Objective:To analyze the clinical characteristics, RAN-binding protein 2 ( RANBP2) gene variations, and prognosis in Chinese acute necrotizing encephalopathy (ANE) patients. Methods:A retrospective analysis of ANE cases registered in the Peking Union Medical College Hospital Encephalitis Registry System from 2022 to 2024, involving patients from Peking Union Medical College Hospital and other hospitals, was conducted. A descriptive study was performed on the clinical characteristics, treatments and prognosis, cerebrospinal fluid examination results, and imaging findings of these patients based on adjusted ANE diagnostic criteria. Whole-exome sequencing technology was used to detect gene mutations in these patients.Results:A total of 18 ANE cases were included, ranged in age from 2 to 72 [20(5, 43)] years. The male-to-female ratio was 4∶5. All patients were found with precipitating infections including COVID-19, influenza A virus and Mycoplasma pneumoniae infections. All patients presented with fever, with varying degrees of consciousness disturbance observed in 16 cases, and seizures in 10 cases. All patients underwent lumbar puncture, with normal or mildly elevated white cell counts [3(2, 13)×10 6/L] and mildly to moderately elevated protein levels [1.90(0.92, 4.65) g/L]. A total of 6 patients were found with extremely elevated interleukin-6 level [950(164, 2 000) pg/ml] in cerebrospinal fluid. Bilateral symmetric thalamic lesions were typical imaging features of ANE, while involvement of other areas such as cortical and subcortical white matter, brainstem, and cerebellum was also observed. A total of 14 patients performed genetic tests while 4 patients were identified with RANBP2 gene mutations (c.1754C>T in 3 cases, c.1966A>G in 1 case). All patients received immunotherapy, and 7 patients died at discharge while other patients presented with neurological sequelae of varying degrees. Conclusions:ANE is a rare and severe parainfectious encephalopathy that can occur in both children and adults. Clinically, it is characterized by rapidly progressing encephalopathy following systematic infection, with bilateral symmetric thalamic lesions. The detection of RANBP2 gene mutations could help make the diagnosis.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy worldwide, accounting for more than 90% of all oral cancers, and is characterized by high invasiveness and poor long-term prognosis. Its etiology is multifactorial, involving tobacco use, alcohol consumption, and human papillomavirus (HPV) infection. Oral leukoplakia and erythroplakia are the main precancerous lesions lesions, with oral leukoplakia being the most common. Both OSCC and premalignant lesions are closely associated with aberrant activation of multiple signaling pathways. Post-translational modifications (such as ubiquitination and deubiquitination) play key roles in regulating these pathways by controlling protein stability and activity. Growing evidence indicates that dysregulated ubiquitination/deubiquitination can mediate OSCC initiation and progression via aberrant activation of signaling pathways. The ubiquitination/deubiquitination process mainly involves E3 ligases (E3s) that catalyze substrate ubiquitination, deubiquitinating enzymes (DUBs) that remove ubiquitin chains, and the 26S proteasome complex that degrades ubiquitinated substrates. Abnormal expression or mutation of E3s and DUBs can lead to altered stability of critical tumor-related proteins, thereby driving OSCC initiation and progression. Therefore, understanding the aberrantly activated signaling pathways in OSCC and the ubiquitination/deubiquitination mechanisms within these pathways will help elucidate the molecular mechanisms and improve OSCC treatment by targeting relevant components. Here, we summarize four aberrantly activated signaling pathways in OSCC―the PI3K/AKT/mTOR pathway, Wnt/β-catenin pathway, Hippo pathway, and canonical NF-κB pathway―and systematically review the regulatory mechanisms of ubiquitination/deubiquitination within these pathways, along with potential drug targets. PI3K/AKT/mTOR pathway is aberrantly activated in approximately 70% of OSCC cases. It is modulated by E3s (e.g., FBXW7 and NEDD4) and DUBs (e.g., USP7 and USP10): FBXW7 and USP10 inhibit signaling, while NEDD4 and USP7 potentiate it. Aberrant activation of the Wnt/β‑catenin pathway leads to β‑catenin nuclear translocation and induction of cell proliferation. This pathway is modulated by E3s (e.g., c-Cbl and RNF43) and DUBs (e.g., USP9X and USP20): c-Cbl and RNF43 inhibit signaling, while USP9X and USP20 potentiate it. Hippo pathway inactivation permits YAP/TAZ to enter the nucleus and promotes cancer cell metastasis. This pathway is modulated by E3s (e.g., CRL4^DCAF1 and SIAH2) and DUBs (e.g., USP1 and USP21): CRL4^DCAF1 and SIAH2 inhibit signaling, while USP1 and USP21 potentiate it. Persistent activation of the canonical NF-κB pathway is associated with an inflammatory microenvironment and chemotherapy resistance. This pathway is modulated by E3s (e.g., TRAF6 and LUBAC) and DUBs (e.g., A20 and CYLD): A20 and CYLD inhibit signaling, while TRAF6 and LUBAC potentiate it. Targeting these E3s and DUBs provides directions for OSCC drug research. Small-molecule inhibitors such as YCH2823 (a USP7 inhibitor), GSK2643943A (a USP20 inhibitor), and HOIPIN-8 (a LUBAC inhibitor) have shown promising antitumor activity in preclinical models; PROTAC molecules, by binding to surface sites of target proteins and recruiting E3s, achieve targeted ubiquitination and degradation of proteins insensitive to small-molecule inhibitors, for example, PU7-1-mediated USP7 degradation, offering new strategies to overcome traditional drug limitations. Currently, NX-1607 (a Cbl-b inhibitor) has entered phase I clinical trials, with preliminary results confirming its safety and antitumor activity. Future research on aberrant E3s and DUBs in OSCC and the development of highly specific inhibitors will be of great significance for OSCC precision therapy.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To analyze the clinical characteristics, RAN-binding protein 2 ( RANBP2) gene variations, and prognosis in Chinese acute necrotizing encephalopathy (ANE) patients. Methods:A retrospective analysis of ANE cases registered in the Peking Union Medical College Hospital Encephalitis Registry System from 2022 to 2024, involving patients from Peking Union Medical College Hospital and other hospitals, was conducted. A descriptive study was performed on the clinical characteristics, treatments and prognosis, cerebrospinal fluid examination results, and imaging findings of these patients based on adjusted ANE diagnostic criteria. Whole-exome sequencing technology was used to detect gene mutations in these patients.Results:A total of 18 ANE cases were included, ranged in age from 2 to 72 [20(5, 43)] years. The male-to-female ratio was 4∶5. All patients were found with precipitating infections including COVID-19, influenza A virus and Mycoplasma pneumoniae infections. All patients presented with fever, with varying degrees of consciousness disturbance observed in 16 cases, and seizures in 10 cases. All patients underwent lumbar puncture, with normal or mildly elevated white cell counts [3(2, 13)×10 6/L] and mildly to moderately elevated protein levels [1.90(0.92, 4.65) g/L]. A total of 6 patients were found with extremely elevated interleukin-6 level [950(164, 2 000) pg/ml] in cerebrospinal fluid. Bilateral symmetric thalamic lesions were typical imaging features of ANE, while involvement of other areas such as cortical and subcortical white matter, brainstem, and cerebellum was also observed. A total of 14 patients performed genetic tests while 4 patients were identified with RANBP2 gene mutations (c.1754C>T in 3 cases, c.1966A>G in 1 case). All patients received immunotherapy, and 7 patients died at discharge while other patients presented with neurological sequelae of varying degrees. Conclusions:ANE is a rare and severe parainfectious encephalopathy that can occur in both children and adults. Clinically, it is characterized by rapidly progressing encephalopathy following systematic infection, with bilateral symmetric thalamic lesions. The detection of RANBP2 gene mutations could help make the diagnosis.

Objective To comprehensively analyze the clinical characteristics and risk factors of children with refractory mycoplasma pneumonia(RMPP),precisely identify the joint predictors in these children,and construct a prediction model.This aims to offer a scientific foundation for the early identification of RMPP and the formulation of accurate treatment and medication strategies.Methods A retrospective analysis was performed on the clinical data of 282 children diagnosed with mycoplasma pneumoniae pneumonia(MPP)who were admitted to the Pediatric Department of the Seventh Medical Center of the Chinese People's Liberation Army General Hospital between August 1,2023,and February 29,2024.Among these children,119 with RMPP were classified into the RMPP group,while the remaining 163 with general MPP(GMPP)were assigned to the GMPP group.The clinical data of both groups,encompassing age,gender,duration of fever,symptoms,laboratory test indices,chest imaging data,complications,etc.,were compared.A logistic probability model(LogP model)for joint application was constructed.The discriminatory ability of the model was evaluated using the area under the receiver operating characteristic(ROC)curve,and the calibration of the model was assessed by means of a calibration curve.Results In comparison with the GMPP group,children in the RMPP group exhibited a significantly longer duration of fever(P=0.002).Moreover,they had a higher incidence of complications,including myocardial damage and coagulation dysfunction(P<0.05).Regarding inflammatory markers,the levels of C-reactive protein(CRP),procalcitonin(PCT),and lactate dehydrogenase(LDH)were notably elevated in the RMPP group(P<0.05),whereas the level of albumin(Alb)was lower(P=0.001).In the RMPP group,the levels of interleukin-2(IL-2),interleukin-5(IL-5),interleukin-8(IL-8),interleukin-1β(IL-1β),and D-Dimer were increased,while the levels of interleukin-6(IL-6)and interleukin-17(IL-17)were decreased(P<0.05).Chest computed tomography(CT)scans revealed a higher proportion of lung consolidation,pleural effusion,and atelectasis in the RMPP group(P<0.05).Multivariate logistic regression analysis demonstrated that CRP,total bilirubin(T-BIL),LDH,IL-17,and prothrombin time(PT)were independent risk factors for RMPP(P<0.05).The receiver operating characteristic(ROC)predictive model established based on these factors had an area under the curve(AUC)of 0.787(95%confidence interval[CI]:0.693～0.880),with a cutoff value of 0.421,a sensitivity of 0.786,and a specificity of 0.660.The calibration curve indicated that the predicted probability matched well with the reference probability,and there was no statistical difference in the results of the Hosmer-Lemeshow test(P>0.05).Conclusions The clinical features of children with RMPP are predominantly characterized by prolonged fever,moderate lung lesions,other organ injuries,and high inflammatory markers.CRP,T-BIL,LDH,IL-17,and PT can act as independent risk factors for RMPP.

Objective To comprehensively analyze the clinical characteristics and risk factors of children with refractory mycoplasma pneumonia(RMPP),precisely identify the joint predictors in these children,and construct a prediction model.This aims to offer a scientific foundation for the early identification of RMPP and the formulation of accurate treatment and medication strategies.Methods A retrospective analysis was performed on the clinical data of 282 children diagnosed with mycoplasma pneumoniae pneumonia(MPP)who were admitted to the Pediatric Department of the Seventh Medical Center of the Chinese People's Liberation Army General Hospital between August 1,2023,and February 29,2024.Among these children,119 with RMPP were classified into the RMPP group,while the remaining 163 with general MPP(GMPP)were assigned to the GMPP group.The clinical data of both groups,encompassing age,gender,duration of fever,symptoms,laboratory test indices,chest imaging data,complications,etc.,were compared.A logistic probability model(LogP model)for joint application was constructed.The discriminatory ability of the model was evaluated using the area under the receiver operating characteristic(ROC)curve,and the calibration of the model was assessed by means of a calibration curve.Results In comparison with the GMPP group,children in the RMPP group exhibited a significantly longer duration of fever(P=0.002).Moreover,they had a higher incidence of complications,including myocardial damage and coagulation dysfunction(P<0.05).Regarding inflammatory markers,the levels of C-reactive protein(CRP),procalcitonin(PCT),and lactate dehydrogenase(LDH)were notably elevated in the RMPP group(P<0.05),whereas the level of albumin(Alb)was lower(P=0.001).In the RMPP group,the levels of interleukin-2(IL-2),interleukin-5(IL-5),interleukin-8(IL-8),interleukin-1β(IL-1β),and D-Dimer were increased,while the levels of interleukin-6(IL-6)and interleukin-17(IL-17)were decreased(P<0.05).Chest computed tomography(CT)scans revealed a higher proportion of lung consolidation,pleural effusion,and atelectasis in the RMPP group(P<0.05).Multivariate logistic regression analysis demonstrated that CRP,total bilirubin(T-BIL),LDH,IL-17,and prothrombin time(PT)were independent risk factors for RMPP(P<0.05).The receiver operating characteristic(ROC)predictive model established based on these factors had an area under the curve(AUC)of 0.787(95%confidence interval[CI]:0.693～0.880),with a cutoff value of 0.421,a sensitivity of 0.786,and a specificity of 0.660.The calibration curve indicated that the predicted probability matched well with the reference probability,and there was no statistical difference in the results of the Hosmer-Lemeshow test(P>0.05).Conclusions The clinical features of children with RMPP are predominantly characterized by prolonged fever,moderate lung lesions,other organ injuries,and high inflammatory markers.CRP,T-BIL,LDH,IL-17,and PT can act as independent risk factors for RMPP.

Objective To analyze the independent influencing factors of intimate partner homicide(IPH)cases,construct an IPH prediction model,and provide a basis for criminal profiling.Methods A total of 476 convicted homicide cases in Guangdong Province from January 1,2014,to December 31,2020,were collected as modeling dataset.They were divided into the IPH group(n=180)and the non-intimate partner homicide(N-IPH)group(n=296)based on whether the offender and victim were intimate partners.Logistic regression was used to build the model,the model was evaluated through the receiver operating characteristic(ROC)curve analysis and a nomogram was drawn.Inter-nal validation was conducted using ten-fold cross-validation method.A total of 126 court judgments from outside Guangdong Province from January 1,2011,to December 31,2020,were randomly col-lected for external validation.Results Through multi-factor Logistic regression analysis,7 variables were ultimately selected for inclusion in the model.The Hosmer-Lemeshow goodness of fit test result of the model was χ2=13.158,P=0.068.The ROC area under the curve(AUC)of the model was 0.939(95%CI:0.919-0.959),the cut-off value was 0.292,the sensitivity was 0.900,and the specificity was 0.865.The calibration curve was close to the ideal curve.The ten-fold cross-validation showed the accuracy of 0.863 and a Kappa value of 0.708.The external validation results showed an AUC of 0.922(95%CI:0.872-0.971),a cut-off value of 0.292,a sensitivity of 0.890,and a specificity of 0.886.The calibration curve tended to the ideal curve.Conclusion The IPH prediction model based on forensic field indicators has good predictive ability,reliable accuracy and stability,and can provide a scientific method for criminal profiling.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective To investigate the attributable risk(AR)of Acinetobacter baumannii(AB)infection in criti-cally ill patients.Methods A multicenter retrospective cohort study was conducted among adult patients in inten-sive care unit(ICU).Patients with AB isolated from sterile body fluid and confirmed with AB infection in each cen-ter were selected as the infected group.According to the matching criteria that patients should be from the same pe-riod,in the same ICU,as well as with similar APACHE Ⅱ score(±5 points)and primary diagnosis,patients who did not infect with AB were selected as the non-infected group in a 1:2 ratio.The AR was calculated.Results The in-hospital mortality of patients with AB infection in sterile body fluid was 33.3％,and that of non-infected group was 23.1％,with no statistically significant difference between the two groups(P=0.069).The AR was 10.2％(95％CI:-2.3％-22.8％).There is no statistically significant difference in mortality between non-infected pa-tients and infected patients from whose blood,cerebrospinal fluid and other specimen sources AB were isolated(P＞0.05).After infected with AB,critically ill patients with the major diagnosis of pulmonary infection had the high-est AR.There was no statistically significant difference in mortality between patients in the infected and non-infec-ted groups(P＞0.05),or between other diagnostic classifications.Conclusion The prognosis of AB infection in critically ill patients is highly overestimated,but active healthcare-associated infection control for AB in the ICU should still be carried out.