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 observe the clinical efficacy of electroacupuncture in treatment of simple obesity patients and its effects on the metabolism-inflammation network. MethodsSeventy-two patients with simple obesity were randomly divided into a electroacupuncture group and a sham electroacupuncture group， with 36 patients in each group. In the electroacupuncture group， conventional acupuncture was applied at Zhongwan （CV 12）， bilateral Tianshu （ST 25）， bilateral Daheng （SP15）， Qihai （CV 6）， Guanyuan （CV 4）， bilateral Zusanli （ST 36） and Fenglong （ST 40）. The sham electroacupuncture group used placebo needle at same acupoints as the electroacupuncture group. Both groups were given sparse-dense waves with a frequency of 2/10 Hz， 30 minutes per session， 3 times a week， for a total of 8 weeks of treatment. The changes of human body parameters including body weight， body mass index （BMI）， waist circumference， and body fat percentage （BFP）， metabolic factors including serum fasting blood glucose （FBG）， triglyceride （TG）， total cholesterol （TC）， high density lipoprotein （HDL）， low density lipoprotein （LDL）， leptin， and ghrelin， and inflammatory factors including interleukin-6 （IL-6） and tumor necrosis factor α （TNF-α） were compared before and after treatment. Correlations between changes in leptin， ghrelin and inflammatory factors were further analyzed. ResultsWaist circumference was significantly reduced after treatment in both groups compared with baseline （P＜0.05）， and the post-treatment waist circumference was lower in the electroacupuncture group than in the sham electroacupuncture group （P＜0.05）； body weight， BMI and BFP decreased significantly after treatment compared to pretreatment levels in the electroacupuncture group （P＜0.05）. Serum leptin， IL-6 and TNF-α declined significantly after treatment in both groups versus baseline （P＜0.05）； the electroacupuncture group had lower post-treatment levels of leptin， IL-6 and TNF-α as well as larger pre-post changes than the sham group （P＜0.05）； ghrelin increased markedly after treatment in both groups （P＜0.05）， with higher post-treatment ghrelin and greater pre-post variation observed in the electroacupuncture group （P＜0.05）. In the electroacupuncture group， changes in ghrelin were negatively correlated with IL-6 and positively correlated with TNF-α （P＜0.01）； in the sham electroacupuncture group， ghrelin changes were negatively correlated with both IL-6 and TNF-α， whereas leptin changes were positively correlated with the two inflammatory markers （P＜0.01）. ConclusionElectroacupuncture can effectively improve clinical manifestations， as well as regulate serum metabolic and pro-inflammatory factors in patients with simple obesity， and discrepant correlation patterns of metabolism-inflammation are observed between the two groups.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

Objective To investigate the safety, economic benefits and psychological effects of day breast conserving surgery for breast cancer. Methods The demographic data and clinical data of breast cancer patients undergoing day (day surgery group) and ward (ward surgery group) breast conserving surgeries in West China Hospital of Sichuan University from March 2020 to June 2021 were retrospectively collected; the demographic data, clinical data, medical and related transportation costs, and preoperative and postoperative BREAST-Q scores of breast cancer patients undergoing day (day surgery group) and ward (ward surgery group) breast conserving surgery in West China Hospital of Sichuan University from June 2021 to June 2022 were prospectively collected. The safety, economic benefit, and psychological satisfaction of day surgery was analyzed. Results A total of 42 women with breast cancer were included in the retrospective study and 39 women with breast cancer were included in the prospective study. In both prospective and retrospective studies, the mean age of patients in both groups were <50 years. There were only statistical differences between the two groups in the aspects of hypertension (P=0.022), neoadjuvant chemotherapy (P=0.037) and postoperative pathological estrogen receptor (P=0.033) in the prospective study. In postoperative complications, there were no statistical differences in the surgical-related complications or anesthesia-related complications between the two groups in either the prospective study or the retrospective study (P>0.05). In terms of the overall cost, we found that the day surgery group was more economical than the ward surgery group in the prospective study (P=0.002). There were no statistical differences in postoperative psychosocical well-being, sexual well-being, satisfaction with breasts or chest condition between the two groups (P>0.05). Conclusion It is safe and reliable to carry out breast conserving surgery in day surgery center under strict management standards, which can save medical costs and will not cause great psychological burden to patients.

The coil adverse events in the U.S.Food and Drug Administration Manufacturer and User Facility Device Experience(MAUDE)database from January 2021 to June 2024 were analyzed retrospectively.The risks of coils during the clinical application and their causes were explored with hospital survey and expert demonstration in Shandong Province.Some improving measures were put forward for the safe use of coils,including implementing the main responsibility of the registrant,enhancing the professional skills of the using institutions and strengthening the supervision of the supervisory authorities.[Chinese Medical Equipment Journal,2025,46(6):83-87]

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Purpose Discover new prostate cancer-related single nucleotide variants.Methods Tissue wax blocks from 21 prostate cancer patients who underwent radical surgery and had relatively complete clinical data were collected for somatic mutation detection to analyze new mutated genes associated with prostate cancer.The levels of cor-responding proteins in the urine of prostate cancer patients were tested according to the results of the selected genes.Results All 21 prostate cancer patients showed obvious somatic mutations,and the mutation types were dominated by C＞T and G＞A.The number of somatic mutations was 521,of which 27 genes had high mutation proportions(≥2 ca-ses),including ZSWIM6(5/21),FOXA1(4/21),SPTA1(2/21),FAM47C(2/21),FLG2(2/21),PRSS3(2/21),TP53(2/21),FLG(2/21),UBR4(2/21),and the mutations occurring in ZSWIM6 were all deletion muta-tions,and the mutations occurring in FOXA1 were missense mutations,deletion mutations,and deletion insertion mu-tations.The urinary levels of UPF1,SPTA1,and IDH1 proteins of the 10 prostate cancer patients were significantly different than those of the healthy controls.Correlation analysis showed that FOXA1 was positively correlated with UBR4(r=0.669,P=0.001),SPTA1 was positively correlated with FLG2(r=1.000,P＜0.001),FAM47C was positively correlated with PRSS3(r=1.000,P＜0.001),and there was a significant positive correlation between TP53 and FLG(r=1.000,P＜0.001).ZSWIM6 and FOXA1 were not correlated with biochemical recurrence.SP-TA1 mutation affected progression-free survival(PFS)[(66.0±0)months vs(30.0±7.8)months,P=0.008].FAM47C was positively correlated with PFS[(66.0±0)months vs(19.0±0)months,P＜0.001].ZNF676 was correlated with PFS[(66.0±0)months vs(26.0±5.0)months,P=0.008].FLG2 was correlated with PFS[(66.0±0)months vs(30.0±7.8)months,P=0.008].PRSS3 was correlated with PFS[(66.0±0)months vs(19.0±0)months,P＜0.001].Conclusion All 21 prostate cancer patients harbored somatic mutations,including ZSWIM6(5/21)and FOXA1(4/21)mutations.SPTA1,FAM47C,ZNF676,FLG2,and PRSS3 may be associated with prognosis.