This study aims to explore the mechanism through which Yishen Jiangtang Decoction(YSJTD) regulates endoplasmic reticulum stress(ERS)-mediated NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome to improve diabetic nephropathy(DN) in db/db mice. Thirty db/db mice were randomly divided into the model group, YSJTD group, ERS inhibitor 4-phenylbutyric acid(4-PBA) group, with 10 mice in each group. Additionally, 10 db/m mice were selected as the control group. The YSJTD group was orally administered YSJTD at a dose of 0.01 mL·g~(-1), the 4-PBA group was orally administered 4-PBA at a dose of 0.5 mg·g~(-1), and the control and model groups were given an equal volume of carboxylmethyl cellulose sodium. The treatments were administered once daily for 8 weeks. Food intake, water consumption, and body weight were recorded every 2 weeks. After the intervention, fasting blood glucose(FBG), glycosylated hemoglobin(HbA1c), urine microalbumin(U-mALB), 24-hour urine volume, serum creatinine(Scr), and blood urea nitrogen(BUN) were measured. Inflammatory markers interleukin-1β(IL-1β) and interleukin-18(IL-18) were detected using the enzyme-linked immunosorbent assay(ELISA). Renal pathology was assessed through hematoxylin-eosin(HE), periodic acid-Schiff(PAS), and Masson staining, and transmission electron microscopy(TEM). Western blot was used to detect the expression levels of glucose-regulated protein 78(GRP78), C/EBP homologous protein(CHOP), NLRP3, apoptosis-associated speck-like protein containing CARD(ASC), cysteinyl aspartate-specific proteinase(caspase-1), and gasdermin D(GSDMD) in kidney tissues. The results showed that compared to the control group, the model group exhibited poor general condition, increased weight and food and water intake, and significantly higher levels of FBG, HbA1c, U-mALB, kidney index, 24-hour urine volume, IL-1β, and IL-18. Compared to the model group, the YSJTD and 4-PBA groups showed improved general condition, increased body weight, decreased food intake, and lower levels of FBG, U-mALB, kidney index, 24-hour urine volume, and IL-1β. Specifically, the YSJTD group showed a significant reduction in IL-18 levels compared to the model group, while the 4-PBA group exhibited decreased water intake and HbA1c levels compared to the model group. Although there was a decreasing trend in water intake and HbA1c in the YSJTD group, the differences were not statistically significant. No significant differences were observed in BUN, Scr, and kidney weight among the groups. Renal pathology revealed that the model group exhibited more severe renal damage compared to the control group. Kidney sections from the model group showed diffuse mesangial proliferation in the glomeruli, tubular edema, tubular dilation, significant inflammatory cell infiltration in the interstitium, and increased glycogen staining and blue collagen deposition in the basement membrane. In contrast, the YSJTD and 4-PBA groups showed varying degrees of improvement in renal damage, glycogen staining, and collagen deposition, with the YSJTD group showing more significant improvements. TEM analysis indicated that the model group had extensive cytoplasmic edema, homogeneous thickening of the basement membrane, fewer foot processes, and widening of fused foot processes. In the YSJTD and 4-PBA groups, cytoplasmic swelling of renal tissues was reduced, the basement membrane remained intact and uniform, and foot process fusion improved.Western blot results indicated that compared to the control group, the model group showed upregulation of GRP78, CHOP, GSDMD, NLRP3, ASC, and caspase-1 expression. In contrast, both the YSJTD and 4-PBA groups showed downregulation of these markers compared to the model group. These findings suggest that YSJTD exerts a protective effect against DN by alleviating NLRP3 inflammasome activation through the inhibition of ERS, thereby improving the inflammatory response in db/db DN mice.
Animals ; Endoplasmic Reticulum Stress/drug effects* ; Diabetic Nephropathies/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Inflammasomes/drug effects* ; Male ; Kidney/pathology* ; Endoplasmic Reticulum Chaperone BiP ; Humans ; Interleukin-18/genetics* ; Mice, Inbred C57BL

Objective To analyze the characteristics of postoperative hospital-acquired infections and drug sensitivity in lung transplant recipients over the past 5 years in a single center. Methods A total of 724 lung transplant recipients at Wuxi People's Hospital from January 2019 to December 2023 were selected. Based on the principles of hospital-acquired infection diagnosis, a retrospective analysis was conducted on the hospital infection situation and infection sites of lung transplant recipients, and an analysis of the distribution of hospital-acquired infection pathogens and their antimicrobial susceptibility test status was performed. Results Among the 724 lung transplant recipients, 275 cases of hospital-acquired infection occurred, with an infection rate of 38.0%. The case-time infection rate decreased from 54.2% in 2019 to 22.8% in 2023, showing a downward trend year by year (Z=30.98, P<0.001). The main infection site was the lower respiratory tract, accounting for 73.6%. The pathogens were mainly Gram-negative bacteria, with the top four being Acinetobacter baumannii (37.1%), Pseudomonas aeruginosa (17.3%), Klebsiella pneumoniae (13.7%), and Stenotrophomonas maltophilia (13.4%), with imipenem resistance rates of 89%, 53%, 58% and 100%, respectively. Gram-positive bacteria were mainly Staphylococcus aureus (3.6%), with a methicillin resistance rate of 67%. Conclusions Over the past 5 years, the hospital-acquired infections in lung transplant recipients have shown a downward trend, mainly involving lower respiratory tract infections, with the main pathogens being Acinetobacter baumannii, Pseudomonas aeruginosa and Klebsiella pneumoniae, all of which have high resistance rates to imipenem.

Objective To explore the risk factors for healthcare-associated infection(HAI)in lung transplant re-cipients(LTRs),and construct a predictive nomogram model.Methods Clinical data of patients who underwent lung transplant in Wuxi People's Hospital from January 2019 to December 2023 were analyzed retrospectively.The patients were divided into a training set(n=506)and a validation set(n=218).Independent risk factors were screened through LASSO regression,and multivariate logistic regression was included to construct a nomogram pre-diction model.The discrimination,calibration,and clinical applicability of the model were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow goodness-of-fit,and decision curves.Results Among the 506 LTRs,201 developed HAIs,with an incidence of 39.72％.The major infection site was lower respiratory tract,and the major pathogen were Gram-negative bacilli(Acinetobacter baumannii).Older age,use of extracorpo-real membrane oxygenation(ECMO),double-lung transplant,surgery duration＞3 hours,long duration of contin-uous fever,frequent abnormal blood routine examination,and long duration of combined use of antimicrobial agents were identified as independent risk factors for HAI after lung transplant.The ROC curve analysis results showed that the areas under the curve(AUCs)of the training set and the validation set were 0.74(95％CI:0.70-0.78)and 0.71(95％CI:0.64-0.78),respectively.The Hosmer-Lemeshow test results showed that there was no sta-tistically significant difference between the predictive and actual probability of HAI(P＞0.05).The clinical decision curve results indicated that the model had clinical benefits at a threshold probability value of 7％-71％.Conclusion The nomogram prediction model constructed in this study can effectively evaluate the risk of postoperative infection in LTRs.The model is stable and has high clinical application value,providing scientific reference for postoperative infection prevention and control.

The 2024 National Education Work Conference pointed out that at the current juncture of the critical period for achieving the goals and tasks of the 14th Five-Year Plan,the implementation of the Education Powerhouse Construction Plan Outline should be taken as the main line of work,and building first-class disciplines is an crucial task for a higher education powerhouse.In 2022,forensic medicine was officially listed as a first-level discipline under the medical category,presenting an un-precedented historical opportunity for the development of forensic medicine.The forensic medicine dis-cipline of Southern Medical University comprehensively improves the quality of talent cultivation and facilitates the construction of first-class disciplines as its main direction.It aims to initiate and imple-ment a high-level faculty team building plan featuring"combining recruitment and cultivation,inter-disciplinary integration";make vigorous efforts to establish a first-level doctoral program,refine advan-tageous second-level disciplines and research directions;and establish an innovative research platform from a high starting point with deep integration.The discipline adheres to moral cultivation and the Five Domains of Education simultaneous development,to build a high-quality talent joint training model.Guided by the construction of the national legal system and industry needs,the discipline will enhance social service capabilities.The forensic medicine construction in our university will continue to contribute to the rule of law in China and educational power.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

Objective:To analyze the applied effect of artificial intelligence(AI)rehabilitation system combined with telemedicine platform in individualized rehabilitation treatment for patients with long-term coma,so as to realize individual management for them.Methods:An AI rehabilitation system that aimed to patients with long-term coma was designed by integrating data of multimodal sensors included inertial sensor,image sensor and bioelectric sensor.A telemedicine platform,which included seven modules(patient management,consultation management,video communication,data transmission,electronic medical records,cost management and system maintenance),was designed by using a browser/server(B/S)architecture.The changes of patients'condition were followed up,and the full process of the management for rehabilitation treatment and the individualization of treatment plan were realized.A total of 60 inpatients whose coma duration exceeded 28 days were selected from the Coma Awakening Center of Dongguan Shipai Hospital between October 1,2021 and September 30,2022.They were randomly divided into control group(n=30)and observation group(n=30)by random number table.The control group received physical therapy and rehabilitation training based on evaluation results of rehabilitation,while the observation group adopted AI rehabilitation system combined with the telemedicine platform to conduct intervention.The consciousness levels,quality of life,hospital stay duration and treatment costs of two group were compared after intervention.Results:The Glasgow Coma Scale(GCS)scores,Coma Recovery Scale-Revised(CRS-R)scores,and Persistent Vegetative State(PVS)scores of two groups after intervention were significantly higher than those before intervention,and these indicators of observation group were significantly higher than them of control group after intervention,and the differences were significant(t=7.187,15.586,9.293,P<0.05),respectively.There were not significant differences in Glasgow Outcome Scale(GOS)scores and Activities of Daily Living(ADL)scores between two groups before intervention(P>0.05),and these scores of two groups were significantly increased after intervention,and these scores of observation group were significantly higher than them of control group after intervention,and the differences were significant(t=7.584,6.755,P<0.05).Additionally,the hospital stay duration and treatment costs of observation group were significantly less than those of control group,and the differences were significant(t=20.965,9.503,P<0.05).Conclusion:The intervention of AI rehabilitation system combines with a telemedicine platform on patients with long-term coma can enhance GCS,CRS-R,PVS,GOS and ADL scores,and reduce hospital stay duration and treatment cost.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To investigate the longitudinal stability of the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Activity of Daily Living Scale (ADL).Methods:The longitudinal cognitive assessment results of 68 dementia patients admitted to the Dementia and Leukoencephalopathy Outpatient Clinic, Department of Neurology, Peking Union Medical College Hospital, from January 2021 to January 2024, were retrospectively analyzed, including the total and sub-items scores of the MMSE, MoCA, and ADL. Two different rules were applied to analyze the abnormality rates: rule 1, where the current test result being better than the previous one was considered an abnormality; rule 2, where the current test result being better than the previous average score was considered an abnormality (If a patient had only 2 cognitive assessments, rule 2 was considered the same as rule 1). Two rules were used to analyze the abnormality rates of the scales. The statistical analyses were repeated after excluding patients with possible anxiety and depression status.Results:In assessing the total score stability, MMSE showed the lowest abnormality rates [27.2% (31/114) under rule 1 and 29.8% (34/114) under rule 2], while MoCA had the highest abnormality rates [41.3% (26/63) and 46.0% (29/63), respectively]. The ADL abnormality rates were 27.7% (23/83) and 33.7% (28/83), respectively. Among MoCA sub-items, category cue, multiple choice cue, second memory trial, orientation, and clock showed higher abnormality rates [31.7%(20/63), 30.2%(19/63), 23.8%(15/63), 22.2%(14/63), 22.2%(14/63), respectively]. After excluding population with possible anxiety and depression status, the relative abnormality rates of MMSE and ADL sub-items did not significantly change, while the abnormality rate of orientation in MoCA sub-items decreased relatively.Conclusion:The MMSE and ADL exhibit good stability in long-term monitoring of dementia patients, serving as essential tools for assessing and following up cognitive changes.

Objective To explore the risk factors for healthcare-associated infection(HAI)in lung transplant re-cipients(LTRs),and construct a predictive nomogram model.Methods Clinical data of patients who underwent lung transplant in Wuxi People's Hospital from January 2019 to December 2023 were analyzed retrospectively.The patients were divided into a training set(n=506)and a validation set(n=218).Independent risk factors were screened through LASSO regression,and multivariate logistic regression was included to construct a nomogram pre-diction model.The discrimination,calibration,and clinical applicability of the model were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow goodness-of-fit,and decision curves.Results Among the 506 LTRs,201 developed HAIs,with an incidence of 39.72％.The major infection site was lower respiratory tract,and the major pathogen were Gram-negative bacilli(Acinetobacter baumannii).Older age,use of extracorpo-real membrane oxygenation(ECMO),double-lung transplant,surgery duration＞3 hours,long duration of contin-uous fever,frequent abnormal blood routine examination,and long duration of combined use of antimicrobial agents were identified as independent risk factors for HAI after lung transplant.The ROC curve analysis results showed that the areas under the curve(AUCs)of the training set and the validation set were 0.74(95％CI:0.70-0.78)and 0.71(95％CI:0.64-0.78),respectively.The Hosmer-Lemeshow test results showed that there was no sta-tistically significant difference between the predictive and actual probability of HAI(P＞0.05).The clinical decision curve results indicated that the model had clinical benefits at a threshold probability value of 7％-71％.Conclusion The nomogram prediction model constructed in this study can effectively evaluate the risk of postoperative infection in LTRs.The model is stable and has high clinical application value,providing scientific reference for postoperative infection prevention and control.

Objective:To analyze the applied effect of artificial intelligence(AI)rehabilitation system combined with telemedicine platform in individualized rehabilitation treatment for patients with long-term coma,so as to realize individual management for them.Methods:An AI rehabilitation system that aimed to patients with long-term coma was designed by integrating data of multimodal sensors included inertial sensor,image sensor and bioelectric sensor.A telemedicine platform,which included seven modules(patient management,consultation management,video communication,data transmission,electronic medical records,cost management and system maintenance),was designed by using a browser/server(B/S)architecture.The changes of patients'condition were followed up,and the full process of the management for rehabilitation treatment and the individualization of treatment plan were realized.A total of 60 inpatients whose coma duration exceeded 28 days were selected from the Coma Awakening Center of Dongguan Shipai Hospital between October 1,2021 and September 30,2022.They were randomly divided into control group(n=30)and observation group(n=30)by random number table.The control group received physical therapy and rehabilitation training based on evaluation results of rehabilitation,while the observation group adopted AI rehabilitation system combined with the telemedicine platform to conduct intervention.The consciousness levels,quality of life,hospital stay duration and treatment costs of two group were compared after intervention.Results:The Glasgow Coma Scale(GCS)scores,Coma Recovery Scale-Revised(CRS-R)scores,and Persistent Vegetative State(PVS)scores of two groups after intervention were significantly higher than those before intervention,and these indicators of observation group were significantly higher than them of control group after intervention,and the differences were significant(t=7.187,15.586,9.293,P<0.05),respectively.There were not significant differences in Glasgow Outcome Scale(GOS)scores and Activities of Daily Living(ADL)scores between two groups before intervention(P>0.05),and these scores of two groups were significantly increased after intervention,and these scores of observation group were significantly higher than them of control group after intervention,and the differences were significant(t=7.584,6.755,P<0.05).Additionally,the hospital stay duration and treatment costs of observation group were significantly less than those of control group,and the differences were significant(t=20.965,9.503,P<0.05).Conclusion:The intervention of AI rehabilitation system combines with a telemedicine platform on patients with long-term coma can enhance GCS,CRS-R,PVS,GOS and ADL scores,and reduce hospital stay duration and treatment cost.