Background: s/Aims: The critical view of safety (CVS) prevents bile duct injury, but severe inflammation hinders its achievement.This study evaluated the parkland grading scale (PGS) as a real-time intraoperative predictor of CVS attainment in a Southeast Asian population, establishing standardized clinical data for future artificial intelligence applications. Methods: This prospective observational study (January–June 2025) included 88 consecutive patients undergoing laparoscopic cholecystectomy by a single surgeon. PGS was assessed upon initial laparoscopic inspection. We analyzed correlations between PGS, CVS attainment (Strasberg’s criteria), operative time, and bail-out procedures using Spearman’s correlation and multivariate logistic regression. Results: Severe inflammation (PGS grade 4–5) was observed in 31.8% of patients. PGS exhibited a strong negative correlation with CVS score (p = −0.652; p < 0.001) and strong positive correlations with operative time and blood loss. A PGS threshold of 4 accurately predicted CVS failure (area under the curve = 0.863). Bail-out procedures were necessary in 11 cases (12.5%), all occurring in the PGS ≥ 4 group. Advanced age and diabetes mellitus were independent risk factors for CVS failure. Conclusions The PGS serves as a precise intraoperative early warning system. A score ≥ 4 indicates a significant risk of CVS failure, prompting safer bail-out strategies. Additionally, this study provides a standardized dataset vital for training future autonomous surgical risk assessment models.

Objective Exploring genetic differences among geographic strains of Anopheles sinensis in China. Methods Based on the mitochondrial cytochrome oxidase subunit I(COⅠ )and ribosomal the second internal transcribed spacer(ITS2), genetic distance analyses were conducted on An. sinensis samples collected nationwide. Results Genetic differentiation was observed within An. sinensis geographic strains, primarily reflected in the genetic distances of COⅠ sequences, with some distances between samples exceeding those between closely related species. However, ITS2 sequences showed relatively low levels of differentiation within geographic strains. No significant separation was observed, and gene exchange exhibited a random pattern among geographic strains. Conclusions Significant differences in genetic distance were observed among An. sinensis samples, along with evidence of interspecific continuity among sibling species. For the identification of closely related species using wild-collected Anopheles mosquitoes samples, reliance solely on commonly used barcodes such as COⅠ and ITS2 may be insufficient. Additional molecular markers, including COⅡ and D3 gene fragments, should also be considered. Caution should be exercised when using COⅠ genes to identify cryptic species, especially within Anopheles mosquitoes.

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.

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:To investigate the efficacy of enteral nutrition combined with trocar puncture with a sump drain in the treatment of Crohn's disease(CD)complicated by intra-abdominal abscess.Methods:A descriptive case series study was conducted.Clinical data of CD patients with intra-abdominal abscess who received enteral nutrition and trocar puncture with a sump drain at the Intestinal Fistula and Intra-abdominal Infection Center of Nanjing BenQ Hospital between June 2018 and December 2022 were retrospectively collected.Laboratory parameters,including white blood cell(WBC)count,C-reactive protein(CRP),and procalcitonin(PCT),were recorded at baseline(pre-puncture)and on days 1,3,and 7 post-puncture.Changes in clinical indicators before and after intervention were compared.Results:All 19 CD patients with intra-abdominal abscess successfully underwent CT-guided trocar puncture with a sump drain,with fistulography confirming the presence of intestinal fistula.No post-procedural complications,including puncture site infection,new-onset intestinal fistula or intra-abdominal hemorrhage,were observed.The median treatment duration was 58.3 days(range:14～300 days).10 patients achieved spontaneous healing while 9 eventually underwent surgical intervention for unresolved fistulas.Post-puncture WBC levels decreased significantly on days 1,3,and 7 compared to baseline(P<0.05).Serum CRP levels increased markedly on day 1 post-puncture(P<0.05)but declined significantly by days 3 and 7(P<0.05).No statistically significant difference was observed in serum procalcitonin levels before and after puncture(P>0.05).Conclusion:The combination of enteral nutrition and trocar puncture with a sump drain is safe,feasible,and effective for managing CD complicated by intra-abdominal abscess.

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.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

OBJECTIVE: To describe survival trends and global patterns of esophageal cancer (EC) using survival data from population-based cancer registries. METHODS: We systematically searched PubMed, EMBASE, Web of Science, SEER, and SinoMed databases for articles published up to 31 December 2023. Eligible EC survival estimates were evaluated according to country or region, period, sex, age group, pathology, and disease stage. RESULTS: After 2010, Jordan exhibited the highest age-standardized 5-year relative survival rates (RSRs)/net survival rates (NSRs) at 41.1% between 2010 and 2014, while India had the lowest, at 4.1%. Survival rates generally improved with diagnostic age across most countries, with significant increases in South Korea and China, of 12.7% and 10.5% between 2000 and 2017, respectively. Survival was higher among women compared to men, ranging from 0.4%-10.9%. Survival rates for adenocarcinoma and squamous cell carcinoma were similar, differing by about 4%. In China, the highest age-standardized RSRs/NSRs was 33.4% between 2015 and 2017. Meanwhile, the lowest was 5.3%, in Qidong (Jiangsu province) between 1992-1996. CONCLUSION Global EC survival rates have improved significantly in recent decades, but substantial geographical, sex, and age disparities still exist. In Asia, squamous cell carcinoma demonstrated superior survival rates compared to adenocarcinoma, while the opposite trend was observed in Western countries. Future research should clarify the prognostic factors influencing EC survival and tailor prevention and screening strategies to the changing EC survival patterns.
Humans ; Esophageal Neoplasms/mortality* ; Registries ; Male ; Female ; Survival Analysis ; Middle Aged ; Survival Rate ; Aged ; Global Health