Objective:A predictive model for cirrhosis-associated hepatic encephalopathy (HE) was constructed and validated using a machine learning algorithm to evaluate the predictive efficacy of the model.Methods:Clinical data of patients with liver cirrhosis (4 537 cases) in the medical record system and laboratory information system of the First Hospital of Jilin University from January 2018 to December 2019 were collected and analyzed retrospectively. Based on the inclusion and exclusion criteria, 474 patients were finally included in the study. Cohort 1 included patients with cirrhosis without HE (113 cases) and patients with cirrhosis complicated with HE (108 cases) from January to December 2018, and was used for feature screening, model building, optimal algorithm selection, and internal validation of the cirrhosis complicated with HE risk prediction model. Cohort 2 included patients with cirrhosis without HE (133 patients) and patients with cirrhosis complicated with HE (120 patients) from January 2019 to December 2019 for external validation. Lasso regression was utilized to identify key predictive variables, and various models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), random forest (RF), and support vector machine (SVM) were employed for model building and internal validation. The DeLong test was used to compare the predictive efficacy of the four models for HE, and the optimal algorithm was selected by combining the specificity or sensitivity. The area under the ROC curve, calibration curve and decision curve were applied to evaluate the predictive efficacy, accuracy of predicted probabilities and clinical utility of the model.Results:The 46 tests with<30% missing data in Cohort 1 were extracted as variables to be selected for modeling. Seven characteristic variables were obtained using Lasso regression screening, including hemoglobin (Hb), total bile acid (TBA), cholinesterase, total bilirubin, creatinine, prothrombin activity, and circulating platelets. The prediction model built by the LightGBM algorithm (HE-Lab7 model) predicted HE with an area under the curve (AUC) of 0.880, which was higher than that of XGBoost, RF, and SVM (all P<0.05), with a sensitivity of 0.825 and a specificity of 0.836. The Brier score of the calibration curve was 0.147, indicating that the predicted probability of the model is in good agreement with the actual probability of occurrence. Decision curves indicate that the model has a high clinical benefit. In Cohort 2, the HE-Lab7 model predicted HE with an AUC of 0.775, a sensitivity of 0.927, and a specificity of 0.758. Conclusion:The predictive efficacy of the cirrhosis-associated HE risk prediction model developed based on the optimal LightGBM algorithm using the large-scale test data based on four machine learning algorithms is good, which provides a reference basis for early prediction and identification of cirrhosis-associated HE.

OBJECTIVE: To explore the efficacy differences among different acupuncture frequencies for pain of temporomandibular disorders (TMD). METHODS: A total of 42 patients with TMD pain were randomly divided into a low-frequency group, a medium-frequency group, and a high-frequency group, with 14 patients in each group. All groups received acupuncture treatment at bilateral Hegu (LI4) and Yanglingquan (GB34), as well as ipsilateral Tinggong (SI19), Jiache (ST6), and Xiaguan (ST7), with each session lasting 30 minutes. The low-frequency group received acupuncture once per week, the medium-frequency group received acupuncture twice per week, and the high-frequency group received acupuncture three times per week, for a total duration of four weeks. The graded chronic pain scale (GCPS) score, visual analogue scale (VAS) score, jaw functional limitation scale-20 (JFLS-20) score, and pressure pain threshold (PPT) were assessed in the three groups before and after treatment, as well as at the four-week follow-up after treatment completion. RESULTS: Compared before treatment, GCPS and JFLS-20 scores were significantly decreased in all the groups after treatment (P<0.05), and VAS scores were significantly decreased in the high-frequency and medium-frequency groups (P<0.05), PPT values at different measurement sites were increased significantly in the high-frequency group (P<0.05). After treatment, GCPS, JFLS-20, and VAS scores in the high-frequency group were lower than those in the medium-frequency and low-frequency groups (P<0.05), while some PPT values were higher than the other two groups (P<0.05). At follow-up, GCPS, JFLS-20, and VAS scores remained significantly lower in all the groups compared to baseline (P<0.05), PPT values were increased significantly in the high-frequency and medium-frequency groups (P<0.05), with the high-frequency group showing lower GCPS, JFLS-20, and VAS scores and higher PPT values compared to the other two groups (P<0.05). CONCLUSION Acupuncture three times per week is more effective in reducing TMD pain intensity compared to once or twice per week, and can also alleviate some mandibular functional impairments. The therapeutic effects persist for at least four weeks after treatment completion.
Humans ; Male ; Female ; Adult ; Acupuncture Therapy/methods* ; Temporomandibular Joint Disorders/physiopathology* ; Middle Aged ; Young Adult ; Treatment Outcome ; Acupuncture Points ; Pain Management ; Adolescent ; Pain Measurement

Objective To explore the effectiveness of multimodal comprehensive diabetes management teaching in the standardized training of endocrinology residents.Methods Fifty-nine resident physicians rotating through the endocrinology department from March to October 2024 were selected.They underwent a multimodal comprehensive diabetes management teaching model that integrated standardized theoretical instruction,case dis-cussions,teaching ward rounds,practical operations,and interdisciplinary pharmaceutical education.The effec-tiveness of this teaching model was evaluated through multiple dimensions using a comprehensive diabetes man-agement evaluation form,Mini-Clinical Evaluation Exercise(Mini-CEX)scale,and satisfaction surveys.Results After two months of multimodal standardized training,the results showed:1)The resident physicians demonstra-ted significant improvements in all aspects of comprehensive diabetes management(P＜0.05).2)The Mini-CEX scores of the resident physicians generally reached excellent levels(P＜0.05).3)Both the resident physicians and teaching faculty showed significantly higher satisfaction with the comprehensive diabetes management program after the training(P＜0.05).Conclusions Multimodal comprehensive diabetes management teaching is effective in enhancing the standardized diagnosis and treatment abilities of resident physicians in the endocrinology depart-ment regarding diabetes.

Objective:To construct and validate a predictive model for postoperative pulmonary complications (PPCs) in patients undergoing robot-assisted laparoscopic urological surgery.Methods:This retrospective study included the medical records of 932 patients who underwent robot-assisted laparoscopic urological surgery at the First Affiliated Hospital of Zhejiang University School of Medicine from January 2020 to February 2022. The patients were divided into a training group ( n=559) and a validation group ( n=373) at a 6∶4 ratio. Logistic regression analysis was used to determine the independent risk factors for PPCs, and a nomogram prediction model was constructed based on these factors. The performance of the model was evaluated using the receiver operating characteristic curve and calibration curve, and the clinical benefit was assessed using the clinical decision curve analysis. Results:The independent risk factors for PPCs included advanced age (>60 yr), smoking history, respiratory tract infection within 1 month, preoperative low SpO 2 (<96%), and prolonged length of postoperative hospital stay ( P<0.05), and the body mass index (18.5-<28.0 kg/m 2) was a protective factor. The nomogram prediction model developed based on the aforementioned 6 influencing factors had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.76-0.86) in training group and 0.80 (95% confidence interval 0.75-0.86) in validation group. The calibration curve indicated a good consistency between the predicted and actual occurrence curves, and the clinical decision curve analysis showed good accuracy and net benefit of the prediction model. Conclusions:The predictive model for PPCs is successfully constructed based on age, low body mass index, smoking history, history of respiratory tract infection within 1 month, preoperative low SpO 2 and prolonged length of postoperative hospital stay and has good predictive performance in patients undergoing robot-assisted laparoscopic urological surgery.

Objective:To construct and validate a predictive model for postoperative pulmonary complications (PPCs) in patients undergoing robot-assisted laparoscopic urological surgery.Methods:This retrospective study included the medical records of 932 patients who underwent robot-assisted laparoscopic urological surgery at the First Affiliated Hospital of Zhejiang University School of Medicine from January 2020 to February 2022. The patients were divided into a training group ( n=559) and a validation group ( n=373) at a 6∶4 ratio. Logistic regression analysis was used to determine the independent risk factors for PPCs, and a nomogram prediction model was constructed based on these factors. The performance of the model was evaluated using the receiver operating characteristic curve and calibration curve, and the clinical benefit was assessed using the clinical decision curve analysis. Results:The independent risk factors for PPCs included advanced age (>60 yr), smoking history, respiratory tract infection within 1 month, preoperative low SpO 2 (<96%), and prolonged length of postoperative hospital stay ( P<0.05), and the body mass index (18.5-<28.0 kg/m 2) was a protective factor. The nomogram prediction model developed based on the aforementioned 6 influencing factors had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.76-0.86) in training group and 0.80 (95% confidence interval 0.75-0.86) in validation group. The calibration curve indicated a good consistency between the predicted and actual occurrence curves, and the clinical decision curve analysis showed good accuracy and net benefit of the prediction model. Conclusions:The predictive model for PPCs is successfully constructed based on age, low body mass index, smoking history, history of respiratory tract infection within 1 month, preoperative low SpO 2 and prolonged length of postoperative hospital stay and has good predictive performance in patients undergoing robot-assisted laparoscopic urological surgery.

Objective:A predictive model for cirrhosis-associated hepatic encephalopathy (HE) was constructed and validated using a machine learning algorithm to evaluate the predictive efficacy of the model.Methods:Clinical data of patients with liver cirrhosis (4 537 cases) in the medical record system and laboratory information system of the First Hospital of Jilin University from January 2018 to December 2019 were collected and analyzed retrospectively. Based on the inclusion and exclusion criteria, 474 patients were finally included in the study. Cohort 1 included patients with cirrhosis without HE (113 cases) and patients with cirrhosis complicated with HE (108 cases) from January to December 2018, and was used for feature screening, model building, optimal algorithm selection, and internal validation of the cirrhosis complicated with HE risk prediction model. Cohort 2 included patients with cirrhosis without HE (133 patients) and patients with cirrhosis complicated with HE (120 patients) from January 2019 to December 2019 for external validation. Lasso regression was utilized to identify key predictive variables, and various models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), random forest (RF), and support vector machine (SVM) were employed for model building and internal validation. The DeLong test was used to compare the predictive efficacy of the four models for HE, and the optimal algorithm was selected by combining the specificity or sensitivity. The area under the ROC curve, calibration curve and decision curve were applied to evaluate the predictive efficacy, accuracy of predicted probabilities and clinical utility of the model.Results:The 46 tests with<30% missing data in Cohort 1 were extracted as variables to be selected for modeling. Seven characteristic variables were obtained using Lasso regression screening, including hemoglobin (Hb), total bile acid (TBA), cholinesterase, total bilirubin, creatinine, prothrombin activity, and circulating platelets. The prediction model built by the LightGBM algorithm (HE-Lab7 model) predicted HE with an area under the curve (AUC) of 0.880, which was higher than that of XGBoost, RF, and SVM (all P<0.05), with a sensitivity of 0.825 and a specificity of 0.836. The Brier score of the calibration curve was 0.147, indicating that the predicted probability of the model is in good agreement with the actual probability of occurrence. Decision curves indicate that the model has a high clinical benefit. In Cohort 2, the HE-Lab7 model predicted HE with an AUC of 0.775, a sensitivity of 0.927, and a specificity of 0.758. Conclusion:The predictive efficacy of the cirrhosis-associated HE risk prediction model developed based on the optimal LightGBM algorithm using the large-scale test data based on four machine learning algorithms is good, which provides a reference basis for early prediction and identification of cirrhosis-associated HE.

Objective To investigate the role of glutamatergic neurons in the dorsomedial periaqueductal grey(dmPAG)in regulating excessive defensive behaviors in mice with post-traumatic stress disorder(PTSD).Methods Eight-week-old male C57BL/6 mice were subjected to stereotactic injections of different recombinant adeno-associated viral vectors(rAAV2/9-CaMKⅡ-mCherry,rAAV2/9-CaMKⅡ-hM3Dq-mCherry and rAAV2/9-CaMKⅡ-hM4Di-mCherry)into the bilateral dmPAG for chemogenetic activation or inhibition of the glutamatergic neurons,followed 2 weeks later by PTSD modeling by single prolonged stress.The looming test,response to whisker stimulation test and contextual fear conditioning(CFC)test were used to observe changes in defensive behaviors of the PTSD mice.The activity of glutamatergic neurons in the dmPAG were observed using immunofluorescence staining.Results Compared with the control mice,the mouse models of PTSD showed a shortened latency of flights with increased time spent in the nest,response scores of defensive behaviors and freezing time(all P<0.01).Immunofluorescence staining revealed significantly increased c-fos-positive glutamatergic neurons in the dmPAG of PTSD mice with defensive behaviors.Activation of the glutamatergic neurons in the dmPAG(in PTSD hM3Dq group)did not cause significant changes in the latency of flights or time in nest but obviously increased response scores of defensive behaviors and freezing time of the mice,whereas inhibiting the glutamatergic neurons in the dmPAG(in PTSD hM4Di group)caused the reverse changes and obviously alleviated defensive behaviors in the PTSD mice(P<0.05 or 0.01).Conclusion Inhibiting the activity of glutamatergic neurons in the dmPAG can alleviate defensive behaviors in mice with PTSD.

Objective To investigate the role of glutamatergic neurons in the dorsomedial periaqueductal grey(dmPAG)in regulating excessive defensive behaviors in mice with post-traumatic stress disorder(PTSD).Methods Eight-week-old male C57BL/6 mice were subjected to stereotactic injections of different recombinant adeno-associated viral vectors(rAAV2/9-CaMKⅡ-mCherry,rAAV2/9-CaMKⅡ-hM3Dq-mCherry and rAAV2/9-CaMKⅡ-hM4Di-mCherry)into the bilateral dmPAG for chemogenetic activation or inhibition of the glutamatergic neurons,followed 2 weeks later by PTSD modeling by single prolonged stress.The looming test,response to whisker stimulation test and contextual fear conditioning(CFC)test were used to observe changes in defensive behaviors of the PTSD mice.The activity of glutamatergic neurons in the dmPAG were observed using immunofluorescence staining.Results Compared with the control mice,the mouse models of PTSD showed a shortened latency of flights with increased time spent in the nest,response scores of defensive behaviors and freezing time(all P<0.01).Immunofluorescence staining revealed significantly increased c-fos-positive glutamatergic neurons in the dmPAG of PTSD mice with defensive behaviors.Activation of the glutamatergic neurons in the dmPAG(in PTSD hM3Dq group)did not cause significant changes in the latency of flights or time in nest but obviously increased response scores of defensive behaviors and freezing time of the mice,whereas inhibiting the glutamatergic neurons in the dmPAG(in PTSD hM4Di group)caused the reverse changes and obviously alleviated defensive behaviors in the PTSD mice(P<0.05 or 0.01).Conclusion Inhibiting the activity of glutamatergic neurons in the dmPAG can alleviate defensive behaviors in mice with PTSD.

Purpose: Total neoadjuvant therapy (TNT) is becoming the standard of care for locally advanced rectal cancer. However, surgery is deferred for months after completion, which may lead to fibrosis and increased surgical difficulty. The aim of this study was to assess whether TNT (TNT-RAPIDO) is associated with increased difficulty of total mesorectal excision (TME) compared with long-course chemoradiotherapy (LCRT) and upfront surgery. Methods: Twelve laparoscopic videos of low anterior resection with TME for rectal cancer were prospectively collected from January 2020 to October 2021, with 4 videos in each arm. Seven colorectal surgeons assessed the videos independently, graded the difficulty of TME using a visual analog scale and attempted to identify which category the videos belonged to. Results: The median age was 67 years, and 10 patients were male. The median interval to surgery from radiotherapy was 13 weeks in the LCRT group and 24 weeks in the TNT-RAPIDO group. There was no significant difference in the visual analog scale for difficulty in TME between the 3 groups (LCRT, 3.2; TNT-RAPIDO, 4.6; upfront, 4.1; P=0.12). A subgroup analysis showed similar difficulty between groups (LCRT 3.2 vs. TNT-RAPIDO 4.6, P=0.05; TNT-RAPIDO 4.6 vs. upfront 4.1, P=0.54). During video assessments, surgeons correctly identified the prior treatment modality in 42% of the cases. TNT-RAPIDO videos had the highest recognition rate (71%), significantly outperforming both LCRT (29%) and upfront surgery (25%, P=0.01). Conclusion TNT does not appear to increase the surgical difficulty of TME.

The Omicron family of SARS-CoV-2 variants are currently driving the COVID-19 pandemic. Here we analyzed the clinical laboratory test results of 9911 Omicron BA.2.2 sublineages-infected symptomatic patients without earlier infection histories during a SARS-CoV-2 outbreak in Shanghai in spring 2022. Compared to an earlier patient cohort infected by SARS-CoV-2 prototype strains in 2020, BA.2.2 infection led to distinct fluctuations of pathophysiological markers in the peripheral blood. In particular, severe/critical cases of COVID-19 post BA.2.2 infection were associated with less pro-inflammatory macrophage activation and stronger interferon alpha response in the bronchoalveolar microenvironment. Importantly, the abnormal biomarkers were significantly subdued in individuals who had been immunized by 2 or 3 doses of SARS-CoV-2 prototype-inactivated vaccines, supporting the estimation of an overall 96.02% of protection rate against severe/critical disease in the 4854 cases in our BA.2.2 patient cohort with traceable vaccination records. Furthermore, even though age was a critical risk factor of the severity of COVID-19 post BA.2.2 infection, vaccination-elicited protection against severe/critical COVID-19 reached 90.15% in patients aged ≽ 60 years old. Together, our study delineates the pathophysiological features of Omicron BA.2.2 sublineages and demonstrates significant protection conferred by prior prototype-based inactivated vaccines.
Humans ; Aged ; Middle Aged ; COVID-19/prevention & control* ; SARS-CoV-2 ; Pandemics/prevention & control* ; China/epidemiology* ; Disease Outbreaks/prevention & control* ; Vaccination