The purpose of this study was to analyze the injury characteristics of patients and therapeutic strategies for patients injured in the last three big earthquakes in China, so as to provide a reference for the improvement of emergency plans for earthquakes. The analysis was based on the data provided by the Mianyang Central Hospital (MCH) from May 12th, 2008 to September 26th, 2017. Microsoft EXCEL software was used for data input, and SPSS was used for statistical analysis. A total of 1,390 earthquake-related patients were hospitalized in MCH. Most patients were admitted to the hospital within the first 2 weeks after the earthquake. The main causes for seismic injuries involved hit/strike by objects or building collapse /burying. Extremity fractures accounted for most injuries, especially 3 days after an earthquake. But soft tissue injuries cannot be neglected. Most earthquake patients were mainly treated by means of surgery and the majority were related to orthopedics. We found that different areas, population, and religions needed a tailored approach to the rescue effort. Therefore, the earthquake magnitude scale has a significant influence on mechanisms, types and severity of the injury of patients injured in earthquakes, as well as their timely transfer, management, and prognosis. Traumatic injuries are very common and thereby various surgical procedures especially orthopedic and neurosurgery are the domain of treatment modalities. Disaster preparedness and combined surgical team effort need to be focused on to reduce both mortality and morbidity. On August 8th, 2017, at 9:19 pm China Standard Time, a Ms 7.0 earthquake struck the Aba prefecture government in the Sichuan province of China. The seismic intensity was higher than level IX on the Mercalli intensity scale, and the earthquake epicenter in Zhangzha County could reach level VIII . It had The Mianyang Central Hospital (MCH), a national-level academic medical center in Sichuan Province, China, is one of the largest city hospitals in the Sichuan province with 1,500 sickbeds. After the Jiuzhaigou earthquake, The MCH immediately launched an emergency response the earthquake sent a rescue team including doctors, nurses, and security experts to the area that was hit by the earthquake, and established field hospitals in 2 days. From August 9th, 2017 to August 11th, 2017, a total of 48 earthquake victims were transferred to the MCH or field hospitals, and all were hospitalized. So far, the MCH had taken part in the rescue after 3 earthquakes and admitted roughly 2,000 earthquake patients from Wenchuan, Yushu, and Jiuzhaigou earthquakes.Although earthquakes are natural disasters that cannot be prevented or forecasted, the damage can be minimized with effective disaster planning as well as successful emergency rescues. Therefore, comprehensive research on the cause, characteristics, and treatment of injuries caused by earthquakes is needed. For example, a lot of victims in earthquakes suffer injuries that need surgical intervention, but the main gaps in knowledge about the earthquake epidemiology of earthquakes compromise the surgical delivery. What is the distribution and burden of surgical conditions following earthquakes? According to the studies after the 1988 Armenian earthquake, the head, superficial and lower extremity injury occupied most of the cases . FurtherIn our study, we retrospectively reviewed the clinical parameters and treatment measures of earthquake patients after three large earthquakes occurred in different geolocations, such as mountain valleys and cities, with different crowds, including tourists and residents, and different ground shaking intensities. In conclusion, our study will draw lessons for the future, and determine effective measures to better deal with different earthquake victims under different circumstances.This is a retrospective study of the clinical characteristics of 48 Jiuzhaigou earthquake patients that were collected between August 9th, 2017 and August 11th, 2017, 160 Yushu earthquake patients between April 16th, 2010 and April 20th, 2010, and 1,182 Wenchuan earthquake patients between May 12th 2008 and June 30th 2008. In this retrospective study, a total of 1,390 earthquake-related patients in the MCH and MCH field hospitals were reviewed. Most patients were sent to the hospital within the first 2 weeks after the earthquake. Inclusion criteria were as follows: patients who were directly or indirectly injured by Jiuzhaigou, Yushu, or Wenchuan earthquakes. Patients with non-injury-related diseases, such as stress disorder, delivery, and internal diseases, or not hospitalized in MCH were excluded from the study.Data collection and evaluation of Jiuzhaigou, Yushu, and Wenchuan earthquake patients were performed according to a standard protocol . UniformInjury causes were based on the primary reason for the injury, such as hit/strike by objects, slips/falls, and building collapse/burying. The attending physician diagnosed injury location and injury type. Injury location and injury type were based on the final hospital diagnosis confirmed by the attending physician. Injury severity was evaluated using the Injury Severity Score (ISS), which is the most commonly used scoring system for assessing a patient's injury level during trauma care . The ISSp < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS 17.0 software.Categorical variables are presented as frequencies and percentages, and continuous variables are presented as averages, standard deviations, and median values. The Chi-square analysis was used for comparison of clinical variables between different parameters, and the Fisher exact test was used in cases where the patient number was <5). A Our study was approved by the Ethical Committee of the Mianyang Central Hospital .p > 0.05). However, a significant difference was observed in age (p < 0.05). We found that most of the patients in the three earthquakes were younger than 60 years old, especially in the Jiuzhaigou earthquake. In addition, educational backgrounds and occupations were also different in the three major earthquakes . The majority wounded in the Wenchuan and Jiuzhaigou earthquakes have a high school education or above. In the Yushu earthquake, however, more patients are below the primary school. Medical staff and teachers were more vulnerable to the earthquake in Wenchuan and Jiuzhaigou earthquakes. But in the Yushu earthquake, most of the wounded were Farmers. Besides, building type and marital status were also different in the three earthquakes . Most of the building types in the Wenchuan earthquake were concrete, whereas it was mud+concrete in the Jiuzhaigou and mud in the Yushu earthquake.As shown in p < 0.05). Wenchuan earthquake patients and Jiuzhaigou earthquake patients were primarily injured by the hit/strike of objects. However, most patients in the Yushu earthquake suffered from building collapse/burying-related injuries. Thus, the most common types of injury between the three earthquakes were different (p < 0.05). Most survivors of the Wenchuan and Jiuzhaigou earthquakes suffered from fractures, while the majority of survivors of the Yushu earthquake suffered from soft tissue injury. In addition, we showed that there was a significant difference in ISS as well as in treatment when comparing Jiuzhaigou earthquake patients to Wenchuan and Yushu earthquake patients (p < 0.05).p < 0.001). In overall surgical procedures, orthopedic surgery appeared to be the most frequent followed by neurosurgery, plastic surgery, and general surgery.By comparing the treatment plans of this three earthquakes, we found that Wenchuan and Jiuzhaigou earthquake patients were mainly treated by surgery, while Yushu earthquake patients were mainly treated by non-operation treatment (p < 0.05), Wenchuan earthquake patients stay in the hospital longer.A total of 42 cases died in the Wenchuan earthquake and the mortality rate of hospitalized patients was 3.55%. The main causes of death were severe chest trauma and head injury (47%), crush syndrome (23%), infection (12%), and multiple organ failure (18%). length of hospital stay between these three earthquakes was different  took up the largest part of the operated orthopedic surgeries, and debridement was the second largest. Similarly, as the main neurosurgical surgery, decompressive craniectomy took on the highest frequency. In terms of plastic surgery, debridement accompanied with incision, split-skin graft, and infection drainage showed the top occurrence rate. Meanwhile, debridement and laparotomy occupied the most part the aspect of general surgery.During the stages of final hospital diagnosis from 12 hours to a week, fractures were the predominant injury type of Jiuzhaigou and Wenchuan earthquake victims, whereas soft tissue injury was the main injury type among Yushu earthquake patients . Thus, aWe further analyzed the distribution of our hospital surgeries at different time points . OrthopeEarthquakes have become a major cause of death and disability worldwide . In the In this study, MCH, one of the best city hospitals in China's Sichuan province, has provided medical support to over 1,500 earthquake-related patients from three major earthquakes . Therefore, we believe that MCH injury analysis is highly representative and reliable.The clinical parameters of patients who survived Wenchuan, Yushu, and Jiuzhaigou earthquake showed that compared to patients who survived the Jiuzhaigou earthquake they were relatively young (36.7 year old), and their age difference was significantly from the age of patients of Wenchuan (42.17 year old) and Yushu earthquake (42.5 year old). This may be because most Jiuzhaigou earthquake patients were tourists, whereas, in the Yushu and Wenchuan earthquakes, most patients were residents. This reason can also explain the difference in marital status between these the patients who survived earthquake. Interestingly, there was a higher possibility that medical and educational workers get harmed in Yushu and Wenchuan earthquakes. The reason lies in that they tended to save others' lives than their own. Teachers bear more responsibility for the safety of students, while doctors and nurses show up and offer help in seriously hit regions. These all render them at a higher risk of getting injured. But the most injured in the Jiuzhaigou earthquake are students, this is because firstly Jiuzhaigou is a tourist attraction, and August is the summer vacation, so many students travel there.p < 0.05). This may be because Yushu is a poverty-stricken area in China where people live under poor conditions. Most buildings were prepared from mud (59.4%), which could easily collapse during an earthquake. Therefore, building collapse or burying was the predominant cause of injuries in patients from Yushu. Most buildings in Wenchuan and Jiuzhaigou were tall buildings from reinforced concrete  or mud + concrete  during an earthquake, something could fall from the building, thereby, injuring individuals by hit/strike of objects. In addition, Wenchuan and Jiuzhaigou are considered mountain areas, and building collapse and falling rocks are common geohazards in these mountainous areas. Lastly, Wenchuan and Jiuzhaigou earthquakes occurred at night and in the early morning. People's field of vision was limited and it was impossible to accurately judge the falling objects. Thus, individuals in Wenchuan and Jiuzhaigou earthquakes are more likely to be injured by the hit/strike of objects, especially during an earthquake.Through analysis of injury characteristics of earthquake patients, injuries among the Jiuzhaigou earthquake patients were mostly due to hit/strike of objects (50%), and this kind of injury causes also had a high proportion (47.8%) in Wenchuan earthquake patients. Our result was in accordance with the findings of a previous report . FurtherIn this study, we showed that arms and legs were the most frequently injured locations, which in Jiuzhaigou, Yushu, and Wenchuan earthquake patients accounted for an injury percentage of 62.5, 72, and 60.6%, respectively. These findings were consistent with data presented in a previous report –21. Headp < 0.001). This may be because both Yushu and Jiuzhaigou earthquakes were less powerful compared to the Wenchuan earthquake.Additionally, our data showed that fractures accounted for 42% of injuries, whereas soft tissue injuries such as blunt trauma and bruise counted for 46.2% of injuries in patients who survived the Yushu earthquake. These findings were not consistent with Wenchuan and Jiuzhaigou earthquakes, in which patients with fractures reached over 70%. The possible reason is most Yushu earthquake patients were caused by the collapse of mud or wooden buildings and burying, resulting in more soft tissue injury. Although soft tissue injuries are not fatal, they can also cause serious infections, myoglobinemia, malnutrition, and internal environmental disorders. The mortality rate of patients with severe infections and crush syndrome will also increase significantly. However, in most Wenchuan and Jiuzhaigou earthquake patients were caused by hit/strike by heavy objects , resulting in fractures. It is important to note that fracture injuries may occur both during and after an earthquake. These results may help emergency response centers predict the type of patient that needs treatment and thereby lead to a more rational allocation of limited medical resources. In addition, based on the ISS, patients were classified as suffering from mild or moderate and severe injuries. In our study, 75% patients of Jiuzhaigou patients had an ISS score <16, and only 25% of patients had an ISS score ≥16, which was similar to that of Yushu earthquake patients. However, in patients of the Wunchuan earthquake, the ISS score ≥16 reached 50.6%. The difference of injury severity between patients who survived the Wenchuan, Yushu, and Jiuzhaigou earthquakes was extremely significant . Most individuals in Yushu are Tibetans, who are important members of the Chinese nation. This population has its own unique culture and medicine named Tibetan Medicine  ranks top in the area of orthopedic surgery (31– 42% in three earthquakes), and debridement the second (14–33% in three earthquakes). Based on prior research, organ failure and pulmonary complications could be greatly reduced by giving early fixation, which would contribute to a higher rate of survival. However, early definitive treatment for multiple injury patients may have side effects, indicating that the best time of operation for multiple injury patients is 2–5 days after injury. In Neurosurgery, decompressive craniectomy (42–57% in three earthquakes) was the commonest surgery performed in our study. Here it appears to be the second most common surgery after orthopedics in the post-earthquake scenario. Nonetheless, patients with multiple injuries may suffer from side effects if they are given definitive treatment early. This means that 2 to 5 days after trauma should be the optimum operating time for them. On the basis of our research, decompressive craniectomy is the most commonly operated in the field of neurosurgery. However, it falls to the second performed after orthopedics if it is a post-earthquake situation. In a similar study of the 1999 Taiwan earthquake , around In our study, only Wenchuan documented 42 cases of mortality (0.4%). The main causes of death were severe head injury, crush syndrome, infection, and multiple organ failure. Especially, chest trauma and craniocerebral trauma tend to cause patients to die quickly within a short period of time after the injury. Therefore, the post-earthquake treatment of patients with chest or craniocerebral trauma greatly affects their therapeutic effects.Analyze the length of hospital stay, treatments are mainly given to the patients directly transferred from the epicenter in the early stage after the earthquake, and the severe patients from other front-line hospitals in the later stage. During a considerable period of time, patients may arrive at hospitals successively. Nonetheless, there may not be an obvious peak in the speed of treatment, which requires hospitals to rationally arrange the allocation of medical resources to avoid the bad consequences caused by the underestimation of the arrival of the wounded in the later stage of rescue. For earthquakes with lower magnitude and better traffic situations, patients may pour in in a short time. As they can all move outward in a short period of time, it is hard for the continued flow of the wounded to happen. In our study, we found out that a low ground shaking earthquake or earthquake area has better traffic conditions than Jiuzhaigou and Yushu earthquakes, most patients (about 80%) arrived at the hospital in a short period of time (<3 days). For this reason, hospitals should make enough preparation in advance to avoid the deficient treatment resulting from insufficient medical resources in the early stage of rescue.It is worth noting that injuries may be further aggravated by traffic, climate problems, and geological. Infections frequently happen among the injured victims during transportation. Yang et al.  indicateAlthough the results of this study provide important information for the clinical management of earthquake-injured patients, there are some limitations. First, in this retrospective study, the medical records of earthquake patients were analyzed, which were created by many different doctors. It is impossible to verify the intra-doctor and inter-doctor variability in making a diagnosis. Hospital medical record keeping was another issue in serious disaster situations. Detailed medical records of all hospitalized patients were often incomplete because of the initial disorganization, especially during the first few hours after an earthquake. In addition, some patients had been treated in other hospitals, which may lead to a selection bias in the data. Lastly, since our research is based on the patients only in our hospital, the research has certain limitations. However, MCH hospital was the major provider of medical services to patients in the Wenchuan, Lushan, and Jiuzhaigou earthquakes, and over 1,000 earthquake victims were admitted to our hospital. Therefore, although a number of records are missing, we are confident about the accuracy and authority of our data.In our study, we found that different geographic locations, populations, and religions need a tailored approach to the rescue effort. First, the geophysical damage degree exerts an obvious influence on the injury mechanism of earthquake casualties. In earthquakes of high magnitude, the number of those injured by heavy objects and total burying rises greatly. Correspondingly, there is a reduction in fall injuries for patients. However, it results in more serious illnesses and an even higher death rate. Besides, the geophysical damage degree clearly affects the evacuation of the injured after the earthquake. The higher the earthquake magnitude is, the later the fastigium of patient evacuation to the hospital would be, and there may even be no obvious peak. Nevertheless, evacuation time can be significantly prolonged, and there will be a lasting impact on the work of hospitals. Correspondingly, in earthquakes of lower magnitude, the impact of which on casualty evacuation is small, the fastigium of their arrival in hospital is obviously earlier, the duration of patient flow shorter, and the time of influence on hospital work less. In the field of earthquake medical rescue, the strength of orthopedics is absolutely the most important, but it is also necessary to strengthen the rescue forces of thoracic neurosurgery and general surgery. Lastly, the popularization of correct knowledge of emergency shelters will help to reduce the damage caused by blindly jumping or escaping while an earthquake happens. We hope that the results of this study will be of use in future natural disasters in China and the rest of the world.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethical Committee of the Mianyang Central Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.DW and XD designed experiments. SX, BS, and JY carried out experiments and took the photos. SX, BS, MH, and PY analyzed experimental results. SX, WX, and GL wrote the manuscript. SX and ZS revised the manuscript. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.Background: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction. Methods: The supervised feature selection (Lasso) and unsupervised feature-selection methods  were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation. Results: In experimental group_A, the best AUCs  were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF , the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF , achieving the highest AUC scores in all three evaluation items . By the method in this study, comparing the best AUC of F Ischemic stroke is the primary reason for disability and the second-leading cause of death worldwide . The surCerebral blood flow (CBF) is an essential physiological parameter to evaluate the state of brain tissue in the clinic. Normal blood flow transmission can provide blood oxygen for brain tissue and maintain a stable brain ,8. HowevIn addition, two factors affecting the rehabilitation of stroke patients are the neurological impairment degree of the patients and the used treatment strategy, and the degree of neurological impairment is one of the influencing factors in deciding the treatment strategy. Therefore, the accurate assessment of neurological impairment in stroke patients is significant for treating and rehabilitating stroke. The main parameter for evaluating the degree of neurological impairment in stroke patients is the National Institutes of Health Stroke Scale (NIHSS) . The NIHAccurate outcome prediction will assist in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect . SeveralThe information in medical images is crucial for the prevention, detection, treatment, and outcome prediction of ischemic stroke. Thus, extracting valuable information from medical images is an effective technique in clinical practice. Nowadays, radiomics, an innovative method to quantify high-dimensional features from medical images, is widely used in medical image processing. For example, it is used to investigate tumor heterogeneity ,31 and i(1)This study explored the role of DRF in ischemic stroke. First, the radiomics features of 3D images in the time series of DSC-PWI were used to obtain the DRF of the whole brain. Then feature selection and dimensionality reduction methods were used to generate various combinations. Finally, by comparing the effects of multiple features in stroke diagnosis, NIHSS evaluation, and outcome prediction, the clinical value of DRF in stroke treatment and outcome prediction can be proved, providing a potential tool for clinical application.(2)In this study, the DRF of the whole brain were extracted instead of lesion features, which reduced the process of lesion segmentation and saved time for clinical treatment.(3)This study can extract useful features related to the target using feature analysis, reducing the problem of enormous computation costs caused by the direct analysis of a four-dimensional (4D) DSC-PWI image.This study aims to explore the role of the whole-brain dynamic radiomics features (DRF) of DSC-PWI in the diagnosis of ischemic stroke, the assessment of neurological impairments, and outcome prediction. The main contributions lie in the following three aspects.Detailed materials and methods are introduced in the following subsections. The materials are described in The Institutional Review Boards approved this retrospective study of Shanghai Fourth People’s Hospital, affiliated with the Tongji University School of Medicine and exempted from informed consent. The datasets in our study were collected by the neurology department of the Shanghai Fourth People’s Hospital, affiliated with the Tongji University School of Medicine, China, from 2013 to 2016. A total of 156 DSC-PWI images from 88 patients were retrospectively reviewed and included. All patients were imaged within 24 h of symptom onset, and 22 patients were screened at least twice during pretreatment and post-treatment. After clinical examination, 78 (50%) DSC-PWI images were diagnosed as ischemic stroke. The primary clinical information includes income NHISS, outcome NHISS, and 90-day mRS. The DSC-PWI image for each patient was scanned on a 1.5T MR scanner , and The proposed method in this study includes four steps: preprocessing DSC-PWI datasets and computing DRF, feature selection and combination strategy, and evaluating the performance of the four combinations of DRF.(A)Registration and smoothing of DSC-PWI datasetsThe preprocessing is intended to reduce noise and position deviation impacts. First, it includes registering all of the volumes in the time series, smoothing the voxel in the time series, and splitting the skull and brain tissue. This study corrected the DSC-PWI datasets for potential patient motion by registering all of the volumes in the time series. Then, a triple moving average filter was selected to smooth the data voxel-by-voxel with a 1 × 3 filtering kernel. The registration method was introduced in Refs. , and j ∈ .(C)Unsupervised feature selectionFeature selection aims to find the most compelling feature representing the target variable and compress the feature space. Lasso has been recognized as one of the most effective feature selection methods for selecting relevant features to the target variable ,48,49. T seen in . The matn_components to 10, each dimensionality-reduction method reduced the significant DRF to 10 features. The obtained dimension-reduction DRF were defined as the name of the dimension-reduction method. For example, the dimension-reduction features obtained from PCA can be defined as PCA, and the dimension-reduction DRF in PCA were defined as {PCA0, PCA1, …, PCA9}.(D)Feature combination strategyThis study adopted unsupervised feature selection methods to extract various additional DRF. As one of the unsupervised feature selection methods, the feature dimension-reduction algorithm (DRA) has been applied. In detail, the DRAs used in this study include principal component analysis (PCA), independent component correlation algorithm (ICA), t-distributed stochastic neighbor embedding (TSNE), uniform manifold approximation and projection (UMAP), and isometric feature mapping (ISOMAP). All of the feature dimension-reduction methods were implemented by importing the corresponding package in python 3.6 seen in . By settt-test, Lasso and DRA } included significant DRF Ft-test, outstanding DRF Lasso, and dimension-reduction feature DRA . Second, experimental group_B was the combination of Lasso and each DRA , which can be defined as group_B {Lasso + DRA }. The signal ‘+’ means combination. Third, the experimental group_ C was the collection of the selected outstanding DRF from DRA  by Lasso, which was defined as group_C {Lasso, item)}. Finally, experimental group D was the combination of Lasso and each group_C, and the set was group D {Lasso + Lasso, item)}. Based on the above, four experimental groups can be obtained for each evaluation item in this study.The feature combination strategy in this study was used to generate four experimental groups for each evaluation item seen in . First, This study used ten supervised machine learning models to fully evaluate the effectiveness of the feature sets in the four experimental groups described above seen in d. The arThe results are divided into four sections, including preprocessing results, generated four experimental groups, and the performance of four experimental groups. The details are shown in the following.According to the ischemic stroke detection by RAPID, this study detected 78 (50%) ischemic stroke cases in 156 DSC-PWI images. The number of NIHSS equal to zero was 95 (60.9%), and the 90-day mRS less than 2 (good outcome) was 101 (66%) seen in . The groFor each 4D DSC-PWI image, 83700 DRF (50 3D images × 1674 features) could be calculated seen in . These Dt-test analysis, this study extracted 25,822 significant DRF for ischemic stroke detection, and there were 5118 DRF in First_order, 7698 in GLCM, 3800 in GLDM, 4117 in GLRLM, 3737 in GLSZM, and 1352 in NGTDM. Their p-values ranged from 0.0123 ± 0.0144. Furthermore, 8324 significant DRF with p-values 0.0232 ± 0.0156 were extracted for NIHSS assessment. Among them, 2061 DRF were in First_order, 2655 in GLCM, 866 in GLDM, 1016 in GLRLM, 1289 in GLSZM, and 437 in NGTDM. Furthermore, 9203 significant DRF with p-values 0.0238 ± 0.0144 were extracted for outcome prediction, and there were 2089 DRF in First_order, 2650 in GLCM, 1304 in GLDM, 1254 in GLRLM, 1439 GLSZM, and 467 in NGTDM. The detailed statistics of each significant DRF were introduced in By the t-test, ischemic stroke detection). Among them, there were only 1 DRF in First_order, 9 in GLCM, 13 in GLDM, 5 in GLRLM, 4 in GLSZM, and 2 in NGTDM. Besides, 32 and 40 outstanding DRF were selected for NIHSS evaluation and outcome prediction, respectively. In the Lasso , there were 7 outstanding DRF in First_order, 5 in GLCM, 5 in GLDM, 2 GLRLM, 4 in GLSZM, and 8 in NGTDM. In the Lasso , there were 6 outstanding DRF in First_order, 21 in GLCM, 9 in GLDM, 2 in GLSZM, 2 in NGTDM, and none in GLRLM. All of the outstanding DRF for the three evaluation items were completely different, with only one DRF (F4) selected for both ischemic stroke detection and outcome prediction. Thus, a total of 105 outstanding DRF were selected. The outstanding DRF were renamed as Fk, and k represents the order of outstanding DRF.This study used the Lasso algorithm to select outstanding DRF from significant DRF for each evaluation item. As a result, 34 outstanding DRF were selected for ischemic stroke detection and defined as Lasso  between outstanding DRF and ground truths for ischemic stroke detection ranged from 0.158 to 0.451. For NIHSS evaluation, the absolute R-values between outstanding DRF and ground truths for NIHSS evaluation ranged from 0.156 to 0.365, and the absolute R-values between outstanding DRF and ground truths for outcome prediction ranged from 0.161 to 0.334. The results showed that the selected outstanding DRF had weak or moderate correlations with ischemic stroke, NIHSS, and outcome seen in b–d.This study’s feature dimension-reduction algorithms are unsupervised feature selection methods, and each feature dimension-reduction algorithm can obtain the same set of dimensionality-reduction DRF for the three evaluation items. The R-value of each dimension-reduction DRF with the corresponding ground truth was calculated and is shown in This study used the Lasso algorithm to extract the outstanding dimension-reduction DRF from the dimension-reduction DRF in PCA, ICA, TSNE, ISMOP, and UMAP. As a result seen in , none ofThis study evaluated the performance of the four constructed experimental groups in classifying and predicting neurological impairment by ischemic stroke detection, NIHSS evaluation, and outcome prediction. Among the four experiments, the combination DRF in experimental group_D achieved the best score in the three evaluation items. In particular, the combinations of Lasso + PCA_Lasso and Lasso + TSNE_Lasso achieved the same highest score, respectively, which proved the potentially significant value of DRF in the clinical treatment and prognosis analysis of stroke. The detailed results were introduced as follows.t-test selected by t-test analysis in the three items. In detail, the significant DRF achieved the best AUC of 0.731 for ischemic stroke detection, 0.652 for NIHSS assessment, and 0.679 for outcome prediction. In terms of ischemic stroke detection, the AUCs of dimension-reduction DRF obtained by PCA, TSNE, UMAP, ICA, and ISOMAP ranged from 0.672 ± 0.033, 0.664 ± 0.036, 0.681 ± 0.040, 0.684 ± 0.034, and 0.687 ± 0.027, respectively. In contrast, all of the AUCs of outstanding DRF selected by Lasso were better than 0.731, ranging from 0.819 ± 0.064, and the best AUC was 0.837. In terms of NIHSS assessment, the AUCs of dimension-reduction DRF obtained by PCA, TSNE, UMAP, ICA, and ISOMAP ranged from 0.540 ± 0.038, 0.529 ± 0.025, 0.502 ± 0.050, 0.531 ± 0.045, and 0.551 ± 0.050, and the best one of them had an AUC of 0.649. The outstanding DRF from Lasso achieved the best AUC of 0.795, and the performance of significant DRF (best AUC = 0.652) was better than all of the dimension-reduction DRF (best AUC = 0.649). In terms of outcome prediction, the AUCs of dimension-reduction DRF obtained by PCA, TSNE, UMAP, ICA, and ISOMAP ranged from 0.544 ± 0.025, 0.544 ± 0.025, 0.560 ± 0.049, 0.562 ± 0.029, and 0.602 ± 0.036. Similarly, the best AUC of 0.818 was achieved by outstanding DRF from Lasso, and the performance of significant DRF (best AUC = 0.679) was better than all of the dimension-reduction DRF (best AUC = 0.646).For experimental group_A seen in , the outt-test) in experimental group_A.For experimental group_B , the perFor experimental group_C , the selFor experimental group_D , the comThe ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value , which ct-test can obtain an AUC of 0.731 for ischemic stroke, 0.652 for NIHSS assessment, and 0.679 for outcome prediction. It can be seen that DRF can reflect the changes in blood flow and assess the state of brain tissue and the degree of damage to brain nerve function.This paper is an exploratory work based on the DRF of the whole brain. In DSC-PWI images, the intensity of voxels changes under the action of the contrast agent, resulting in changes in 3D image features. Therefore, the 3D image features can reflect the process of the propagation of the contrast agent in the brain tissue and then indicate the state of blood flow propagation. Furthermore, ischemic stroke is a vascular disease that causes tissue and neurological damage due to vascular blockage. Therefore, there is an inevitable relationship between the extracted time-related DRF and ischemic stroke. Some studies reported that the intensity drop of the ischemic tissue was less than normal tissue, and the decline rate was slower than in normal tissues . In addit-test. This also means that Lasso was the best one among all of the feature selection methods. Finally, the outstanding DRF in experimental group_A and outstanding dimension-reduction DRF in experimental group_C were combined, achieving the best score for the three evaluation items. Thus, it can be concluded that different combinations of Lasso and dimension-reduction algorithms could achieve different results for stroke detection, NIHSS assessment, and outcome prediction.Lasso and dimension-reduction algorithms have been used in various scenes of ischemic stroke analysis ,49,63,64Although the results showed that the combination of outstanding DRF and outstanding dimension-reduction DRF selected from Lasso could improve performance in stroke detection, NIHSS assessment, and outcome prediction, further optimization of the models can be regarded as one future work. We will validate our improved method with the more extensive and varied datasets before applying it to clinical trials in future work. The results in this study do not mean that the models can be used alone for stroke treatment decision-making. Instead, this study should be considered a support tool in stroke treatment guidance.t-test in experimental group_A and the best_AUC experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.In conclusion, although different feature selection methods on whole-brain DRF achieved diverse performance, the critical role of whole-brain DRF in ischemic stroke detection, NIHSS assessment, and outcome prediction has been proven. From experimental group_A to experimental group_D, it can be concluded that the combination of outstanding DRF with outstanding dimension-reduction DRF in experimental group_D performed best in all of the three evaluation items. Comparing the best AUC of F


In 2020, many students lost summer opportunities due to the COVID-19 pandemic. We wanted to offer students an opportunity to learn computational skills and be part of a community while stuck at home. Because the pandemic created an unexpected research and academic situation, it was unclear how to best support students to learn and build community online. We used lessons learned from literature and our own experience to design, run and test an online program for students called the Science Coding Immersion Program (SCIP). In our program, students worked in teams for 8 hours a week, with one participant as the team leader and Zoom host. Teams worked on an online R or Python class at their own pace with support on Slack from the organizing team. For motivation and career advice, we hosted a weekly webinar with guest speakers. We used pre- and post-program surveys to determine how different aspects of the program impacted students. We were able to recruit a large and diverse group of participants who were happy with the program, found community in their team, and improved their coding confidence. We hope that our work will inspire others to start their own version of SCIP. In the summer of 2020, the COVID-19 pandemic caused many summer research programs and internships to be delayed or canceled. A survey in the United States reported that 44.5% of research opportunities for undergraduate students were canceled , which lAt San Francisco State University (SF State) in-person learning was halted in March of 2020 and the SF State campus stayed almost entirely closed for summer research in biology and chemistry, which led to a huge loss of opportunity for our students. To provide our students a way to learn new skills despite the campus closure, we decided to create a new virtual program called the Science Coding Immersion Program (SCIP); this program was partly based on our previous experience with an on-campus summer coding program for non-computer science undergraduates  and otheBased on our aims, we had three major objectives for SCIP: (1) create a virtual workspace structure that could work while students were sheltered in place during the pandemic; (2) provide a space to build community for students; and (3) increase student confidence in coding as a scientific skill and enthusiasm for coding.virtual space was created for students to meet and communicate with other science students, using Zoom for team meetings and webinars, a Slack space for announcements and questions, and Google documents for instructions. The second objective of SCIP was addressed through our method for community building, in which participants work in teams for 8 hours a week; the team meetings were structured like study groups with a designated (volunteer) team leader who hosted the Zoom meetings and kept track of time. SCIP focused on community building primarily because of the known positive impact of community on learning. In the social theory of learning, learning communities can reduce attrition and advance learning  offering multiple scheduling options to accommodate other summer activities of participants; (2) offering participants the choice to learn either Python or R; and (3) creating a system for successful communication between participants, team leaders and program coordinators.The base structure of SCIP was adapted from the ten-step guide to support non-computer science undergraduates in learning how to code in a summer program . For SCISCIP was designed to be flexible with participants’ summer schedules and interests. Potential conflicts included summer classes, other virtual summer programs, part-time jobs and family commitments. When the program was introduced rapidly in 2020 due to the short turnaround of program cancellations from the pandemic, the program included staggered start dates, which allowed coordinators to start with a small number of participants and to incorporate time for changes in running the program. It also allowed the large volume of registrants to join at a time that best suited their schedules. Therefore, participants chose their preferred Monday start date from 5 consecutive Mondays in 2020. The duration of the program varied from 8 weeks for the early start dates to 6 weeks for the later start dates. With sufficient time to advertise and organize as such in 2021, the program established one start date for all participants, with the same duration for the program. In 2021, all participants therefore started on the first Monday of June for a 6-week program.Along with program start dates, participants were also asked in the registration to indicate their (1) available times , (2) programming language of choice, and (3) experience level with coding prior to the program. Each participant was also asked to indicate their willingness to be a team leader. To be a team leader, interested participants were expected to have leadership experience of any kind, but they did not need to have previous coding experience. Coordinators then created teams of 4–8 participants and 1 team leader based on these factors to ensure participants’ availability, as well as similar interests and experience.Data Analysis with R  and Introduction to Python Programming . In addition to R and Python, we added an image analysis option in 2021, using ImageJ as a learning platform. This was a small part of the program with just two image analysis teams, mostly with students who were slated to participate in another workshop at the end of the summer offered by the NSF-supported Center for Cellular Construction. With these selected courses, participants were encouraged to work at their own pace and not rush to finish the course. For those who were interested in more structure, we provided a timeline with suggested course sections to go through per week but with the emphasis that participants did not have to follow it if it did not support their learning.Each team worked independently on a course that was chosen by the program coordinators . The couSlack was used as the primary communication tool between the SCIP coordinators and the participants . To assure efficient communication, all participants were required to join our Slack workspace before starting the program. The purpose of the platform was to provide a space for participants to communicate with their teams, SCIP coordinators, and any other participant in the program. General channels were accessible to everyone for webinar reminders and general announcements, and a question forum was available for participants to ask questions and share tips. In addition, a separate channel for each team was created to discuss coding and meeting topics outside of their team meetings, if necessary. These team channels only included members of the team and no coordinators in order to provide a space for team participants to freely discuss questions, answers, and messages.Zoom was used for all team meetings and for the webinars. Teams used Zoom to meet virtually with their group and work on their Python or R courses four times a week for two hours. At the start of the program, team leaders were tasked to create a Zoom link and facilitate team meetings. Weekly webinars were also held through Zoom to provide participants insight on research and career options within the Biology and Chemistry/Biochemistry fields that use coding skills. Guest speakers ranged from professors to SF State alumni in graduate programs and/or biotechnology jobs.immersion aspect of the program, which allows participants to learn both as part of a community and individually. In SCIP, teams meet for 8 hours a week and about 4 of those hours are designated as “quiet working time”, which was based on the “Shut Up and Write” idea that is used in writing groups . Participants were encouraged to work on their online course during the “quiet working time” in their team meetings only, and not outside of the team meetings. Therefore, participants learned new skills while they were in Zoom meetings with each other during the reserved meeting time, rather than working on it in their own time. This structure allowed for participants to work on their course individually, but have a community and support system in which they can reach out to when in need of support and guidance. Knowing that learning computer programming can be very frustrating, we structured our program this way so that participants would never struggle with coding tasks on their own .What sets SCIP apart from other online learning structures is the Zoom meetings were hosted and facilitated by team leaders who were also peers. Studies have shown that peer-led teams have a positive impact on participants’ performance, retention, and attitude . Most of our team leaders had little-to-no coding experience (survey data not shown). To better align with the coding experiences of the participants, team leaders were assigned to teams with similar coding experience and learned the same material as everyone else in the team. Additionally, SCIP coordinators created a schedule for them to use in the meetings with very detailed information for the first few days and less detailed later on . The matTeam leaders were instructed to make sure that each Zoom meeting consisted of time to talk  and time to study  while the Zoom meeting was muted . Teams wThe program provided a curated list of suggested readings, inspired by the idea of scientist spotlights . Scientist spotlights are curriculum supplements that let students learn about counter-stereotypical scientists. In addition to scientist spotlights, the curated reading list also included blog posts and short articles about computer programming. The goal of these readings was to let SCIP participants learn about both frustrations and successes from others who have come before them. In order to achieve this specific goal, we included short interviews with SF State alums who have dealt with frustration as they were learning to code  and how they have overcome difficulties  and found success  .Evaluation of program effectiveness was analyzed through data from two participant surveys: the pre- and post-program assessments. Participation in the survey was voluntary, and all participant survey responses were collected anonymously. Because group start dates were staggered in 2020, the pre-assessment survey was given after the last offered start date of the program and the post-assessment survey was given at the end of the entire program. For 2021, pre-assessment was given on the first day of the program since all participants started on the same day and post-assessment was sent out at the end of the program.Numerical rating scale questions, in the form of Likert-type data, were asked in both assessments to evaluate confidence in coding as well as effectiveness of the program design. In the post-assessment, some questions were open-ended to elicit broader comments or suggestions about the SCIP program. The study was IRB exempt. Data and code for data analysis are available in a GitHub repository  of the participants were undergraduate students, 35.7% were master’s students and the remaining participants comprised of post-baccs, faculty and staff . In the Slack and Zoom - were chosen and used throughout the program (https://seo.sfsu.edu/), as well as by the Department of Biology.In order to streamline communication, two communication platforms -  program . These pAccording to our post-survey assessment, 95.7% (88/92) of our participants felt that using the Zoom meetings with their teams were “Effective”, “Somewhat Very Effective” or “Very Effective” in 2020, and 89.5% in 2021 . For SlaParticipation and retention rates were assessed in three different ways within the program. First, we counted the number of students who let us know that they left the program. Based on these numbers, 95.9% and 92% of participants retained throughout the program in 2020 and 2021, respectively. Secondly, retention of participants in the program was assessed through the completion rate of the pre-assessment and post-assessment surveys. The survey completion rate for the pre-assessment was 76.2% in 2020 (112 out of 147 participants) and 92.8% for 2021 (128 out of 138) in 2021. Survey completion rate for the post-assessment survey was 62.6% (92 out of 112) for 2020 and 55.1% (76 out of 128) for 2021. Third, we collected Zoom team photos, which were taken in the last two weeks of the program, and referenced them as a form of retention evaluation. Referencing these photos and accounting for the 7 teams (40 participants) who did not submit a team photo, 74.8% of the (80 out of 107) participants were present in the team photo in 2020. In 2021, we received photos from all teams and 81.2% (112 out of 138) of participants were present in their team photo. We believe that the team photos from the last week of the program are a good measure of retention, which means that more than 80% of our participants stayed for the entire program.Would you recommend SCIP to others?”. Nearly all of participants would indeed recommend SCIP to others (99% 2020 and 97% 2021)  /C.We were interested in determining participants’ coding experience prior to SCIP and participants’ coding confidence before and after SCIP. In both cohorts, the majority of the participants assessed had no or little experience with coding: 50.7% and 50.8% had no experience in coding, and 18.7% and 31.7% had less than 1 semester of experience, in 2020 and 2021 respectively . To findHere, we report only on the participants for which we could pair the pre-assessment and post-assessment surveys. In 2020, we were able to pair 75 pre- and post-assessments. In 2021, we were able to pair 63 pre- and post-assessments. 7 paired survey observations from 2021 were excluded because the participants took part in the Image Processing groups that did not learn coding skills. For SCIP Summer 2020 and Summer 2021, median coding confidence from all participants increased significantly in both cohorts. The median coding confidence for all participants increased 2 points - from 1 in the pre-assessment to 3 in the post-assessment - with a p-value of 4.32e-16 (df=4) and n = 75 for Summer 2020 and a p-value of 3.71e-14 (df=4) and n = 63 for Summer 2021. Since the p-values are less than 0.05, we can infer that the increase in coding confidence for all participants (team leader and regular participant) was significant /BFor team leaders, the median coding confidence for both cohorts increased by 1 point - from 2 in the pre-assessment to 3 in the post-assessment - with a p-value of 1.65e-04 (df = 4) and n = 23 for Summer 2020 and a p-value of 0.04 (df=4) and n = 15 for Summer 2021. Based on the p-value, we can infer that team leaders had a significant increase in median coding confidence. Furthermore, we analyzed regular participants’ median coding confidence. The median coding confidence for regular participants increased 2 points in both cohorts, from 1 in the pre-assessment to 3 in the post-assessment - with a p-value of 1.07e-11 (df=4) and n = 52 for Summer 2020 and a p-value of 1.03e-12 (df = 4) with an n = 48 for Summer 2021 /D/E/F. AWhat is one thing you liked about the program?” to determine whether our aim of creating a community had been achieved. Survey responses were first split into a list of words, and then the frequency of the words was determined. Common conversational filler words such as “the”, “a”, and “is” were omitted. We created a word cloud to illustrate the most commonly utilized words in the open-ended prompt  and recorded whether the response mentioned any of the following: the team aspect of the program, the community aspect, support, not graded, learning, own pace, skills. In 2020, 23% of the respondents wrote something positive about the team aspect chemistry students, which we started in response to the pandemic in 2020. We have described how we organized the program, who we were able to recruit and how it created a community, and how it impacted student’s coding confidence. While we started SCIP because of the lockdown in 2020, we have now learned that there is a lot of interest even when there is no lockdown. For students with summer jobs or family obligations, it is often hard to commit to full time internships or summer programs, yet these students can more easily join an online and part time summer program like SCIP.Overall, participants seemed to generally approve of SCIP’s program design, and we can infer that our program design can be used to provide participants a good opportunity to learn a skill for their career and build community. Participants generally approved of the Zoom meetings and Slack environment. Slack data shows that around 120 participants were active on Slack every week and around 80 posted each week , so the Slack environment was used well. We found that participants were somewhat less happy with the webinars, although it is not clear if that was because they didn’t participate or because they didn’t find the content useful. Future iterations of the program will look into this.We used several different methods to estimate retention rates for the program and believe that the team photos from the last week of the program are a good measure of retention, which means that more than 80% of our participants stayed for the entire program. While we will always strive for higher retention rates, we feel that 80% retention is good for a program that is entirely voluntary and doesn’t give academic credit. If the students stay it is because they enjoy the program and/or feel that it is useful.One goal of SCIP was to help students feel part of a community during a period when they were not on campus. Although labs began to open up by 2021, live classes at SF State were still very limited and a significant amount of time was spent with remote learning. We gauged the scientific community by (1) analyzing who joined SCIP, (2) participation and retention in the SCIP program and (3) the level of satisfaction in the program, including plans to continue learning coding and (4) an analysis of how students felt about the program using open-ended questions on the post-assessment survey.We recruited students mainly from the Biology and Chemistry/Biochemistry departments at San Francisco State University. We included messages in the department newsletters, sent emails to students in our classrooms , and to all students in our research training programs . It was made clear that the program was free and students would not earn academic credit for it. All students majoring in Biology, Chemistry, or Biochemistry who applied were accepted to the program. We also accepted a few students from other schools, some alums from our own departments and several interested faculty and staff.Latinx and Black students are often underrepresented in computing fields. Therefore, we wanted to determine whether Latinx and Black students are represented in this program as well. Our program had a somewhat lower number of Latinx students compared to the departments we recruited from. This could be in part due to the fact that we allowed participants to check multiple ethnicities, in which case we counted them as mixed-race. Furthermore, although the Black student population is not large in general at SF State, our demographics mirrored those in the Departments of Biology and Chemistry/Biochemistry, and increased in 2021. Based on our findings, we see that Latinx and Black student representation in our program mostly matches the proportion of students from the departments we recruit from.What is one thing you liked about the program?” and we found that participants especially appreciated the team-based and community aspects of the program.To get a deeper understanding of what part of our program’s learning structure worked well for participants, we offered survey respondents an open-ended prompt to discuss what they enjoyed about the program. The wording of the prompt was as follows: “Most of our participants had little or no coding experience, and we were interested to determine if our program increased their coding confidence. We found that the median coding confidence for participants  increased by 1–2 points. These results showed that SCIP participation increased individual levels of coding confidence, providing evidence for the effectiveness of a virtual learning program for science students learning programming in R and Python.Assessment of confidence in coding was also analyzed through interest in taking a coding class in the future through the survey responses. Of the participants who completed a post-program survey, over 75% of individuals were planning or were considering taking a coding class in the future. Even of those with no prior experience, more than two-thirds of participants responded that, after the program, they were considering or planning to take a class with a coding component in order to further their coding experience .Our program and assessment have some limitations. In 2020, the “pre-program” survey was actually done mid-program. This could have impacted the coding confidence results, but they were actually very similar in both years, which suggests that the results were valid even in 2020. In addition, because the program was not graded and participants did not submit any work to provide documentation for learned materials, we cannot easily judge how much they learned. However, we did ask participants how far they progressed in the online class that they were assigned. We found that in 2021 42% of participants who did the post-program survey indicated they finished the entire class and another 42% indicated that they finished three-quarters of the class. In 2020 35.2% indicated they finished the entire class and 46% indicated that they finished three-quarters of the class. All others completed one quarter or half of the class. Future iterations of this program may include more concrete survey questions to determine how much students have learned.The impact of the pandemic on our student population included loss of income, loss of family members, and loss of research and internship experiences. We developed a virtual program that provided students with opportunities to learn a new skill and acquire confidence in this skill, while also building a community from behind a computer screen. Two faculty and three part-time staff members managed over 130 participants in this virtual environment in 2020 and 2021. Retention was high over the 6–8 week period and participant feedback was very positive. Coding confidence increased in nearly all participants, and the participants appreciated the community and support they found in the self-paced program.“I liked the fact that my team was open to communicating in and out of zoom. I felt comfortable talking to them and asking them questions”, “I liked that we all had fun doing it, struggling, teaching each other” and “It was a safe supportive environment: everyone was really nice. I liked getting to know my team members”.Participant comments in the open-ended questions suggested that the community was an important part of the learning environment: It has become clear that the challenges of shifting from in-person learning to online learning, have been far greater than just technical adjustments. Studies worldwide have shown that the lockdown associated with the COVID-19 pandemic has had many impacts on student mental health, including changes in eating behaviors, sleep, anxiety, depression and suicidal thoughts . Given tFinally, as science becomes more interdisciplinary, coding and data science are clearly taking a prominent role in biological and (bio)chemistry research. The increase in confidence gained by our participants, many of them from historically underserved groups, could lead to more racial and gender diversity in fields not typically diverse, but where these computational skills are particularly important - such as neuroscience, computational biology, evolutionary biology and ecology. Taking advantage of the successes of our virtual program, we aim to offer this program to more students and also hope that other campuses will learn from our program. The SCIP method can be used within specific research fields, to draw students from across the country with this free format.Our future goals are to create new online courses with diverse representation amongst the course instructors to offer as part of future SCIP programs, as a mechanism to increase coding skills amongst our students and to increase diversity in computational fields.Supplement 1Supplement 2Supplement 3Supplement 4


Tobamovirus and one of the most significant plant-infecting viruses, for the first time in a chrysanthemum in Thailand using reverse-transcription polymerase chain reaction (RT–PCR). The TMV-infected chrysanthemum leaves exhibited mosaic symptoms. We conducted a sequence analysis of the coat protein (CP) gene and found that the TMV detected in the chrysanthemum had 98% identity with other TMV isolates in GenBank. We carried out bioassays and showed that TMV induced mosaic and stunting symptoms in inoculated chrysanthemums. We observed the rigid rod structure of TMV under a transmission electron microscope (TEM). To enhance the speed and sensitivity of detection, we developed a colorimetric RT loop-mediated isothermal amplification (LAMP) technique. We achieved LAMP detection after 30 min incubation in isothermal conditions at 65 °C, and distinguished the positive results according to the color change from pink to yellow. The sensitivity of the LAMP technique was 1000-fold greater than that of RT–PCR, and we found no cross-reactivity with other viruses or viroids. This is the first reported case of a TMV-infected chrysanthemum in Thailand, and our colorimetric RT–LAMP TMV detection method is the first of its kind.We detected tobacco mosaic virus (TMV), a member of the genus  Chrysanthemum x morifolium) are one of the most popular ornamental plants worldwide due to their cultural significance and variations in shape and color [Chrysanthemums , chrysanthemum virus B (CVB), impatient necrotic spot virus (INSV), tobacco mosaic virus (TMV), tomato aspermy virus (TAV), tomato spotted wilt virus (TSWV), tomato leaf curl New Dehli virus (ToLCNDV), turnip mosaic virus (TuMV), and zucchini yellow mosaic virus (ZYMV) ,7,8,9,10Tobamovirus of the family Virgaviridae, poses a substantial threat to many tropical and subtropical plants [TMV, a member of the genus l plants . This vil plants . TMV is l plants . The genl plants . Researcl plants ,15,16. Tl plants  successfTraditionally, researchers have used polymerase chain reaction (PCR) techniques to detect plant viruses and reverse-transcription (RT)–PCR for RNA viruses . However® Reagent  according to the manufacturer’s instructions and employed it as a template for the synthesis of complementary DNA (cDNA) using ReverTra AceTM qPCR RT Master Mix with gDNA Remover  according to the manufacturer’s instructions.We collected chrysanthemum leaves exhibiting virus-like symptoms, such as mosaic, mottling, chlorosis, vein banding, and malformation, from three cultivation areas in Chiang Mai and one in the Chiang Rai Province of Thailand for detection of TMV using RT–PCR. We extracted total RNA from chrysanthemum leaves using TRIzol® PLUS & PLUS GREEN 2x Master Mixes  and two primers specific to the TMV coat protein (CP) gene (TMV-spec (5′-CGGTCAGTGCCGAACAAGAA-3′) and Tob-Uni1 (5′-ATTTAAGT GGASGGAAAAVCACT-3′)) [TM Nucleic Acid Staining Solution  and visualized them under a DUT-48 Blue Light Transilluminator . We calculated the percentage of disease incidence (PDI) according to the formula presented in [We performed PCR using EconoTaqACT-3′)) . We carrented in .TM 3730xl DNA sequencers . We aligned and analyzed the obtained sequence using Molecular Evolutionary Genetics Analysis (MEGA) version X [We purified the PCR product using PCR Clean-Up and Gel Extraction Kits  according to the manufacturer’s instructions and analyzed it using fluorescent dye terminator sequencing with ABI Prismersion X  and BLASersion X . We consTo confirm the pathogenicity of TMV in chrysanthemums, we examined seedlings (1 month old) of chrysanthemum cultivar Huay Leuk-4. We ground the chrysanthemum leaf that tested positive for TMV with 0.1 M phosphate buffer and used it to mechanically inoculate carborundum-dusted chrysanthemum leaves.To study the host range and symptom induction, we examined 13 indicator plants. We kept the inoculated plants in a greenhouse at 25–28 °C and observed the symptoms daily. We then subjected all inoculated plants to RT–PCR to confirm the presence of TMV.To observe the TMV particles under TEM, we prepared the naturally infected chrysanthemum leaves by dipping and negative staining according to the method described in . Briefly® LAMP Primer Design Tool version 1.1.0  default setting, based on the TMV CP gene sequence retrieved from GenBank (accession no. V01408.1) as a template. All LAMP primers were checked by BLAST to confirm the specificity to TMV.We designed the LAMP primers (forward inner (FIP), backward inner (BIP), forward (F3), backward (B3), loop-forward (LF), and loop-backward (LB)) using the NEB® Colorimetric LAMP 2x Master Mix with UDG . The 25 µL LAMP component contained 12.5 µL WarmStart® Colorimetric LAMP 2x Master Mix with UDG, 2.5 µL LAMP Primer Mix (10×), 1.0 µL target DNA, and 9.0 µL nuclease-free water. We initially tested the LAMP reactions for 60 min incubation at 61, 63, 65, and 67 °C in a dry bath incubator; we then applied the temperature that successfully amplified the LAMP products to determine the optimal incubation duration . We analyzed the LAMP products using 1.5% agarose gel electrophoresis (RT–LAMP–AGE), with staining achieved using RedSafeTM Nucleic Acid Staining Solution . Regarding the colorimetric analysis of the RT–LAMP reaction, a color change from pink to yellow after incubation indicated a positive result, whereas no change indicated a negative result.We performed the colorimetric RT–LAMP reaction using WarmStart0–10−10) and measured the amount of cDNA using NanoDrop 2000c . We performed RT–LAMP according to the optimal conditions.To evaluate the limit of detection (LOD) of the RT–LAMP reaction, we prepared cDNA using a 10-fold serial dilution method , chrysanthemum stunt viroid (CSVd), and cDNA from a healthy chrysanthemum. We performed the RT–LAMP reaction according to the optimal conditions.A total of 110 leaf samples from 13 chrysanthemum cultivars were collected . RT–PCR CP gene and submitted it to GenBank with the accession no. ON075087. After multiple alignment with 13 other TMV sequences from plants in different countries, including Thai peppers (GenBank accession no. AY633749.1) and Egyptian chrysanthemums (GenBank accession no. GU982315.1), the BLAST analysis and SDT-based color-coded percent identity matrix showed that the CP gene of the Thai chrysanthemum TMV shared approximately 95–99% identity with the TMV isolates retrieved from the GenBank database , while the Egyptian chrysanthemum TMV belonged to a nearby branch . The phyCapsicum annum (chlorotic spots and vein clearing); Chenopodium amaranticolor and C. quinoa (necrotic spots); Datura stramonium and Nicotiana glutinosa (necrotic spots that developed into necrosis); and N. tabacum cv. Samsun NN, Solanum lycopersicum, and Vigna unguiculata (necrotic spots) (Capsicum annum (mosaic); Chrysanthemum x morifolium (mottling); C. coronarium ; N. benthamiana, N. tabacum cv. Xanthi, and Petunia x hybrida ; and Solanum lycopersicum (mosaic and vein necrosis)  . We obseecrosis) .We observed rigid rod particles approximately 200–300 nm in length under TEM in a naturally infected chrysanthemum leaf prepared by dipping and negative staining b.We designed six LAMP primers: FIP, BIP, F3, B3, LF, and LB . We prep−8, which was 103-fold greater than that of RT–PCR  for RT–LAMP by observing the appearance of LAMP products was 10f RT–PCR a. The LO–PCR , chrysanthemum chlorotic mottle viroid (CChMVd), and chrysanthemum stunt viroid (CSVd), we performed RT–LAMP according to the optimal conditions with other viruses and viroids. We only detected LAMP products in the TMV lane, which was corroborated by the colorimetric results c, showinCassia siamea, Capsicum annum, N. tabacum cv. Xanthi, Petunia x hybrida, and Solanum lycopersicum) to test the efficiency of colorimetric RT–LAMP. All 10 chrysanthemum samples tested negative. The TMV-inoculated indicator plants exhibiting systemic symptoms  tested positive, whereas the inoculated cassia, which exhibited no symptoms, tested negative (We used 10 newly collected chrysanthemum samples from 4 cultivars including Huay Leuk-4 (4 samples), New Day (2 samples), Candor Pink (2 samples) and Celebrate (1 sample) and 6 TMV-inoculated indicator plants  and CP amino acid sequences, respectively [Tobamovirus protein sequences [The genus families . Subsequectively . Presentequences .Researchers have used the RT–LAMP technique to detect many viruses in various plants, such as strawberries and watermelons ,34. The The authors of  developeThe addition of loop primers to the reaction reduced the DNA amplification time to half or one-third that of the original LAMP method by hybridizing the hairpin loops and extending the region between loops . We usedWe employed colorimetric RT–LAMP to test newly collected chrysanthemum samples and obtained no positive results, indicating that chrysanthemum TMV infections in Thailand are uncommon. However, to prevent further TMV infections, we should eliminate any TMV host plants to minimize the spread by aphids. TMV constitutes a potential threat to the production of various crops in Thailand, including chrysanthemums, and further research into TMV transmission in the field, TMV-resistant cultivars and TMV-free plantlets, and effective detection methods is necessary to improve the management of this virus.3-fold that of RT–PCR. The specificity assay found no cross-reactivity with other viruses or viroids. To evaluate the performance of the colorimetric RT–LAMP technique, we tested newly collected chrysanthemum samples and TMV-inoculated indicator plants, which demonstrated effective TMV detection. The colorimetric RT–LAMP method for TMV developed in this study is the first of its kind and can be used for routine detection of TMV due to its speed, accuracy, sensitivity, and specificity.Having identified the first reported TMV infection of a chrysanthemum in Thailand, we isolated and characterized the virus according to its biological and molecular properties. We developed a colorimetric RT–LAMP technique with an enhanced detection efficiency that required 30 min incubation at an isothermal temperature of 65 °C. We detected ladder-like patterns of LAMP products that corresponded to the colorimetric results. We easily determined the colorimetric results by observing the color change from pink to yellow, which indicated positive detection. The LOD of colorimetric RT–LAMP was 10