Objective: To develop a RHCE genotyping assay based on kompetitive allele-specific PCR (KASP) and assess its clinical accuracy for RhCE blood group determination. Methods: KASP primers were designed to interrogate three RHCE loci: the 109 bp insertion/deletion in intron 2, c. 307T>C, and c. 676C>G. A total of 1 194 RhD-positive inpatients from Chengdu were typed by both KASP genotyping and manual tube serology. Discordant samples (n=10) were retested by both methods and further resolved by Sanger sequencing. An additional 377 cases were tested for the c. 48C>G locus to evaluate the predictive accuracy of individual loci and combined locus testing for RhC antigen. Results: Genotyping concordance with serology was 100.0% for both the c. 676C>G locus (RhE/Rhe) and the c. 307T>C locus (Rhc). For RhC prediction using the 109 bp insertion, overall accuracy was 99.7% (1 191/1 194); the 3 discordant cases were confirmed by Sanger sequencing to be false negatives attributable to 109 bp deletion in intron 2. Testing the c. 48C>G allele for RhC prediction yielded 7 false positives, with an accuracy of 98.1% (370/377). RhC antigen status was determined by combining the 109 bp insertion and the c. 48C allele. After excluding 10 samples with inconsistent results between the two loci, the accuracy reached 100% in the remaining 367 samples. When both loci were applied in combination, accuracy reached 100% in the 367 cases with concordant results. Among the 1 194 patients, CCee (45.8%) and CcEe (31.7%) were the most common RhCE phenotypes. The e antigen had the highest positivity rate (92.2%), and the Ce haplotype was the most frequent (66.9%). Conclusion: The KASP-based RHCE genotyping method achieves high accuracy for clinical RhCE typing. Combining the 109 bp insertion/deletion with the c. 48C allele significantly improves RhC antigen prediction compared with either locus alone. This method was applied to RhCE genotyping of 1 194 RhD-positive inpatients in Chengdu, providing local RhCE phenotype and haplotype distribution data to support RhCE-matched transfusion practice.

Objective To explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). Methods The Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. Gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox regression, least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients’ 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. Results Differentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poorer prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. The scRNA-seq analysis showed that RORA and KLF10 were mainly expressed in natural killer cells. Conclusion The prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.

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.

To explore the clinical significance of decreased serum immunoglobulin (Ig) A levels in patients with IgG4-related disease (IgG4-RD).

Chronic heart failure(CHF)is the end-stage of a variety of heart diseases with complex pathogenesis,which may involve myocardial fibrosis,inflammation reaction and oxidative stress.Chinese materia medica has the characteristics of multiple pathways,levels,and targets in the treatment of CHF,with significant therapeutic effects.Based on the TCM theories,this article described the research progress of TCM and its effective components,TCM compounds and Chinese patent medicines in regulating TGF-β signaling pathway in the treatment of CHF,and summarized the mechanism and target of TCM regulating TGF-β pathway in the treatment of CHF,including inhibiting myocardial fibrosis,reducing inflammatory reaction and apoptosis,so as to provide a reference for the related research of Chinese materia medica in the treatment of CHF.

Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

Objective Prepubertal mice are administered subcutaneously with letrozole sustained-release pellets behind the neck and treated with a high-fat diet to establish a mouse model of polycystic ovary syndrome(PCOS).The liver transcriptomes of the model mice are compared with those of the placebo control mice to investigate the underlying mechanisms of liver involvement in the pathogenesis of PCOS.Methods A customized 2 mg dose of letrozole sustained-release pellets with a 40-day release period was used.The control placebo and letrozole pellets were implanted subcutaneously in the dorsal cervical region of 3-4-week-old C57BL/6J mice(8 mice per group)to establish the control group and letrozole-induced PCOS model group.Both groups were treated with a high-fat diet starting the day after administration.The modeling period lasted for 5 weeks,during which body weight and 24-hour food intake were monitored in each group every week.When samples were collected,liver weight was recorded.Pathological changes in ovarian and hepatic tissues were examined by hematoxylin-eosin(HE)staining,while hepatic lipid deposition was observed by Oil Red O staining.The extent of macrophage infiltration in the liver was evaluated via F4/80 immunohistochemical staining,and hepatic fibrosis levels were observed by Masson's trichrome staining.Transcriptomic sequencing was performed to analyze differentially expressed genes(DEGs)in liver tissues between the control and model groups,followed by enrichment analysis of significant DEGs.Quantitative real-time fluorescent quantitative PCR(qPCR)was subsequently used to validate the expression of significant DEGs in liver tissues of both groups.Results Compared with the control group,the model group which received subcutaneous letrozole sustained-release pellets combined with a high-fat diet exhibited significantly increased body weight(P＜0.001),prominent polycystic ovarian morphology,and significantly decreased liver-to-body weight ratio(P＜0.05).However,no significant changes were observed in absolute liver weight(P＞0.05),hepatic histomorphology,or lipid deposition.Transcriptome sequencing identified 119 upregulated and 217 downregulated DEGs in the liver tissues of letrozole-treated mice,which were predominantly enriched in pathways related to cholesterol and steroid biosynthesis,steroid hormone metabolism,and inflammatory responses.qPCR validation demonstrated that mRNA expression of HSD3B2 and HMGCR was significantly upregulated in liver(P＜0.01),while mRNA expression of IL4,CCL2 and COL1A1 was downregulated(P＜0.05)in the model group compared with the control group.However,Masson's trichrome staining and F4/80 immunohistochemical analysis showed no significant changes in hepatic fibrosis or macrophage infiltration.Conclusion Subcutaneous administration of letrozole sustained-release pellets combined with a high-fat diet successfully establishes a mouse model of PCOS.The model mice exhibited significant changes in hepatic gene expression.Liver may contribute to PCOS pathogenesis through regulating cholesterol and steroid metabolism.

BACKGROUND:3D printing technology can design and construct models,surgical guides and personalized implants or fixture according to the actual condition and treatment demand of patients,which has great application prospects in the repair of traumatic fractures.OBJECTIVE:To review the application of 3D printing technology in traumatic fractures.METHODS:Web of Science,PubMed and CNKI databases were searched for relevant literature on the application of 3D printing technology in the field of trauma orthopedics published from 2020 to 2024.Chinese and English search terms were"traumatic fracture,3D printing technology,digital model,surgical guide."Finally,60 articles were included for analysis.RESULTS AND CONCLUSION:(1)Traumatic fracture is a fracture phenomenon with interruption of bones continuity and destruction of integrity caused by various injurious factors.Improving reduction and healing effects with reliable schemes has become a hot issue that needs to be handled urgently in orthopedics related field.(2)3D printing technology is a three-dimensional entity technology to meet demands based on digital model data,in which adhesive forming materials(powdered metal or polymer),stereolithography,deposition modeling,and optical polymer spray are used,and it is widely used in the field of digital orthopedic biomedicine.(3)3D printing technology has played significant advantages in disease diagnosis,preoperative planning,reconstructing fracture 3D model,customizing orthopedic implants,customizing fixation braces and prosthetics,fabrication of surgical guide and repair of bone defects,which can design and construct models,surgical guides,and personalized implants or fixture according to the actual conditions and treatment demands of patients.It can provide new ideas for the treatment of traumatic fractures.

To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

A deep learning based microwave imaging model which can directly map the scattered electric field to the dielectric property distribution image of the target object is developed,and its potential for medical applications is explored.The two-dimensional time-domain finite difference method is used for numerical simulation to obtain a dataset of scattered electric fields;a deep convolutional autoencoder based imaging model is constructed to perform imaging studies on two types of target objects;the imaging results are quantitatively evaluated using relative error,and the model's ability to distinguish different types of strokes is also analyzed.The results show that the imaging network based on deep convolutional autoencoder exhibits excellent imaging performance when processing both numerical models.For simple objects,the model can accurately locate and preliminarily reconstruct the shape of the object,with an average relative error of 0.3012,while for the stroke models,it can effectively reconstruct the location and shape of the stroke area,and preliminarily reconstruct other brain tissues,with an average relative error of 0.077 8.The microwave imaging network based on deep convolutional autoencoder has great promise for fast and accurate image reconstruction,and the numerical example of stroke detection demonstrates its significant application potential in biomedical imaging.