Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To establish a nomogram model for predicting the hyper-progression recurrence after hepatectomy in patients with hepatocellular carcinoma (HCC) based on circulating tumor cells (CTC).Methods:Clinical data of 231 HCC patients undergoing hepatectomy at the Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital from January 2013 to December 2022 were retrospectively analyzed, including 200 males and 31 females, aged 46(39, 52) years old. Patients were divided into two groups: the modeling group ( n=154) and the validation group ( n=77). According to the state of postoperative hyper-progression recurrence, patients in the modeling group were subdivided into hyper-progression recurrence ( n=39) and non-hyper-progression recurrence group ( n=115). Patients in the validation group were also subdivided into hyper-progression recurrence ( n=16) and non-hyper-progression recurrence group ( n=61). Clinicopathological data such as the total CTC count, alpha-fetoprotein, and postoperative pathology were collected. Logistic regression analysis was used to analyze the influencing factors of postoperative hyper-progression recurrence. A nomogram model was established based on the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were used to validate the nomogram model. Results:Multivariate logistic regression analysis showed that HCC patients with age ≤45 years old ( OR=6.704, 95% CI: 1.619-27.760, P=0.009), incomplete tumor capsule ( OR=13.292, 95% CI: 3.084-57.295, P=0.001), high total numbers of CTC ( OR=1.101, 95% CI: 1.023-1.186, P=0.011) and high Ki67 index ( OR=52.659, 95% CI: 3.215-862.604, P=0.005) had a high risk of hyper-progression recurrence after hepatectomy. The above three preoperative variables were integrated to construct a nomogram model. The calibration curve showed that the predicted results of the nomogram model were in good agreement with the actual results. The ROC curves of the nomogram model for predicting hyper-progression recurrence after hepatectomy in HCC patients were plotted, and the area under the curve was 0.907 (95% CI: 0.856-0.959) and 0.833 (95% CI: 0.721-0.945) in the modeling group and validation group, respectively. DCA showed that the nomogram model could be used as a valuable predictive tool for the hyper-progression recurrence after hepatectomy. The CIC showed that the population judged by the nomogram model was highly matched with the actual population with hyper-progression recurrence. Conclusions:This study established a nomogram model based on age, tumor capsular integrity and total CTC count, which could accurately predict the postoperative hyper-progression recurrence in HCC patients before hepatectomy. The model is promising in guiding clinical practice after further validation.

Ketone bodies have beneficial metabolic activities, and the induction of plasma ketone bodies is a health promotion strategy. Dietary supplementation of sodium butyrate (SB) is an effective approach in the induction of plasma ketone bodies. However, the cellular and molecular mechanisms are unknown. In this study, SB was found to enhance the catalytic activity of 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2), a rate-limiting enzyme in ketogenesis, to promote ketone body production in hepatocytes. SB administrated by gavage or intraperitoneal injection significantly induced blood ß-hydroxybutyrate (BHB) in mice. BHB production was induced in the primary hepatocytes by SB. Protein succinylation was altered by SB in the liver tissues with down-regulation in 58 proteins and up-regulation in 26 proteins in the proteomics analysis. However, the alteration was mostly observed in mitochondrial proteins with 41% down- and 65% up-regulation, respectively. Succinylation status of HMGCS2 protein was altered by a reduction at two sites (K221 and K358) without a change in the protein level. The SB effect was significantly reduced by a SIRT5 inhibitor and in Sirt5-KO mice. The data suggests that SB activated HMGCS2 through SIRT5-mediated desuccinylation for ketone body production by the liver. The effect was not associated with an elevation in NAD⁺/NADH ratio according to our metabolomics analysis. The data provide a novel molecular mechanism for SB activity in the induction of ketone body production.
Mice ; Animals ; Butyric Acid/metabolism* ; Ketone Bodies/metabolism* ; Liver/metabolism* ; Hydroxybutyrates/metabolism* ; Down-Regulation ; Sirtuins/metabolism* ; Hydroxymethylglutaryl-CoA Synthase/metabolism*

In this study, we designed a core competency-oriented formative assessment system for standardized residency training. A formative assessment information platform was established according to this formative assessment system. We described the business process design in detail and how to use information technology for assessment data application. The corresponding data were fed back to residents, instructors, rotation departments, specialty bases, and residency training management departments to promote continuous quality improvement. Meanwhile, we demonstrated the difficulties, deficiencies, and future direction of the construction of formative assessment information platform.

A novel method for the detection of moroxydine ( ABOB) residue in tomatoes was developed based on the enhancement of the fluorescence intensity of CdTe quantum dots( QDs) in the presence of ABOB. The factors influencing the performance of the QDs fluorescent probes were investigated, and the optimum conditions were determined:the concentration of mercaptoacidic acid ( TGA) capped-CdTe quantum dot was 1×10-4 mol/L, and the reaction time was 20 min at pH=5. 6. Under the optimum conditions, the relative fluorescence intensity increases linearly proportional to the ABOB concentration in the range of 1. 0×10-12-5. 0×10-10 mol/L with a limit of detection of 5. 2×10-13 mol/L, R=0. 9981, the recovery was 97%-106%, without obvious interference on the determinations of moroxydine from the common coexisting ions, antibiotics, and vitamins. The proposed method has been successfully applied in the detection of trace moroxydine hydrochloride residue in tomatoes.

Objective To evaluate the significance of serum prealbumin, bilirubin/albumin( B/A )ratio and brainstem auditory evoked potential (BAEP)for early predicting the bilirubinic neurotoxicity caused by early neonatal hyperbilirubinemia. Methods According to the gestational age, birth weight and the intervention criterion of neonatal jaundice, 94 neonates (newborn infants with a age of 1-7days) with hyperbilirubinemia were divided into two groups, one was term neonates with normal BAEP, the other was term neonates with abnormal BAEP. We detected their serum levels of total bilirubine, prealbumin, BAEP and calculated B/A ratio. Result The prealbumin levels was significantly lower and B/A ratio was higher in group of abnormal BAEP than those of control group ( P < 0. 01 ), while the levels of total bilirubin were not significantly different between the two groups ( P > 0. 05 ). In group of abnormal BAEP , there was a negative correlation between the level of serum prealbumin and the B/A ratio (r = -0. 637, P <0. 01 ). Conclution Prealbumin may be a sensitive indicator for early predicting bilirubinic toxicity like B/A ratio and BAEP.

Objective To explore the relationship between tumor necrosis factor (TNF) gene polymorphisms in donors and recipients and the incidence and severity of acute graft-versus-host diseases (aGVHD) after unrelated allogeneic hematopoietic stem cell transplantation (alIo-HSCT). Methods Single nucleotide polymorphisms (SNPs) of TNFα-238 (G/A), TNFα-857 (C/T), TNFα-863 (C/A), TNFα-1031 (T/C), TNFβ + 252 (A/G) were analyzed by Multiplex SNaPshot analysis in 76 pairs of donors and recipients. Results Transplantation involving donors with TNFα-857 CC genotype resulted in a higher incidence of grade Ⅱ-Ⅳ aGVHD than donors with CT genotype (91.3% vs 8. 7% , P =0. 039). In the 23 patients with grade Ⅱ-Ⅳ aGVHD, no patients had TNFβ +252 AA genotype, 19 (82.6%) had GA genotype and 4 (17.4%) had GG genotype. There was a significant difference in the distribution pattern of the TNFβ +252 (AA, GA and GG) genotypes in these patients (P =0.03). There was no significant association of TNFα-238 (G/A), TNFα-863 (C/A) and TNFα-1031 (T/C) polymorphisms with the risk of aGVHD. Conclusion These results suggest donor TNFα-857 CC genotype is related to a higher incidence of grade Ⅱ -Ⅳ aGVHD, and patients with TNFβ +252 AA genotype have protection against the risk of grade Ⅱ -Ⅳ aGVHD.

52 cases of the patients with L4-S1 intervertebral disc protrusion were first treated by traditional Tuina manual techniques, including the rolling method, pressing method,oblique-plucking method and shaking method, and then treated by electric acupuncture on Shenshu (BL 23), Yaoyangguan (GV 3), Dachangshu (BL 25), Xiaochangshu (BL 27),Mingmen (GV 4) and Shangliao (BL 31). After 7-28 sessions of the treatments, the results showed cure in 40 cases, remarkable effect in 8 cases, effect in 3 cases, failure in 1 case, and the total effective rate in 98.1%.