Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

ObjectiveTo explore the pharmacological mechanism involved in the treatment of allergic cough （AC） by Xiaoer Huatan Zhike granules （XEHT） based on proteomics and network pharmacology. MethodsAfter sensitization by intraperitoneal injection of 1 mL suspension containing 2 mg ovalbumin （OVA） and 100 mg aluminum hydroxide， a guinea pig model of allergic cough was constructed by nebulization with 1% OVA. The modeled guinea pigs were randomized into the model， low-， medium- and high-dose （1， 5， 20 g·kg^-1， respectively） XEHT， and sodium montelukast （1 mg·kg^-1） groups （n=6）， and another 6 guinea pigs were selected as the blank group. The guinea pigs in drug administration groups were administrated with the corresponding drugs by gavage， and those in the blank and model groups received the same volume of normal saline by gavage， 1 time·d^-1. After 10 consecutive days of drug administration， the guinea pigs were stimulated by 1% OVA nebulization， and the coughs were observed. The pathological changes in the lung tissue were observed by hematoxylin-eosin staining. The enzyme-linked immunosorbent assay was performed to measure the levels of C-reactive protein （CRP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， superoxide dismutase （SOD）， and malondialdehyde （MDA） in the bronchoalveolar lavage fluid （BALF） and immunoglobulin G （IgG） and immunoglobulin A （IgA） in the serum. Immunohistochemistry （IHC） was employed to observe the expression of IL-6 and TNF-α in the lung tissue. Transmission electron microscopy was employed observe the alveolar type Ⅱ epithelial cell ultrastructure. Real-time PCR was employed to determine the mRNA levels of IL-6， interleukin-1β （IL-1β）， and TNF-α in the lung tissue. Label-free proteomics was used to detect the differential proteins among groups. Network pharmacology was used to predict the targets of XEHT in treating AC. The Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed to search for the same pathways from the results of proteomics and network pharmacology. ResultsCompared with the blank group， the model group showed increased coughs （P<0.01）， elevated levels of CRP， TNF-α， IL-6， and MDA and lowered level of SOD in the BALF （P<0.05， P<0.01）， elevated levels of IgA and IgG in the serum （P<0.05， P<0.01）， congestion of the lung tissue and infiltration of inflammatory cells， increased expression of IL-6 and TNF-α （P<0.01）， large areas of low electron density edema in type Ⅱ epithelial cells， obvious swelling and vacuolization of the organelles， karyopyknosis or sparse and dissolved chromatin， and up-regulated mRNA levels of IL-6， IL-1β， and TNF-α （P<0.01）. Compared with the model group， the drug administration groups showed reduced coughs （P<0.01）， lowered levels of CRP， TNF-α， IL-6， and MDA and elevated level of SOD in the BALF （P<0.05， P<0.01）， alleviated lung tissue congestion， inflammatory cell infiltration， and type Ⅱ epithelial cell injury， and decreased expression of IL-6 and TNF-α （P<0.01）. In addition， the medium-dose XEHT group and the montelukast sodium group showcased lowered serum levels of IgA and IgG （P<0.05， P<0.01）. The medium- and high-dose XEHT groups and the montelukast sodium showed down-regulated mRNA levels of IL-6， IL-1β， and TNF-α and the low-dose XEHT group showed down-regulated mRNA levels of IL-6 and TNF-α （P<0.05， P<0.01）. Phospholipase D， mammalian target of rapamycin （mTOR）， and epidermal growth factor receptor family of receptor tyrosine kinase （ErbB） signaling pathways were the common pathways predicted by both proteomics and network pharmacology. ConclusionProteomics combined with network pharmacology reveal that XEHT can ameliorate AC by regulating the phospholipase D， mTOR， and ErbB signaling pathways.

Objective:To investigate the iodine nutrition status of children aged 8 - 10 and pregnant women in Xining City, Qinghai Province.Methods:From 2019 to 2021, a stratified cluster sampling method was used to divide 7 counties (districts) under the jurisdiction of Xining City, Qinghai Province into 5 sampling areas according to east, west, south, north, and center each year. One township (town, street) was selected from each area. Forty non boarding students aged 8 to 10 from each primary school (half male and half female, age balanced) and 20 pregnant women from each township (town, street) location were selected to collect edible salt samples at home and a random urine sample to measure salt iodine and urinary iodine level. B-ultrasound was used to measure thyroid volume in children and the goiter rate was calculated.Results:A total of 6 534 samples of household edible salt were collected from children and pregnant women, with an average salt iodine concentration of 25.58 mg/kg. The coverage rate of iodized salt was 97.50% (6 371/6 534), and the qualified iodized salt consumption rate was 89.46% (5 845/6 534). A total of 4 362 urine samples were collected from children, with a median urinary iodine level of 183.10 μg/L. The difference between different years was statistically significant ( H = 20.27, P < 0.001). A total of 2 169 urine samples were collected from pregnant women, with a median urinary iodine level of 168.90 μg/L. The difference between different years was statistically significant ( H = 107.09, P < 0.001). A total of 3 336 cases of thyroid gland examination were conducted in children, including 33 cases of thyroid enlargement, with a goiter rate of 0.99%. There was a statistically significant difference between different years (χ 2 = 15.00, P < 0.001). Conclusion:From 2019 to 2021, children aged 8 to 10 and pregnant women in Xining City are at an appropriate level of iodine, and the achievements in prevention and treatment of iodine deficiency disorders still need to be continuously consolidated.

Objective:To explore the application value of the artificial intelligence (AI) morphological assisted analysis system in the pre-classification of blood cells in patients with acute myeloid leukemia, myelodysplasia-related (AML-MR).Methods:A retrospective analysis was conducted on the bone marrow and peripheral blood cell morphology of patients initially diagnosed with AML-MR at Taizhou Hospital in Zhejiang Province from September 1, 2022, to December 31, 2023. A total of 44 patients, including 25 males and 19 females, with a median age of 71 (63.5, 75.3) years. Bone marrow and peripheral blood morphology were examined using the Morphogo cell morphology assisted analysis system, with the artificial classification results serving as the gold standard. A confusion matrix was constructed to evaluate the precision, sensitivity, and specificity of the AI system in identifying various cell types in bone marrow and peripheral blood for AML-MR diagnosis. The impact of dysplastic hematopoiesis on AI pre-classification was analyzed by comparing AI and manual classification results.Results:The AI system completed the pre-classification of 44 bone marrow smears and 42 corresponding peripheral blood smears from AML-MR patients. For bone marrow smears, the precision, sensitivity, and specificity of AI in pre-classifying blast cells were 85.78%, 91.01%, and 94.58%, respectively. For peripheral blood smears, these values were 87.11%, 87.05%, and 98.29%, respectively. The precision and sensitivity of AI in pre-classifying promyelocytes were 54.26% and 46.93%, respectively, while for monocytes, they were 58.16% and 68.34%, both lower than those for blast cells. The precision and sensitivity of AI in identifying myelocytes and metamyelocytes also decreased (77.47%, 66.25% and 81.91%, 63.29%, respectively). The precision and sensitivity of AI in pre-classifying erythroblasts/proerythroblasts (67.71%, 69.89%) were lower than those for polychromatic and orthochromatic normoblasts (83.43%, 85.53% and 92.97%, 86.96%, respectively). The confusion matrix and comparative analysis of AI and manual classification indicated that the decline in AI pre-classification precision and sensitivity was due to frequent misclassification between promonocytes and monocytes, as well as between monocytes and promyelocytes. Additionally, this decline is associated with dysplasia. However, the impact of dysplasia on the AI pre-classification of mature-stage granulocytes was minimal.Conclusion:The AI system demonstrated high precision, sensitivity, and specificity in pre-classifying blast cells in bone marrow and peripheral blood smears from AML-MR patients. The AI-assisted morphological analysis system can be effectively utilized for the pre-classification of blood cells in AML-MR patients.

Objective:To explore the immunophenotypic differences between acute promyelocytic leukemia (APL) and APL-like NPM1 mutant acute myeloid leukemia (NPM1mutAML) using flow cytometry, and to investigate early diagnostic markers for differentiating APL from NPM1mutAML.Methods:A retrospective study was conducted on 72 cases of APL diagnosed at Taizhou Hospital, affiliated with Wenzhou Medical University, from February 2nd, 2018 to December 16th, 2023, including 42 male and 30 female patients with a median age of 42 (32, 57) years old. Based on morphology, 51 cases were classified as the coarse-granular type and 21 cases as the fine-granular type. Additionally, 45 cases of NPM1mutAML, comprising 20 male and 25 female patients with a median age of 58 (47, 65) years old, were included. Of these, 12 cases were classified as the coarse-granular type and 33 as the fine-granular type. Immunophenotypic analysis was performed using multiparameter flow cytometry, and all patients underwent cytogenetic analysis for chromosome karyotyping. FISH analysis was used for detecting the PML-RARα fusion gene in APL cases, and sequencing was used for identifying NPM1 mutations in NPM1mutAML patients. The antigen expression parameters (expression rate, median fluorescence intensity [MdFI], and coefficient of variation [ CV]) were analyzed using principal component analysis (PCA). The antigen expression rates were compared using the Wilcoxon rank-sum test, and the positive rates of antigens were compared using the Chi-square test. Sensitivity and specificity for diagnosis by the some antigens were evaluated using ROC curve analysis. Results:The immunophenotypic analysis revealed that the expression rates of CD123, CD64, CD13, and CD9 were significantly higher in APL compared to NPM1mutAML ( Z values of-6.72, -6.29, -5.63, -7.67, P<0.01). In the coarse-granular type, the expression rates of CD123 and CD9 in APL were also significantly higher than those in NPM1mutAML ( P<0.01). In the fine-granular type, the expression levels of CD123, CD13, CD64, and CD9 were significantly higher in APL than in NPM1mutAML ( P<0.01). ROC curve analysis showed that in the fine-granular type, the areas under the curve (AUC) for CD64, CD13, CD123, and CD9 in diagnosing APL and NPM1mutAML were 0.96, 0.89, 0.86, and 0.89, respectively ( P<0.01). In the coarse-granular type, the AUC for CD64 and CD13 were 0.49 and 0.51 ( P>0.05), while the AUC for CD123 and CD9 were 0.96 and 0.96 ( P<0.01). Principal component analysis (PCA) of antigen expression (expression rate, MdFI, CV) showed complete separation of the APL and NPM1mutAML groups. Conclusion:APL and APL-like NPM1mutAML patients exhibit distinct antigen expression profiles. Specifically, a combined detection of CD64, CD13, CD123, and CD9 can help to rapidly differentiate APL from APL-like NPM1mutAML at initial diagnosis.

OBJECTIVE To investigate the management status of small pressure steam sterilizers in dental institu-tions across seven major regions of China and provide references for establishing standardized monitoring and eval-uation protocols for sterilization efficacy.METHODS From Aug.to Nov.2023,a convenience sampling method was employed to select 885 dental institutions of various levels from seven geographical regions.An online survey using a self-designed questionnaire was conducted to assess the management status of small pressure steam steri-lizers in dental institutions.RESULTS A total of 885 questionnaires were distributed,with 770 valid responses col-lected,covering 770 dental institutions at all levels.The survey involved 2 056 small pressure steam sterilizers,of which 43.14％were pre-vacuum type and 89.40％were benchtop models.Tertiary dental institutions had the high-est proportion of imported small pressure steam sterilizers(60.15％),with most devices in use for 5-10 years(36.88％).Secondary and low er-level institutions predominantly used domestically produced small pressure steam sterilizers(74.60％),which used for 3-5 years accounting for the highest proportion(34.29％).While 85.58％of dental institutions employed dedicated sterilization personnel,only 45.84％performed proper maintenance for small pressure steam sterilizers.Training rates were 80.00％for department of stomatology in ungraded general hospitals and primary medical institutions,76.19％in prejob and 71.43％during the work for department of sto-matology in primary clinics,respectively.CONCLUSIONS Although most dental institutions demonstrate sound policy formulation,challenges persist in equipment maintenance,policy implementation and grassroots training.Future efforts should be made on strengthening maintenance and inspection of equipment,enhancing sys-tem supervisory mechanisms,and optimizing training systems to ensure dental treatment safety.

Objective:To develop a deep learning model based on super-resolution endorectal ultrasound（ERUS）images for the preoperative prediction of perineural invasion（PNI）in patients with rectal cancer，thereby providing a reference for risk stratification and individualized treatment planning.Methods:A retrospective analysis was conducted on 382 patients with rectal cancer who underwent total mesorectal excision at Fujian Medical University Union Hospital between June 2019 and February 2024. Patients were randomly divided into a training set（ n=305）and a test set（ n=77）at a ratio of 8∶2，and further grouped into PNI-negative group and PNI-positive group subgroups based on pathological results. Super-resolution ultrasound images were generated from original ERUS images using a generative adversarial network（GAN）. Deep convolutional neural networks were developed based on features from intratumoral and peritumoral regions to identify the optimal region of interest（ROI）. The dSR5_ResNet18 and dSR5_ResNet50 models were constructed using the super-resolution images with a 5-pixel peritumoral extension. Representative clinical features were selected for subgroup analysis based on sample size and intergroup statistical differences between PNI-positive and PNI-negative patients. Forest plots were used to evaluate model applicability and robustness across subgroups. Results:The dSR5_ResNet18 model，built using super-resolution images of the tumor combined with a 5-pixel peritumoral region，achieved the best predictive performance，with an AUC of 0.867（95% CI=0.782 - 0.952）in the test set. Decision curve analysis demonstrated that the dSR5_ResNet18 model provided the greatest net clinical benefit. Forest plot analysis indicated strong generalizability of the models across subgroups such as pathological N stage，maximum lesion length，and lymph node enlargement，though relatively weaker performance was observed in the carcinoembryonic antigen（CEA）subgroup. Among all models，dSR5_ResNet18 exhibited the most consistent performance across subgroups，with the narrowest confidence intervals and highest robustness. Conclusions:The deep learning model incorporating ERUS-based super-resolution reconstruction demonstrated excellent performance in the preoperative prediction of PNI in rectal cancer. It offers significant advantages in image quality and generalizability，and may serve as a valuable tool to assist clinicians in formulating personalized treatment strategies.

BACKGROUND: Chronic kidney disease (CKD) is associated with common pathophysiological processes, such as inflammation and fibrosis, in both the heart and the kidney. However, the underlying molecular mechanisms that drive these processes are not yet fully understood. Therefore, this study focused on the molecular mechanism of heart and kidney injury in CKD. METHODS: We generated an microRNA (miR)-26a knockout (KO) mouse model to investigate the role of miR-26a in angiotensin (Ang)-II-induced cardiac and renal injury. We performed Ang-II modeling in wild type (WT) mice and miR-26a KO mice, with six mice in each group. In addition, Ang-II-treated AC16 cells and HK2 cells were used as in vitro models of cardiac and renal injury in the context of CKD. Histological staining, immunohistochemistry, quantitative real-time polymerase chain reaction (PCR), and Western blotting were applied to study the regulation of miR-26a on Ang-II-induced cardiac and renal injury. Immunofluorescence reporter assays were used to detect downstream genes of miR-26a, and immunoprecipitation was employed to identify the interacting protein of LIM and senescent cell antigen-like domain 1 (LIMS1). We also used an adeno-associated virus (AAV) to supplement LIMS1 and explored the specific regulatory mechanism of miR-26a on Ang-II-induced cardiac and renal injury. Dunnett's multiple comparison and t -test were used to analyze the data. RESULTS: Compared with the control mice, miR-26a expression was significantly downregulated in both the kidney and the heart after Ang-II infusion. Our study identified LIMS1 as a novel target gene of miR-26a in both heart and kidney tissues. Downregulation of miR-26a activated the LIMS1/integrin-linked kinase (ILK) signaling pathway in the heart and kidney, which represents a common molecular mechanism underlying inflammation and fibrosis in heart and kidney tissues during CKD. Furthermore, knockout of miR-26a worsened inflammation and fibrosis in the heart and kidney by inhibiting the LIMS1/ILK signaling pathway; on the contrary, supplementation with exogenous miR-26a reversed all these changes. CONCLUSIONS Our findings suggest that miR-26a could be a promising therapeutic target for the treatment of cardiorenal injury in CKD. This is attributed to its ability to regulate the LIMS1/ILK signaling pathway, which represents a common molecular mechanism in both heart and kidney tissues.
Animals ; MicroRNAs/metabolism* ; Angiotensin II/toxicity* ; Mice ; Renal Insufficiency, Chronic/chemically induced* ; Mice, Knockout ; Disease Models, Animal ; Male ; Signal Transduction/genetics* ; LIM Domain Proteins/genetics* ; Mice, Inbred C57BL ; Cell Line ; Humans

Objective In this study,we investigated the correlation between the scores for different Liebowitz Social Anxiety Scale(LSAS)subscale models and the metabolic activity in specific regions of the brain using 18F-fluorodeoxyglucose(FDG)positron emission tomography/computed tomography(PET/CT)(18F-FDG PET),thereby improving the understanding of the neurobiological characteristics of social anxiety.Methods A total of 39 cognitively normal participants(29 men and 10 women,aged 30-63 years)were enrolled.All participants underwent LSAS assessment and brain 18F-FDG PET scanning.Correlations between metabolic activities in various brain regions and scores from the different LSAS subscales were analyzed accordingly.Results LSAS subscale scores were significantly correlated with metabolic activity in specific brain regions.In the Safren model,the score for the observation by others subscale was positively correlated with the left fusiform gyrus(P<0.001,false discovery rate[FDR]-corrected)and the left caudate tail(P<0.001,FDR-corrected),suggesting a close association between mood states related to observation by others and the metabolic activity in these regions.In the Baker model,the score for the eating and drinking subscale was negatively correlated with the right precuneus(P<0.001,FDR-corrected),while the score for the assertiveness subscale was positively correlated with the left caudate nucleus(P<0.001,FDR-corrected).These findings revealed the complex associative patterns between various mood and behavioral dimensions and metabolic activities in specific brain regions.Conclusion Social anxiety symptoms are closely associated with metabolic changes in specific brain regions,including the left insula,left caudate tail,and right precuneus.Moreover,different social situations activate distinct brain regions.Compared with individuals with social anxiety disorder,normal individuals exhibit involvement of fewer brain regions when experiencing social anxiety.These findings provide new experimental evidence for understanding the neural mechanisms underlying social anxiety.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.