Dining is an essential part of human life. In order to pursue a healthier self, more and more people enjoy homemade cuisines. Consequently, the amount of recipe websites has increased significantly. These online recipes represent different cultures and cooking methods from various regions, and provide important indications on nutritional content. In recent years, the development of data science made data mining a popular research area. However, only a few researches in Taiwan have applied data mining in the studies of recipes and nutrients. Therefore, this work aims at utilizing machine learning models to discover health-related insights from recipes on social media. First, we collected over 15,000 Chinese recipes from the largest recipe website in Taiwan to build a recipe database. We then extracted information from this dataset through natural language processing methodologies so as to better understand the characteristics of various cuisines and ingredients. Thus, we can establish a classification model for the automatic categorization of recipes. We further performed cluster analysis for grouping nutrients to recognize the nutritional differences for each cluster and each cuisine type. The results showed that using the support vector machine (SVM) model can successfully classify recipes with an average F-score of 82%. We also analyzed the nutritional value of different cuisine categories and the possible health effects they may bring to the consumers. Our methods and findings can assist future work on extracting essential nutritional information from recipes and promoting healthier diets. Under the economic prosperity and the rapid development of science and technology, the social pattern has changed dramatically. For example, the rise of social network has greatly changed the culinary culture. The choice of eating or cooking has become a hot topic, as well as the social influence of dietary choices . The immThe choice of recipes reflects one’s preference on ingredients and diet habits, which in turn has a strong correlation with diseases, including incidence rate of cancer , the deaTaking Taiwan as an example, the prevalence of obesity, diabetes, hypertension, and triglycerides is rapidly rising, which is related to Taiwanese people’s traditional lifestyle . SpecifiRecent studies have focused on nutritional analysis of recipes uploaded by users to well-known recipe sharing websites. A cross-sectional study included the nutritional composition of 5237 recipes on the AllRecipe website. They examined substances such as protein, carbohydrate, sugar, sodium, fat, saturated fat and dietary fiber in the recipe. Using the World Health Organization (WHO) health score  and the Food Safety Light Mark  of the Food Safety Authority of the United Kingdom as an indicator, they analyzed whether online recipes meet high health standards. They found that most recipes are high in protein, fat, saturated fat and sodium, and the dietary fiber content is low, which is less healthy than recipes from TV programs. In addition, when analyzing the user’s favorite recipes, it is also found that popular recipes are usually less healthy . Said anContrastively, only a few studies investigated the correlation between recipes and health conditions in Taiwan. Considering recent advances in machine learning technologies, we believe applying them to this topic can be fruitful. Therefore, this paper aims to discover the correlation between food and health by exploring cuisines and its unique ingredients, building cuisine classification models and clustering nutrients. We start by collecting information from the largest Chinese recipe website in Taiwan, iCook.com. It is a social platform for amateurs to share and discuss cooking recipes. However, they lack standardized nutrient lists. Therefore, we use natural language processing (NLP) techniques to process the data in order to establish a food-centric vocabulary and database. Subsequently, we construct machine learning models to automatically classify the recipes. This research also extracts unique ingredients for each cuisine type and the nutritional information of each recipe by linking the recipe ingredients to the nutrient database. We then establish a clustering model of the recipe based on the nutrient characteristics, and finally explore the relationship between diet and health. Our contributions include findings from online Chinese recipes, the ingredients and nutrients of various cuisines, an analysis of the characteristics of various cuisines and their nutritional value, and the correspondence between diet and diseases. This research can help improve awareness of the effect of what we eat on our body, as well as propose customized recipes or recommendation services to individual users.Our study relies on the recipes data retrieved from iCook.tw, the largest Chinese recipe-sharing website in Taiwan. After data collection and preprocessing, ingredients and nutrients are regarded as features and machine learning as well as data exploration techniques are utilized to analyze cuisine types, dietary habits and its influences on health. The entire process of our system is presented in At the outset, we collect data from the recipe website including the most popular cuisines in Taiwan. Eight categories of cuisines are retained for further analysis, namely, Chinese (C), Japan (J), Korea (K), Thailand (T), America (A), Italy (I), France (F) and Spain (S) cuisines. The numbers of recipes in each cuisine category are 1321 (C), 1231 (J), 1333 (K), 1021 (T), 670 (A), 1836 (I), 949 (F), and 821 (S), thus resulting in 9182 classified recipes in total. In addition, there are also 6121 non-classified (N) recipes. This class of recipes are to be classified by the trained model later.For the nutritional content, due to the fact that the website does not have nutritional labels, we attempt to map the ingredients to the 2017 version of the Taiwan FDA Food Composition Databases (TFDA) for gaining nutritional insights. This process is illustrated in ”, and all other non-ingredient-related words like kitchen appliances, cooking techniques such as “julienne.” Finally, in order to precisely analyze ingredients, the recipes that use “few, appropriate,” etc. quantity of the ingredients are replaced with an average amount. Decorative ingredients with a very small amount are removed. After preprocessing, we discover that there are some unrecognizable recipes or recipes with only one ingredient left. These exceptional cases are examined manually by the authors and excluded from the database.The free-form data uploaded by users of the recipe website requires the following preprocessing steps. To start with, we need to normalize synonyms. It is common for the same ingredient to be written in different ways. Moreover, different physical forms of the same ingredient are commonly shown as different terms, for instance, “diced scallions” and “chopped scallions” both are scallions and should map to the same term in the nutrient database. Besides, we also remove parentheses, punctuations, and emojis, such as “「”, “【”, “】”, “！”, “https://en.wikipedia.org/wiki/Tf%E2%80%93idf (accessed on 22 May 2021).) is used for calculating a vector representation of the ingredients. Then, we experiment with four widely used machine learning models, including naïve Bayes (NB) [This step is to identify common and featured ingredients, in other words, those that appear across multiple types and only in a few types. To achieve this, each recipe is considered as a document, while each ingredient yes (NB) , decisioyes (NB) , random yes (NB) , and supyes (NB) , to consMore specifically, we calculate the importance and uniqueness of the ingredient terms in each recipe by using the tf-idf model, and then combine the above weights to obtain the “ingredient characteristic value”i in the category j cuisine as j. The numerator j that includes ingredient i. At the experimental stage, a critical value j.The more times a certain ingredient appears in the recipe, the more important it is. Hence, we consider the frequency of an ingredient appearing in the cuisine type as a measure of its importance of the ingredient i as the following equation. The numerator N is the total number of cooking categories, and the denominator i.On the other hand, we define i in the category j, denoted as Finally, by multiplying the importance score i.Seasonings are usually set as the mean value.ii.When the unit is described as “a long piece of,” “a piece of,” “a carton of,” etc., we use the food replacement table as a reference for the replacement of nutrients.iii.The ingredients for decoration or spices with small amounts such as white sesame and mint leaf are excluded in calculation.To understand the nutrition attributes of each cuisine, we extract nutritional information of nutrients by referring to the TFDA database mentioned in the previous section. We calculate nutritional facts per 100g of each recipe from each ingredient, and select seven of the most important and common nutrients, including carbohydrates, proteins, fat, saturated fat, dietary fiber, sugars and sodium. For an ingredient that cannot be mapped to the TFDA database, if the usage of it is low in the recipe, it is excluded in the calculation. Otherwise, it is replaced by the most similar ingredient in the TFDA database. Additionally, the quantity of some ingredients in the recipe is not specified using a precise unit, but usually described as “properly,” “a few,” etc. Therefore, we define the unspecified quantity of an ingredient by the following criteria:https://en.wikipedia.org/wiki/Unsupervised_learning (accessed on 22 May 2021).) methods to find groups that contain similar nutritional features withing the recipe. In other words, we do not use predefined category labels but rather their actual nutrients as the categorization criteria. In our experiments, we employ the k-means algorithm to distribute 13,323 recipes into 20 clusters, and use tf-idf to determine the most representative ingredients of each cluster. Through the steps mentioned in this section, we can quantitatively examine the correlation between recipe, its ingredients, and the well-being of our body.We utilize unsupervised learning , Japan (J), Korea (K), Thailand (T), America(A), Italy (I), France (F), and Spain (S) cuisines. In addition, due to the fact that only using accuracy as the metric for performance evaluation can be prone to bias, we include Macro-average F1-score as a comprehensive metric for multi-class models.First, we perform basic preprocessing steps mentioned above, and the numbers of samples before and after preprocessing are listed in The tf-idf algorithm is then used to calculate the “Specialty Score” of ingredients. Recall that, the higher the Specialty Score, the more important the ingredient is for the cuisine type. The scores are listed in We compare different classification models with regards to their ability to categorize recipes into eight cuisine types. As The macro average of three metrics from all classifiers are compared in The k-means algorithm is employed to distribute 13,323 recipes into 20 clusters. Afterwards, tf-idf is used to find representative ingredients of each cluster. We then manually label and merge them with similar characteristics. In the end, 20 clusters are reorganized into 5 groups, and results are as follows.Group A: This group includes the 1st, 5th, and 13th clusters. The recipes in this group constitute a third of the total recipes. Carbohydrates, protein and fat are relatively average in this group (see Table 10 for a comparison). It also shows that the 13th cluster mainly uses low fat meat, such as salmon and chicken, as ingredients. Past researches indicate that high protein, low fat and low carbohydrates are good for weight loss . TherefoGroup B: It contains the 2nd, 8th, 11th, and 19th clusters. This group has higher carbohydrates. Past studies have found that, under the same calorie limit, a diet that maintains a high carbohydrate ratio daily is not helpful to weight loss . The 2ndGroup C: It consists the 0th, 6th, and 12th clusters. This group has high sodium content, that is, high salt content. Many studies have shown that excessive sodium content in the diet has been considered to be associated with hypertension, cardiovascular disease, and chronic kidney disease ,36,37. NGroup D: It is made of the 4th, 10th, 14th, and 16th clusters. The main characteristic of this group is high dietary fiber. There is considerable epidemiological evidence that higher daily dietary fiber intake can reduce the risk of diseases including cardiovascular disease , Type 2 Group E: It includes the 3rd, 7th, 9th, 15th, 17th, and 18th clusters. This group has an overall higher portion of fat. We observe that the recipes in this group are mostly salads, which contains ingredients such as sesame oil, olive oil and coconut oil. Olive oil is rich in monounsaturated fatty acids, and several studies have shown that it can lower the chance of stroke for patients with cardiovascular diseases  and has To summarize, our model finds 20 clusters of recipes that may have various influences on human health. To acquire a deeper insight, we analyze common ingredients and featured ingredients of various cuisine types. We observe that Chinese, Japanese, and Korean cuisines use similar ingredients, whereas American, Italian, French, and Spanish cuisines are comparable in the same manner. Thai food is more distinct, where common ingredients such as salt, sugar, and pepper are rare. The reason may be that Thailand is in Southeast Asia, where the preference of flavor is different from Northeast Asia. More precisely, it focuses on sour, spicy, and umami. Lemon, chili, and fish sauce are common ingredients in this region. It is also customary for people in this area to use fish sauce to replace salt and/or sugar, so there is a considerable difference in the use of ingredients from other cuisines.When Sajadmanesh et al.  used tf-In addition to the SVM classification model, the current study also uses naïve Bayes, decision tree, and random forest to compare the results. SVM was previously used by Su et al.  to underHsiao and Chang  stated thttps://www.dietaryguidelines.gov/sites/default/files/2020-12/Dietary_Guidelines_for_Americans_2020-2025.pdf (accessed on 22 May 2021)). It states that foods and beverages with added sugar, fat, sodium, or alcohol should be reduced. This corresponds to Groups B, C, and E in http://www.fao.org/nutrition/education/food-dietary-guidelines/regions/countries/italy/en/ (accessed on 22 May 2021)) advocate for small amounts of fat and sugar in foods. To sum up, our method can successfully find groups of recipes and nutrients that are consistent with most up-to-date dietary recommendations around the world.For the purpose of suggesting a healthier diet, we refer to the Dietary Guidelines for Americans 2020–2025, Guideline 4 of Chapter 1 (The strengths of our study include the use of state-of-the-art machine learning models to efficiently categorize recipes and discover relations among ingredients and nutrients, which enable an objective measure of healthy diets. Another advantage of our methods is its accuracy. A study of recipes provided by restaurants on university campuses  mentioneThis work examines ingredients and nutrients on a recipe sharing social network site iCook.tw to explore the health effects of various cuisines and ingredients. The online recipes are processed and nutrients within them are linked to a standard nutrient database. Multiple machine learning approaches are explored, and the SVM classifier is found to be superior in the recipe classification experiment than three other methods, with an F1-score of 0.82. We further analyze the healthiness of cuisines by clustering nutrients and organizing possible health effects of different clusters of recipes. We observe that only a third of the online recipes contain high protein, low fat and low carbohydrates, which are indications of a healthier diet. As for the most notable relationship between nutrients, sugar is negatively correlated with protein and dietary fiber, in other words, sweeter dishes are usually low in protein and fiber content. On the other hand, dietary fiber is positively correlated with carbohydrates and protein, which are essential nutrients of human health. Our findings can help the public to better understand the impact of dietary habits. We foresee more nutritious and healthy cooking styles to emerge after our proposal of the awareness of healthy cuisines and ingredients.


Staphylococcus aureus bacteremia (SAB) are incompletely understood. We used Geographic Information Systems (GIS) and spatial statistics to analyze demographic and geographic epidemiology of SAB in the community.Risk factors for community-associated S. aureus Bacteremia Group Prospective Cohort Study (SABG-PCS) at Duke University Medical Center to obtain demographic and clinical data. We used the American Community Survey and U.S. Census to supply neighborhood variables. Secular trends in demographic and clinical characteristics of SAB patients prospectively enrolled between 1995 and 2015 (n = 2478) were determined using linear regressions. To characterize spatial patterns in Methicillin-resistant S. aureus (MRSA) bacteremia compared to Methicillin-susceptible S. aureus (MSSA) bacteremia, we used GIS mapping and selected a subgroup of patients (n = 667) living in and around Durham County, North Carolina. We then created generalized additive models (GAMs) using this subgroup to detect geographic heterogeneities in probabilities of MRSA infections compared to MSSA infections.We used the We found evidence of changing demographic and clinical characteristics of SAB patients over the 21-year period. The proportion of infections acquired in the community increased significantly (p < 0.0001). However, we did not detect spatial heterogeneities of MRSA infections in Durham County. Patient location of residence was not significantly associated with antimicrobial-resistant infections. Patient age and year of hospital admission were the only statistically significant covariates in our spatial models.S. aureus to identify robust risk factors for infection.We utilized a novel method to analyze SAB in the community using GIS and spatial statistics. Future research should prioritize community transmission of Vance G. Fowler, Jr., MD, MHS, Achaogen (Consultant)Advanced Liquid Logics (Grant/Research Support)Affinergy Affinium (Consultant)Akagera (Consultant)Allergan (Grant/Research Support)Amphliphi Biosciences (Consultant)Aridis (Consultant)Armata (Consultant)Basilea Bayer (Consultant)C3J (Consultant)Cerexa Contrafect Debiopharm Destiny (Consultant)Durata Genentech Green Cross Integrated Biotherapeutics (Consultant)Janssen Karius (Grant/Research Support)Locus (Grant/Research Support)Medical Biosurfaces (Grant/Research Support)Medicines Co. (Consultant)MedImmune Merck (Grant/Research Support)NIH (Grant/Research Support)Novadigm (Consultant)Novartis Pfizer (Grant/Research Support)Regeneron sepsis diagnostics Tetraphase (Consultant)Theravance Trius (Consultant)UpToDate Valanbio xBiotech (Consultant)


The immunotherapeutic treatment of various cancers with an increasing number of immune checkpoint inhibitors (ICIs) has profoundly improved the clinical management of advanced diseases. However, just a fraction of patients clinically responds to and benefits from the mentioned therapies; a large proportion of patients do not respond or quickly become resistant, and hyper- and pseudoprogression occur in certain patient populations. Furthermore, no effective predictive factors have been clearly screened or defined. In this review, we discuss factors underlying the elucidation of potential immunotherapeutic resistance mechanisms and the identification of predictive factors for immunotherapeutic responses. Considering the heterogeneity of tumours and the complex immune microenvironment , checkpoint expression levels may not be the only factors underlying immunotherapy difficulty and resistance. Researchers should consider the tumour microenvironment (TME) landscape in greater depth from the aspect of not only immune cells but also the tumour histology, molecular subtype, clonal heterogeneity and evolution as well as micro-changes in the fine structural features of the tumour area, such as myeloid cell polarization, fibroblast clusters and tertiary lymphoid structure formation. A comprehensive analysis of the immune and molecular profiles of tumour lesions is needed to determine the potential predictive value of the immune landscape on immunotherapeutic responses, and precision medicine has become more important. Over the past few years, remarkable results have been achieved with the availability of cancer checkpoint inhibitors (CPIs), which have revolutionized the oncology battlefield by making the host immune response a target for anticancer therapeutic intervention. However, a significant fraction of patients has no respond to CPI treatment; and moreover, a proportion of patients showed resistance to CPI, and some cancers may pseudo- or hyperprogress. Moreover, some patients are often heavily treated with different chemotherapy regimens prior to being treated with CPI, which increases the complexity of the TME. Taking anti-programmed cell death 1 (PD-1) monotherapy as an example, physicians or investigators have come to a clinical consensus that the PD-1 ligand 1 (PD-L1) expression level can serve as a criterion for treatment with CPIs, such as anti-PD-1 therapy. However, depended on the understanding of the tumour microenvironment (TME) and the tumour mutational burden (TMB), other related biomarkers are also used as criteria for CPI treatment. Immunotherapeutic research may be currently in the middle-to-late stage, and researchers have gradually realized that tumours and TMEs progress over time and thus must be reconsidered and evaluated from a continuous perspective rather than by simply using the expression of certain biomarkers at certain time points as single measures; the idea of high-dimensional biomarkers for tumour immunotherapy is depicted in Bearing this in mind, we firstly argued the intrinsic and extrinsic resistance mechanisms of immunotherapies and the advantages and shortcomings of using PD-L1 expression levels as biomarkers of PD-1 therapy and nonresponse to PD-1 treatment due to a limited understanding of PD-L1 cut-offs. And in turn we summarize the criteria for the immunotherapeutic treatment of “hot and cold tumours” from certain perspectives and try to determine the impact of immune cells on the TME in both temporal and spatial dimensions. Moreover, we also report some new findings on tertiary lymphoid structures (TLS) associated with hot/cold tumours to clarify that CD8 toxic T cell infiltration affects CPI treatment and that subtle structural features of the TME play an important immunotherapeutic role. We also discuss the problems and dilemmas of hyperprogression as an integral factor underlying immunotherapeutic resistance in cancer.Immunotherapies based on antibodies targeting the PD-1 and PD-L1 axis have profoundly changed the strategies for treated advanced tumours. Between 2015 and 2017, various ICIs were approved for the second- or first-line treatment of tumours with high PD-L1 expression  exhibited only a 20% sustained response rate for 5-10 years; pembrolizumab (anti-PD-1) also achieves an initial response rate of only 70-80%, which is reduced to 33% at the 3-year follow-up. Meanwhile, for the anti-PD-1 and anti-CTLA-4 combination clinical study, only 61% objective response was observed, and significant toxicity also exists at the same time  less T cell infiltration or T cell exclusion indicates a poor CPI response, and 2) more T cells in the tumour area indicates a more favourable CPI response  influence clinical outcomes Figure 1Therefore, the limitations of PD-L1 as biomarker of anti-PD1 therapy should be considered, and some confusion remains. Researchers should understand that different drugs, assay systems , clinical decision points, tumour indicators and cut-offs will influence the conclusions.And what is more, thinking in terms of different dimensions, PD-L1 as IO biomarker is dynamic and heterogeneous both spatially and temporally. PD-L1 expression can be also temporal and heterogeneous, this can make the situation even more complicated. This has forced us to rethink from the beginning of the mechanisms of immunotherapy resistance.Immunotherapeutic resistance is categorized as either primary resistance or acquired resistance. Primary resistance  is a clinical scenario that a cancer does not respond to an immunotherapeutic strategy. The incidence rates of various types of cancer can change drastically . The so-Tumours that initially respond to immunotherapy effectively but either stop responding or grow over time are said to have acquired resistance . With thExtensive evidence suggests that the TMB is a predictive biomarker of immunotherapy response independent of PD-L1 , 17. TheTMB is correlated with increased expression of tumour-specific antigens (TSAs) and tumour-associated antigens (TAAs) that can target reinvigorated T cells, whereas a high TMB tends to enhance tumour immunogenicity and ICI responses across tumour types –21. SeveThe TMB is associated with certain biomarkers, including MSI-H (high microsatellite instability), which results from dMMR (deficient DNA mismatch repair) and is detected in a subset of human cancers. Tumours with an MSI-H/dMMR status typically display high TMB levels, and MSI-H/dMMR is an established predictive biomarker of ICI efficacy –27.As reported, MSI has the highest incidence in endometrial ~30%), gastric ~20%), and colorectal (~15%) cancers and likewise occurs in lower proportions in other different tumour types. About 20,000 stage IV dMMR tumours are diagnosed each year in the United States %, and co. The MMR%, gastriHowever, the correlation of the TMB with ICI response is not consistent neither across nor within tumour types. As reported, renal cell cancers (RCCs), Merkel cell carcinoma (MCC), and mesothelioma have higher ICI response rates than predicted based on their TMBs, which is potentially due to the higher antigen qualities in these tumour types that result from a high number of indel mutations (in RCC), viral antigens (in MCC), and complex chromosomal rearrangements (in mesothelioma) , 33. AnoAntigen or neoantigen processes and presentation are essential for the T cell recognition of tumour cells and engagement of the T cell receptor (TCR). Neoantigens are newly generated antigens following somatic tumour mutations which confer the immunogenicity of tumour and are necessary for the effectiveness of PD‐1 and PD‐L1 monoclonal antibodies . In addiWhile loss of antigen presentation has been found to be associated with ICI resistance, it may also be a component of metastatic heterogeneity with clonal evolution . DownregFive already known oncogenic viruses, human papillomavirus (HPV), Epstein-Barr virus (EBV), human T cell leukaemia virus (HTLV 1), human herpesvirus 8, and Merkel cell polyoma virus, are relevant with around 15% of malignant tumours . For exaFurthermore, tumour immunotherapies mainly depend on the tumour-associated antigens expression and the responses of T cells to tumour antigens. Cancer/testis antigens (CTAs) are encoded by 276 genes, such as LAGE-1, MAGE-A, SCP-1, NY-ESO-1 and TTK, from more than 70 gene families . CTAs arHowever, the absence of characterization of genes associated immune responses in cancer cells has hindered further development of indicators for selecting and optimizing immunotherapy.Increased tumour heterogeneity (ITH) describes the variability between cancer cells within a single tumour. Cancer originates from a tumour cell clone that acquires the ability to proliferate uncontrollably while evading detection and clearance by the immune system. As the cancer progresses, genomic instability leads to the development of tumour cell subclones that acquire various genomic alterations  in whichClonal expansion occurs due to genomic alterations, individual mutations and changes in gene expression between tumour compartments. In patients with metastatic tumours, heterogeneity between the primary tumour and metastases is thought to underlie the complexity of the cancer . For exaPD-1/PD-L1 blockade induced primary and acquired resistance suggests other therapeutic mechanisms and biomarker possibilities for tumor patients. Hugo et al. analysed pre-treatment melanoma biopsies of the somatic mutagenomes and transcriptomes to recognize factors potentially influencing resistance to anti-PD-1 therapy or innate sensitivity . While aIn addition to PD-1/PD-L1, various studies have showed that tumour-infiltrating lymphocytes (TILs) in and around neoplastic cells reflect host immunity in a range of cancers, such as breast cancer , gastricIshigami et al.  showed tThe TME is responsible for the coexistence of immune cells and tumour cells, and the spatial distribution among cells, as well as the degree of cytotoxic T cell infiltration in the tumour nest, affects the efficacies of immunotherapies such as PD-1 to some extent. Both tumour heterogeneity and minute structural differences are the main factors underlying T cell infiltration. Thus, investigators have proposed the concept of hot and cold tumours, which will be reviewed in subsequent chapters. However, it should be noted that the concept of cold and hot tumours may not be applicable to only a single cell type (T cells), as the infiltration of myeloid cells (macrophages) and fibroblasts may also inhibit or promote tumour development. The complexity of this process increases over time and may alter the balance between tumour promotion and suppression depending on the degree of macrophage polarization.It was observed that the infiltration of immunosuppressive cells in the TME, such as T regulatory cells (Tregs), myeloid-derived suppressor cells (MDSCs), M2 tumour-associated macrophages (TAMs) is always associated with immunosuppression as well as by the release of IL-10, TGF-β like immunosuppressive cytokines and other chemokines . The immTAMs are important microenvironment components of solid tumours that differentiate along the spectrum of M1 tumour-killing macrophages to M2 tumour-promoting macrophages . TAMs exvia several mechanisms -MDSCs reduced around 75% T cell proliferation and around 80% IFN-γ release. Thus, the high frequency of PMN-MDSCs was closely associated with a poor OS, and CD11b+/CD16+ PMN-MDSCs subpopulation was most closely related with poor survival of HNSCC patients   . Activats (PRRs) . Severals (PRRs) , 120. Fos (PRRs) –124. Fraescaccae . Broadlyescaccae –123. Theescaccae . Given tescaccae , and howCancer immunotherapy using ICI has revolutionized the treatment and physician’s perspective of advanced cancer. However, response rates to immunotherapy are still comparatively low in the major resistant cases. One main factor associated with initial CPI resistance is the lack of tumour T-cell infiltration, the so-called “non-inflammatory” or “cold tumour” feature. The lack of T cells in tumours may be due to lack of tumour antigens, lack of antigen presenting cells (APCs), lack of priming/activation of T cells, and impaired transport of T cells to the tumour bed.As previously mentioned in the section on the extrinsic mechanism of ICI resistance, the formation of “hot” and “cold” tumours is complex and influenced by multiple factors, such as the chemokine distribution in the tumour nest, TLSs, B cell signature, and CAF-associated protein secretion and structures. Furthermore, defective recruitment of APCs or lack of T cell activation or co-stimulation after antigen presentation can be an influencing factor.“Cold tumours” are usually defined as a lower infiltration rate of effector T cells in TME, a low mutational load, and a low neoantigen burden and are often characterized by an immunosuppressive TME . SeveralMany attempts have been used to try to turn cold tumours into hot ones, such as the intervention of various small molecule drugs, antibody drugs, combo therapies and even oncolytic viruses. We have listed a few interesting cases for reference. For example: Demonstrated in mouse breast, pancreatic and glioblastoma tumour models that anti-PDL1, anti-VEGFR2, and anti-LTβ receptor (LTβR) therapies were showed to induce high endothelial venules (HEVs) and to enhance cytolytic of TME, which leading to the destruction of tumours and transforming immune-cold glioblastomas into immune-rich ones . The genThe TME includes a complex network of chemokine or cytokines which may affect cell trafficking to the tumour nest. Adhesion molecules also involve into the recruit effector T cells to the TME and to specific regions within the tumour. For example, CX3CL1 attracted majority of Th1 cells and effector-activated cytotoxic T cells, but CXCL9 and CXCL10 recruited more memory CD45RO T cells . In addiNew evidence indicates that tumour-infiltrating B cells have also been reported to play an essential role in the clinical outcome of cancer patients receiving anti-PD-1 therapy. A higher rate of melanoma-infiltrating B cells with a plasma cell phenotype prior to treatment was correlated with longer survival in patients treated with anti-PD-1 . It was TLSs, the ectopic lymphoid structures, are ectopic lymphoid organs that develop in nonlymphoid tissues at sites of chronic inflammation and have been identified in several types of cancer –139. WelTumour-associated TLSs are often associated with good prognosis in the majorities of cancer types, including breast cancer, CRC, lung cancer and melanoma, demonstrating capacity to induce a systemic and long-lasting antitumour response , 146. HoIn a retrospective study of NSCLC patients, researchers demonstrated that TLSs, referred to as tumour-induced BALT, were correlated with increased OS, disease-specific survival, and disease-free survival (DFS) . B-cell For CRC patients, CD3+ TLSs are prognostic biomarkers in patients with both primary and metastatic CRC . T cell-To present, several newer investigations have proven that cancer-associated TLSs have immunosuppressive, pro-tumorigenic effects. Indeed, the link between tumour-associated TLSs and patient outcomes seems to be dependent on many factors, including the type of TLSs, cancer type and disease stage.Increasingly evident suggest that a successful antitumour immune response requires the presence, activation and synergistic stimulation of all lymphatic components of the immune system, including CD8+ T cells, CD4+ T cells, B cells and innate lymphocytes within the TME. This is particularly reflected in the discovery of TLSs, which represent well organized clusters of TILs and elicit advanced immune responses . AssessiA high proportion of desmoplastic melanomas have been reported to exhibit formation of TLSs, and patients also have a high response to PD-1 blockade , 157. TwTumour-infiltrating B cells are well characterized, but their overall functional role in cancer is not fully understood. Some studies suggest that they have a tumor-promoting role, while others suggest that they are positively associated with better cancer prognosis, especially when they are associated with organized lymphocyte aggregates (known as TLSs).B cells and TLSs are potential biomarkers and therapeutic targets in response to ICB in patients with melanoma and renal cell carcinoma , 146. InA former neoadjuvant ICB trial in melanoma patients showed an enrichment of B-cell markers in tumours of patients who responded to treatment by targeted expression profiling compared to those in nonresponding patients . Immune In patients with metastatic melanomas, the co-existence of tumour-associated CD20+ B cells and CD8+ T cells was correlated with improved survival, which was revealed by immunofluorescence staining that the development of tertiary lymphoid structures was found in CD8+CD20+ tumours. Moreover, B cell-rich tumours were associated with increased levels of TCF7+ naive and memory T cells, suggesting that TLs showed a critical role in the immune microenvironment of melanoma, by imparting a distinct T cell phenotype . These oICI therapies consist not only of monoclonal antibodies targeting the traditional check point pathways , but theThe occurrence of HPD after ICI treatment was initially characterized in 2016 . Then a It has been suggested that amplification of MDM2/4 gene, EGFR gene mutation and chromosome 11 region 13 (CCND1/FGF3/FGF4/FGF19) may be associated with the development of HPD. When overexpression of the ubiquitin ligase MDM2 can disrupt its regulation of wild-type (WT) p53, it blocks the activation of the transcriptional domain of the p53 gene and leads to p53 inactivation through down-regulating of the ubiquitin-dependent p53 proteins . From a The pathophysiological MOA of HPD is still unknown to a large extent. Nevertheless, an accumulating number of investigations suggest that alterations in the TME during checkpoint therapy, for example, activation of PD-1-expressing Treg cells and CD8+ T cells, may initiate an increase in accelerated tumour development. In addition, changes in the tumour immune microenvironment, aggravation of innate immunosuppression, activation of carcinogenic signals, and regulation of tumour-promoting cytokines may be critical for the development of HPD .More recently, Champiat et al.  proposedvs 3%), JAK3 (22 vs 2%) and SOX9 (17 vs 1%) to be more frequent in patients with hyperprogression than in those without hyperprogression  or circulating tumour DNA (ctDNA) show promise as potential biomarkers for immunotherapy , for exaversus 43% (6 of 14) of standard progression patients (p = 0.01)  . Meanwhi = 0.01) , 185, wh = 0.01) . In addiAlthough ICIs have substantially progressed the treatment of cancer in recent years, tumour progression due to immune resistance remains a substantial challenge for oncology treatment. Several issues deserve deeper consideration, such as the TME in immunotherapy-resistant cancers having multiple immunosuppressive signals that must be bypassed to achieve a clinical response; a better understanding of the heterogeneity within tumours from the same patient; and the requirement of high-quality T cell induction for immune checkpoint function. Additionally, the interaction of draining lymph nodes with TLSs during tumour progression and immune cell infiltration suggest that the subtle structure of the TME may be equally important, not only for the T cells activation but also the impacts of CAFs and macrophages are important to build up the whole TME. Previous research has focused on tumour and immune cell suppression mechanisms within the tumour, but it is increasingly recognized that tumour and immune suppressor cells interact with stromal cells to form a complex signalling network that may also be essential for T cell exclusion. An increasing number of studies have also elucidated the roles of stromal cells in promoting immune evasion and supporting cancer progression and metastasis, the introduction of the concept of spatial-omics has also enhanced the understanding of researchers in this field Figure 1In the search for a cancer cure, ICIs are potentially the best treatment developed in recent years but may not be the final end point. The evolutionary process of tumours serving as a microenvironment for development has become more complex over time. Current immunotherapies rely on a snapshot of tumours at a certain time point as a cut-off, which is why some chemotherapeutic and ICI treatment strategies must be continually altered. It is believed that a better understanding of tumours at the molecular, protein and cellular levels as well as over time will lead to more appropriate treatments. This is why precision medicine and companion diagnoses are particularly important.SW contributed to the idea generation and article writing. KX contributed to the article writing and wording. TL contributed to the overall design, idea generation and article writing. SW and TL contributed equally to this work. All authors contributed to the article and approved the submitted version.This paper was supported by the Foundation of Scientific Research Project of Education Department of Jilin Province  and the Foundation of Department of Human Resources and Social Security of Jilin Province . We also thank the China Scholarship Council for supporting the overseas research program of KX (No.201508130060) and TL (No.201308130088).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


EHR has streamlined care and documentation. Further developments such as Computerized Physician Order Entry (CPOE) has been noted as one of the most promising functionalities of Health Information Technology (HIT), as it allows providers to enter orders, medications, diagnostic tests, and procedures, with the intent of improving the clarity and specificity of physician orders, facilitating the rapid communication of orders to pharmacies, and providing significantly enhanced decision support capabilities compared to traditional handwritten orders.In our experience, initiation of CPOE has been beneficial in many ways, namely in decreasing medication errors related to handwritten orders. However, in the clinical scenario of acute resuscitation of a critically injured burn patient, the CPOE structure did not address each and every need that would arise, especially if fluid titration was necessary. Nursing staff were left unsure of what to do in terms of their role in adjusting fluids and assessing for adequacy of resuscitation. This led to gaps in care in which potential critical situations needed to be addressed. For instance, the possible development of abdominal compartment syndrome and how to respond to it was not part of CPOE set that was implemented. This was placing the patient at risk by delaying initiation of hemodynamic monitoring and delaying electrolyte replacement as well.The goal of this study is to report the outcomes of this quality improvement initiative and to describe the resultant research that is in place to evaluate its effectiveness.This is a QI project that will identify and describe how a protocol was developed post- CPOE implementation to address gaps in nursing care during fluid resuscitation of critically ill burned patientsWe created a protocol that allows the nurse to have better insight into what is happening with the patient and what physician orders are most pertinent at any particular time. The protocol sets parameters that alerts the nurse when additional intervention is necessary. For instance, monitoring for abdominal compartment syndrome begins once resuscitation exceeds 6 mL/kg/TBSA and allowing for the nurse to call the primary physician for hypotension that is refractory to fluid bolus. This was not clear before and nurses were not intervening appropriately, which resulted in gaps in delivery of care. We have not had to report any adverse or sentinel event related to fluid resuscitation since the implementation of this protocol.A nurse-driven protocol helped address gaps in care for nurses at the bedside during fluid resuscitation


Tumour hypoxia promotes an aggressive tumour phenotype and enhances resistance to anticancer treatments. Following the recent observation that the mitochondrial inhibitor atovaquone increases tumour oxygenation in NSCLC, we sought to assess whether atovaquone affects tumour subregions differently depending on their level of hypoxia.n = 15) received atovaquone treatment, whilst cohort 2 (n = 15) did not. Hypoxia-related metrics, including change in mean tumour-to-blood ratio, tumour hypoxic volume, and fraction of hypoxic voxels, were assessed using hypoxia PET imaging. Tumours were divided into four subregions or distance categories: edge, outer, inner, and centre, using MATLAB.Patients with resectable NSCLC participated in the ATOM trial (NCT02628080). Cohort 1 (Atovaquone-induced reduction in tumour hypoxia mostly occurred in the inner and outer tumour subregions, and to a lesser extent in the centre subregion. Atovaquone did not seem to act in the edge subregion, which was the only tumour subregion that was non-hypoxic at baseline. Notably, the most intensely hypoxic tumour voxels, and therefore the most radiobiologically resistant areas, were subject to the most pronounced decrease in hypoxia in the different subregions.This study provides insights into the action of atovaquone in tumour subregions that help to better understand its role as a novel tumour radiosensitiser.Trial registration: ClinicalTrials.gov, NCT0262808. Registered 11th December 2015, https://clinicaltrials.gov/ct2/show/NCT02628080The online version contains supplementary material available at 10.1186/s13550-021-00871-x. Lung cancer is the leading cause of cancer-related death in the UK with a five-year overall survival rate only in the region of 16%  and is aOxygen deprivation or hypoxia is defined as an oxygen tension under physiological normoxia  . HypoxiaTo reduce tumour hypoxia and therefore enhance radiosensitivity, decreasing cellular oxygen consumption rate (OCR) via metabolic reprogramming has arisen as a promising approach . Mathemabc1 complex) of the electron transport chain (ETC)  and outer [1.37 (95% CI 1.2–1.5)] subregions exceeded baseline mean TBRmean in the centre subregion [1.19 (95% CI 1.0–1.3)]. Change in mean TBRmean in the non-hypoxic edge subregion following treatment was negligible . In contrast, no significant change was observed in TBRmean in untreated patients, neither in tumours overall nor in any tumour subregion . TBRmean increased from baseline in the edge region in both cohorts similarly, by + 8.0% in atovaquone-treated and by + 4.6% in untreated patients. Thus, atovaquone caused a decrease in TBRmean in tumours overall and in all tumour subregions except the edge region. Interestingly, the reduction in TBRmean in the centre subregion was lower than in the inner and outer subregions. TBRmax values showed a similar trend with no significant changes from baseline in each distance category and overall for untreated patients  atovaquone-treated patients had an overall and meaningful  decrease in HV from baseline, and the median change was − 28% (95% CI − 58.2 to − 4.4). Meanwhile, only two (13.3%) untreated patients had an overall reduction in HV equal to or greater than 10%, and the median change was + 15.5% (95% CI − 6.5 to 35.5), as previously reported [In both cohorts, the magnitude of tumour HV at baseline was not in itself predictive of hypoxia reduction to atovaquone. For instance, the two untreated patients that displayed a meaningful reduction in HV in tumours overall had either medium (44.8 mL) or very small (3.5 mL) baseline HV. Similarly, no meaningful decrease in HV was observed in four atovaquone-treated patients, who had large (366.9 mL), medium (45.9 and 59.3 mL), and small (24.6 mL) baseline HVs.As median HV decreased from baseline in the centre, inner, and outer tumour subregions, analysis of more intense tumour hypoxia was performed. TBR greater than 1.6 and 1.8 were used to define tumour hypoxic voxels. Only tumours that had sufficient HV (predefined as ≥ 1.5 mL) at these TBR thresholds to accurately assess change were included. Using TBR greater than 1.6 and 1.8, atovaquone-induced reduction in HV was even more pronounced in all tumour subregions except in the edge subregion where no median change in HV was consistently observed Table .Table 2SGiven that the treatment duration differed between patients, its influence on tumour HV changes was evaluated  contrary to untreated patients where no significant change was observed (P = 0.63)  and outer (P = 0.01) subregions. The reduction was non-significant in the centre (P = 0.05) and negligible in the edge subregion (P = 0.86)  Table a. In unt6) Table b.P = 0.96) nor in centre (P = 0.31), inner (P = 0.95), outer (P = 0.91), and edge (P = 0.21) subregions (Table P = 0.01) (Table P < 0.001) and in the inner (P < 0.01) and outer (P = 0.01) subregions  Table b. In thens Table c. In untThus, it seems that atovaquone did decrease the global fraction of hypoxic voxels significantly in tumours overall and in all subregions except the edge subregion. The fraction of the most intensely hypoxic voxels also showed the greatest reduction among tumour hypoxic voxels and in most tumour subregions.mean in this subregion was markedly lower than the hypoxia threshold set at 1.4, and although TBRmean did increase during treatment, it was insufficient to classify these voxels as hypoxic, thus explaining the absence of change in median HV in the edge tumour subregion.Following the recent demonstration that the mitochondrial inhibitor atovaquone decreases tumour hypoxia in patients with NSCLC, this study shows that the reduction in TBR and HV occurred in the outer, inner, and centre tumour subregions. The centre subregion displayed a lower TBR at baseline which is likely to be due to lack of hypoxic PET tracer uptake due to a lack of viable tumour cells (e.g. necrotic regions) and may explain the lesser reduction observed in these regions. Besides, atovaquone did not seem to act at the edge subregion of the tumour. Baseline TBRmean from baseline in tumours of untreated patients, signifying that tumour hypoxia intensified over time, reinforced the reduction observed in atovaquone-treated patients. Moreover, as the nitroimidazole-based tracers FMISO and FAZA used accumulate exclusively in viable cells at oxygen levels responsible for hypoxia-related radioresistance [The increase in median HV and TBRsistance , any decsistance , 22. NotAs hypoxia appeared to decrease to a greater extent in the inner and outer tumour subregions than in the centre subregion in atovaquone-treated patients, we hypothesise that blood perfusion may impact the reported treatment outcome. TBR indices at 4 hours post-injection seemed to fall towards tumour centres, and it is possible that TBR could underestimate the extent of hypoxia, especially at tumour centres. These voxels could be poorly perfused , leading to very low rates of FMISO influx which would cause TBR values to remain relatively low despite generating hypoxic conditions which would cause long-term tracer accumulation. Thus, it would be interesting to assess the relationship between tumour blood perfusion and TBR using baseline perfusion CT and hypoxia PET scans.Fixed distance categories were used to assign voxels to a tumour subregion, in accordance with a previous study on FMISO PET kinetics in NSCLC . Yet, tumean in a voxel. As shown by Grimes et al , one woOur analysis of hypoxia PET scans shows differences in hypoxia in different NSCLC tumour regions at baseline, which could be useful for RT dose guidance. Hypoxia-based RT dose painting is an appealing approach for cancer therapy , 27. AddTBRmean between the two scans for the overall tumour or individual tumour regions.Hypoxia PET image repeatability has been demonstrated to be high with modern PET/CT systems using FMISO head and neck cancer (HNC)  and NSCLIn this study, we provide insights into the action of atovaquone in different subregions of NSCLC tumours. Following the demonstration that atovaquone reduces tumour hypoxia in patients with NSCLC, we show that this reduction mostly occurs in the inner and outer tumour subregions, and to a lesser extent in the centre subregion. Importantly, the most intensely hypoxic tumour voxels, and therefore the most radiobiologically resistant areas, are subject to the most pronounced decrease in hypoxia. This enables a better understanding of the role of atovaquone as a novel tumour radiosensitiser. Further investigation is required to assess whether the PET radiotracer may underestimate the level of hypoxia in the innermost tumour subregion and whether perfusion is perhaps responsible, at least in part, for this observation.Additional file 1: Supplementary Figure 1. Change in tumour HV measured by hypoxia PET-CT. Supplementary Figure 2. Influence of treatment time on tumour hypoxic volume changes. Supplementary Figure 3. Relationship between baseline tumour hypoxic volume and total baseline tumour volume.


Fluid overload (FO) after resuscitation is frequent and contributes to adverse outcomes among postinjury open abdomen (OA) patients. Bioelectrical impedance analysis (BIA) is a promising tool for monitoring fluid status and FO. Therefore, we sought to investigate the efficacy of BIA-directed fluid resuscitation among OA patients.A pragmatic, prospective, randomized, observer-blind, single-center trial was performed for all trauma patients requiring OA between January 2013 and December 2017 to a national referral center. A total of 140 postinjury OA patients were randomly assigned in a 1:1 ratio to receive either a BIA-directed fluid resuscitation (BIA) protocol that included fluid administration with monitoring of hemodynamic parameters and different degrees of interventions to achieve a negative fluid balance targeting the hydration level (HL) measured by BIA or a traditional fluid resuscitation (TRD) in which clinicians determined the fluid resuscitation regimen according to traditional parameters during 30 days of ICU management. The primary outcome was the 30-day primary fascial closure (PFC) rate. The secondary outcomes included the time to PFC, postoperative 7-day cumulative fluid balance (CFB) and adverse events within 30 days after OA. The Kaplan–Meier method and the log-rank test were utilized for PFC after OA. A generalized linear regression model for the time to PFC and CFB was built.n = 66; TRD, n = 68). The BIA patients were significantly more likely to achieve PFC than the TRD patients . In the BIA group, the time to PFC occurred earlier than that of the TRD group by an average of 3.66 days (P < 0.001). Additionally, the BIA group showed a lower postoperative 7-day CFB by an average of 6632.80 ml (P < 0.001) and fewer complications.A total of 134 patients completed the trial (BIA, Among postinjury OA patients in the ICU, the use of BIA-guided fluid resuscitation resulted in a higher PFC rate and fewer severe complications than the traditional fluid resuscitation strategy.The online version contains supplementary material available at 10.1186/s40779-021-00329-0. Open abdomen (OA) has been accepted as a therapeutic option for managing abdominal catastrophes over the last two decades –4. HowevTraditionally, the FO calculation method was based on fluid balance measurements and body weight variations in critically ill patients. However, fluid balance studies using the difference in fluid inputs and outputs do not include insensible water loss and are less accurate , 28. MorBioelectrical impedance analysis (BIA) technology measures the total body, regional or segmental impedance of tissues and is derived from resistance (R) and reactance (Xc) by the transmission of electric currents at different frequencies , 31. RapTherefore, we performed the trial to investigate the effects of fluid resuscitation protocols with adjustment determined according to the hydration level measured by BIA in compared with the traditional fluid resuscitation strategy determined by clinicians according to usual clinical parameters among OA patients. Our study hypothesis was that application of BIA to assess the hydration status among postinjury OA patients during ICU resuscitation could decrease fluid overload, promote PFC, and reduce complications caused by fluid overload.clinicaltrials.gov Identifier: NCT03466684) conducted in two parallel groups  was a pragmatic, prospective, randomized, observer-blind, single-center trial  that provide trauma and acute critical care surgery (ACCS) services for eastern China. After an abbreviated laparotomy, trauma patients with nonclosure of the abdominal fascia were admitted to the SICU.Severely-injured adult patients admitted to SICU with OA who underwent emergent abbreviated laparotomy were considered eligible. The OA was defined as nonclosure of the abdominal fascia and skin after laparotomy that required temporary abdominal closure (TAC) . The decThe TAC method was standardized among all available patients. TAC at the initial operation was performed with a vacuum-assisted and mesh-mediated fascial traction (VAWCM) as described previously , 34. In The exclusion criteria were as follows: a) age less than 18 years; b) pregnancy; c) lactation; d) limb amputations; e) mental disorders; f) diabetes mellitus; g) pre-existing blood disorders; h) pre-existing abdominal fistulas; i) pre-existing terminal illness; j) liver dysfunction (Child-Pugh class C); k) New York Heart Association (NYHA) class IV; l) chronic renal failure requiring dialysis; m) therapy with an extracorporeal membrane oxygenator (ECMO); n) enrolled in an ongoing, interventional randomized controlled trial (RCT); and o) expected to die within 24 h of ICU admission due to devastating injuries.For patient allocation, a computer-generated randomization list gave an allocation table in permuted blocks . Allocation concealment was conducted at the bedside using a sealed envelope to allow randomization by blinded administrative assistants immediately after the participant was deemed eligible.Eligible patients were randomly allocated to the traditional fluid resuscitation group (defined as TRD) or BIA-directed fluid resuscitation group (defined as BIA). After allocation, a multifrequency BIA with eight tactile electrodes  was used to assess the body fluid status every 6 h within the first 72 h after admission to the ICU and daily for 4 days in both groups.An alternating current of 250 mA at the frequency bands of 5, 50, 250, and 500 kHz was used, and two electrodes were built into every grip and plate as single-frequency BIA with eight electrodes. The patient’s skin was cleaned with saline before applying the electrodes to the arm and leg. The segmental impedance values in the arm, leg, and trunk were measured using a multifrequency analyzer for all frequencies. The hydration level (HL), fat mass (FM), body cell mass (BCM), internal cell water (ICW), external cell water (ECW), and regional fluid distribution were measured and automatically displayed. The HL and total body hydration status indices are evaluated by resistance and reactance through BIA. The migration of the vector in the nomogram results in variations of the numerical value of the HL. The OA patients were stratified into three hydration level categories . According to the numerical scale for BIA, normal hydration was between 72.7 and 74.3%. Higher and lower values represented states of hyperhydration and dehydration, respectively. Hyperhydration was classified into three categories as follows: mild (74.3–81.0%), moderate (81.1–87.0%), and severe (> 87.0%) . Similar2 when < 70%. The colloid used for fluid resuscitation was mainly blood products. If the patients had hypoproteinemia, albumin would be applied. Persistent anuria was assessed by senior clinicians, but we did not use the urine output to guide intravenous fluid resuscitation. When the patient CVP, MAP, and ScvO2 parameters met the above requirements, the patients were committed to the next stabilization period directed by the two fluid resuscitation strategies described below. The end point of resuscitation was postoperative day 7 in ICU after OA.First, all patients received aggressive hemodynamic support according to the standard protocol in our department during initial salvage resuscitation. Briefly, a 500-ml bolus of crystalloid was applied every 30 min to maintain central venous pressure (CVP) of 8 to 10 mmHg. If the mean arterial pressure (MAP) was below 65 mmHg, vasopressors were provided to maintain a MAP ≥65 mmHg. Red blood cells were transfused to promote ScvOIn the TRD group, the patients received a restricted intravenous fluid regimen or dehydration therapy based on the decision made by two senior clinicians according to the cumulative fluid balance recording and hemodynamic condition, such as the heart rate, blood pressure, CVP, MAP, urine output, cardiac echocardiography, lactate and body weight changes. BIA information was not available to the clinicians managing the patients in the TRD group, nor adjusted during fluid restriction or pharmacological and mechanical therapy.2 goals, then the following fluid management strategy was applied for each 6-h period. If the HL was above 87.0% (severe level), fluid infusion was restricted, a furosemide drip was used, and continuous renal replacement therapy (CRRT) was initiated with an ultrafiltration rate when the patients were in failure or had an inadequate response to the above diuretic therapy that gave a net negative fluid balance of at least 1500 ml during the next 6 h. If the HL was 81.1–87.0% (moderate level), the above methods were used to trigger a net negative fluid balance (approximately 1000 ml) for the next six h. Similarly, if the HL was 74.3–81.0% (mild level), a net negative fluid balance of approximately 500 ml was achieved during the next 6 h of ICU hospitalization. If the HL was below 71.0% , the CVP, MAP, and ScvO2 were maintained as described above during ICU resuscitation. Vasoactive agents were used if necessary combined with close monitoring of the cardiovascular response, and a net negative fluid balance was abandoned during periods of hemodynamic instability.In contrast, in the BIA group, the protocol for patients who received BIA-directed resuscitation was as follows Fig. . If hypeBecause implementation of the different fluid intervention regimens required clinician intervention, blinding of research clinicians participating in the protocol was inappropriate and infeasible. However, the other medical staff, sponsors, patients, and data and safety monitoring team remained unaware of the interventions during the trial.Demographic and clinical characteristics were systematically recorded, including age, gender, mechanism of injury, indication for the open abdomen, intervention, laboratory tests, complications, and day of hospital and ICU admission. Patient populations were classified as blunt or penetrating according to the injury mechanism. The illness severity was based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score, Sequential Organ Failure Assessment (SOFA) score, Abbreviated Injury Score (AIS) and Injury Severity Score (ISS) for trauma patients.The fluid resuscitation volumes for the patients were systematically collected for postoperative 7 days. The total fluid intake was accurately monitored, which included administration of blood products, intravenous fluids, and drug infusions and provision of various nutrition support, such as enteral and parenteral nutrition. The fluid output included the CRRT output, urine output, tube drainage output, actual blood loss, nasogastric feeding tube output, stool output, insensible perspiration from both the skin and respiratory tract, and fluid drainage from TAC dressings. The cumulative sum of total blood product transfusions in the first 24 h, cumulative fluid balance and 24-h fluid balance over the first 7 days of OA management were recorded. During the first 72 h after OA, the vasopressors administered in the SICU were collected, including norepinephrine, vasopressin, phenylephrine, and dobutamine.The primary outcome measured was the primary fascial closure rate  within 30 days. The secondary outcomes measured included days to PFC, postoperative 7-day cumulative fluid balance, 30-day mortality, hospital and ICU length of stay (LOS), length of mechanical ventilation and complications within 30 days after OA. All patients were followed 30 days from enrollment, and the outcomes at hospital discharge were included.Postinjury OA patients with sIAI admitted to our referral center were anticipated to have a low fascial closure rate according to previous studies  and similar studies in other centers 33–60%) , 33, 37.–60% , 33P < 0.2 in the statistical analyses. The data are reported as the means ± standard deviations (SDs) and medians  for continuous variables and as percentages for categorical variables. A P value of less than 0.05 was deemed statistically significant. The analyses were performed with the SAS statistical analysis software, version 9.3 .Continuous data were analyzed using Student’s t-test. Categorical data were analyzed using the Chi-square test or Fisher’s exact test whenever appropriate. The Kaplan–Meier method and the log-rank test were utilized for primary fascial closure up to 30 days after OA. Trends in the daily fluid input, daily fluid output, daily fluid balance, and cumulative fluid balance were illustrated using GraphPad Prism  as the means and 95% confidence intervals (CIs) and compared using one-way ANOVA. A generalized linear regression model for the time until closure and cumulative fluid balance was built, including the covariates of BIA usage and any covariates with Between January 2013 and December 2017, a total of 160 patients undergoing OA with temporary abdominal closure were admitted to the two SICUs in our department. Of these patients, 20 were excluded . A total of 140 participants were randomized , and 134 participants were treated , we found that resuscitation guided by BIA reduced the time to PFC by an average of 3.66 days . Among the extra-abdominal complications, patients resuscitated by TRD were also significantly more likely to have pneumonia  and an acute lung injury (ALI) . Compared with the patients in the TRD group, their BIA counterparts had fewer ventilator days , shorter ICU lengths of stay  and a reduced hospital length of stay . No significant difference was found in 30-day mortality between the BIA and TRD groups.Complications were not infrequent in the two groups Table . An enteCurrently, the fluid resuscitation strategy among postinjury OA patients in the ICU is predominantly guided by traditional protocols. Several retrospective studies of fluid resuscitation therapy have been implemented to achieve PFC and better outcomes among OA patients , 23. HowOptimizing postoperative fluid therapy to promote PFC by alleviating fluid overload remains a challenge. Frequent development of FO in OA patients cannot be detected on time and that the degree of hydration cannot be appropriately assessed with effective measures. However, BIA, as promising tools, can be used to measure the body composition and fluid volume with an electric current transmitted through the body and has been validated in patients –40. Our Reduced fluid overload is an advantage of resuscitation directed by BIA. As an instantaneous technique to measure changes in the body fluid status of ICU patients, BIA can reflect the fluid overload state earlier and bypass errors due to fluid balance accounting. Additionally, serial BIA measurements accurately quantify the degree of edema, which establishes a target value to guide optimized fluid therapy and treatment targets for diuretics or ultrafiltration for CRRT patients. With the intervention based on the BIA protocol, an adequate circulating blood volume could be established, and complications resulting from fluid removal might be prevented. In a nutshell, BIA technique improved detection of the fluid overload state and guided optimized therapy, thereby preventing fluid overload in OA patients during resuscitation.Notably, the application of the BIA technique was associated with an increased primary fascial closure rate and earlier fascial closure. Goussous and colleagues reported that a smaller fluid balance over the first 10 days was associated with PFC success among nontrauma patients . RecentlIn the present study, the use of BIA was associated with a decreased incidence of adverse events, particularly EAF. Large volume resuscitation was associated with an increased frequency of complications, such as fistula formation, longer time to recovery from intestinal edema, and higher mortality . Miller This study has several limitations. First of all, weight-based methods to determine the fluid overload status were not studied in this trial. Moreover, obesity, the size of the abdominal incision, the effect of intra-abdominal pressure on CVP, and other accompanying conditions might be important factors that we failed to control. Furthermore, we did not compare BIA-guided fluid resuscitation with other restrictive fluid management, and future prospective studies should be implemented to solidify the clinical results. Besides, problems inherent in the nature of this single-center study limit the generalizability of the research results to a broader population. Therefore, multicenter trials with a broader spectrum, including the use of BIA technique in balanced resuscitation practices may be sufficient to answer more critical clinical questions.Among postinjury OA patients in the ICU, the application of fluid resuscitation protocols with adjustments determined according to hydration level measured by BIA compared with the traditional fluid resuscitation strategy determined by the treating clinicians according to the usual clinical parameters resulted in higher PFC rates and fewer severe complications. Thus, close monitoring of the hydration status with the BIA technique may be routinely used in postinjury OA patients.Additional file 1: Table S1 Intraoperative and postoperative fluid volume and drug management. BIA bioelectrical impedance analysis, TRD traditional fluid resuscitation, POD postoperative day.


Visceral disseminated varicella zoster virus (VZV) infections frequently affect immunocompromised patients. Diabetes mellitus has been associated with VZV infection, and most cases of disseminated infection involve patients with poorly controlled blood glucose levels. It initially presents as severe abdominal pain, which is evaluated as an acute abdomen, however, the cause is typically unidentified due to unremarkable computed tomography (CT) findings. We report a case of visceral disseminated VZV infection in a patient with well-controlled diabetes mellitus with fat stranding around the celiac and superior mesenteric artery on CT.A 61-year-old Japanese woman with well-controlled diabetes mellitus presented to the emergency department with severe abdominal pain that gradually worsened. She had stable vital signs and skin rashes, suggestive of varicella. Abdominal CT showed fat stranding around the celiac and superior mesenteric arteries. The patient tested positive for the VZV antigen and was diagnosed with a visceral disseminated VZV infection. Acyclovir was administered, and the patient was discharged on the 14th day.Visceral disseminated VZV infection may affect patients with well-controlled diabetes mellitus and causes acute abdomen. Periarterial fat stranding on CT is associated with abdominal pain due to visceral disseminated VZV infection. Varicella zoster virus (VZV) infections have a severe clinical course in immunocompromised patients. Specifically, patients who underwent bone marrow transplantation have the highest risk of developing disseminated infection . AbdominThe cause of severe abdominal pain, associated with visceral disseminated VZV infection, remains unclear with negative imaging findings , 8. ReceWe report a case of visceral disseminated VZV infection in a patient with well-controlled DM with severe abdominal pain and periarterial fat stranding on CT.A 61-year-old Japanese woman with postoperative ovarian cancer and DM, controlled with oral antidiabetic medications or drugs, presented to the emergency department (ED) with a complaint of abdominal pain that worsened over four days. She underwent surgery for ovarian cancer 11 years ago and received no chemotherapy as the disease was still at an early stage. She had been followed up as an outpatient for approximately six years and was confirmed to have no recurrence. She had other medical histories of hypertension and hyperlipidemia, with no recent history of taking any steroids or immunosuppressive medications.On the day of her visit to the ED, skin rashes were noted on her face, abdomen, and thighs. Within the four days between the onset of abdominal pain and the patient’s visit to the ED, she had experienced few episodes of vomiting. Abdominal examination revealed tenderness in the epigastric area without muscular guarding or rebound tenderness. The skin rashes were accompanied by blisters. The patient had a Glasgow Coma Scale of 15 (E4V5M6), blood pressure of 139/81 mmHg, heart rate of 76/min, respiratory rate of 16/min, body temperature of 35.9 ℃, and oxygen saturation level of 100% (room air). Blood tests in the ED showed a slight increase in the C-reactive protein level, as shown in Table Blood tests after admission showed that both VZV-immunoglobulin (Ig)M and VZV-IgG were positive;similarly, blood VZV DNA was elevated. There was an increase in antinuclear antibodies; however, other specific antibody tests were negative Table . Her abdThis case highlighted two important clinical learning points. First, visceral disseminated VZV infection affected patients with well-controlled DM. Second, CT findings of periarterial fat stranding around the CA and SMA were associated with abdominal pain in visceral disseminated VZV infection.Visceral disseminated VZV infection has commonly been reported among immunocompromised patients, such as bone marrow or renal transplant recipients, and patients under immunosuppressive therapy , 8–10. IAdditionally, disseminated VZV infection has been reported among patients with poorly controlled DM (glycated hemoglobin > 10%) , 11. PreVisceral disseminated VZV infection causes severe abdominal pain in 80–100% of cases . VZV infIn 20–60% of visceral disseminated VZV infections, abdominal pain appeared before the skin rash , 9. It iIn conclusion, visceral disseminated VZV infections may affect patients with well-controlled DM. CT findings of fat stranding around the CA and SMA suggest visceral disseminated VZV infection as the cause of abdominal pain. Nonetheless, further cases are needed to confirm these findings.


Anti‐silencing function 1 (ASF1) is a conserved histone H3–H4 chaperone protein. ASF1B, a paralog of ASF1, acts by promoting cell proliferation and influencing cell cycle progression. Although there is some evidence demonstrating that ASF1B plays a key role in the development, progression, and prognosis of certain cancers, there are no pan‐cancer analyses of ASF1B.We used a range of bioinformatics approaches to investigate the predictive role of ASF1B, including its correlation with prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), and immune cell infiltration, in diverse cancer types.We found that ASF1B was highly expressed in 22 cancers and was negatively correlated with the prognosis of multiple major cancer types. Furthermore, ASF1B expression was correlated with TMB in 21 cancers and with MSI in 7 cancers. We found that ASF1B was coexpressed with genes encoding immune activators, immune suppressors, major histocompatibility complexes, chemokines, and chemokine receptors. We further found that the role of ASF1B in the infiltration of different types of immune cells varied across tumor types. ASF1B may potentially affect several key immune‐related pathways, such as those involved in antigen processing and presentation, natural killer cell‐mediated cytotoxicity, and autoimmune thyroid disease.Our findings show that ASF1B may serve as a prognostic marker and potential immunotherapeutic target for several malignancies due to its role in tumorigenesis and immune infiltration. Our first pan‐cancer analysis of ASF1B results showed that this gene was highly expressed in 22 tumor tissues compared to normal tissues and revealed an association between ASF1B expression and clinical prognosis. Our results also suggested that ASF1B may be an independent prognostic factor in multiple cancers and that high ASF1B expression is relatedto poor prognosis in major tumor types. Furthermore, ASF1B expression was associated with TMB, MSI, and immune cell infiltration in various cancers. Its effect on tumor immunity varied depending on the cancer type. These discoveriesmay contribute to clarifying the role of ASF1B in tumor development and provide new regulatory targets for more precise and personalized immune antitumor strategies. Anti‐silencing function 1 (ASF1), a conserved histone H3–H4 chaperone protein, regulates transcription by derepressing silenced mating‐type sitesBidirectional communication between the microenvironment and cells is essential for both tumor growth and normal tissue homeostasis.We conducted a pan‐cancer analysis of ASF1B using multiple databases, including Oncomine, HPA, CCLE, and TGCA, to characterize ASF1B expression and its correlation with prognosis, immune response, clinicopathology, and tumor microenvironment in diverse cancers. Our findings revealed that ASF1B is correlated with the development of multiple cancers, has the potential to be a good diagnostic, therapeutic and prognostic marker, and is promising as a new immune checkpoint inhibitor. This work offers a basis for understanding the mechanism of action of ASF1B in various cancers and provides a rationale for targeting ASF1B with immunotherapies.22.1https://www.oncomine.org/), an online cancer microarray database, contains approximately 48 million gene expression data points from over 80,000 samples of various cancer types.p‐value: 0.001. Data sets with statistical significance were noted.Oncomine . We then extracted ASF1B expression data from TCGA (https://tcga.xenahubs.net) using Perl software and performed pan‐cancer analyses. The “wilcox.test” method was applied to investigate ASF1B mRNA expression levels across cancers. Thereafter, we investigated mRNA sequencing in different cancer cell lines from Cancer Cell Line Encyclopedia . The cutoff was set as a false discovery rate (FDR) value <0.05. The R package “ggpubr” was used to design the box plot.We downloaded 33 cancer‐related RNA sequencing datasets and their associated clinicopathological and survival data from the UCSC Xena website (2.2p < 0.05) and then plotted survival curves using the R packages “survminer” and “survival.” The R packages “survival” and “forestplot” were used for Cox analysis to identify the correlation between ASF1B and survival. The R packages “ggpubr” and “limma” were used for clinicopathological correlation analyses.We extracted the survival information for each TCGA sample. We then selected several indicators  to investigate the association of ASF1B expression with the prognosis of patients with various cancers. We used the Kaplan–Meier method and log‐rank test to perform survival analyses in 33 cancer types , a web server for comprehensive analysis of the infiltration of tumor immune cells, was used to explore the relationship between prognosis and the infiltration of tumor immune cells, including CD4+ T cells, CD8+ T cells, B cells, neutrophils, dendritic cells (DCs), and macrophages. We then explored the relationship between ASF1B expression levels and the abundance of tumor‐infiltrating immune cells in 15 cancers, in which ASF1B had a correlation with overall survival. We next applied the ESTIMATE algorithm from the R packages “estimate” and “limma” to calculate immune and stromal scores.n = 33) based on ASF1B expression data using CIBERSORT, which was developed to estimate the abundance of particular cells in hybrid cell populations using gene expression datasets.p < 0.001).TIMER (Tumor Immunization Estimation Resource) database (2.5https://www.gsea‐msigdb.org/gsea/downloads.jsp). GO and KEGG functional annotations and enriched pathways associated with ASF1B were analyzed using the R packages “limma,” “org. Hs.eg.db,” “clusterProfiler,”The R packages “limma,” “reshape2,” and “RColorBrewer” were used for coexpression analyses. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets were obtained from the Gene Set Enrichment Analysis website .All gene expression data were subjected to log2 transformative normalization. Comparisons between normal and cancerous tissues were evaluated using two‐group 33.1We investigated ASF1B expression levels in normal tissues and various cancer tissues using Oncomine. The results indicated that the expression of ASF1B was significantly increased in most cancer types, including bladder cancer (BLCA), brain and central nervous system (CNS) cancer, BRCA, cervical cancer (CESC), colorectal cancer (COAD), esophageal cancer (ESCA), gastric cancer, head, and neck cancer (HNSC), kidney cancer, liver cancer (LIHC), lung cancer, lymphoma, myeloma, ovarian cancer (OV), pancreatic cancer (PAAD), and sarcoma (SARC) Figure . In contTo further assess ASF1B expression across cancers, we used R software to analyze RNA sequencing data obtained from TCGA Figure . We also3.2p < 0.001), CESC (p < 0.001), DLBC (p = 0.042), KICH (p < 0.001), KIRC (p < 0.001), KIRP (p < 0.001), acute myeloid leukemia (LAML) (p = 0.035), lower grade glioma (LGG) (p < 0.001), LIHC (p = 0.001), LUAD (p = 0.003), mesothelioma (MESO) (p < 0.001), PAAD (p < 0.001), PCPG (p = 0.004), STAD (p = 0.028), THCA (p = 0.037), THYM (p = 0.023), and uveal melanoma (UVM) (p =< 0.022) , KIRC , KIRP , LGG , LIHC , LUAD , MESO , PAAD , and UVM  patients with high ASF1B expression displayed decreased survival. However, among the individuals with CESC , GBM , LUSC , SKCM , STAD , and THYM , those with high ASF1B expression had increased survival.To further investigate the correlation between ASF1B expression and prognosis, we conducted survival correlation analyses in 33 cancers using the following metrics: overall survival (OS), disease‐free survival (DSS), disease‐free interval (DFI), and progression‐free interval (PFI). Cox analysis showed that ASF1B expression was significantly correlated with OS in ACC  Figure . Our resp < 0.001), CESC (p = 0.003), KICH (p < 0.001), KIRC (p < 0.001), KIRP (p < 0.001), LGG (p < 0.001), LIHC (p = 0.034), LUAD (p = 0.0018), LUSC (p = 0.030), MESO (p < 0.001), PAAD (p < 0.001), PCPG (p = 0.002), PRAD (p = 0.002), and UVM (p = 0.0039). In these analyses, PCPG showed the highest hazard ratio (HR = 5.061). Kaplan–Meier analyses also indicated that high ASF1B expression was correlated with decreased DSS in patients with ACC , KIRC , KIRP , LGG , LUAD , MESO , PAAD , and PRAD . However, this association was reversed in CESC , DLBC , LUSC , and STAD . We further detected a correlation between high ASF1B expression and decreased DFI , LIHC (p = 0.012), LUAD (p = 0.004), PAAD (p = 0.004), PRAD (p < 0.001), SARC (p = 0.013), STAD (p = 0.015), and THCA (p = 0.001). Kaplan–Meier analyses also indicated that high ASF1B expression was associated with diminished DFI in individuals with KIRP , LIHC , LUAD , and THCA . Conversely, increased ASF1B expression was predictive of increased DFI in STAD . Forest plots indicated that high ASF1B expression was correlated with lower PFI in ACC (p < 0.001), CESC (p = 0.003), COAD (p = 0.043), KICH (p < 0.001), KIRC (p = 0.001), KIRP (p < 0.001), LGG (p < 0.001), LIHC (p = 0.002), LUAD (p = 0.036), MESO (p = 0.002), PAAD (p < 0.001) PCPG (p < 0.001), PRAD (p < 0.001), THCA (p = 0.041), and UVM (p = 0.001) , KIRP , LGG , LUAD , MESO , PAAD , and THCA . Meanwhile, increased ASF1B expression was correlated with increased PFI in individuals with CESC , COAD , LUSC , and STAD . Interestingly, patients with LIHC  showed decreased PFI in early disease stages but increased PFI in late disease stages, while the opposite trend was observed in individuals with uterine carcinosarcoma UCS .Moreover, DSS analyses showed that high ASF1B expression predicted adverse outcomes in individuals Figure  with ACCI Figure  in KIRP ) Figure . KM surv3.3p = 0.0066), CHOL , ESCA , LIHC , LUAD , LUSC , and THYM  had lower ASF1B expression. However, patients >65 years with LGG  and PRAD  had higher expression of ASF1B relative to patients <65 years. No prominent correlation between age and ASF1B expression was observed in patients with other cancer types , ESCA , KICH , KIRC , KIRP , and LUAD . Moreover, in individuals with ACC , KIRC , KIRP , LIHC , and LUSC , ASF1B expression was higher in stage III tumors than in stage I tumors. Intriguingly, in patients with THCA  and BRCA , ASF1B expression was higher in stage II tumors than stage I tumors but was not dramatically different in stage III or IV tumors compared to stage I tumors. Therefore, we surmised that, in patients with these advanced cancers, high ASF1B expression was directly responsible for decreased survival. Notably, although the ASF1B expression differences between stage I and IV tumors were remarkable, the expression differences between tumors of other stages were relatively small . Overall, the expression of ASF1B was positively correlated with TMB in 21 cancer types, including ACC, UCS, UCEC, THCA, TGCT, STAD, SKCM, SARC, PRAD, PAAD, MESO, LUSC, LUAD, LGG, KIRC, KICH, HNSC, GBM, COAD, BRCA, and BLCA  across cancers.CA Table , and negCA Table . We furtCA Table , and negCA Table .3.5An increasing number of studies have revealed that the TME plays a crucial role in tumorigenesis and progression.3.6+ T cell, CD4+ T cell, macrophage, neutrophil, and DCs. We found that ASF1B was positively correlated with the infiltration of B cell (p < 0.001), CD8+ T cell (p < 0.001), CD4+ T cell (p < 0.001), macrophage (p < 0.001), neutrophil (p < 0.001), and DCs (p < 0.001) in LGG. Besides, high levels of B cells (p < 0.001), CD8+ T cells (p = 0.01), CD4+ T cell (p < 0.001), macrophage (p < 0.001), neutrophil (p < 0.001), and dendritic cell (p = 0.01) was related to poor prognosis of patients , which was negatively correlated with survival rate (p = 0.035), indicating that high expression of ASF1B predicted poor prognosis in patients with KIRP, which was in accordance with the results in Figure p = 0.012), and higher B cell infiltration suggested a better prognosis (p < 0.001). Although the mechanisms were different, high levels of ASF1B also predicted poor prognosis of LUAD patients, which was compatible with our previous findings  and low levels of neutrophils in tumor tissue leaded to poor patient prognosis (p = 0.001). Hence, high expression of ASF1B was correlated with poor outcomes of patients with MESO  and increase the infiltration of neutrophil (p < 0.001) in UVM to reduce the survival time of patients with UVM , chemokine, and chemokine receptor proteins. Heat map results showed that nearly all immune‐associated genes except CCL27 were coexpressed with ASF1B and that the major immune‐related genes were positively correlated with ASF1B in HNSC, KIRC, KIRP, LGG, LIHC, and THCA Figure . We alsoNext, we analyzed GO functional annotations and KEGG pathways associated with ASF1B in various cancers Figure . The dat4The present study demonstrated that the ASF1B gene was highly expressed in 20 cancers; immunohistochemical (IHC) results supported this trend at the protein level. Our findings in CESC, BRCA, and LUAD resemble the results of previous studies.Kaplan–Meier survival analyses revealed that high ASF1B expression is associated with poor prognosis in several cancers. A previous study revealed that the upregulation of ASF1B promotes the proliferation of CESCs by forming a stable complex with CDK9.Moreover, we found that ASF1B expression is associated with age in some cancers. ASF1B expression was lower in younger patients with BRCA, CHOL, ESCA, LIHC, LUAD, LUSC, and THYM than in older individuals, while higher ASF1B expression was detected in younger patients with LGG and PRAD. These discoveries may have implications for the selection of immunotherapy therapy regimens for patients of different ages. Our study also found that in most tumors, ASF1B expression correlated with tumor stage; it was particularly relevant in distinguishing between stage I and stage IV tumors. In patients with ACC, BLCA, BRCA, ESCA, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, and UVM, ASF1B expression was higher in stage IV tumors than in stage I tumors. These results clearly suggest that ASF1B can be used as a prognostic biomarker in multiple cancers.Previous studies have shown that TMB reflects the overall neoantigen load, thus influencing the efficacy of immunotherapy.TME characteristics can affect the sensitivity of tumor cells to immunotherapy and influence clinical results.At present, few studies have reported on the immunological role of ASF1B in cancer, and it is generally believed that ASF1B is a promoter of cell proliferation that can also act on the cell cycle.In conclusion, our first pan‐cancer analysis of ASF1B shows that this gene is highly expressed in most tumor tissues compared to normal tissues and reveals an association between ASF1B expression and clinical prognosis. Our results also suggest that ASF1B may be an independent prognostic factor in multiple cancers and that high ASF1B expression is related to poor prognosis in major tumor types. However, the specific role of ASF1B in each tumor type should be further investigated. Furthermore, ASF1B expression is associated with TMB, MSI, and immune cell infiltration in various cancers. Its effect on tumor immunity varies depending on the cancer type. These discoveries may contribute to clarifying the role of ASF1B in tumor development and provide new regulatory targets for more precise and personalized immune antitumor strategies.The authors declare that there are no conflicts of interest.Ximing Xu and Xiaoqin He designed this study. Hua Zhu and Xiaoyu Zhang analyzed the data. Hua Zhu and Xinyao Hu wrote the article. All authors read and approved the final manuscript.This study was approved by the Ethics Committee of Renmin Hospital of Wuhan University.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.