Objective To explore the pathogenesis and prognostic factors of liposarcoma in the head and neck region, and simultaneously analyze the efficacy of different treatment regimens. Methods A retrospective analysis was performed on all patients with primary untreated head and neck liposarcoma who were diagnosed and underwent surgical treatment at our hospital from January 2008 to January 2024. All patients were monitored during follow-up, and their prognoses were analyzed using SPSS software. Results A total of 30 patients were included in the study. Liposarcoma accounted for up to 60% of the cases in the orbit, while the remaining liposarcomas were primarily located in various interspaces of the neck. Dedifferentiated liposarcoma was the most common type, comprising 33%, while myxoid pleomorphic liposarcoma was the rarest at 4%. The tumor pathological type (P<0.001) and Ki67 (P=0.014) significantly affected the tumor control rate. However, an analysis of disease-specific survival rates revealed no significant differences across various factors (all P>0.05). Conclusion The prognosis of head and neck liposarcoma is better compared to that of liposarcomas in other parts of the body. However, myxoid pleomorphic liposarcoma, pleomorphic fat sarcoma, and high Ki67 levels are indicators of poor prognosis. Additionally, postoperative adjuvant radiotherapy does not significantly enhance disease-specific survival rates.

OBJECTIVE To evaluate the efficacy， safety and cost-effectiveness of serplulimab as a first-line treatment of small- cell lung cancer （SCLC）， and provide an evidence-based basis for drug selection in hospitals. METHODS Rapid health technology assessment was adopted； PubMed， Cochrane Library， Embase， CNKI， Wanfang， VIP and official websites of domestic and international health technology assessment agencies were systematically searched from the inception to Oct. 2024. Two reviewers independently screened the literature， assessed the quality of included studies and carried out the qualitative analysis according to the inclusion and exclusion criteria. RESULTS A total of 13 systematic reviews/meta-analyses and 9 economic studies were included， and the literature quality was generally good. In terms of effectiveness， compared with chemotherapy alone， serplulimab combined with chemotherapy significantly improved progression-free survival， overall survival， and objective response rate in patients with SCLC. In terms of safety， serplulimab combined with chemotherapy showed no significant difference in the incidence of ≥3 grade adverse events compared with chemotherapy alone in the treatment of SCLC， indicating a good safety profile； compared with combination therapies involving other immunosuppressive agents， the incidence rate of adverse events was also lower. In terms of cost-effectiveness， compared with chemotherapy alone， serplulimab combined with chemotherapy is not cost- effective， which may be related to the high price of serplulimab. CONCLUSIONS Serplulimab is effective and safe in the treatment of SCLC， but has no obvious advantage in terms of cost-effectiveness.

Most of the medical colleges and universities in China follow the traditional three-stage teaching mode centering on subjects, and a number of colleges and universities have implemented the teaching mode of organ-system-based curriculum (OSBC). With the OSBC course for the nervous system in our university as an example, this article analyzes the advantages and challenges of OSBC course of the nervous system in the context of integrative medicine.
Neurology

To elucidate the mechanism by which steaming affects the quality of Curcuma kwangsiensis root tubers, methods such as LSCM, RVA, dual-wavelength spectrophotometry, LF-NMR, and LC-MS were employed to qualitatively and quantitatively detect changes in starch gelatinization characteristics, water distribution, and material composition of C. kwangsiensis root tubers under different steaming durations. Based on multivariate statistical analysis, the correlation between differences in gelatinization parameters, water distribution, and terpenoid material composition was investigated. The results indicate that steaming affects both starch gelatinization and water distribution in C. kwangsiensis. During the steaming process, transformations occur between amylose and amylopectin, as well as between semi-bound water and free water. After 60 min of steaming, starch gelatinization and water distribution reached an equilibrium state. The content of amylopectin, the amylose-to-amylopectin ratio, and parameters such as gelatinization temperature, viscosity, breakdown value, and setback value were significantly correlated(P≤0.05). Additionally, the amylose-to-amylopectin ratio was significantly correlated with total free water and total water content(P≤0.05). Steaming induced differences in the material composition of C. kwangsiensis root tubers. Clustering of primary metabolites in the OPLS-DA model was distinct, while secondary metabolites were classified into 9 clusters using the K-means clustering algorithm. Differential terpenoid metabolites such as(-)-α-curcumene were significantly correlated with zerumbone, retinal, and all-trans-retinoic acid(P<0.05). Curcumenol was significantly correlated with isoalantolactone and ursolic acid(P<0.05), while all-trans-retinoic acid was significantly correlated with both zerumbone and retinal(P<0.05). Alpha-tocotrienol exhibited a significant correlation with retinal and all-trans-retinoic acid(P<0.05). Amylose was extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and α-tocotrienol(P<0.05). Amylopectin was significantly correlated with zerumbone(P<0.05) and extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and 9-cis-retinoic acid(P<0.01). The results provide scientific evidence for elucidating the mechanism of quality formation of steamed C. kwangsiensis root tubers as a medicinal material.
Curcuma/chemistry* ; Starch/chemistry* ; Multivariate Analysis ; Water/chemistry* ; Terpenes/analysis* ; Plant Roots/chemistry* ; Plant Tubers/chemistry* ; Drugs, Chinese Herbal/chemistry*

The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T ²KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T ²KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 ⁶ vs. 106.1×10 ⁶, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
Humans ; Breast Neoplasms/diagnostic imaging* ; Female ; Ultrasonography, Mammary/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Breast/diagnostic imaging*

OBJECTIVES: Pelvic floor dysfunction is common among pregnant and postpartum women and significantly impacts quality of life. This study aims to translate the German Pelvic Floor Questionnaire for Pregnant and Postpartum Women into Chinese and to evaluate its reliability and validity in the Chinese population. METHODS: The questionnaire was translated using the Brislin model. A cross-sectional study was conducted among pregnant and postpartum women to assess the content validity, construct validity, Cronbach's α coefficient, test-retest reliability, and split-half reliability of the Chinese version. RESULTS: A total of 72 women were included, with 6.9% being pregnant and 93.1% postpartum; the age was (32.3±3.6) years. The Chinese version of the questionnaire contains 4 dimensions and 45 items. The content validity index of individual items ranged from 0.833 to 1.000, with a scale-level content validity index of 0.977 and intraclass correlation coefficients (ICCs) exceeding 0.90. The overall Cronbach's α coefficient was 0.891, with subscale coefficients ranging from 0.732 to 0.884 (all ICCs>0.70). The test-retest reliability of the total scale was 0.833, and for the 4 dimensions, bladder, bowel, prolapse, and sexual function, the values were 0.776, 0.579, 0.732, and 0.645, respectively. The split-half reliability was 0.74. CONCLUSIONS The Chinese version of the questionnaire demonstrated good reliability and validity, indicating its applicability in assessing pelvic floor dysfunction and associated risk factors during pregnancy and postpartum.
Humans ; Female ; Surveys and Questionnaires ; Pregnancy ; Adult ; Postpartum Period ; Psychometrics ; Pelvic Floor Disorders/diagnosis* ; Cross-Sectional Studies ; Quality of Life ; Pelvic Floor/physiopathology* ; Reproducibility of Results ; China ; Translations ; Young Adult

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

OBJECTIVE To evaluate the cost-utiliby of capivasertib combined with fulvestrant for the second-line treatment of hormone receptor-positive/human epidermal growth factor receptor-2-negative （HR+/HER2－） advanced breast cancer from the perspective of the Chinese healthcare system. METHODS A partitioned survival model was constructed using clinical data from the CAPItello-291 trial. Costs and quality-adjusted life years （QALYs） were used as the output indicators of the model， and the incremental cost-effectiveness ratio （ICER） was used as the evaluation indicator of the model. Using three times the per capita gross domestic product （GDP） of China in 2024 as the willingness-to-pay threshold （WTP）， this study analyzed the cost-utility of capivasertib combined with fulvestrant versus fulvestrant monotherapy in the treatment of HR+/HER2－ advanced breast cancer， and conducted sensitivity analysis and scenario analysis under conditions where the price of capivasertib was reduced by 50%， 60%， 70% and 95%， respectively. RESULTS The results of the basic analysis showed that compared with the fulvestrant monotherapy regimen， the ICER of capivasertib combined with fulvestrant was 843 038.46 yuan/QALY， which was higher than the WTP（287 247 yuan/QALY）. The one-way sensitivity analysis revealed that the top three factors with the most substantial influence on ICER were the utility value in the progression disease state， the price of capivasertib， and the utility value inthe progression free survival state. Probabilistic sensitivity analysis demonstrated the robustness of the basic analysis results. Scenario analysis revealed that even if the price of capivasertib were reduced by 95%， capivasertib combined with fulvestrant did not exhibit cost-effectiveness at the current WTP. CONCLUSION At a WTP of three times China’s GDP per capita in 2024， compared to fulvestrant monotherapy， capivasertib combined with fulvestrant as the second-line treatment for HR+/ HER2－ advanced breast cancer is not cost-effective.

Henoch-Sch?nlein purpura is also known as IgA vasculitis. The kidney involvement, namely Henoch-Sch?nlein purpura nephritis (HSPN), is one of IgA vasculitis's main clinical manifestations. Galactose-deficient IgA1 plays an important role in the pathogenesis of HSPN, which not only is similar to the "four-hits hypothesis" of IgA nephropathy, leading to IgA1 deposit in the mesangial region of the kidney, but also is closely related to inflammation with more complicated compositions of its immune complex. Therefore, abnormal glycosylation of IgA1 is expected to be a biomarker for diagnosis and prediction of HSPN pathogenesis, disease activity determination and prognosis prediction. Here, the paper reviews the research progress on the pathogenesis and clinical application of abnormal glycosylation of IgA1 in HSPN.