ObjectiveTo investigate the influencing factors and independent risk factors for lower extremity deep vein thrombosis （DVT） in patients with acute necrotizing pancreatitis （ANP）， to analyze the effectiveness of three commonly used risk assessment models for thrombosis （Caprini score， Padua score， and Wells score）， and to provide a reference for clinical identification of high-risk individuals and optimization of prevention and treatment strategies. MethodsA retrospective analysis was performed for the clinical data of 320 patients with ANP who were admitted to Luzhou People’s Hospital and The Affiliated Hospital of Southwest Medical University from April 2013 to April 2024， and according to the presence or absence of DVT during hospitalization， the patients were divided into thrombosis group with 25 patients and control group with 295 patients. After propensity score matching， the two groups were compared in terms of past history and various examination results during hospitalization. The risk factors for lower extremity DVT in ANP patients during hospitalization were analyzed through univariate and multivariate Logistic regression， and a DVT risk prediction model was established based on independent influencing factors. The receiver operating characteristic （ROC） curve was used to assess the performance of models， and the DeLong test was used for comparison of the area under the ROC curve （AUC）， sensitivity， and specificity. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups； the chi-square test was used for comparison of categorical data between groups. ResultsAfter matching， the patients were divided into thrombosis group with 24 patients and control group with 112 patients. The clinical characteristics analysis showed that compared with the control group， the thrombosis group had significantly higher degree of pancreatic necrosis， D-dimer level， Bedside Index for Severity in Acute Pancreatitis （BISAP） score， and proportion of patients undergoing dialysis （all P<0.05）. The multivariable Logistic regression analysis showed that BISAP score， degree of pancreatic necrosis， and D-dimer level were independent risk factors for lower extremity DVT in ANP patients during hospitalization （all P<0.05）. The BISAP-Caprini score model had an AUC of 0.832 （95% confidence interval： 0.722 — 0.942， P<0.001） in predicting the risk of lower extremity DVT， with a Youden index of 1.661， an optimal cut-off value of 0.26， a sensitivity of 75.0%， and a specificity of 91.1%. ConclusionD-dimer， BISAP score， and the degree of pancreatic necrosis are independent risk factors for lower extremity DVT in patients with ANP during hospitalization， and the BISAP-Caprini score model can effectively predict the risk of DVT in ANP patients.

Objective To investigate the role and potential mechanism of secreted phosphoprotein 1 (SPP1)⁺ macrophages in the formation of chronic renal allograft fibrosis. Methods The expression features of SPP1⁺ macrophages in renal allografts of chronic allograft dysfunction (CAD) patients were analyzed based on single-cell transcriptome data of renal tissues from patients with CAD. Transcription factor VIPER analysis and DoRothEA transcription factor activity analysis were performed on the single-cell transcriptome data. Renal tissue samples were collected from kidney transplant recipients, including the CAD group (n=5) and the non-renal allograft fibrosis group (CTL group, n=5). A mouse model of chronic allograft rejection was established and divided into the allogeneic kidney transplantation group (CAD group, n=3) and the syngeneic kidney transplantation group (SYN group, n=3). Hematoxylin-eosin staining was used to detect renal tissue injury in mice, and Masson staining was used to detect renal tissue fibrosis. Immunofluorescence staining was performed to detect SPP1 expression in renal tissues of transplant recipients and mouse renal allografts. Bone marrow-derived macrophages (BMDMs) were extracted from mice and subjected to hypoxia stimulation. The expression of hypoxia-inducible factor (HIF)-1α and SPP1 was detected by Western blot, and SPP1 expression was detected by flow cytometry. BMDMs were transfected with HIF-1α overexpression plasmid and HIF-1α small interfering RNA (siRNA) followed by hypoxia intervention, and the expression of HIF-1α and SPP1 was detected by Western blot. Mouse aortic endothelial cells (MAECs) were co-cultured with the supernatant of BMDMs, and the expression of endothelial-mesenchymal transition (EndMT)-related markers was detected by Western blot and immunofluorescence. Results Single-cell transcriptome analysis showed that the proportion of SPP1⁺ macrophages in renal allograft tissues was significantly higher in the CAD group than in the CTL group (P<0.05). The renal injury score and the percentage of interstitial fibrotic area in the CAD group were significantly higher than those in the SYN group (both P<0.05). Immunofluorescence staining showed that the proportion of SPP1⁺ macrophages was increased in the CAD group compared with the CTL group, and also increased in the CAD group compared with the SYN group (both P<0.05). VIPER analysis and DoRothEA transcription factor activity analysis revealed activation of the hypoxia pathway and upregulated expression of transcription factors such as HIF-1α in SPP1⁺ macrophages. SPP1 expression was elevated in BMDMs under hypoxic conditions. Knockdown of HIF-1α inhibited hypoxia-induced SPP1 protein expression, whereas overexpression of HIF-1α upregulated SPP1 protein levels. After co-culture of hypoxia-induced BMDMs with MAECs, the expression levels of EndMT-related markers were increased. Conclusions SPP1⁺ macrophages differentiated under hypoxia are significantly infiltrated in the formation of chronic renal allograft fibrosis, and may promote renal allograft fibrosis by inducing EndMT in renal vascular endothelial cells.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

Objective To explore the clinical characteristics and related prognostic factors of hypertriglyceridemia acute pancreatitis (HTG-AP). Methods A retrospective analysis was conducted on 350 patients with HTG-AP admitted to HanZhong Central Hospital from March 2019 to March 2024. All patients received conventional treatment. They were followed up for one year after treatment. The prognosis of the patients was statistically analyzed, and logistic regression was used to analyze the related factors of prognosis. Results HTG-AP patients had an acute onset, with clinical symptoms of sudden upper abdominal pain (329/350), nausea and vomiting (275/350), acidosis (101/350), and multiple organ failure (38/350). All patients had elevated serum TG. During the follow-up period, 123 cases had a poor prognosis (poor prognosis group), and 227 cases had a good prognosis (good prognosis group). Compared with the good prognosis group, the patients in the poor prognosis group had higher levels of TG, creatinine and C-reactive protein at admission, lower levels of serum calcium and albumin, and higher proportions of diabetes mellitus history and severe conditions (P<0.05). Logistic regression analysis found that the factors related to the prognosis of patients with HTG-AP were TG level, C-reactive protein level, albumin level, history of diabetes mellitus, and moderate to severe condition (P<0.05). Conclusion HTG-AP patients have an acute onset and have main clinical symptoms of sudden upper abdominal pain, nausea and vomiting. Some patients experience systemic inflammatory reactions such as acidosis and multiple organ failure, and they may also have significantly increased serum TG. TG, C-reactive protein, albumin, history of diabetes mellitus, and severe disease conditions are associated with the prognosis of HTG-AP patients.

To investigate the risk factors for acute kidney injury (AKI) following the use of amphotericin B deoxycholate and to develop a predictive model to guide clinical monitoring and intervention.

OBJECTIVE To investigate the neuroprotective effect and mechanism of eleutheroside B （ELB） on Parkinson’s disease （PD） model mice by regulating the IκB kinase β （IKKβ）/nuclear factor-κB （NF-κB） signaling pathway. METHODS Fifty mice were randomly divided into normal control group， model group， positive control group （selegiline hydrochloride， 10 mg/kg）， and ELB low-dose and high-dose groups （80， 160 mg/kg）， with 10 mice in each group. Each group was given relevant medicine or normal saline intragastrically for 14 consecutive days. Starting from the 10th day of administration， the model group and all administration groups were intraperitoneally injected with 1-methyl-4-phenyl-1，2，3，6-tetrahydropyridine （MPTP） 30 mg/kg， for five consecutive days to establish the chronic PD model. After the last administration for 24 h， six mice were randomly selected from each group to test their behavioral abilities； detect the levels of interleukin-1β （IL-1β）， IL-10， tumor necrosis factor-α （TNF-α） in brain tissue and their mRNA expressions were measured， and positive expression of tyrosine hydroxylase （TH）， protein expressions of TH， α -synuclein （ α -syn）， ionized calcium-binding adaptor molecule 1 （Iba-1）， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in the brain tissue were detected. The ultrastructure of neurons in substantia nigra was observed. RESULTS Compared with the model group， rotarod endurance time and climbing score of each administration group （except for the ELB low-dose group） were increased significantly （ P ＜0.05）， while the levels and mRNA expressions of IL-1β， TNF-α， α -syn， and Iba-1， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in brain tissue were decreased significantly （except for TNF-α in the ELB low-dose group）. Conversely， the level and mRNA expression of IL-10 （except for the ELB low-dose group）， TH positive expression and protein expressions were significantly increased （ P ＜0.05）. Typical neurodegenerative pathological changes， such as neuronal karyopyknosis， mitochondrial swelling and vacuolization， and endoplasmic reticulum dilation， all showed varying degrees of improvement. CONCLUSIONS ELB may exert neuroprotective effects by inhibiting the activation of the IKKβ/NF-κB signaling pathway， alleviating inflammatory responses， reducing abnormal α -syn aggregation and neuronal loss， and further improving motor dysfunction in PD mice.

BACKGROUND:Sarcopenia is an age-related systemic skeletal muscle disease,which is associated with a variety of adverse outcomes such as falls,functional decline,frailty,and death.Postmenopausal women are one of the high-risk groups for sarcopenia.OBJECTIVE:To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.METHODS:Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study(CHARLS),and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019(AWGS2019)recommended metrics.The study cohort was randomized into a training set(70％)and a validation set(30％).Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator,ten-fold cross-validation,and logistic regression.Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors,and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS AND CONCLUSION:The prevalence of sarcopenia in this study was 23.50％and age,place of residence,sleep quality,cognitive function,depression,and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women.The nomogram model showed good discrimination between the training and validation sets,with an AUC value of 0.751(95％confidence interval=0.724-0.778,P＜0.001),a specificity of 72.2％,and a sensitivity of 63.2％in the training set,and an AUC value of 0.763(95％confidence interval=0.721-0.805,P＜0.001),with a specificity of 69.6％and a sensitivity of 70.8％.The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations,and the decision curve analysis demonstrated broad and good clinical utility.To conclude,the nomogram to assess the risk of sarcopenia constructed based on age,place of residence,sleep quality,cognitive function,depression,and number of chronic diseases,provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women,and helps to reduce the incidence of sarcopenia.

BACKGROUND:Sarcopenia is an age-related systemic skeletal muscle disease,which is associated with a variety of adverse outcomes such as falls,functional decline,frailty,and death.Postmenopausal women are one of the high-risk groups for sarcopenia.OBJECTIVE:To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.METHODS:Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study(CHARLS),and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019(AWGS2019)recommended metrics.The study cohort was randomized into a training set(70％)and a validation set(30％).Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator,ten-fold cross-validation,and logistic regression.Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors,and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS AND CONCLUSION:The prevalence of sarcopenia in this study was 23.50％and age,place of residence,sleep quality,cognitive function,depression,and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women.The nomogram model showed good discrimination between the training and validation sets,with an AUC value of 0.751(95％confidence interval=0.724-0.778,P＜0.001),a specificity of 72.2％,and a sensitivity of 63.2％in the training set,and an AUC value of 0.763(95％confidence interval=0.721-0.805,P＜0.001),with a specificity of 69.6％and a sensitivity of 70.8％.The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations,and the decision curve analysis demonstrated broad and good clinical utility.To conclude,the nomogram to assess the risk of sarcopenia constructed based on age,place of residence,sleep quality,cognitive function,depression,and number of chronic diseases,provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women,and helps to reduce the incidence of sarcopenia.

With the rapid development of computer science, the application of artificial intelligence (AI) in the field of medical imaging has become increasingly extensive. The temporomandibular joint (TMJ) is structurally complex, with a high incidence of related disorders and diverse clinical manifestations. This review analyzes the current state of research on AI in TMJ imaging diagnosis. Deep learning models based on U-Net and its derivatives have demonstrated outstanding performance in segmentation of condyle and articular disc. Various object detection and feature extraction algorithms have shown excellent diagnostic efficacy for common conditions, such as osteoarthrosis and disc displacement, with some models even achieving expert-level performance on test datasets. Meanwhile, explainable AI provides intuitive justification for model decisions through techniques such as heatmap visualization. Notably, current studies still face critical challenges, including coverage of disease spectra, integration of multimodal data, and model generalizability. Future studies should focus on developing integrated systems that combine diagnosis, segmentation, generation, and interpretation functions. Through multicenter data validation and algorithmic optimization, these efforts will enhance the clinical applicability and decision transparency of models, ultimately laying the foundation for precise imaging diagnosis and intelligent management of TMJ disorders.

Objective: To evaluate the performance of platelet efficacy score (PEscore) in predicting platelet transfusion efficacy in hematological patients. Methods: A total of 485 patients with hematological diseases, including 298 males (62.09±15.45 years) and 187 females (59.17±16.52 years) who received platelet transfusion from January 1, 2021 to December 31, 2024 were enrolled in this study. Clinical data of the patients such as diagnosis, gender, age, number of platelet transfusion, and platelet antibody data were analyzed to investigate the incidence and influencing factors of platelet transfusion refractoriness in hematological patients at our hospital. ROC curve was used to evaluate the performance of PEscore model in predicting platelet transfusion efficacy. The predictive performance of PEscore model was validated by calculating its sensitivity, specificity, and accuracy in 115 clinical cases. Results: The incidence of platelet transfusion refractoriness in 485 cases was 29.90% (145/485). Significant differences (P<0.05) were observed between the effective and ineffective platelet transfusion groups regarding the following factors: diagnosis: lymphoma [55.32% (26/47) vs 44.68% (21/47)], the number of previous platelet transfusions [≥25: 60.78% (31/51) vs 39.22% (20/51)], platelet antibody screening result [positive: 33.76% (53/157) vs 66.24% (104/157)], and platelet transfusion volume (×10 /L) [>6: 62.71% (74/118) vs 37.29% (44/118)]. The area under the ROC curve of PEscore was 0.876. The cut-off points and corresponding sensitivity and specificity were 19.90.59% and 69.44%, respectively. The results of clinical application showed that the sensitivity, specificity and accuracy of the PEscore model for predicting platelet transfusion were 87.50%, 93.41% and 92.17%, respectively. Conclusion: The incidence of platelet transfusion refractoriness in hematological patients is relatively high. PEscore prediction model has a good performance in predicting the effect of platelet transfusion, which can provide a reliable basis for predicting the effect of platelet transfusion in hematological patients before blood transfusion.