Objective: To develop a prediction model for mild cognitive impairment (MCI) risk among the elderly, so as to provide a tool for MCI early screening. Methods : From July 2022 to September 2024, a multi-stage stratified random cluster sampling method was used to recruit permanent residents aged ≥65 years from the Xinjiang Uygur Autonomous Region as study participants. Data on sociodemographic characteristics, nutritional status, body composition indices, bone mineral density, and handgrip strength were collected through questionnaires and physical examinations. Sarcopenia was defined based on appendicular skeletal muscle index and handgrip strength. MCI was assessed using the Mini-Mental State Examination, with adjustments for educational level. Participants were randomly divided into a training set and a validation set in a 7∶3 ratio. LASSO regression and multivariable logistic regression models were employed to screen for predictors and construct an MCI risk prediction model. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 1 641 participants were surveyed, including 755 males (46.01%) and 886 females (53.99%). The majority of participants were aged 65-<75 years, comprising 1 154 individuals (70.32%). MCI was detected in 517 participants, corresponding to a detection rate of 31.51%. Resultsfrom LASSO regression and multivariate logistic regression analysis showed that residence (rural, OR = 2.323, 95% CI: 1.682-3.210), age (75-<85 years, OR = 1.405, 95% CI: 1.019-1.937; ≥85 years, OR = 3.655, 95% CI: 1.696-7.875), educational level (primary school, OR = 0.341, 95% CI: 0.247-0.472; junior high school, OR = 0.255, 95% CI: 0.160-0.408; high school, OR = 0.286, 95% CI: 0.154-0.531; bachelor's degree or above, OR = 0.120, 95% CI: 0.041-0.351), history of alcohol consumption (yes, OR = 3.216, 95% CI: 2.164-4.779), risk of malnutrition (yes, OR = 1.464, 95% CI: 1.064-2.014), sarcopenia (yes, OR = 3.197, 95% CI: 2.332-4.385), and waist-to-hip ratio (abnormal, OR = 1.540, 95% CI: 1.159-2.048) were identified as predictive factors for MCI among the elderly. In the training set, the area under the ROC curve, sensitivity, and specificity were 0.788, 0.719, and 0.712, respectively. In the validation set, the corresponding values were 0.784, 0.913, and 0.542, respectively. DCA demonstrated that the model provided a higher clinical net benefit for predicting MCI risk when the risk threshold probability ranged from 0.124 to 0.764. Conclusion The prediction model developed in this study demonstrates good discriminative ability and clinical utility, indicating its substantial value for predicting the MCI risk among the elderly.

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.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

OBJECTIVE: To investigate the clinical efficacy and safety of ultrasound-guided closed reduction and internal fixation using Kirschner wire for the treatment of olecranon fractures of the ulna in children. METHODS: Between January 2019 and January 2021, 13 children with olecranon fracture were treated with ultrasound-guided closed reduction and percutaneous Kirschner wire internal fixation, including 10 males and 3 females. The age ranged from 3 to 14 years old. Children with ulnar olecranon fractures were evaluated using the Gicquel scoring system. The clinical evaluation encompassed postoperative pain, functional status, and range of motion, with a maximum score of 15 points. The radiological assessment contributed an additional 4 points. A cumulative score of more than 18 scores was classified as excellent, more than 17 scores as good, more than16 scores as fair, and less than 16 scores as poor. Clinical assessment:A score of 14 indicates excellent performance, a score of 13 reflects good performance, a score of 12 denotes fair performance, and a score of less than 11 signifies poor performance. RESULTS: A total of 13 patients were followed up, with a duration ranging from 6 to 12 months. According to the Gicquel scoring criteria, the comprehensive evaluation of clinical and radiographic findings yielded 10 excellent and 3 good outcomes. Evaluation based solely on clinical findings resulted in 13 excellent outcomes. CONCLUSION Ultrasound-guided percutaneous cross Kirschner wire fixation for children's olecranon fracture has the advantages of less trauma, rapid recovery, less fluoroscopy, and good recovery of elbow function. The clinical effect is satisfactory.
Humans ; Child ; Male ; Female ; Fracture Fixation, Internal/instrumentation* ; Ulna Fractures/physiopathology* ; Bone Wires ; Child, Preschool ; Adolescent ; Olecranon Process/surgery* ; Ultrasonography ; Closed Fracture Reduction/methods* ; Olecranon Fracture

OBJECTIVES: To study the clinical and genetic characteristics of osteopetrosis (OPT) in children. METHODS: A retrospective analysis was performed on the clinical data of 14 children with OPT. Whole-exome sequencing was used to detect pathogenic genes, and clinical phenotypes and genotypic features were summarized. RESULTS: Among the 14 children (10 males and 4 females), the median age at diagnosis was 8 months. Clinical manifestations included systemic osteosclerosis (14 cases, 100%), anemia (12 cases, 86%), infections (10 cases, 71%), thrombocytopenia (9 cases, 64%), hepatosplenomegaly (8 cases, 57%), and developmental delay (5 cases, 36%). Malignant osteopetrosis (MOP) cases had lower platelet counts, creatine kinase isoenzyme, and serum calcium levels, but higher white blood cell counts, lactate dehydrogenase, and alkaline phosphatase levels compared to non-MOP cases (P<0.05). Genetic testing identified 15 variants in 12 patients, including 8 variants in the CLCN7 gene (53%), 6 in the TCIRG1 gene (40%), and 1 in the TNFRSF11A gene (7%). Three novel CLCN7 variants were identified: c.2351G>C, c.1215-43C>T, and c.1534G>A. All four patients with TCIRG1 variants exhibited MOP clinical phenotypes. Of the seven patients with CLCN7 variants, 4 presented with intermediate OPT, 2 with benign OPT, and 1 with MOP. CONCLUSIONS Clinical phenotypes of OPT in children are heterogeneous, predominantly involving CLCN7 and TCIRG1 gene variants, with a correlation between clinical phenotypes and genotypes.
Humans ; Osteopetrosis/genetics* ; Male ; Female ; Infant ; Child, Preschool ; Retrospective Studies ; Vacuolar Proton-Translocating ATPases/genetics* ; Child ; Chloride Channels/genetics* ; Mutation ; Receptor Activator of Nuclear Factor-kappa B

OBJECTIVES: Transcervical resection of adhesions (TCRA) under hysteroscopy is the mainstay treatment for intrauterine adhesions (IUA), but its effectiveness varies depending on the surgical approach. This study aims to investigate the impact of different surgical techniques on endometrial repair and pregnancy outcomes in patients with secondary infertility and moderate-to-severe IUA. METHODS: A retrospective analysis was conducted on 225 patients who underwent TCRA followed by in vitro fertilization and embryo transfer between January 2021 and December 2022. Patients were grouped based on the surgical method: A cold knife group (n=127) and an electrosurgical group (n=98). Adhesions were separated using either cold knife or electrosurgical instruments. Postoperative visualization of uterine angle and tubal ostia, endometrial restoration, vascular endothelial growth factor (VEGF) expression in adhesion tissues, and clinical pregnancy outcomes were compared. Univariate and multivariate Logistic regression analyses were performed to identify factors influencing pregnancy outcomes. A LightGBM model was constructed to predict pregnancy outcomes. RESULTS: Compared with the electrosurgical group, patients in the cold knife group had significantly greater postoperative endometrial thickness [(8.86±0.53) mm vs (8.10±0.87) mm, P<0.05], higher live birth rates (64.57% vs 30.61%, P<0.05), and lower VEGF expression (1.31±0.09 vs 1.53±0.16, P<0.05). Logistic regression analyses identified age, number of visible tubal ostia postoperatively, and surgical method as significant factors affecting pregnancy outcomes (P<0.05). The LightGBM model based on surgical method had an area under the curve (AUC) of 0.882 (0.838-0.926), with internal validation AUC of 0.817 (0.790-0.840). CONCLUSIONS Cold knife surgery promotes faster recovery of the endometrial microenvironment and earlier improvement of fertility in patients with secondary infertility and IUA Surgical method is a key factor influencing pregnancy outcomes, and the LightGBM model based on surgical approach shows good predictive performance for pregnancy outcomes in patients with moderate-to-severe IUA.
Humans ; Female ; Pregnancy ; Tissue Adhesions/surgery* ; Retrospective Studies ; Adult ; Pregnancy Outcome ; Uterine Diseases/surgery* ; Hysteroscopy/methods* ; Infertility, Female/etiology* ; Electrosurgery/methods* ; Fertilization in Vitro ; Endometrium/surgery* ; Embryo Transfer ; Vascular Endothelial Growth Factor A/metabolism*

Objective To analyze the correlation between tongue image features and the risk levels of disease in patients with idiopathic membranous nephropathy(IMN).Methods Based on IMN clinical research electronic data acquisition system,a cross-sectional study method was used to analyze the clinical diagnosis and treatment data of 135 IMN patients from Guangdong Provincial Hospital of Chinese Medicine.The patients were grouped according to the risk levels of disease,and then the correlation between the risk levels of disease and tongue image features was analyzed.During the description of tongue image features,TB is for tongue body,TC is for tongue coating,L is for luminance,a is for red-green axis,G is for the value of green,B is for the value of blue,and AUT is for the value of autocorrelation.Results The comparison of tongue image feature indicators of patients with different risk levels of IMN showed that:(1)the higher the level of disease risk of IMN patients,the greater the values of TB-L,TB-G and TB-B(P<0.05 or P<0.01).The values of tongue image indicator TB-a and TC-a of the patients with different risk levels of IMN were shown in decreasing sequence:low-risk group>high-risk group>middle-risk group>extremely-high-risk group(P<0.05).(2)Linear regression analysis showed that TB-L,TB-G,and TB-B were significantly increased in the high-risk group compared with those in the middle-and low-risk groups(P<0.05 or P<0.01),whereas there were no significant differences between the middle-risk group and low-risk group(P>0.05).(3)The results of correlation analysis showed that there was a positive correlation among most of the tongue image feature indicators(including TB-L,TB-G,TB-B,TB-AUT,TC-L,TC-G,and TC-B,etc.)and the risk level of disease,while TB-a was negatively correlated with the risk level of disease,and the differences were all statistically significant(P<0.05 or P<0.01).(4)All patients were treated with Chinese medicine and/or Chinese patent medicine,and 46.7%of patients were given hormones and immunosuppressants,and there was no statistically significant difference in the the use of hormones and immunosuppressants among various groups(P=0.637).Conclusion There is a correlation between the tongue image features of IMN patients and the risk level of disease,and the results will provide an objective reference for the assessment of illness state and traditional Chinese medicine(TCM)syndrome differentiation of IMN patients.With reference to the changes in the tongue image features,the illness state can be precisely identified,which is more accurate than the inspection of four diagnostic methods of TCM.

BackgroundPrenatal depression has an important impact on maternal health and pregnancy outcomes. Previous studies have shown that maternal prenatal depression is associated with social support， and social support is related to fear of childbirth. However， there is limited research on the relationship among maternal prenatal depression， social support and fear of childbirth， and no studies have specifically explored the influence of social support and fear of childbirth on prenatal depression in primiparous women. ObjectiveTo investigate the current status of prenatal depression among primiparous women， and to analyze the correlation between social support and fear of childbirth， and to further explore the influence of social support and fear of childbirth on prenatal depression in this population， so as to provide references for improving their mental health. MethodsA total of 380 primiparous women admitted to the inpatient department of Chengdu Wenjiang District People's Hospital from December 2022 to September 2023 were enrolled as study subjects. A self-made questionnaire， Edinburgh Postnatal Depression Scale （EPDS）， Social Support Rating Scale （SSRS） and Childbirth Attitudes Questionnaire （CAQ） were used to conduct the survey. Pearson correlation analysis was employed to examine the relationships between scale scores. Multiple linear regression analysis was conducted to identify influencing factors of prenatal depression. ResultsA total of 380 questionnaires were distributed， with 372 （97.89%） valid responses collected. Among the participants， 222 cases （59.68%） were identified with prenatal depression. Pearson correlation analysis revealed that EPDS score was negatively correlated with SSRS score （r=-0.283， P<0.01） and positively correlated with CAQ score （r=0.341， P<0.01）. Multiple linear regression analysis indicated that social support （β=-0.166， P<0.01） and fear of childbirth （β=0.269， P<0.01） were influencing factors of prenatal depression in primiparous women. ConclusionThe prevalence of prenatal depression among primiparous women is concerning， with depression levels showing significant associations with both social support and fear of childbirth.

OBJECTIVE To assess the cost-effectiveness of durvalumab combined with chemotherapy as a first-line treatment for advanced biliary tract cancer from the perspective of the Chinese healthcare system. METHODS Using data from the TOPAZ-1 clinical trial， a three-state Markov model comprising progression-free survival （PFS）， progressive disease （PD） and death was developed， with a cycle length of 21 days and a 10-year time horizon. Patients in the observation group received durvalumab in combination with gemcitabine and cisplatin， whereas those in the control group received placebo plus the same chemotherapy regimen. The evaluation indexes were quality-adjusted life year （QALY） and the incremental cost-effectiveness ratio （ICER）. The willingness-to-pay （WTP） threshold was set at three times the 2024 Chinese per capita gross domestic product （GDP） （287 247 yuan/QALY）. The sensitivity analyses， along with scenario analyses， were performed. RESULTS In the base-case analysis， the ICER of observation group compared to control group was 1 166 344.46 yuan/QALY， far exceeding the WTP threshold， indicating that the regimen was not cost-effective. One-way sensitivity analysis identified the PD state utility， discount rate， cost of durvalumab， and PFS state utility as the main drivers of ICER variation. Probabilistic sensitivity analysis showed that， at the above WTP threshold， the probability of the acceeptance of this regimen was 0， further supporting the robustness of the base-case findings. In the scenario analysis， inclusion of a patient assistance program reduced the ICER to 235 885.16 yuan/ QALY， below the above WTP threshold， suggesting cost-effectiveness under this assistance program. However， when applying a regional WTP threshold set at three times the per capita GDP （158 475 yuan/QALY） of Gansu Province （the province with the lowest GDP in China in 2024）， the ICER remained above the threshold， indicating that the regimen was not cost-effective at the regional level. CONCLUSIONS At current pricing， durvalumab plus chemotherapy as a first-line treatment for advanced biliary tract cancer is not cost-effective in China. Although the introduction of a patient assistance program can substantially reduce the ICER and achieve cost-effectiveness at a WTP threshold set at three times the 2024 per capita GDP of China， due to limited affordability in low-income areas， the program remains not cost-effective.