BACKGROUND:Primary cortical neurons and microglial cells play a crucial role in exploring cell therapies for neurological disorders,and most of the current methods for obtaining the two types of cells are cumbersome and require separate extraction.It is therefore crucial to find a convenient and rapid method to extract both types of cells simultaneously. OBJECTIVE:To explore a novel method for simultaneous extraction of primary cortical neurons and microglial cells. METHODS:Newborn suckling SD rats were taken within 24 hours.The brain was removed and placed in a dish with DMEM,and the pia mater was removed for later use.Primary neurons were extracted from the same brain tissue,and then the remaining brain tissue was used to extract microglial cells.The whole process was performed on ice.Extraction and culture steps of primary cortical neurons:The cerebral cortex was taken 2.0-3.0 mm with forceps,and the tissue was digested with papain for 20 minutes.After aborting digestion,the blown tissue presented an adherent tissue suspension.The supernatant cell suspension was obtained,filtered,and dispensed into 15 mL centrifuge tubes.After centrifugation and re-suspension,the cells were inoculated onto 6-well plate crawls coated with L-polylysine.Neuronal morphology was observed at 1-day intervals,and staining could be performed for identification using immunofluorescence staining of MAP2 and β-Tubulin by day 7.Microglia extraction and culture steps:The remaining brain tissue at 8-10 mm thick was subjected to microglial cell extraction,digested by trypsin for 20 minutes.After digestion was stopped,the tissue was blown to a homogenate,and then the homogenate was transferred to the culture bottle for culture.On day 14,the culture flasks were sealed and subjected to constant temperature horizontal shaking for 2 hours.Microglial cells were shed in the supernatant.Purified microglial cells were taken and continued to be cultured for 3 days for identification by Iba1 immunofluorescence staining. RESULTS AND CONCLUSION:(1)After 24 hours of culture,the neurons were adherent to the wall,the cytosol was enlarged,and some neurons developed synapses.After 3 and 5 days of culture,the cytosol was further enlarged,and most of the neurons were in the form of synapses,and some neurons were growing in clusters.On day 7,neuronal synapses were prolonged and thickened,and they were connected with each other to form a network.The neurons were identified by β-Tubulin and MAP2 immunofluorescence staining.(2)The cells grew close to the wall on day 1 of culture.On days 3,5,and 7,the density of microglial cells was small,and the cell morphology was bright oval or round,but the cells basically grew in clumps on the upper layer of other cells.On day 10,the density of microglial cells increased significantly.On day 14,microglial cells grew in dense clumps on the upper layer of other cells,and then they could be isolated and purified.The isolated and purified cells were taken and re-cultured to day 3 and identified as microglial cells by Iba1 immunofluorescence;their purity was greater than 95%.(3)The results show that primary cortical neurons and microglial cells obtained by this method after extraction and culture are of high purity,good morphology,and high viability.

BACKGROUND:Chondrocyte apoptosis is an important factor in the development of osteoarthritis,and Complanatoside A has a flavonoid effect,which can inhibit apoptosis of various cells,but its effect on chondrocyte apoptosis and the mechanism of action are not clear. OBJECTIVE:To investigate the intrinsic association and mechanism of Complanatoside A in chondrocyte apoptosis based on the Wnt/β-catenin signaling pathway. METHODS:(1)The cartilage tissues of the femur and tibia transected during knee arthroplasty were collected,and chondrocytes were isolated,cultured in vitro,and identified.(2)Cell counting kit-8 was used to detect the optimal intervention concentration of Complanatoside A in the concentration range of 0-160 μmol/L.(3)Chondrocytes were divided into blank group,sodium nitroprusside(1.5 mmol/L)-induced group,and sodium nitroprusside(1.5 mmol/L)+Complanatoside A(5 μmol/L)group.The viability and apoptosis rate of the cells in each group were detected by cell counting kit-8 and flow cytometry.The expression of type Ⅱ collagen and SOX9 was detected by immunofluorescence staining.The expression of apoptosis-related proteins and Wnt/β-catenin pathway proteins was detected by western blot assay. RESULTS AND CONCLUSION:The cells extracted in vitro were cultured and stained,and were clearly identified as chondrocytes.Complanatoside A had no obvious cytotoxicity to chondrocytes in the concentration range of 0-80 μmol/L,and significantly improved the chondrocyte viability in the concentration range of 2.5-10 μmol/L,especially when the concentration was 5 μmol/L.The apoptotic rate of chondrocytes was higher in the sodium nitroprusside-induced group than the blank control group,while the apoptotic rate was lower in the sodium nitroprusside+Complanatoside A group than the sodium nitroprusside-induced group.The fluorescence intensity of type Ⅱ collagen and SOX9 in chondrocytes was weaker in the sodium nitroprusside-induced group than the blank control group,while the fluorescence intensity of type Ⅱ collagen and SOX9 in the sodium nitroprusside+Complanatoside A group was higher than that of the sodium nitroprusside-induced group.In the sodium nitroprusside-induced group,the protein expression of Bax,Caspase-3,matrix metalloproteinase 13,Wnt3a,Wnt5a and β-catenin was higher than that of the blank control group,while the protein expression of Bcl-2 was lower than that of the blank control group.In the sodium nitroprusside+Complanatoside A group,except for the protein expression of Bcl-2 which was higher than that of the sodium nitroprusside-induced group,the expression of the other aforementioned proteins was lower than that of the sodium nitroprusside-induced group.To conclude,Complanatoside A has a certain inhibitory effect on chondrocyte apoptosis,which could regulate apoptosis-related proteins and promote the expression of chondrocyte regulatory factors,and presumably might play a role through inhibiting the Wnt/β-catenin signaling pathway.

Objective: To explore the relationship between visual acuity and physical fitness of university freshmen, so as to provide reference for myopia prevention and control for freshmen. Methods: From October to November 2022, 2 160 college freshman without majoring in public safety administration, selected from Guangxi Police College in 2022 by using the stratified cluster random sampling method, were reviewed for the results of visual acuity test and physical fitness scores. The physical fitness indices were evaluated by using the Z scores of physical fitness test scores, and the strength of association between the level of physical fitness index and myopia was analyzed by using Logistic regression model. Results: Among 2 160 college freshman without majoring in public safety administration, 917 (42.5%) students were diagnosed screening myopia, including 66 (3.1%) cases of high myopia, 383 (17.7%) cases of moderate myopia and 468 (21.7%) cases of mild myopia. The differences in the distribution of visual acuity tests among students with different physical fitness indices, body mass index, and gender were statistically significant ( Z/H=54.50, 49.53, 15.51, P <0.01). Low level and low middle level physical fitness indices were associated with screening myopia among freshmen[ OR (95% CI )=2.81(1.93-4.08),1.87(1.38-2.54)], and low level physical fitness indexes were associated with high myopia [ OR (95% CI )=7.22(2.33-22.32)] ( P <0.01). Conclusions Screening myopia among college freshman without majoring in public safety administration is related to physical fitness, and low level and low middle level physical fitness index are risk factors for myopia. Improving the level of physical fitness might be effective in preventing myopia.

AIM: To analyze the influencing factors of capsular constriction syndrome(CCS)in cataract patients after phacoemulsification(Phaco)combined with intraocular lens(IOL)implantation.METHODS: Retrospective study. The data of 2 900 cataract patients(2 900 eyes)in our hospital's information system from January 2021 to January 2024 were collected. All patients were treated with Phaco combined with IOL implantation, and the incidence of CCS within 30 wk after surgery was recorded. Patients were categorized into CCS(116 cases, 116 eyes)and N-CCS group(2 784 cases, 2 784 eyes)based on the occurrence of CCS. The basic data of the two groups were compared, and the influencing factors of CCS within 30 wk after Phaco combined with IOL implantation in cataract patients were analyzed by multivariate Logistic regression.RESULTS: Among 2 900 patients(2 900 eyes)included, 116 cataract patients(116 eyes)developed CCS within 30 wk after Phaco combined with IOL implantation, with an incidence rate of 4.00%. The single factor and multi-factor Logistic regression analysis showed that the complicated diabetes, high myopia, complicated glaucoma, and axial length(AL)>30 mm were the risk factors for the occurrence of CCS after Phaco IOL implantation in cataract patients(all P<0.05).CONCLUSION: Attention should be paid to cataract patients with diabetes, high myopia, glaucoma and AL>30 mm, which will increase the risk of CCS within 30 wk after Phaco combined with IOL implantation in cataract patients.

Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.