Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective To evaluate the application value of fascial platform versus commercial platform in transumbilical single-port laparoscopic transabdominal preperitoneal repair(TAPP)for inguinal hernia.Methods One hundred patients with inguinal hernia admitted between January and December 2024 were prospectively enrolled and randomly assigned to either the fascial platform group(n=50)or the commercial platform group(n=50)by using a random number table.The following parameters were compared between the two groups:platform establishment time,operative time,intraoperative blood loss,incision suture time,operative difficulty score,camera manipulation difficulty score,postoperative 24-hour Visual Analogue Scale(VAS)score of pain,postoperative hospital stay,total hospitalization cost,comprehensive satisfaction score,and postoperative complications.Results The fascial platform group demonstrated significantly superior outcomes compared to the commercial platform group in platform establishment time[(3.4±1.0)min vs.(8.1±1.5)min,t=-18.389,P＜0.001],operative time[(67.5±7.9)min vs.(72.2±11.5)min,t=-2.380,P=0.019],intraoperative blood loss[(8.3±4.3)ml vs.(11.1±5.7)ml,t=-2.798,P=0.006],incision suture time[(7.0±1.4)min vs.(11.8±2.7)min,t=-11.212,P＜0.001],operative difficulty score(14.4±3.6 vs.23.1±4.1,t=-11.215,P＜0.001),camera manipulation difficulty score(11.4±2.6 vs.18.3±2.5,t=-13.370,P＜0.001),VAS score of postoperative 24-hour pain(2.4±1.0 vs.3.7±1.1,t=-6.139,P＜0.001),total hospitalization cost[(15.8±1.6)thousand yuan vs.(18.3±1.6)thousand yuan,t=-7.841,P＜0.001],and comprehensive satisfaction score(7.3±1.1 vs.6.3±1.0,t=5.271,P＜0.001).However,there were no statistically significant differences in postoperative hospital stay or postoperative complication between the two groups(P＞0.05).All the patients were followed up for 1-12 months postoperatively[(5.3±1.8)months vs.(5.7±2.0)months,t=-0.945,P=0.347],with no recurrence observed in either group.Conclusions Both fascial platform and commercial platform can be used for transumbilical single-port TAPP.The fascial platform offers advantages including shorter platform establishment time,reduced incision suture time,lower operative and camera manipulation difficulty,decreased cost,less postoperative pain,and higher patient satisfaction.

Metabolic dysfunction-associated steatotic liver disease (MASLD), the most prevalent chronic liver condition globally, lacks adequate and effective therapeutic remedies in clinical practice. Recent studies have increasingly highlighted the close connection between the ubiquitin-proteasome system (UPS) and the progression of MASLD. This relationship is crucial for understanding the disease's underlying mechanism. As a sophisticated process, the UPS govern protein stability and function, maintaining protein homeostasis, thus influencing a multitude of elements and biological events of eukaryotic cells. It comprises four enzyme families, namely, ubiquitin-activating enzymes (E1), ubiquitin-conjugating enzymes (E2), ubiquitin-protein ligases (E3), and deubiquitinating enzymes (DUBs). This review aims to delve into the array of pathways and therapeutic targets implicated in the ubiquitination within the pathogenesis of MASLD. Therefore, this review unveils the role of ubiquitination in MASLD while spotlighting potential therapeutic targets within the context of this disease.

The transcription factor HOXB13 plays crucial roles in cancer development. HOXB13 is abnormally expressed in most cancers, which makes it a valuable therapeutic target for cancer therapy. The level of HOXB13 differs significantly between healthy and cancer tissues, which indicates that the level of HOXB13 is closely related to carcinogenesis. The regulatory network mediated by HOXB13 in cancer proliferation, metastasis, and invasion has been systematically investigated. Moreover, HOXB13 variants play distinct roles in different cancers and populations. By understanding the molecular mechanisms and mutation features of HOXB13, we provide a comprehensive overview of carcinogenesis networks dependent on HOXB13. Finally, we discuss advancements in anticancer therapy targeting HOXB13 and the roles of HOXB13 in drug resistance to molecular-targeted therapies, which serves as a foundation for developing HOXB13-targeted drugs for clinical diagnosis and cancer therapies.
Humans ; Neoplasms/metabolism* ; Homeodomain Proteins/metabolism* ; Carcinogenesis/genetics* ; Mutation ; Gene Expression Regulation, Neoplastic ; Molecular Targeted Therapy ; Drug Resistance, Neoplasm/genetics*

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.