Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

To develop and validate a model for predicting intensive care unit (ICU) mortality risk in patients with malignant tumors complicated by acute respiratory failure (ARF) based on an explainable machine learning framework.

Objective To investigate the role and regulatory mechanism of LINC00657 in the progression of cervical cancer. Methods Bioinformatics analysis predicted potential binding sites between LINC00657 and miR-30a-5p and between miR-30a-5p and Skp2. These sites were verified by using RNA immunoprecipitation and dual-luciferase reporter experiments. LINC00657, miR-30a-5p, and Skp2 mRNA expression levels in cervical cancer tissues and cell lines were assessed by utilizing RT-qPCR. Western blot analysis was employed to examine the protein levels of Skp2 in cells and subcutaneous xenograft tumor models in nude mice. Immunohistochemistry was applied to analyze Skp2 expression in animal tissues. The cellular processes of cervical cancer cell lines were evaluated through CCK-8, scratch, and Transwell assays. Results LINC00657 and Skp2 presented binding sites for miR-30a-5p. In cervical cancer, LINC00657 and Skp2 showed high expression levels (P<0.05), whereas miR-30a-5p displayed low expression (P<0.05). Functional experiments demonstrated that linc00657 upregulates Skp2 expression, a process that is dependent on its sequestration of miR-30a-5p. Conclusion LINC00657 promoted the malignant progression of cervical cancer by upregulating Skp2 expression through specifically sequestering miR-30a-5p, thereby relieving its inhibitory effect on the target gene Skp2.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Objective To summarize the clinical efficacy of modified Morrow surgery in the treatment of hypertrophic obstructive cardiomyopathy. Methods A retrospective analysis was conducted on the clinical data of patients with hypertrophic obstructive cardiomyopathy treated with modified Morrow surgery at Zhongshan Hospital Affiliated to Fudan University from 2020 to 2023. Results A total of 318 patients were enrolled, including 156 males and 162 females, with an average age of (55.6±13.1) years. Preoperative echocardiography showed a mean interventricular septal thickness of (18.1±3.8) mm, peak left ventricular outflow tract pressure difference of (86.4±24.9) mm Hg. The surgery time was (162.3±51.0) min, extracorporeal circulation time was (80.9±31.0) min, and aortic occlusion time was (44.8±20.8) min. After the surgery, transesophageal echocardiography showed that the interventricular septal thickness was (11.0±1.8) mm and left ventricular outflow tract peak pressure difference was (9.4±5.1) mm Hg. The incidence rate of postoperative complete left bundle branch block was 45.3%, Ⅲ° atrioventricular block was 3.8%, and postoperative newly developed atrial fibrillation was 3.1%. The postoperative hospital stay was (6.6±4.9) days, and one perioperative death occurred, with a mortality rate of 0.3%. The follow-up time was (10.3±9.4) months, during which the transthoracic echocardiography revealed a ventricular septal thickness of (12.9±2.9) mm and a peak left ventricular outflow tract pressure difference of (13.9±10.0) mm Hg. Conclusion The modified Morrow procedure for the treatment of hypertrophic obstructive cardiomyopathy is safe and effective, with good results in the short and medium term.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Objective This study aims to investigate the effects of varying durations of light exposure on body weight and learning and memory abilities of pubertal NIH mice. Methods Forty pubertal NIH mice, evenly split by gender and with similar initial weights, were subjected to a 12 h light-dark cycle for one week. They were then randomly assigned to groups with daily light exposure durations of 0, 6, 12, 18, and 24 hours, with 8 mice in each group. The experimental period lasted for 7 weeks, with the first 5 weeks as the feeding phase under different light exposure conditions, and the last 2 weeks as the behavioral testing phase. Their body weight was monitored, and learning and memory abilities were assessed using the T-maze, object location test, and eight-arm maze tests. Results During the light exposure period, there were no significant differences in body weight among groups (P>0.05). However, the weight gain of mice in the 24 h group was significantly higher than that of the 0 h group and the 6 h group during the second and third weeks of light exposure (P<0.05). After five weeks of light exposure, in the T-maze test, the latency time of the 0 h light exposure group was significantly longer than that of the 12 h group (P<0.01), and the latency time of the 24 h light exposure group was significantly longer than that of the 12 h group (P<0.05). In the object location test, the mice in 12 h group exhibited a higher discrimination index and spent more time observing the new location compared to the other groups, with significant differences in comparison to the 18 h group (P<0.01) and the 24 h group (P<0.05). In the eight-arm maze test, the time to find food, the reference memory error rate, and the working memory error rate in the 12 h group were all lower than those in the 0 h group, with significant differences (P<0.05). Moreover, the working memory error rate in the 24 h group was higher than that in the 12 h group, with significant differences (P<0.05). Conclusion Continuous 24 h light exposure affects body weight gain, while light exposure durations exceeding 18 h or below 6 h per day weaken the learning and memory abilities of NIH mice.

BACKGROUND:Lijin manipulation can promote skeletal muscle repair and treat skeletal muscle injury.However,the formation of fibrosis and scar tissue hyperplasia are closely related to the quality of skeletal muscle repair.To study the regulatory effect of Lijin manipulation on the formation of fibrosis and scar tissue hyperplasia is helpful to explain the related mechanism of Lijin manipulation to improve the repair quality of skeletal muscle injury. OBJECTIVE:To explore the mechanism of Lijin manipulation to improve the repair quality of skeletal muscle injury in rabbits,thereby providing a scientific basis for clinical treatment. METHODS:Forty-five healthy adult Japanese large-ear white rabbits were randomly divided into blank group,model group and Lijin group,with 15 rats in each group.Gastrocnemius strike modeling was performed in both model group and Lijin group.The Lijin group began to intervene with tendon manipulation on the 3rd day after modeling,once a day,and 15 minutes at a time.Five animals in each group were killed on the 7th,14th and 21st days after modeling.The morphology and inflammatory cell count of gastrocnemius were observed by hematoxylin-eosin staining,the collagen fiber amount was observed by Masson staining,the expression of interleukin-6 and interleukin-10 in gastrocnemius was detected by ELISA.The protein and mRNA expressions of paired cassette gene 7,myogenic differentiation factor,myoblastogenin,alpha-actin,transforming growth factor beta 1,and type Ⅰ collagen were detected by western blot and RT-PCR,respectively,and the expression of type Ⅰ collagen protein was detected by immunohistochemistry. RESULTS AND CONCLUSION:Hematoxylin-eosin staining and Masson staining showed that compared with the model group,inflammatory cell infiltration and collagen fiber content decreased in the Lijin group(P<0.01),and the muscle fibers gradually healed.ELISA results showed that compared with the model group,the expression of interleukin-6 in the Lijin group continued to decrease(P<0.05),and the expression of interleukin-10 increased on the 7th day after modeling(P<0.05)and then showed a decreasing trend(P<0.05).Western blot and RT-PCR results showed that compared with the model group,the protein and mRNA expressions of paired cassette gene 7,myogenic differentiation factor,myoblastogenin in the Lijin group were significantly increased on the 14th day after modeling(P<0.05),but decreased on the 21st day(P<0.05);the protein and mRNA expressions of alpha-actin,transforming growth factor beta 1,and type Ⅰ collagen in the Lijin group were significantly decreased compared with those in the model group(P<0.05).Immunohistochemical results showed that the expression of type Ⅰ collagen in the Lijin group was significantly lower than that in the model group(P<0.05).To conclude,Lijin manipulation could improve the repair quality of skeletal muscle injury by inhibiting inflammation,promoting the proliferation and differentiation of muscle satellite cells,and reducing fibrosis.

BACKGROUND:Piezoelectric materials can catalyze the generation of reactive oxygen species,which can destroy bacteria by multiple ways without causing drug resistance.This indiscriminately attack bacteria strategy has obvious advantages over traditional antibiotic therapy,thus providing a novel idea for antibacterial strategies. OBJECTIVE:To summarize the properties and antibacterial mechanisms of piezoelectric materials and discuss the application status of several piezoelectric materials in the field of anti-bacteria. METHODS:The literature search was performed in PubMed,Web of Science,CNKI,and WanFang databases.Chinese search terms were"piezoelectric materials,piezoelectric catalysis,reactive oxygen species,antibacterial,bacterial infection,anti-infection,drug resistance."English search terms were"piezoelectric materials,piezoelectricity,piezoelectric catalysis,piezocatalysis,reactive oxygen species,ROS,bacterial infection,antibacterial strategies,anti-infection,drug resistance,drug-resistant bacteria."Retrieval time was from January 2013 to December 2023.Primary screening was conducted by reading the titles and abstracts.Repetitive studies and irrelevant articles were excluded.Finally,68 articles were included for review after literature quality evaluation. RESULTS AND CONCLUSION:(1)Piezoelectric materials are stable and environment-friendly materials,most of which show good biocompatibility.(2)Piezoelectric materials can catalyze a large amount of reactive oxygen species in the process of piezoelectric effect,combined with extracellular oxidation and intracellular oxidation,reactive oxygen species can destroy the membrane of bacteria,intracellular proteins,enzymes,and nucleic acids,disorder the structure and function,even kill the bacteria.The antibacterial performance is related to the rate of catalytic generation of reactive oxygen species,and the catalytic efficiency is related to many factors such as material system,morphology,and external conditions.(3)Reactive oxygen species producted by piezoelectric catalysis can kill bacteria without selectivity and show spectral antibacterial activity.This strategy does not rely on antibiotics and does not cause drug resistance.(4)Combined with the advantages of non-invasive,controllable,and penetrating ultrasound,piezoelectric materials will have significant value and great potential in the future as adjunctive or alternative treatments for drug-resistant bacterial infections and other fields.(5)The current challenge of low catalytic efficiency of piezoelectric materials limits its application in the field of antibacterial,how to improve the piezoelectric catalytic efficiency has become the focus of scholars'attention.