 period; it clustered topic keywords related to factors that contribute to effective kicking, such as kick speed, angular velocity, and the use of the (right or left) foot. Topic 42 was similar to Topic 31 in the pre-COVID-19 period and clustered around keywords such as “GPS” and “Polar,” which are used as distance markers and measuring instruments in performance research in athletic and training situations. Topic 46 was clustered with research topics that explore goalkeepers’ behavioral characteristics in match situations, either throughout the match or in special situations such as penalty kicks. Topic 49 was similar to Topic 13 in the pre-COVID-19 period and was clustered with keywords that represent the athletic demands of effective performance on the field, such as distance traveled, maximum velocity, and acceleration.Topics 4, 10, 22, 29, 34, 36, and 51 include a subtopic domain named “Sport Science-2: Performance Training.” Topic 4 was similar to Topic 2 in the pre-COVID-19 period and was clustered with research keywords related to jump training interventions for various performance enhancements, including sprinting, which is one of the most important motor skills in soccer. Topic 10 was similar to the pre-COVID-19 period (2018–2020), clustered with keywords that refer to topics related to players’ training load and performance in and out of season. Topic 22 was clustered with keywords for research exploring the relationship between small-sided games (SSG), a soccer training method, and player performance. Topic 29 was clustered with keywords for research topics that explore the relationship between external loading and athlete session ratings of perceived exertion during training in an attempt to determine effective training volume and training strategies. Topic 34 was clustered under the topic keywords exploring the relationship between training and various maturation and developmental factors, such as neuromuscular and respiratory factors, in athletes. Topic 51 was clustered with topic keywords that explored stretching methods, intensities, methods , postures, and areas (soleus) for effective lower extremity training.Topics 36 and 45 included a subtopic domain named “Sport Medicine-4: Injury-2.” Topic 36 was similar to Topic 18 in the pre-COVID-19 period. However, the word “season” was removed and clustered with a slightly different keyword composition, specifically, with topics that explored the relationship between training load and injury, such as “chronic” and “Injury.” Topic 45 also appeared to be similar to Topic 36 in the pre-COVID-19 period; it was clustered with keywords representing topics that explored differences in bone mineral content and bone mineral density according to demographic characteristics such as sport and gender, as well as types of injuries.Topics 1, 2, 19, 25, 32, and 43 were included in the topic domain “Sport Medicine-5: Injury-3.” Topic 1 appeared similar to the head and brain injury topics in the pre-COVID-19 period but was implied as a single topic. Topic 1 was clustered with topic keywords covering injuries such as concussions due to impact exposure when heading. Topic 2 was clustered with keywords describing two topics, one related to the performance of athletes returning from injury and the other related to the performance of athletes returning after COVID-19. Topic 19 was clustered with keywords describing topics related to the careful measurement and diagnosis of an athlete’s injury using MRI images to verify a precise causal relationship with symptoms. Topic 25 was clustered with keywords describing different research approaches to repetitive injuries to the same and surrounding areas after anterior cruciate ligament reconstruction. Topic 32 had the same number as Topic 32 in the pre-COVID-19 period and was clustered under the keyword “infiltration for safe and effective turf surface stability,” with topics comparing injury rates and player fatigue on natural and artificial turf. Topic 43 was clustered under the keyword “research,” which refers to topics that validated various interventions for the recovery and prevention of hamstring injuries.Topic Area-8 was labeled “Sport Science-3: Exercise Performance-2.” Topic 13 was clustered under the keyword “relative age effect” (REA), which refers to various research topics approaching REA from a performance perspective. Topic 16 appeared to be similar to Topic 39 in the pre-COVID-19 period and was clustered under the keyword “maturity,” which refers to a variety of research topics that explored the relationship between maturity and soccer performance, including anthropometrics, demographic characteristics such as age, and athletic performance such as running. The Topic Areas and Topics from the period during the COVID-19 pandemic (2021–2022) are shown in 4.3.Visualization was also conducted to ensure that well-defined clusters with interpretable topics were acquired. An intertopic distance map is a visualization technique that shows the similarity between topics represented as the distance between each topic. A topic is a node, and the set between nodes is organized as a topic area. Topics that resulted in similar topic areas overlapped.Despite the variations in location across the quadrants, the social science topic areas were located close together. The sports science and sports medicine topic areas were also located close to each other, although there was a shift in position in the quadrant. However, during COVID-19, Topic Area-4 was clustered with keywords focusing on movement-related factors associated with lower extremity injuries, which may have distanced it from the sports science and sports medicine areas, including Topic Area-6. While the pre-COVID-19 period is characterized by a clustering of injury-related topics, the period during the COVID-19 pandemic is characterized by an overlap of performance-related topics and a scattering of injury-related topics. Insights into visual changes are derived in the Discussion section.In addition, we utilized hierarchical clustering, a technique in topic modeling to visualize the hierarchical structure of topics, to analyze the topics between 2018–2020 and 2021–2022. We also used a ward-linkage function to perform hierarchical clustering based on the cosine distance matrix between topic embeddings . Figure In addition to the hierarchical clustering, we also utilized a heatmap visualization technique to gain further insights into the similarities between the topics in our paper. A heatmap based on the cosine similarity matrix between topic embeddings was created to show the similarities between topics. Furthermore, we were able to visually confirm both strong and weak similarities between topics by reviewing and analyzing the heatmap visualization results, which provided valuable insights into the relationships between them. The resulting visualization enabled us to gain a deeper understanding of the underlying patterns and themes present in the data and provided a useful framework for further analysis and interpretation of our results.5.5.1.The topic clusters and topic areas established in this study demonstrate the close relationship between the social sciences, sports science, and sports medicine in soccer. Topic areas and topics were separated or merged over time, and keywords were changed, too. Nevertheless, we derived insights by keeping the connections between complex and interrelated topics as tight as possible.5.1.1.In terms of general factors, we found that the research on the psychology of soccer exhibited similar prominent thematic keywords both before and during the COVID-19 pandemic. “Self-efficacy,” “coach leadership,” and “relationship with parents” were common in both periods . During Interestingly, the process of exploring positive and negative factors related to mental health was also common in both periods but changed between them. In the pre-COVID-19 period, multiple studies were found that investigated soccer players’ mental health or tested its causal relationship with various intervention variables, including the digital environment, and applied soccer interventions for people with mental health problems . During Additionally, studies from the pre-COVID-19 period on soccer players’ visual skills were categorized as “performance” . However5.1.2.The sociological study of soccer was discovered to be more interdisciplinary than general sociological research, with a variety of topic meanings and keywords that were similar in both time periods studied. Nevertheless, soccer studies that addressed sex and gender, such as the perceptions and gender roles of female soccer players and the attitudes and impact of LGBTQ fans, were identified in both periods . In addiAlthough the keywords organized in each topic vary by time period, there were many sociological studies centered on fan culture. In the pre-COVID-19 period, keywords such as “ideology” and “politics” were used to identify fans’ sociocultural behavioral characteristics . During Indeed, the establishment of club–fan relationships and identities conveyed to fans through social activities and promotions, online interactions, community club activities, etc., can be studied, and such studies can be characterized as sociological research toward the ultimate goal of a viable and successful business approach that includes the branding of football clubs . In factOn the other hand, research topics emerged that could be approached in the unique context of the COVID-19 pandemic. For example, the scholarly soccer literature explored whether there was still a significant home-advantage effect in soccer matches played away from home during the pandemic . Through5.1.3.Business research in soccer changed significantly during the pandemic compared to before it. Only the topic keyword cluster related to determinants of the market value of professional soccer clubs was similar in both periods . We founIn the pre-COVID-19 period, several business-focused keywords related to soccer were found in the topics. Research topics inferred from keywords such as “spectator attendance determinants,” “brand image,” “sponsorship,” and “loyalty” are typical of business research and are expected to continue being explored in the post-COVID-19 era, just as they were in the pre-COVID-19 era . During In fact, it could be inferred from the topic modeling results that soccer business research during the COVID-19 pandemic assumed a relational approach to preventing the loss of fans, which loss was predicted to be catastrophic due to the physical void caused by COVID-19. During the pandemic, soccer businesses tried to maintain and rebuild relationships with fans as potential consumers to prevent catastrophic losses due to physical voids such as lockdowns, unattended games, and shrinking consumption . The topFurthermore, the topic modeling results demonstrate that the keyword clusters generally differ between both periods. The change in keyword clusters does not imply a change in research topics but rather a difference in the amount of research that was actively conducted at different times. The keyword clusters and topics found in both periods are those that have been studied in the soccer business field from the past to the present. 5.2.5.2.1.Technology research in soccer was divided into independent Topic Areas in both periods. In the pre-COVID-19 period, various types of research were conducted. Research on soccer match analysis has been ongoing since the late 1990s when the importance of match analysis was raised, for example, regarding the development and application of technology for more sophisticated analysis or proposing analysis results from different perspectives using recorded items utilized for specific match analysis factors . In addiSimilarly, we found studies from both periods regarding the development of individual technologies such as image processing and humanoids to improve the AI performance of soccer robots . The soc5.2.2.In soccer, as in all sports disciplines, the search for new processes to improve player performance has been the focus of many experts and researchers. Research on soccer player performance has been actively conducted in soccer research, including exploring various determinants of performance ability and seeking effective interventions . In otheFor example, soccer performance research in the pre-COVID-19 period explored the relationship between performance-determining factors such as BMI and athletic performance related to sprinting  and soccer performance in actual matches . In addiRegarding the period during the COVID-19 pandemic, we found several similar types of studies to those published before the pandemic. Except for studies exploring goalkeeper behavioral traits and soccer players’ sleep quality, habits, and performance, performance studies focusing on kicking and measurements via GPS had mostly similar keyword compositions . HoweverNevertheless, the relationship between diet and performance and approaches to talent discovery and skill development in elite soccer players has been a consistent theme throughout the years . During 5.2.3.Performance training topics that emerged during the COVID-19 pandemic included jump training effectiveness studies validating improvements in various performance components, the link between stretching and performance, and training-induced improvements in muscular and aerobic capacity, as well as several interventions that are specific to improving performance . The smaAll competitive sports require planned, controlled training to achieve high performance and improve team competitiveness . Soccer The reduced opportunities for actual matches due to lockdowns and the reduction in matches during the pandemic suggest that performance training in soccer has become relatively more prominent as an external factor. During the COVID-19 pandemic, many football organizations had to be adaptable and flexible in the way they trained their players . In a stHowever, no keywords were found to recognize the application of remote training methods and their underlying technologies, including virtual reality, AI, and wearables, which were used to a large extent during the pandemic. The time required to develop new approaches to respond to extraordinary change, including how long it takes for technologies to be fielded and studied and results to be interpreted, applied, and published, makes us hopeful for creative research trends in the future. In addition, new research topics that can be explored include the comparison of face-to-face training, as identified in this study, with newly published, technology-based training programs and the effectiveness of hybrid training programs that incorporate both elements.5.2.4.Two injury-related topics emerged in the pre-COVID-19 period, consisting of keywords for specific injury sites in the lower extremities and factors that lead to injury. In soccer research on injuries in general, lower limb injuries have the highest prevalence , but theHowever, during the COVID-19 pandemic, lower-injury research has seen an increase in topics and a more granular focus, including studies of specific injury triggers and interventions for recovery. The injury sites studied were the hip, pelvis, hamstring, ankle, and knee, and the research covered a variety of topics, including injury triggers, repetitive injuries to the same site, injury prevention, and rehabilitation .Several reasons have been speculated for the change in research topics related to head and lower extremity injuries during COVID-19 as opposed to pre-COVID-19. It could be assumed that there would inevitably be more training situations than matches during the pandemic and that research based on training environments would be more active. Even in pre-COVID-19 practical match situations, dynamic headers that could cause injury were discouraged to the extent that regulations were implemented to minimize head injuries , and durThese different causes may have contributed to the increased representation of lower limb injury research in the topic modeling results over time. More importantly, however, the problem of functional decline and injury in the lower extremities due to reduced training and playing opportunities has been reported in much of the current research on lower extremity injuries . Demonst5.2.5.In contrast to the lower limbs, in the pre-COVID-19 era, head injury research was divided into at least two topic areas. One topic area attempted to explore the relationship between impact size and injury by measuring the impact of a header  . The othOne cluster of topics emerged during the pandemic that addressed brain injuries, including concussions. Because of the reduced number of matches and the lockdowns during the COVID-19 pandemic, the head injury topic might have been underrepresented more dramatically than the overall physical injury topic. While there were many factors contributing to this relative decline in topics, the variables of attrition and decreased frequency of research cannot be overlooked. As previously described, the relative contraction during the coronavirus period could be attributed to a relative reduction in live matches compared to previous periods, resulting in a decrease in exposure to dangerous head injuries, including headers, and an increase in lower limb injuries .However, 5.2.6.In addition to the lower limbs and head, injuries in soccer occur in a variety of other areas, and research has been published on these injuries. Although not organized into independent topic areas, discrete clusters of topics identified thematic areas of injury research. Some topics consisted of many of the same keywords, regardless of the time period. First, studies exploring the association between bone density and mineral content and injury were published before and during COVID-19, with various studies focusing on bone composition, including differences in bone density and injury in soccer players according to gender and bone density and injury according to sport .Keyword clusters indicative of cardiac injuries in soccer players were also found in both periods. Soccer research on the heart can be categorized into studies examining soccer players’ heart disease and cardiac responses after matches and training . The topIn both periods, we also found studies that explored turf type as a factor, as opposed to specific injury sites. Natural and artificial turf comprise two of the many influential external environments in which players play . ImportaDietary habits, energy intake, and metabolism were always clustered as one or more themes in performance studies, but there was also a strong presence of studies demonstrating a link between these themes and injury . This clIn addition, the relationship between the soccer environment and injury, which is bound to have changed through the pandemic, is a timely topic. Topics describing the relationship between training load and performance, which were found in the performance research area during the pre-COVID-19 period, have been partially transformed into keyword clusters linking training load to injury . The risOn the other hand, the psychosocial and medical issues of soccer players and other practitioners who had to face restrictions not only in matches but also in training and daily life due to league suspensions, curtailments, and lockdowns  that wer6.In the present research, we utilized the Web of Science (WOS) database to carry out a topic modeling analysis, encompassing articles indexed in the Science Citation Index Expanded, the Social Sciences Citation Index, and the Arts & Humanities Citation Index. We anticipate that a more integrated approach in data collection and analysis could be achieved by incorporating diverse academic databases such as Scopus and SportDISCUS, alongside WOS. Furthermore, it is pertinent to highlight the potential role of the BERT model, which forms the foundation of BERTopic, in enhancing the quality and depth of analysis. BERT, known for its capability to acquire intensive domain knowledge in diverse fields, can facilitate the extraction of more pertinent keywords and topics when fine-tuned with specialized domain knowledge. Future research endeavors could explore the outcomes of employing a BERT model that has been fine-tuned using sports or soccer-centric documents. Lastly, as the global society transitions into the post-pandemic period, it would be significant to adopt a longitudinal perspective in examining whether the trends in soccer research are reverting to the patterns observed before the advent of the COVID-19 pandemic.7.The COVID-19 pandemic has had a clear impact on soccer research trends. Topic modeling revealed a context in which the pandemic led to changes in research priorities that reflect changes in soccer participation, training routines, and even the sports business environment. First, the interdisciplinary nature of topics across both time periods emerged, spanning psychology, sociology, business, and technology. This emphasizes that socioeconomic phenomena are emerging from soccer that are relevant to a variety of academic disciplines. On the business side, concerns about the economic impact of the COVID 19 pandemic were reflected in the research. It also emphasized the importance of team-fan and player-fan relationships to overcome this crisis. Second, before and during the pandemic, sports, including soccer, still valued performance. In both periods, there was a strong emphasis on training, an expected theme given soccer players’ constant efforts to improve their performance. However, during the COVID-19 pandemic, a shift in themes revealed a focus on developing fast-paced, highly efficient training sessions in response to various constraints. Third, this change in the soccer environment during the pandemic was inferred as the reason for the shift in research focus from head injuries to lower limb injuries. It could be that training routines changed as match play was restricted, which affected the types of injuries players faced, or it could be that the pandemic led to less physical contact during matches, which led to fewer head-related injuries. This shift in focus could be a direct result of the pandemic, but it could also be due to new rules, advances in technology, or a shift in awareness of the issue of injury sites. Furthermore, the relative decline in head and brain injury research during the pandemic does not necessarily mean that the issue has become less important; rather, it may be an opportunity for researchers to explore the long-term effects of these injuries and the potential benefits of reducing exposure to dangerous situations. In conclusion, while the COVID-19 pandemic has clearly impacted soccer research trends, the overall direction of research on player performance, injury prevention, and technology integration remains macroscopically consistent compared to pre-COVID-19. However, as the world adapts to the new normal, soccer research must evolve to optimize athlete health and training methods and continue to advance performance. Future research must bridge these gaps and develop a holistic strategy to meet the ever-evolving challenges of this popular sport.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.JL and DH contributed to the conception and design of this study. SS collected the database. YK performed the topic modeling analyses. JL, S-BP, and DH wrote sections of the manuscript, contributed to manuscript revision, read, and approved the submitted version. All authors contributed to the article and approved the submitted version.


To the Editor—Lost progress in the fight against antimicrobial resistance (AMR) is a frightening reality, and challenges in maintaining the healthcare infrastructure used to address the public health threat of AMR are increasingly prevalent. Shortages and attrition among clinical microbiologists, antimicrobial stewardship pharmacists, and physicians are common, particularly with the recent healthcare system strains of the coronavirus disease 2019 (COVID-19) pandemic. AMR was linked to 4.95 million global deaths in 2019, and the COVID-19 pandemic the following year made things dramatically worse. For instance, the Centers for Disease Control and Prevention (CDC) reported a 15% increase in antimicrobial-resistant infections in hospitals in 2020. At the same time, 80% of hospitalized patients with COVID-19 received an antibiotic between March and October 2020. Additionally, the CDC noted significant increases in infections with organisms like carbapenem-resistant Acinetobacter spp, which increased by 78% in 2020. AMR has the potential to undermine modern medicine and requires substantial immediate and sustained resources and action. The CDC recommends antimicrobial stewardship programs (ASPs) to combat AMR by ensuring the appropriate use of antimicrobials, and ASPs are most effective when co-led by infectious disease-trained pharmacists and physicians. Additionally, microbiologists are critical stakeholders in the success of this team in ensuring the ability to accurately and rapidly diagnose infections in addition to tracking AMR. This frontline healthcare team is the backbone of the response against AMR yet faces substantial sustainability issues.Together, this team performs essential collaborations such as reporting antimicrobial resistance to the CDC, which is also critical to hospitals participating in the Promoting Interoperability Program by CMS Medicare as a condition of hospital participation. Recent survey data have demonstrated increased vacancy rates for multiple medical laboratory areas and projected medical laboratory scientist retirement rates >20% for some departments. Filling vacancies was a top concern among many respondents (14.2%), exacerbated by COVID-19–related staffing challenges, inadequate salaries, the need for a heightened profile of the profession, and concerns about a lack of adequately trained staff. Importantly, staffing shortages in the medical laboratory are not benign and are associated with an increased risk of medical error. Furthermore, a reduction in adequately trained staff results in limitations in the extent of testing offered, leading to discontinuation of local testing and expanded off-site testing, which contributes to delays in appropriate treatment for patients.The COVID-19 pandemic highlighted how vital diagnostic testing and microbiologists are to healthcare systems and public health. For example, developing a diagnostic stewardship test utilization committee may help prevent burnout by reducing unnecessary testing while highlighting the important role of the laboratory in stewardship initiatives. In a recent survey of laboratorians in Canada demonstrated, 55% of respondents felt that involvement in stewardship initiatives was a valuable use of their time and they have an important role to play (70.6%). However, nearly 60% of respondents expressed that lack of time is a barrier driven by limited staffing and competing priorities. Although only one-third of hospitals in the United States report efforts to optimize diagnostic testing, this is 1 of the 6 leading stewardship practices and an area ripe for multidisciplinary improvement. In collaboration with stewardship personnel, microbiologists are responsible for guiding actions like diagnostic test selection, providing input on appropriate reporting, and following up on critical results. Optimizing existing test use and rapid diagnostic testing with adjunctive reporting algorithms can be crucial in building more sustainable and resilient healthcare systems for addressing AMR.Lack of visibility and perceived lack of upward career mobility have been cited as drivers of laboratory staff retention, which could be addressed by further engaging the laboratory in stewardship efforts. Moreover, most stewards have noted less ability to perform traditional ASP activities, and 71% noted a positive screening for burnout. Beyond pandemic-related issues, attrition of clinical pharmacists has been noted related to a lack of ability to buy down time for nonclinical work , lack of capacity to produce revenue for activities, and limited career advancement opportunities. Similarly, supply challenges of infectious diseases physicians were highlighted in the 2020 fellowship match, where >1 in 5 US ID fellowships went unmatched. Reasons for this include being among the lowest-paid specialty due to revenue activities favoring procedure-based specialties, compounded by the significant financial burden of medical education, leading many to choose other specialties. These threats to recruiting and maintaining personnel with physicians and pharmacists are problematic because these leaders are critical to the success of ASPs. Survey data have suggested that for each 0.5 increase in pharmacist and physician FTE, an ∼1.5× increase in the effectiveness of ASPs occurs. In contrast, lower-resourced facilities  have been associated with less prospective audit and feedback, optimized diagnostic testing, and measurement of antibiotic use, which may contribute to rural health inequities in AMR. Robust and systematic investment in human resources is integral to the growth and sustainability of ASPs.Like clinical laboratory scientists, chronic and pandemic-related acute issues threaten the sustainability of ASPs among pharmacists and physicians. A recent survey of ASPs reported an increase of 5 new duties related to COVID-19, whereas 18% of stewards noted less stewardship full-time equivalent (FTE) support during this period.In conclusion, AMR is a critical public health issue, and the healthcare infrastructure and related policy should prioritize ensuring the support and sustainability of the ASP team as the frontline responders to AMR. As a clinical microbiologist, antimicrobial stewardship pharmacist, and infectious diseases physician, we see this as an urgent issue that must be addressed immediately to safeguard current and future progress in combating AMR.


However, building accurate andtransferable potential models using GNNs remains challenging, as thedata are greatly limited by the expensive computational costs andlevel of theory of QM methods, especially for large and complex molecularsystems. In this work, we propose denoise pretraining on nonequilibriummolecular conformations to achieve more accurate and transferableGNN potential predictions. Specifically, atomic coordinates of samplednonequilibrium conformations are perturbed by random noises, and GNNsare pretrained to denoise the perturbed molecular conformations whichrecovers the original coordinates. Rigorous experiments on multiplebenchmarks reveal that pretraining significantly improves the accuracyof neural potentials. Furthermore, we show that the proposed pretrainingapproach is model-agnostic, as it improves the performance of differentinvariant and equivariant GNNs. Notably, our models pretrained onsmall molecules demonstrate remarkable transferability, improvingperformance when fine-tuned on diverse molecular systems, includingdifferent elements, charged molecules, biomolecules, and larger systems.These results highlight the potential for leveraging denoise pretrainingapproaches to build more generalizable neural potentials for complexmolecular systems.Recent advances in equivariant graph neural networks(GNNs) havemade deep learning amenable to developing fast surrogate models toexpensive  Efforts have been madeto develop empirical force fields that work for different applications,6 including small organic molecules,7 biomolecules,9 and materials.10 Nevertheless, these curated methods may failto model complex systems involving significant polarization and many-bodyinteractions. Also, they can be difficult to transfer to differentsystems, as empirical force fields are tailored to match the datafrom QM or experiments of certain systems.13 How to designaccurate and transferable approximations of ab initio QM methods has been one of the major challenges in modern computationalchemistry.The development of efficientand transferable molecular potentialsplays a key role in performing accurate simulations of molecular systems.However, accurate and transferable 15 Physical constraints, including energy conservation and roto-translationalinvariance, are required for ML potentials,18 and variousapproaches have been developed to meet these constraints. For example,Gradient domain ML (GDML) predicts force fields directly and appliesa kernel-based method to guarantee conservative energies.19 Symmetric GDML (sGDML) extends GDML to incorporatespace group symmetries and dynamic nonrigid symmetries.20 Gaussian approximation potentials (GAPs) learnan energy decomposition as a summation of each atomic-centered environment,22 and different kernels or descriptors for local atomic contributionsare also investigated.24Machine learning (ML) has emerged as a powerful toolto learn interatomicpotentials or force fields by fitting the data in computational chemistry,promising near-QM accuracy with faster computations.25 DNNs are built on top of geometricdescriptors of molecular systems to model potentials. Initially, interatomicdistances and angles were used, but such descriptors fail to expressthe permutational invariance of atomic order in the input.27 High-dimensional neural network potential (HDNNP)28 is introduced, which breaks down the molecular system intolocal atomic environments. These atomic environments are encoded usingatom-centered symmetry functions (ACSFs)29 consisting of two- and three-body terms. The HDNNP has been extendedby ANI32 and TensorMol.33 However,such models are limited in transferability; namely, reparameterizationfor new chemical elements requires retraining the whole model. Itis notable that the size of the descriptor grows quadratically withthe number of elements.Neural potentials use deepneural networks (DNNs) to model therelationship between molecular geometries and potential energy surface(PES).34 are utilized to learn representations from molecularstructures, with nodes representing atoms and edges representing interatomicinteractions. MLPs are implemented on top of the representations learnedby GNNs to model the potentials. GNNs learn chemical and physicalinteractions through aggregation of neighboring atomic informationand updating the nodes. SchNet implements continuous convolution filtersto model interatomic distances35 and DimeNetincorporates angles to model three-body interactions in message-passing.36 Other works attempt to include chemical domainknowledge to develop better neural potentials.39 However, themessage-passing operations in these models fail to encode directionalinformation. In neural potentials, we are interested in the equivariancewith respect to three-dimensional (3D) rigid-body transformation suchthat the neural network commutes with any 3D rotations, translations,and/or reflections. The first category of equivariant GNNs is basedon irreducible representations in group theory including Tensor fieldnetwork (TFN),40 Cormorant,41 SE(3)-Transformer,42 SEGNN,43 etc. These models build equivariantvia Clebsch Gordon coefficients and a spherical harmonic basis aswell as learnable radial neural networks. The second category of equivariantGNNs relies on linear operations  on vectorial features instead.47 PaiNN48 and TorchMD-Net49 are two examples of such equivariant GNNs. PaiNN proposesto keep track of scale features and vector features separately anddevelops linear operations to pass information between them to keepthe equivariance. TorchMD-Net extends such an architecture with themultihead self-attention mechanism.50 Inour work, we focus on equivariant GNNs due to their superior performancein neural potential benchmarks.49End-to-end neural potentials, on theother hand, are constructeddirectly on Cartesian coordinates and element types rather than onhandcrafted descriptors. Graph neural networks (GNNs) based on message-passing52 have been investigated. GNNs are first pretrained via SSL to learnexpressive molecular representations and then fine-tuned on downstreamprediction tasks. Predictive SSL methods rely on recovering the originalinstance from partially observed or perturbed samples, including maskedattribute prediction,53 context prediction,54 and motif tree generation.55 Also, GEM56 proposes to pretrainGNNs via predicting 3D positional information, including interatomicdistances, bond lengths, and bond angles. On the other hand, contrastivelearning, which aims at learning representations via contrasting positiveinstances against negative instances, has been widely implementedin GNNs for chemical sciences.60 MolCLR61 applies randommasking of atom and edge attributes to generate contrastive instancesof molecular graphs. Recent works have also investigated multilevelsubgraphs in contrastive training.63 GraphMVP64 and 3D infomax65 incorporate3D information into 2D graphs via contrasting molecules representedas 2D topological graphs and 3D geometric structures. It is notedthat most SSL works neglect 3D information. Even though a few SSLapproaches incorporate 3D information, they are not built upon equivariantGNNs, which greatly limits the applications to accurate neural potentialpredictions. Recently, a few works propose denoising as an SSL approachto pretrain GNNs.68 By predicting the noise added to atomic coordinatesat the equilibrium states, GNNs are trained to learn a particularpseudoforce field. Such a method has demonstrated effectiveness onQM property prediction benchmarks like QM9.69 Nevertheless, it only leverages molecules at equilibrium states,which is far from sufficient for accurate neural potentials, sincepotential predictions require evaluation of nonequilibrium molecularstructures in simulations. Overall, SSL has not been well studiedfor neural potential predictions.To improve the performance of GNNs on molecular property predictions,self-supervised learning (SSL) approachesAlthough neural potentialshave been extensively investigated,their accuracy and transferability are still limited due to the relianceon expensive QM calculations to obtain training data. In particular,for large and complex molecular systems, collecting accurate and sufficientQM data can be extremely challenging or even infeasible. This raisestwo questions: (1) can we use SSL to improve the accuracy of neuralpotentials with currently available data; and (2) can we use SSL todevelop more transferable neural potentials that leverage relativelyrich molecular potential data  and apply pretrainedmodels to large and complex molecular systems with limited data? Toaddress these challenges, we propose an SSL pretraining strategy onnonequilibrium molecules to improve the accuracy and transferabilityof neural potentials ( passedfrom its neighboring atoms fmt) 1. All th feature 2.122.2g ∈ G in inputspace X and g if there exists a transformation Tg(x)) = Sg(ϕ(x)), ∀g ∈ G and 70 Equivariant GNNs introduce the equivariance as an inductivebias to molecular modeling and demonstrate superior performance inmany energy-related property predictions.In this work, we investigate GNNs for molecular potential predictions,which require 3D positional information of atoms. To this end, equivariantGNNs are introduced, which extend the message-passing functions tokeep the physical symmetry of the molecular systems. Let 42 TFN40 introduces the Clebsch-Gordon coefficient, radial neural network,and spherical harmonics as building blocks for SE(3)-equivariant messagepassing as given in rij = xi – xj is the directional vectorbetween the Cartesian coordinates xi and xj of twoatoms i and j, hjk is a type-k feature ofnode j, and k features to type-18One strategy of designingequivariant message-passing operations is based on irreducible representationsin group theory.48 Following the insight, a straightforward way to build E(3)-equivariantoperations is to keep track of a vectorial feature hi for each node. Thus, the message passingfunction and update function for vectorial features are shown in fv maps the message mij to a scalar, and the vectorial feature of each atom is updatedas a linear combination of directional vectors rij in each layer t. It shouldbe noted that only linear operations can be applied to vectorial featuresin the message function fm to keep the equivariance. Usually, atoms within a cutoff distance dcut from atom i are includedin the neighboring list Another method to build the equivarianceis to apply only linearoperations  to vectorial features in message-passing.T equivariant message-passing layers, globalpooling over all node features can be applied to obtain a representationof the whole molecule. In our work, we apply summation over all thenode features as the pooling.71 The pooledrepresentation is then fed into an MLP to predict the molecular potentialas Ê = MLP(∑ihiT).After Tg(x)) = ϕ(x), ∀g ∈ G, and Sg is the identitymapping for ∀g ∈ G. Building an invariant GNN is more straightforward than buildingan equivariant one. By simply replacing Besides, invariance indicates that the transformations  of the input will not change the output, such thatϕ(35) and three equivariant GNN models-Transformer,42 EGNN,70 and TorchMD-Net49) to validate the generalizability of the proposedpretraining method. SchNet adapts a continuous-filter convolutionthat contains elementwise multiplication between node features anda weight matrix that depends only on interatomic distances. SE(3)-Transformerfalls into the first category of equivariant GNNs which extends TFNby introducing a scalar self-attention50 term αij to ij is calculated as the normalized dot productof the key and query. EGNN and TorchMD-Net, on the other hand, followthe second strategy of equivariant GNNs that apply linear operationsto vectorial features. EGNN directly manipulates the coordinates ofatoms as vectorial features. It implements fm in fm. The self-attention term is utilizedas a scalar term for directorial vectors between atoms to update vectorfeatures, and scalar features are updated with the dot product ofvectorial features. These models represent a wide variety of invariant/equivariantGNNs and have achieved competitive performance on multiple molecularbenchmarks related to force fields or energies.49In this study, we implement one invariant GNN  measures the probabilitydistribution of perturbed molecular conformations given the atoms.The pretraining strategy is based on predicting the artificial noiseadded to sampled conformations of molecules. A conformation of a moleculeis denoted as 73 In this work, we extend sucha concept from equilibrium molecular conformations to nonequilibriumconformations, which is pivotal in molecular simulations. For a givenmolecule with N atoms encoded as V, the probability of a molecular conformation X is p ∼ exp) following theBoltzmann distribution, where E is the potential energy. The force field ofeach atom in the conformation is −∇XE= −∇X log p. The molecular conformation X can be sampled via molecular dynamic simulation, normalmode sampling, and torsional sampling, etc. These methods providediverse physically and chemically feasible nonequilibrium conformationsaround the equilibrium states which help inform the energetics ofmolecular systems.30 It should be notedthat in our case, we refer to nonequilibrium states as molecular conformationsthat are not at energy minima, which is different from the terminologyin statistical mechanics. By adding random noise to each atomic coordinate,unrealistic conformation X̂ can be obtained,which has higher energy than the sampled conformation X. Driven by this, we assume p is approximated by a Gaussian distribution X. Followingthe assumption, the force field is proportional to the perturbationnoise for a given variance σ, as shown in T in Boltzmann distribution is considered as a fictitiousterm when approximated with Gaussian, meaning T isnot explicitly included in the denoising pretraining. Such a simplificationhelps make use of data sampled from a normal mode or torsional samplingthat does not include temperature. Also, it is worth noting that thetemperature term T in the Boltzmann distributionof a molecular system can be related to the selection of the optimalstandard deviation σ of the Gaussian distribution of randomnoise in pretraining. Investigation of data and pretraining strategiesthat connect σ with T is out of the scope ofthis work but could be an interesting direction to explore.Such a denoising strategy in a self-supervised manner is relatedto learning a pseudoforce field at the perturbed states.It should be noted that although Gaussian distribution can be agood approximation of the distribution of states around a local minimum,it may fail for states with high energies. Such an assumption is relatedto the pretraining data set and the selection of the standard deviationσ of the Gaussian noise added to atomic positions. The pretrainingmolecular conformations should be physically reasonable and not overlydistorted. Consequently, adding noise to these conformations leadsto higher energy states, ensuring that the denoise pretraining processenables GNNs to learn a score function that leads to lower energyconformations. Details regarding the pretraining data set can be foundin 2.4XT), which still keeps the equivariance.To predict thenoise given the perturbed molecular conformation, the GNN models areexpected to output vectors instead of a single scalar. For SchNetand SE(3)-Transformer that lack the explicit modeling of vectorialfeatures, models first output the predicted energy Ê .The negative gradient with respect to the perturbed atomic coordinatesX̂ is calculated to evaluate the artificial noise, such that 33.130 selects a subset of GDB-17,69 samples more than 24 million conformations vianormal mode sampling, and calculates DFT total energy. ANI-1x74 extends ANI-1 by obtaining over 5 million newconformations through an active learning algorithm.31To evaluate the performanceof denoise pretraining strategies on molecular potential predictions,five data sets containing various nonequilibrium molecular conformationswith DFT-calculated energies are investigated as listed in 75 selects ab initio molecular dynamics (AIMD) trajectoriesof molecular isomers with a fixed composition of atoms , where each molecule contains5,000 conformations with DFT-calculated energy and in total 129 moleculesare included. Unlike the previous data sets which only investigatesmall organic molecules, MD2277 and SPICE76 cover a wider variety of molecule types. Inparticular, MD22 includes the AIMD trajectories of proteins, carbohydrates,nucleic acids, and supramolecules .Most molecular systems in MD22 contain more atoms than ANI-1 and ANI-1xand more details of MD22 can be found in Supporting Information S1. SPICE covers more than 1.1 million conformationsand is constituted by different molecular systems, including smallmolecules, dimers, dipeptides, and amino acids. While other data setscontain only H, C, N, and O, SPICE involves molecules with halogensand metals, adding up to 15 different elements. It also includes chargedand polar molecules, which further broadens the chemical space itcovers.Besides ANI-1 and ANI-1x, other data sets concerningmolecularenergetic predictions are studied. ISO1775 for ISO17.In this study, we combine ANI-1 and ANI-1x as the pretrainingdataset since they include various small organic molecules with differentconformations. In pretraining, all conformations of each moleculeare split into the train and validation sets by a ratio of 95%/5%.All data sets including ANI-1 and ANI-1x are benchmarked in fine-tuningfor potential predictions. By this means, we investigate whether invariantor equivariant GNNs pretrained on small molecules generalize to othervarious molecular systems. During fine-tuning, we split the data setbased on the conformations of each molecule by a ratio of 80%/10%/10%into the train, validation, and test sets for ANI-1, ANI-1x, MD22,and SPICE. Besides, we follow the splitting strategy reported in theoriginal literature3.2–4 and zero weight decay.All models are pretrained on the combination of ANI-1 and ANI-1x andfine-tuned on each data set separately. In pretraining, we employthe AdamW optimizer78 with the batch size256, and a linear learning rate warmup with cosine decay79 is applied. During fine-tuning, the models aretrained for 10 epochs on ANI-1 and ANI-1x while being trained for50 epochs on SPICE and ISO17. We apply different experimental settingsfor each molecule in MD22 since molecules greatly vary in the numberof atoms. In comparison to pretrained models, we also train GNNs fromscratch on each data set following the same setting as their denoisepretrained counterparts. Detailed fine-tuning settings can be foundin Supporting Information S2. In fine-tuning,the parameters of pretrained message-passing layers are transferred.Atomic features hK frommessage-passing layers are first summed and fed into a randomly initializedMLP to predict the energy of each molecular conformation. Both thepretrained layers and the MLP head are fine-tuned for potential predictions.Hyperparameters for each GNN are based on the recommendations fromthe original literature. More details of GNNs implemented in thiswork can be found in Supporting Information S3. To evaluate the performance of the neural potentials, we reportboth the rooted mean square error (RMSE) and the mean absolute error(MAE) with the DFT potentials.During pretraining,each invariant or equivariant GNN is trained for 5 epochs with a maximallearning rate 2 × 103.3Supporting Information S4. Also, theinvestigation of fine-tuning for more epochs can be found in Supporting Information S5. These results demonstratethat denoising pretraining of invariant and equivariant GNNs on themolecular potential data sets can directly boost prediction accuracy.To investigatethe performance of denoise pretraining, we pretrain the invariantand equivariant GNN models on the combination of ANI-1 and ANI-1xand fine-tune the models on each data set separately. 3.4abinitio methods. Therefore, the ability to transfer GNN modelspretrained on small molecules (with sufficient training data) to largerand more complex molecular systems (with limited training data) wouldbe highly beneficial. To this end, we fine-tune the pretrained fourGNNs, either invariant or equivariant, on three other molecular potentialbenchmarks , whichcover different molecular systems from the pretraining data sets.The results presentedin the previous section demonstrate that denoise pretraining is highlyeffective for improving the accuracy of neural potentials when thedownstream tasks and pretraining data cover similar chemical space. However, real-world simulations often involvemuch larger and more complex molecular systems, making it challengingto obtain the energies of such systems using expensive 7O2H10. It is demonstrated that pretrained GNN models achievebetter prediction accuracy when evaluated by both RMSE and MAE. Inparticular, pretraining significantly improves the performance ofSE(3)-Transformer by approximately 50%. Pretrained models are shownto be transferable to a specific molecular system containing fixedatoms.Supporting Information S6.Lastly, we fine-tune the pretrained models on MD22 which consistsof larger molecular systems with numbers of atoms up to 370. Thisprovides an appropriate benchmark to validate the transferabilityof GNN models pretrained on small molecules. Test MAE of the fourGNN models on each molecule is shown in Overall, our experiments suggest that the proposedpretrainingmethod can improve the accuracy and transferability of GNN modelsacross diverse molecular systems, making it a promising method forpredicting the energies of complex molecular systems with limitedtraining data.3.5To further evaluatethe benefits of denoise pretraining for molecular potential predictions,we train GNNs with different data set sizes. As shown in 3.6The standard deviationσ in the Gaussian distribution of the noise to perturb molecularconformations affects the performance of pretrained equivariant GNNs.Small perturbation noise can be too trivial for the model to predict,while large noise may break the molecular conformations and fail toprovide useful information for molecular simulations. To select thesuitable σ, we enumerate different σ values {0.01, 0.02,0.05, 0.1, 0.2, 0.5, 1.0}Å. 4To summarize, our proposeddenoise pretraining method for invariantand equivariant graph neural networks (GNNs) on nonequilibrium molecularconformations enables more accurate and transferable neural potentialpredictions. Our rigorous experiments across multiple benchmarks demonstratethat pretraining significantly improves the accuracy of neural potentials.Furthermore, GNNs pretrained on small molecules through denoisingexhibit superior transferability and data efficiency for diverse molecularsystems, including different elements, polar molecules, biomolecules,and larger systems. This transferability is particularly valuablefor building neural potential models on larger and more complex systems,where sufficient data are often challenging to obtain. Notably, themodel-agnostic nature of our pretraining method is confirmed by theperformance improvements observed across different invariant and equivariantGNN models, including SchNet, SE(3)-Transformer, EGNN, and TorchMD-Net.Our proposed denoise pretraining method thus paves the way for improvingneural potential predictions and holds great potential for broaderapplications in molecular simulations.


In a sense, “human” is a multicomponent complex composed of the human body and symbiotic flora. There are large numbers of parasitic microorganisms in the human gut. Gut microorganisms affect human obesity, enteritis, autoimmune diseases, responses to cancer treatment drugs, and even life expectancy. The gut microbiota influences the physiological characteristics of the host during the earliest stages of life. Alterations in gut microbiota may damage key processes in the development of organ systems, including the brain. Accumulating evidence has revealed the degree of interdependence between humans and human gut microbiota, emphasizing the importance of the brain–gut axis.Regular exercise is increasingly being prescribed as an adjunctive therapy for a variety of human diseases.Eubacterium and Coprococcus. Furthermore, utilizing untargeted metabolomics, the metabolites in the gut linked to these particular intestinal bacteria were identified as FAAs, notably N‐oleoylethanolamide (OEA). FAA bind to the endogenous cannabinoid receptor CB1 and stimulate the dorsal root ganglia (DRG) to express the vanilloid receptor TRPV1 in the gut, which connects to the brain through the spine. During exercise, the intestinal sensory nerves expressed CB1 receptors are stimulated, leading to the inhibition of monoamine oxidase (MAO) expression, which degrades dopamine and other neurotransmitter molecules in the ventral striatum region of the brain, prompting the release of neurotransmitter dopamine. The striatum is a key node in the brain's reward and motivation network; therefore, higher dopamine levels in this region during exercise can increase the desire to exercise and improve athletic performance. Furthermore, the authors discovered that bouts of dopamine triggered by physical exercise activate particular neuronal subpopulations in the striatum area of the brain.Using different classes of antibiotics, the authors identified two gut bacteria that were strongly associated with better exercise performance: Inhibition of dopamine signaling reduces exercise tolerance, suggesting that exercise‐induced dopamine surges and subsequent striatal neuronal activity are critical for maintaining a willingness to exercise. The activation of dopamine signaling restored the exercise performance of antibiotic‐treated mice, as the authors have noted. Furthermore, the authors have indicated that the expression of the dopamine‐degrading enzyme, MAO, decreased in the exercising mice, but not in those where the microbiota was depleted, indicating that the latter's sluggish dopamine response was due to MAO‐driven dopamine turnover. Restoration of the gut microbiome, inhibition of MAO, and increased artificial dopamine signaling in the striatum are adequate measures for reinstating exercise capacity in mice that lack gut bacteria. Additionally, the microbiota's influence on dopamine signaling requires the involvement of intestinal neurosensory neurons that expressed TRPV1 and CB1. Most importantly, the authors have demonstrated that microbiota‐derived FAAs promote exercise‐induced neuronal activation, striatal dopamine release, and enhance motor performance.In conclusion, the work by Dohnalová et al.Wenhuan Sun drew the images and wrote the article. Fangfang Zhou and Zhenjiang Bai provided modifications. All authors have read and approved the final manuscript.The authors declare they have no conflicts of interest.No ethical approval was necessary for this work.


Kaposi sarcoma (KS) is a rare disease that was not frequently identified before the widespread occurrence of AIDS. Even today, it remains a challenge for physicians to diagnose, particularly in its early stages, often requiring referral to specialists and further investigations. Dermoscopy, a non-invasive imaging technique, reveals a distinctive rainbow pattern that strongly indicates KS. Moreover, advanced imaging tools like optical coherence tomography (OCT) can provide additional information though specific disease-related patterns have not been fully established yet. These emerging techniques show promise in facilitating early diagnosis of skin-related KS and monitoring the effectiveness of treatments. However, biopsy remains the definitive method for confirming the disease. In this study, we present two cases of cutaneous Kaposi sarcoma, documented using OCT, both before and after treatment with imiquimod 5% cream. The study highlights the potential of OCT in evaluating disease progression and treatment response, as well as the usefulness of dermoscopy in detecting early indicators of KS. By integrating these advanced imaging techniques, the diagnosis and management of cutaneous KS could be improved, leading to timely interventions and better patient outcomes. Kaposi sarcoma (KS) is considered a low-grade vascular tumor, induced by human herpesvirus-8 (HHV-8), also known as Kaposi’s sarcoma-associated herpesvirus (KSHV). KS predominantly affects mucocutaneous sites but may involve all organs .KS has four distinct clinical and epidemiological forms: classic, endemic (common in African regions), epidemic (associated with HIV-positive patients), and iatrogenic . Classic KS primarily affects elderly men and is more prevalent among people from Mediterranean regions. This vascular tumor is typically found on the lower extremities, particularly the ankles and feet. However, in individuals with AIDS-related lesions, the trunk is frequently affected ,3.In addition to HHV-8 , other cIn affluent regions such as the U.S. and Europe, suspected cases of KS are routinely confirmed through histopathology. Given that the skin lesions are typically visible, obtaining tissue for microscopic confirmation is easily achieved with a simple skin punch biopsy. However, in sub-Saharan Africa, where KS is clinically relevant, the diagnosis is often based on visual inspection of the lesions without histologic confirmation .The infectious cause of all types of KS  has been identified as HHV-8 . HHV-8 eSeveral technologies have been developed as novel non-invasive diagnostic methods, including dermoscopy , reflectDermoscopy is a technique commonly used to examine skin tumors and other skin conditions ,18. It oRCM is an advanced imaging method that offers non-invasive, high-resolution visualization of the epidermis and papillary dermis at the cellular level. Utilizing the distinctive contrast offered by melanin and melanosomes in RCM images, this technique facilitates a microscopic analysis of tissue morphology in both healthy skin and melanocytic lesions, effectively providing a “virtual” skin biopsy with detailed en-face views of various skin layers . In the OCT holds tremendous potential in the field of skin cancer detection, offering a promising pathway for future advancements. Initially developed in the late 1980s for ophthalmology applications ,22, OCT No universally accepted staging classification exists for KS. However, patient management considers three clinical scenarios: localized non-aggressive, locally aggressive, and disseminated KS . Local t® device equipped with advanced high-definition (HD) OCT processing software version 4.1 .Imiquimod functions as an immune response modulator, operating via toll-like receptor 7, thereby instigating innate and adaptive immune responses. Importantly, it lacks direct antiviral efficacy but rather elicits the upregulation of interferon alpha genes, alongside interleukin 6 and 8 (IL-6 and IL-8) . In a prWe present a case involving a 49-year-old African American man with a medical history that includes disseminated endemic KS (previously treated with doxorubicin), pulmonary tuberculosis, and pulmonary aspergilloma. During a period of systemic remission, the patient developed discrete violaceous papules on his right leg . The patDermoscopy revealed faint nodular structures on a red whitish background a. OCT waThe lesion was effectively managed by applying imiquimod 5% cream once daily for five days, with a total treatment duration of six weeks a–c. PostIn our second case, we present a 60-year-old woman who reported multiple pinkish papules on her left ankle that had been present for the past four months. Her medical history includes a lung transplant performed approximately ten years ago, and she has been on continuous immunosuppression regimens ever since.The clinical examination and dermoscopic analysis were conducted before a,b and aTo gain further insights into the lesion’s characteristics, pre- and post-treatment OCT scans were performed c–e. ThesUpon histological examination, plump sheets of spindle cells compressing the vasculature were notably present c, which The successful management of the pinkish papules on the patient’s ankle using imiquimod 5% cream showcases its efficacy in treating such lesions, even in individuals with complex medical histories, such as the patient who had undergone a lung transplant with long-term immunosuppressive therapy.Regular monitoring and follow-up will continue to be an essential part of the patient’s care to ensure the long-term efficacy and safety of the treatment plan.The purpose of presenting these two cases of KS is to underscore the potential significance of non-invasive diagnostic techniques, particularly OCT, in effectively monitoring the response of KS to topical therapies. As previously mentioned, it is essential to note that a universally accepted staging system for KS is yet to be established.Both of the cases we have reported are characterized by localized KS, involving limited and non-rapidly progressive lesions in the lower extremities. In this specific clinical scenario, there exists a diverse array of local therapeutic options, encompassing both destructive and pharmacological approaches .However, these two cases serve to elucidate how the concept of localized disease must be contextualized in the clinical setting. In the first case, the localized KS represents a limited recurrence of a previously systemic disease, while in the second case, it denotes the initial onset of the disease. For both patients, topical therapy with imiquimod was selected, given that the lesions were only slightly elevated. The existing literature on the use of this topical immunomodulator in KS includes some case reports, one prospective phase II cohort study, and one comparative single-blinded non-controlled clinical study; however, these studies are insufficient to define the precise regimen dosage scheme ,29.In both cases, OCT was utilized for assessing the lesions during the diagnostic stage and subsequently to evaluate the response to therapy. While OCT has primarily been studied in non-melanoma skin cancers, it is being explored for new indications. In the context of KS, the current OCT examination reveals features that distinguish it from actinic keratosis (AK) and in situ squamous cell carcinoma.Notably, KS exhibits a nodular, inhomogeneous area with signal attenuation in the dermis, accompanied by vascular-like signal-poor structures . Our casIn contrast, AK and in situ squamous cell carcinoma are characterized by irregular epidermal thickening and hyperkeratosis. Furthermore, the OCT examination can differentiate KS from basal cell carcinoma (BCC), as the latter typically exhibits lobular tumoral structures in the dermis .Recently, a case report detailed the use of OCT in extramammary Paget’s disease. Before radiation therapy, Low-Coherence OCT was conducted, revealing specific characteristics of Paget cells, which were larger than keratinocytes, displaying a dark cytoplasm and a slightly bright nucleus, and organized in nest-like structures. Following radiotherapy, both clinical examination and OCT confirmed a complete response to treatment. Additionally, the pre-treatment OCT findings have potential applicability in monitoring any sub-clinical residual disease .In the context of BCC, OCT shows promise in assessing treatment efficacy, particularly with Imiquimod therapy, aiding in detecting residual disease within healed tumors . OCT is In another study by Forsea et al. , prelimiIn the study conducted by Cappilli et al. , distincIn contrast, KS exhibited highly distinctive LC-OCT features, specifically linear dark regions within the upper dermis  and dark intersections involving multiple irregular vessels. This specific LC-OCT pattern effectively differentiates KS from other vascular lesions. The incorporation of these OCT features into the diagnostic process holds significant potential for enhancing our ability to discriminate KS from its mimicking conditions, thus contributing to more precise identification and improved management of this complex disease .Finally, in both of our cases, follow-up OCT examinations demonstrated a reduction in vascularization after therapy, with notable decreases in tortuosity and vessel thickness. These findings underscore the potential significance of OCT parameters in assessing the response to topical therapy.Our case reports offer a comprehensive longitudinal assessment of KS lesions using dermoscopy and OCT. Moreover, they reinforce the appropriateness and safety of imiquimod as a viable treatment option for localized forms of KS.


The construction of multifunctional, single-molecule nanocircuits to achieve the miniaturization of active electronic devices is a challenging goal in molecular electronics. In this paper, we present an effective strategy for enhancing the multifunctionality and switching performance of diarylethene-based molecular devices, which exhibit photoswitchable rectification properties. Through a molecular engineering design, we systematically investigate a series of electron donor/acceptor-substituted diarylethene molecules to modulate the electronic properties and investigate the transport behaviors of the molecular junctions using the non-equilibrium Green’s function combined with the density functional theory. Our results demonstrate that the asymmetric configuration, substituted by both the donor and acceptor on the diarylethene molecule, exhibits the highest switching ratio and rectification ratio. Importantly, this rectification function can be switched on/off through the photoisomerization of the diarylethene unit. These modulations in the transport properties of these molecular junctions with different substituents were obtained with molecule-projected self-consistent Hamiltonian and bias-dependent transmission spectra. Furthermore, the current–voltage characteristics of these molecular junctions can be explained by the molecular energy level structure, showing the significance of energy level regulation. These findings have practical implications for constructing high-performance, multifunctional molecular-integrated circuits. In recent years, the design and construction of high-performance, multifunctional molecular devices have become a significant research focus in molecular electronics ,2,3,4. IRectification is a fundamental function of a single-molecule device, often combined with other functions, such as gate regulation, to enhance performance and multifunctionality. However, previous studies have shown limited effectiveness in modulating the rectification ratio of single-molecule devices via a gate ,22. Rece3  a–c. As dThe molecular structures in the ground state of M1~M3 with ring-closed and ring-open forms were first optimized with the Gaussian09 program . The lonk-sampling, we employed a 2 × 2 × 130 grid. The convergence criterion for the Hamiltonian during the self-consistent calculations was set to be a 1.0 × 10−5 Hartree.The single-molecule junction models are illustrated in The tunneling currents through the molecular junctions can be calculated using the Landauer–Büttiker formula :(1)IV=2eV is the bias voltage applied to the molecular junctions, T is the bias-dependent transmission coefficient, Here, 3 over a wide bias range. Additionally, its rectification function can be switched on/off through photoisomerization. This rational design offers a general and effective strategy to enhance switching performance and enable the integration of multiple functions, which is of great importance for the study of molecular properties and the construction of high-performance devices. Furthermore, the differences in transport properties among these molecular devices can be explained by their relative energy levels, highlighting the importance of adjusting energy level structures for designing single-molecule devices with specific functions. In summary, we propose a design strategy for achieving a high-performance, photocontrolled rectifier based on a DAE molecular switch. The electronic transport properties of single-molecule junctions, where a DAE molecule is substituted with electron donor/acceptor groups and connected to two gold electrodes, were investigated using the NEGF and DFT methods. The influence of substituents on transport behavior and its underlying mechanism were discussed. Compared to symmetrically substituted molecules, the asymmetric molecule with both donor and acceptor groups exhibits a superior switching performance, with a switching ratio above 10