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.

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.

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.

OBJECTIVE: To investigate the Wnt signaling pathway and miRNAs mechanism of extracts of Plastrum Testudinis (PT) in the treatment of osteoporosis (OP). METHODS: Thirty female Sprague Dawley rats were randomly divided into 5 groups by random number table method, including sham group, ovariectomized group (OVX), ovariectomized groups treated with high-, medium-, and low-dose PT (160, 80, 40 mg/kg per day, respectively), with 6 rats in each group. Except for the sham group, the other rats underwent bilateral ovariectomy to simulate OP and received PT by oral gavage for 10 consecutive weeks. After treatment, bone mineral density was measured by dual-energy X-ray absorptiometry; bone microstructure was analyzed by micro-computed tomography and hematoxylin and eosin staining; and the expressions of osteogenic differentiation-related factors were detected by immunochemistry, Western blot, and quantitative polymerase chain reaction. In addition, Dickkopf-1 (Dkk-1) was used to inhibit the Wnt signaling pathway in bone marrow mesenchymal stem cells (BMSCs) and miRNA overexpression was used to evaluate the effect of miR-214 on the osteogenic differentiation of BMSCs. Subsequently, PT extract was used to rescue the effects of Dkk-1 and miR-214, and its impacts on the osteogenic differentiation-related factors of BMSCs were evaluated. RESULTS: PT-M and PT-L significantly reduced the weight gain in OVX rats (P<0.05). PT also regulated the bone mass and bone microarchitecture of the femur in OVX rats, and increased the expressions of bone formation-related factors including alkaline phosphatase, bone morphogenetic protein type 2, collagen type I alpha 1, and runt-related transcription factor 2 when compared with the OVX group (P<0.05 or P<0.01). Meanwhile, different doses of PT significantly rescued the inhibition of Wnt signaling pathway-related factors in OVX rats, and increased the mRNA or protein expressions of Wnt3a, β-catenin, glycogen synthase kinase-3β, and low-density lipoprotein receptor-related protein 5 (P<0.05 or P<0.01). PT stimulated the osteogenic differentiation of BMSCs inhibited by Dkk-1 and activated the Wnt signaling pathway. In addition, the expression of miR-214 was decreased in OVX rats (P<0.01), and it was negatively correlated with the osteogenic differentiation of BMSCs (P<0.01). MiR-214 mimic inhibited Wnt signaling pathway in BMSCs (P<0.05 or P<0.01). Conversely, PT effectively counteracted the effect of miR-214 mimic, thereby activating the Wnt signaling pathway and stimulating osteogenic differentiation in BMSCs (P<0.05 or P<0.01). CONCLUSION PT stimulates bone formation in OVX rats through β-catenin-mediated Wnt signaling pathway, which may be related to inhibiting miR-214 in BMSCs.
Animals ; MicroRNAs/genetics* ; Female ; Rats, Sprague-Dawley ; Wnt Signaling Pathway/genetics* ; Osteogenesis/genetics* ; Mesenchymal Stem Cells/cytology* ; Cell Differentiation/drug effects* ; Bone Density/drug effects* ; Ovariectomy ; Osteoporosis/drug therapy* ; beta Catenin/metabolism* ; Rats ; Intercellular Signaling Peptides and Proteins/metabolism* ; Drugs, Chinese Herbal/pharmacology*

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Objective:To explore the genetic and clinical characteristics of Guizhou patients with enlarged vestibular aqueduct（EVA） syndrome through combined SLC26A4 variant analysis and clinical phenotype analysis. Methods:Seventy-two EVA patients underwent comprehensive genetic testing using a multiplex PCR-based deafness gene panel and next-generation sequencing（NGS）. The audiological and temporal bone imaging characteristics were compared across mutation subtypes. Results:A total of 27 pathogenic loci of SLC26A4 were detected in 72 patients, including c.919-2A>G in 79.2%（57/72）. A novel deletion（c.1703_1707+6del） was discovered. Among 65 cases, truncated mutations were 89.2%（58/65）, 52.3%（34/65）, 28（43.1%） and 7（10.8%）. No significant differences were observed in the midpoint diameter of the vestibular aqueduct and the incidence of incomplete partitioning typeⅡ（IP-Ⅱ） of the cochlea among the three groups of patients. Moreover, there was no difference in the midpoint diameter of different vestibular pipes or the combination with IP-Ⅱ. Conclusion:The most common mutation site of SLC26A4 in EVA patients in Guizhou is c.919-2A>G, though genotype-phenotype correlations remain elusive. The detection of 27 mutation sites and the discovery of new mutation sites suggested the precise diagnostic significance of NGS technology in EVA patients in Guizhou.
Humans ; Sulfate Transporters ; Vestibular Aqueduct/abnormalities* ; Mutation ; Membrane Transport Proteins/genetics* ; Hearing Loss, Sensorineural/genetics* ; Male ; Female ; Child ; Adolescent ; Child, Preschool ; Adult ; Young Adult ; Phenotype ; High-Throughput Nucleotide Sequencing

Objective To evaluate the application of shear wave elastography(SWE)in the diagnosis of peripheral neuropathy in patients with diabetes.Methods Totally 85 patients with type 2 diabetes(T2DM)were selected from the Chengdu Office Hospital of People's Government of Tibetan Autonomous Region,including 46 patients with peripheral neuropathy(DPN)and 39 patients without peripheral neuropathy(NDPN).Compared for clinical data(gender,age,disease duration),cross-sectional area of the median nerve measured by high-frequency ultra-sound(CSA)and shear wave elastography(SWE)parameters(mean Young's modulus value,Emean)and shear wave velocity(SWV)between two groups of patients.Multifactor Logistic regression analysis was carried out on the indicators between the above groups to screen independent predictors in the diagnosis of peripheral neuropathy in diabetes patients,and a combined model was constructed.The area under the operating characteristic curve(AUC),sensitivity and specificity of the subjects were used to evaluate the diagnostic efficacy of the single model and com?bined model of the quantitative parameters(CSA,Emean,SWV)measured by clinical data,high?frequency ultra?sound and SWE in the diagnosis of peripheral neuropathy in diabetes patients.Results Age,course of disease,Emean,SWV and CSA were statistically significant in the diagnosis of peripheral neuropathy in diabetes patients(all P<0.05).AUC was 0.658,0.754,0.839,0.822 and 0.736,respectively.The combination model based on disease course,CSA and SWV showed the highest diagnostic efficiency,with AUC,sensitivity,and specificity of 0.887(0.800-0.946),80.43％,and 84.62％,respectively.Conclusions The combined model based on the course of disease,CSA and SWV have a high diagnostic efficiency in peripheral neuropathy of diabetes patients,and has good clinical application value.