A 51-year-old male presented with nasal obstruction, followed by progressive hearing loss and blurred vision. Imaging identified space-occupying lesions in the paranasal sinuses, orbits, and paraspinal regions, while laboratory tests confirmed positive anti-proteinase 3 anti-neutrophil cytoplasmic antibody(PR3- ANCA) immunoglobulin G (IgG)and markedly elevated serum IgG4. Despite treatment with corticosteroids, immunosuppressants, and radiotherapy, the patient exhibited steroid dependency with relentless disease progression. Following multidisciplinary consultation, a diagnosis of inflammatory myofibroblastic tumor (IMT) coexisting with ANCA- associated vasculitis (AAV) was favored, though IgG4-related disease remained a critical differential. Ultimately, profound immunosuppression precipitated a severe herpesvirus infection, leading to disseminated intravascular coagulation and multiple organ dysfunction syndrome. This case underscores the rarity and diagnostic complexity of concurrent IMT and AAV, highlights the therapeutic dilemma of balancing primary disease control against fatal opportunistic infections, and emphasizes the critical role of multidisciplinary collaboration in the diagnosis and treatment of complex diseases.

Objective To establish a stable and reliable sepsis model of rat after liver transplantation (LT) for clinical translational research and analyze its characteristics. Methods The "two-sleeve method" was used to establish the in situ LT model of SD rats, and the sepsis model was constructed through cecal ligation and puncture (CLP) at 3 d after the operation. SD rats were randomly divided into 3 groups: sham operation group (Sham group), LT group, and LT + CLP group, with 6 rats in each group. The changes in body weight, rectal temperature and survival rate were compared, and the sepsis score was used for evaluation. The levels of blood biochemical indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea (Urea), creatinine (Cr), creatine kinase (CK), lactate dehydrogenase (LDH)] and inflammatory factors [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α] in each group were detected, and the pathological changes and cell apoptosis in different organs were observed. Results Compared with the Sham group, the body weight of the LT group and LT + CLP group decreased (all P<0.05). The rectal temperature of the LT + CLP group showed a continuous downward trend after the operation, the sepsis score increased sharply after the operation, and the survival rate dropped to 16.7%, and the differences between the Sham group, LT group and LT + CLP group were statistically significant (all P<0.05). The levels of ALT, AST, Urea, Cr, CK, LDH, and serum IL-1β, IL-6, IL-10 and TNF-α in the LT + CLP group were higher than those in the Sham group and LT group rats within 72 hours after the operation(all P<0.05). The pathological examination of the LT + CLP group showed severe tissue structure destruction, necrosis and infiltration of inflammatory cells in multiple organs, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining showed an increased level of cell apoptosis in multiple organs. Conclusions Using liver transplantation combined with CLP, a stable animal model of liver transplantation infection is successfully established, which exhibits a high mortality rate, significant multi-organ damage and intense inflammatory response, providing an ideal animal model for transplantation infection research.

Liver cirrhosis recompensation refers to the process in which patients with decompensated cirrhosis achieve the disappearance of decompensated complications and restoration of liver function and clinical phenotype to a compensated state after etiological treatment or symptomatic treatment. In recent years， with effective control of the etiology of cirrhosis and treatment or prevention of decompensated complications， the clinical significance of recompensation has gained increasing attention. This article elaborates on recompensation from the aspects of definition， diagnostic criteria， influencing factors， mechanisms affecting long-term prognosis， and clinical management strategies， and with reference to the latest research evidence， this article also discusses the core value of recompensation in improving quality of life and reducing the risk of end-stage events， in order to provide a basis for optimizing the comprehensive management of liver cirrhosis.

Objective: To explore the intervention effects of high intensity interval training (HIIT) on glucose metabolism, cortisol (Cor), and sleep quality among college students with comorbid depressive symptoms and obesity, so as to provide a reference for improving sleep quality among college students with comorbid depressive symptoms and obesity. Methods: In March 2023, 45 college students with comorbid depressive symptoms and obesity were recruited and randomly assigned to an exercise group ( n =23) and a control group ( n =22) by random number table method. The exercise group received HIIT intervention for 12 weeks, three times a week, while the control group received no intervention. Blood samples were collected from participants to measure fasting insulin (FINS), fasting blood glucose (FBG), homeostatic model assessment of insulin resistance (HOMA-IR), Cor, and Pittsburgh Sleep Quality Index (PSQI) before and after intervention. Statistical analysis was performed using t-test, repeated measures analysis of variance (ANOVA), simple effect analysis. Results: The repeated measures ANOVA revealed statistically significant time×group interaction effects for body composition (weight, body mass index, percentage of body fat, fat mass, waist to hip ratio), depressive symptoms, PSQI scores and its subdimensions (subjective sleep quality, sleep onset time, sleep efficiency, sleep disorders, daytime dysfunction), as well as FBG, FINS, and HOMA-IR between the exercise group and control group before and after intervention ( F =7.10-53.38, all P <0.05). Simple effect analysis showed that compared to the control group, the exercise group demonstrated significant improvements in body composition (body mass index, fat mass, waist to hip ratio), depressive symptoms, PSQI scores and its sub dimensions (subjective sleep quality, sleep onset time, sleep efficiency, sleep disorders, daytime dysfunction), FBG, FINS, HOMA-IR, and Cor (all P <0.05). Conclusion HIIT can improve the sleep quality of college students with comorbid depressive symptoms and obesity by enhancing glucose metabolism and regulating Cor levels.

Objective:To investigate the impact of different ventilation modalities during initial resuscitation on short-term outcomes in adult patients with in-hospital cardiac arrest (IHCA).Methods:This retrospective study included adult patients (age ≥18 years) admitted to the emergency resuscitation or observation units of our hospital from September 2019 to December 2021. Demographic data, comorbidities, and short-term outcomes of IHCA patients who underwent airway management during resuscitation were recorded. Participants were stratified into non-advanced airway and advanced airway groups based on ventilation modality. The primary outcome was defined as sustained return of spontaneous circulation (ROSC) ≥20 min, and secondary outcomes included survival to discharge and favorable neurological status at discharge. Logistic regression analyses were performed to assess the impact of different ventilation modalities on short-term outcomes among adult IHCA patients. and developed a prediction model of ROSC for adult IHCA patients, and its predictive performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic.Results:Among 285 IHCA patients (non-advanced airway: n=75; advanced airway: n=210), 127 achieved ROSC ≥20 min, 51 survived to discharge, and 35 had favorable neurological outcomes. Logistic regression identified ventilation modality, epinephrine dose, and arrest location as independent predictors of ROSC in adult IHCA patients. Advanced airway management demonstrated significantly higher ROSC rates compared to non-advanced interventions ( OR=3.698, 95% CI:1.844-7.419, P<0.001). However, no significant associations were observed between ventilation modalities and survival to discharge ( OR=1.097, 95% CI:0.506-2.376, P=0.815) or favorable neurological outcomes at discharge ( OR=0.548, 95% CI:0.224-1.339, P=0.187). Ventilation modality, epinephrine dose, and arrest location were incorporated as predictors in a multivariable logistic regression model to develop a ROSC prediction model for adult IHCA patients. The discriminative ability of model was evaluated through receiver operating characteristic (ROC) curve analysis, yielding an AUC of 0.735 (95% CI:0.678-0.793). Subgroup analyses demonstrated that early advanced airway management significantly enhanced ROSC rates in noncardiac etiology cases, whereas no such benefit was observed in cardiac etiology cases, while this intervention correlated with decreased survival to discharge rates and deteriorated neurological outcomes among survivors. Conclusions:Advanced airway management demonstrated improved ROSC rates in adult IHCA cases, while showing no significant improvement in survival rates or favorable neurological outcomes at discharge. Ventilation modality, epinephrine dose, and arrest location are independent predictors of ROSC. A model integrating these factors exhibits moderate predictive utility for IHCA outcomes.

OBJECTIVE To construct a predictive tool for surgical site infections(SSIs)in spinal surgery for Chi-nese population to provide evidence support for reducing SSIs.METHODS A systematic review of Chinese and English database literature was conducted for Meta-analysis to obtain pooled risk values for influencing factors,and a risk prediction scoring tool was constructed based on the logistic regression model.Patients who underwent spinal surgery and completed postoperative follow-up in a tertiary hospital in Beijing from Jan.to Dec.2021 were selected to validate the predictive effect of the tool.RESULTS The predictive model for SSIs in Chinese spinal sur-gery patients was Logit(P)=—3.47+0.63(age 60 years)+0.31 ×(patient with cardiovascular disease)+0.69 ×(rheumatoid arthritis)+1.07 ×(diabetes)+1.06 ×(operation duration＞3 h)+1.17 ×(preopera-tive albumin＜35 g/L)+0.71 ×(history of spinal surgery)+0.67 ×(carrying internal implants)+0.73 ×(blood transfusion).The total score of the predictive tool was 92,with a cutoff score of≥24.50 indicating high-risk individuals.The area under the curve was 0.733,with the sensitivity 58.30％and the specificity 79.60％.CONCLUSION The established predictive model for SSIs in Chinese spine surgery demonstrates good predictive performance and can be used as a reference assessment tool in clinical practice.

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.

Obesity has become a global public health challenge,and metabolic and bariatric surgery(MBS)remains one of the most effective treatments for severe obesity.However,substantial variability in patient characteristics,surgical complexity,and postoperative adherence leads to heterogeneous outcomes.The rapid evolution of artificial intelligence(AI)offers new opportunities to address these limitations.By integrating multidimensional clinical,imaging,and longitudinal follow-up data,machine learning and large language models support key aspects of MBS,including candidate selection,surgical decision-making,perioperative risk prediction,skill assessment,and long-term outcome management.Recent studies have demonstrated notable progress in decision support,complication forecasting,robotic surgery optimization,patient counselling,and postoperative weight-trajectory prediction.Nevertheless,challenges remain regarding model generalizability,ethical and regulatory oversight,data privacy,and transparency in AI-assisted decision-making.This review summarizes current advances,limitations,and future directions of AI applications in MBS,providing a reference for clinicians seeking to understand and apply these emerging technologies.

To address the problem of large reconstruction errors in 3D pulse signals caused by excessively small out-of-plane displacement of the contact membrane in the existing traditional Chinese medicine fingertip tactile binocular vision detection technology, this study proposes a 3D pulse image detection method based on subtle motion magnification technology and explores its application in pulse pattern recognition. Firstly, a 3D pulse image detection system based on binocular vision to obtain pulse image signals is developed as experimental data. Then, the phase motion video magnification algorithm is used to amplify the original signals, and the amplified signals are reconstructed in three dimensions to obtain 3D pulse signals. On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. The experimental results show that compared to the methods without subtle motion magnification technology, the proposed method significantly improves waveform clarity, amplitude stability, and periodic regularity. Meanwhile, the average accuracy in pulse pattern recognition reaches 96.29%±0.26%.
Algorithms ; Imaging, Three-Dimensional/methods* ; Pattern Recognition, Automated ; Medicine, Chinese Traditional ; Motion ; Humans ; Pulse ; Signal Processing, Computer-Assisted ; Machine Learning

Objective To optimize scanning parameters of SPECIAL 1H-MR spectroscopy(MRS),and to observe the feasibility of combining with self-made simulated metabolite spectral data set(B set)of LCModel software for quantitative analysis of pig liver glucose(Glc)in vitro.Methods Metabolite mixture of cod liver oil,Glc and choline with different concentrations of Glc(10,20,30,40,50 mmol/L)and the fixed concentration of cod liver oil(0.125 mg/ml)and choline(100 mmol/L)were prepared with saline to simulate liver metabolism phantoms.There were 5 tube models for each Glc concentration,with 25 tube models configured.SPECIAL sequence was used to scan tube models with different parameters,and 1H-MRS images were obtained.The acquired SPECIAL data of tube models were analyzed using LCModel software and built-in metabolite basic set(A set)and B set,respectively.SPECIAL 1H-MRS images were generated,and signal-to-noise ratio(SNR),standard deviation％(SD％)and Glc signal intensity were obtained.The quality of SPECIAL 1H-MRS images were evaluated according to SNR and SD％,and the optimal scanning parameters were selected.The correlation of Glc signal intensity of phantoms obtained by combining optimal parameters with B set and Glc concentration were analyzed.The optimal SPECIAL sequence was used to scan pig liver in vitro(n=5).Then updated fat suppression(FS)-SPECIAL sequence scanning were performed,the corresponding 1H-MRS images were obtained based on B set,and the quality was observed.Results The optimal scanning parameters of SPECIAL sequence included TR 3 500 ms,TE 4.42 ms,TM 20.00 ms,and the number of repetitions(averages)was 256.SNR of phantoms SPECIAL 1H-MRS acquired with the optimal scanning parameters and B set was 40.5±1.1 and SD％was(13.5±1.0)％,with clearer spectral lines,smoother baselines and higher Glc peak resolution.There was positive correlation between Glc signal intensity obtained with 1H-MRS and Glc concentration of phantoms(r=0.997,P＜0.001).SNR of SPECIAL 1H-MRS of pig liver in vitro was 24.0±2.7 and SD％was(13.5±1.1)％,while SNR of FS-SPECIAL 1H-MRS was 29.5±2.3 and SD％was(4.0±0.8)％,the methylene peak was suppressed and the resolution of Glc peak was higher.Conclusion SPECIAL 1H-MRS with optimized parameters combining with self-made simulation data set of LCModel software could be used for accurately quantitative analysis of pig liver Glc in vitro.