Postoperative cognitive dysfunction (POCD) is a clinically significant complication in elderly patients, largely driven by surgery-induced neuroinflammation. The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway, a crucial regulator of innate immunity, has been implicated in neuroinflammatory activation. This study investigated whether electroacupuncture (EA) improves POCD by modulating this pathway in aged rats. Aged male Sprague-Dawley rats were subjected to sevoflurane anesthesia and splenectomy to establish a POCD model. Animals were divided into sham, POCD, POCD+EA, and POCD+EA+cGAS inhibitor groups. The EA groups received stimulation at Baihui (Governor Vessel 20 (GV20)), Neiguan (Pericardium 6 (PC6)), and Zusanli (Stomach 36 (ST36)) acupoints. Cognitive function was evaluated using the Morris water maze, while hippocampal expression of cGAS-STING pathway components and downstream effectors—interferon regulatory factor 3 (IRF3), nuclear factor kappa B (NF-κB), and interleukin-1 beta (IL-1β)—was assessed by Western blot. Cellular localization was determined by immunofluorescence staining. Compared with the Sham group, rats in the POCD group showed significant impairments in spatial memory, accompanied by upregulated protein expression of cGAS, STING, NF-κB, IRF3, and IL-1β in the hippocampus. Upon pathway activation, cGAS and STING proteins were predominantly co-localized with neurons, and their fluorescence intensity in the hippocampus was markedly increased. These behavioral deficits and molecular alterations were partially reversed in both the POCD+EA and POCD+EA+inhibitor groups. Electroacupuncture alleviates POCD in aged rats by inhibiting hippocampal cGAS-STING pathway activation and reducing neuroinflammation, suggesting a promising non-pharmacological strategy for POCD management.

Negative logarithm of the acid dissociation constant(pKa)significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pKa prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pKa prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pKa values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pKa values,making it a valuable tool in the field of pKa prediction.

Objective To observe the effect of specialist team-led walking-cognition dual-task train-ing combined with active self-disclosure on control and balance abilities in elderly patients with acute ischemic stroke(AIS).Methods A total of 90 elderly AIS patients treated in our hospital from January 2022 to January 2024 were enrolled and randomly assigned into the control group and the observation group,with 45 cases in each group.The control group received routine walk-ing training,while the observation group received specialist team-led walking-cognition dual-task training combined with active self-disclosure intervention.Control ability,balance ability,walking ability,cognitive function and psychological status were compared between the two groups.Results After intervention,the scores of Sheikh Trunk Control Scale and Fugl-Meyer Assessment(FMA),and the static balance score,dynamic balance score and total score of Berg Balance Scale(BBS)were significantly increased in both the observation and the control groups(P＜0.05),and all above scores were obviously higher in the former group than the latter one(P＜0.01).The two groups also obtained notably shorter single-and dual-task walking time after intervention,but there were no statistical difference in the single-task walking time in both groups before and after intervention(P＞0.05).After intervention,the observation group had significantly shorter dual-task walking time(22.87±7.36 s vs 27.52±8.71 s,P=0.008)and lower walking time cost of dual task[(11.16±4.07)％vs(25.61±7.82)％,P=0.000]when compared with the control group.After intervention,the scores of Mini-Mental Status Examination were increased,and the scores of Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale were decreased in the two groups(P＜0.05).Conclusion Specialist team-led walking-cognition dual-task training com-bined with active self-disclosure intervention can effectively improve trunk control ability,balance ability,walking ability,cognitive function and psychological state in elderly AIS patients,has cer-tian clinical application value.

Objective:To observe the effect of ankle joint proprioception training on ankle joint proprioception, balance and gait in patients with thalamic infarction.Methods:Fifty-six patients with thalamic infarction were divided into a control group and a treatment group, each of 28, using a random number table. Both groups were given conventional lower limb rehabilitation training, but the treatment group was additionally provided with ankle joint proprioception training. Before and after 4 weeks of the treatment, the Tecnobody proprioception testing system was used to determine the average trajectory error rate (ATE) and the time taken in the test. The Berg Balance Scale (BBS) and a balance tester were used to assess balance. A gait analyzer was used to collect spatial-temporal measures of the patients′ walking, including the stride amplitude, stride rate, the proportion of the time spent in the swing phase, and foot dorsiflexion and plantarflexion angles.Results:After the treatment, the time used, ATE, ankle proprioception, BBS scores, static balance test scores, stability limits, stride length, stride rate, swing phase time percentage, and foot dorsiflexion and plantarflexion angles had improved in both groups compared with before the treatment ( P≤0.05). Compared with the control group, the treatment group had a smaller average ATE, spent less time on the ankle proprioception test, had higher BBS scores, had lower scores on the static balance test, had larger limits of stability, took longer strides at a faster rate, and spent a greater percentage of time in the swing phase. That group also showed greater ankle dorsiflexion and plantarflexion on average ( P≤0.05). ATE difference of the affected lower limb and the time to complete the ankle proprioception test were positively correlated with the gap in the static balance ability test, and negatively correlated with the gaps in the BBS score, the limits of stability, stride length, stride rate, and the time share of the swing phase, as well as the dorsiflexion and plantarflexion angles of the foot. Conclusions:Ankle proprioception training, in addition to effectively improving ankle proprioception, can improve the balance and gait of persons with thalamic infarction. It is worthy of clinical application and promotion.

Objective:To evaluate the clinical value of peripheral blood monocyte subset analysis in the diagnosis and treatment of chronic myelomonocytic leukemia (CMML) .Method:We retrospectively enrolled 51 patients newly diagnosed with CMML at Guangdong Provincial People's Hospital between June 1, 2020, and December 31, 2024, according to the WHO 2022 diagnostic criteria. Twenty-three patients with other myeloid neoplasms (excluding CMML) and peripheral monocytosis (absolute count ≥0.5×10 9/L and percentage ≥10%) were included as the control group. All patients underwent bone marrow aspiration for examinations including bone marrow smears, biopsies, cytogenetics, and gene mutation analysis to establish a definitive diagnosis. Concurrently, flow cytometry was used to determine the proportions of peripheral blood monocyte subsets: classical (MO1, CD14 +CD16 -) , intermediate (MO2, CD14 +CD16 +) , and non-classical (MO3, CD14 lowCD16 +) . Differences between the groups were compared, and diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. Result:Among the 51 CMML patients, the proportion of the peripheral blood MO1 subset was significantly higher than that in patients with other myeloid neoplasms ( P=0.027) , whereas there were no significant differences in the MO2 and MO3 subsets (all P>0.05) . Further analysis revealed that 43 (84.31%) of the CMML patients met the WHO diagnostic threshold for the MO1 subset (≥94%) , while the remaining 8 patients did not; 46 patients (90.20%) had MO3 subset proportions below the threshold proposed by Hudson (≤1.13%) , while the remaining 5 patients were above this threshold. In-depth analysis showed that among the 8 patients who did not meet the WHO criteria, 7 were experiencing inflammation. Similarly, all 5 patients who did not meet the Hudson criteria were in an inflammatory state. Subsequent ROC curve analysis of this cohort identified a cut-off value for the MO1 subset of 97.55% [Area Under the Curve (AUC) =0.661, P=0.027], which aligns with the WHO criteria. Conclusion:Peripheral blood monocyte subset analysis, particularly MO1 subset analysis, can effectively assist in CMML diagnosis, but exclusion of inflammatory conditions is required.

Tuberculosis is a infectious disease caused by Mycobacterium tuberculosis( Mtb), which seriously threatens human health. Innate immune cells can synergistically resist the invasion of Mtb by phagocytosing pathogens, releasing inflammatory cytokines, and activating other immune cells, playing a crucial role in the host′s defense against Mtb infection. A comprehensive and in-depth understanding of the role of innate immune cells in Mtb infection will help reveal the complexity of immune response and understand the mechanism of early elimination of Mtb and immune escape of Mtb, and provide new ideas and strategies for the prevention and treatment of tuberculosis. This article will provide an overview of the research progress on the mechanisms of several important innate immune cells, including macrophages, neutrophils, natural killer cells, dendritic cells, and constant natural killer T cells, in Mtb infection.

Objective:To investigate the association between triglyceride glucose index (TyG) and early vascular aging measured by brachial ankle pulse wave velocity (baPWV) in young and middle-aged population.Methods:It was a cross-sectional study. A total of 5 680 subjects aged 20 to 59 years who underwent health check-ups at the Health Management Center of General Hospital of Tianjin Medical University from January to December in 2020 were selected as the research subjects. All the research subjects completed the health risk assessment questionnaire, physical examination, laboratory test, and multi-functional vascular lesion detection. The TyG was calculated and the research subjects were divided into four groups with the quartiles of TyG (Q 1 to Q 4, with a cut-off value of 8.22, 8.60 and 9.01, respectively). The baPWV value was converted into a Z-score, and those with a Z-score above the 95th percentile were defined as having early vascular aging. The Spearman correlation method, multiple linear regression model, binary logistic regression model and the area under the receiver operating characteristic curve (AUC) were used to analyze the association between TyG and early vascular aging. Results:Among the 5 680 middle-aged and young people included in the analysis, there were 3 117 males and 2 563 females, with an age of 46 (39, 52) years, a TyG of 8.60 (8.22, 9.01), and a baPWV of 1 279.25 (1 147.50, 1 434.25) cm/s. The prevalence rate of early vascular aging was 5.02% (285/5 680). Taking group Q 1 as the reference, in the multiple linear regression model adjusted for multiple factors, group Q 4 was significantly associated with a 47.64 (95% CI: 28.18-67.11) cm/s increase in baPWV ( P for trend<0.001). In the multivariate adjusted binary logistic regression model, compared with that in the Q 1 group, the OR of early vascular aging occurrence in the Q 2, Q 3, and Q 4 groups was 1.52 (95% CI: 0.75-3.07), 1.78 (95% CI: 0.89-3.58), and 3.04 (95% CI: 1.47-6.31), respectively. Elevated TyG level was positively correlated with the occurrence of early vascular aging ( P for trend<0.001). The AUC of TyG in predicting early vascular aging was 0.732 (95% CI: 0.704-0.759), with the optimal cut-off value being 8.86. The AUC of TyG in predicting early vascular aging in males was lower than that in females [0.665 (95% CI: 0.628-0.702) vs 0.796 (95% CI: 0.748-0.843)] ( P<0.001). Conclusions:There is a correlation between TyG and early vascular aging measured by baPWV in the middle-aged and young population. When TyG≥8.86, clinical intervention measures should be taken in a timely manner.

Objective:To analyze the effect of influenza vaccination on influenza cluster epidemic in primary and secondary schools in Beijing during the 2023-2024 surveillance season and provide evidence for the improvement of influenza vaccination strategies.Methods:The incidence data of influenza cluster epidemic and influenza vaccination coverage in the schools in Beijing during 2023-2024 were collected. Descriptive epidemiological methods were used to analyze cluster epidemic characteristics, and χ2 test was used to compare incidence differences between groups, and OR value and vaccine effectiveness [VE=(1- OR)×100%] were calculated. A negative binomial regression model was used to evaluate the association between school vaccination rates and cluster epidemic risk. Joinpoint regression was used to analyze trends in relative risk ( RR) with increasing vaccination coverage and to determine the optimal vaccination threshold. Results:A total of 126 influenza cluster epidemic were reported in 115 primary and secondary schools in Beijing during 2023-2024 with the median size of 15 case, the average attack rate was 36.26% (2 033/5 607). The epidemics mainly occurred in urban area (70, 55.56%). Primary schools were the main setting (78, 61.90%), and influenza A(H3N2) was the predominant subtype (108, 85.71%). The overall influenza vaccination coverage in the primary and secondary students was 54.26%, while the average vaccination in classes affected by the epidemics was 58.57%. The overall protection rate was 47.62%, the protection rate was higher in primary schools (49.65%) than in secondary schools (46.60%). The protection rates against influenza A(H1N1)pdm09 (80.93%) and influenza B (Victoria lineage) (81.65%) were significantly higher than that against influenza A(H3N2) (44.19%). When school vaccination coverage reached ≥76.00%, the epidemic risk decreased by 52.82%.Conclusions:Even the match between influenza vaccine strains and circulating strains is suboptimal, increasing influenza vaccination coverage in schools can effectively reduce the risk for influenza cluster epidemic. In the future, measures such as policy guidance and public health education should be taken to further improve vaccination coverage, thereby establishing herd immunity and reducing the transmission risk of influenza in schools.

Objective:To explore the trend of influenza positive rate in Beijing by using classic autoregressive integrated moving average (ARIMA) model, autoregressive integrated moving average model with exogenous variables (ARIMAX) and vector autoregression model (VAR) to compare the performance of three models in influenza prediction and select the most suitable one for Beijing.Methods:The weekly positive rate of influenza virus nucleic acid test and meteorological data in Beijing from week 1 of 2013 to week 40 of 2024 were collected. The data were divided into four groups with expanding training sets and corresponding testing sets. The training set of the first group was from week 1 of 2013 to week 40 of 2016, and the testing set was from week 41 of 2016 to week 40 of 2017. Subsequent groups extended the training set by one year each time. Data from 2020 to 2023 were excluded due to COVID-19 pandemic. The fourth group used data from the week 1 of 2013 to week 40 of 2023 for training and from the week 41 of 2023 to week 40 of 2024 for testing.Results:The incidence of influenza had seasonality in Beijing with higher incidence in winter and spring. The positive rate of influenza virus was positively correlated with the weekly average atmospheric pressure ( r=0.482, P<0.001) and weekly average wind speed ( r=0.003, P=0.034), and negatively correlated with the weekly average temperature ( r=-0.541, P<0.001). The ARIMAX model incorporating meteorological factors had the best prediction performance, with test set's root mean square error ( RMSE) of 0.115 3 and mean absolute error ( MAE) of 0.076 7 (the RMSE and MAE values for ARIMA and VAR models were 0.117 1 and 0.163 8, and 0.078 6 and 0.122 3, respectively). The prediction results of the optimal model showed that the positive rate of influenza virus would continue to rise in Beijing after October 2024 and reach peak in the second week of 2025, but the peak positive rate would be lower than that of previous influenza season. Conclusions:Compared with the ARIMA model and the VAR model,the ARIMAX model which used meteorological parameters is more suitable for prediction of long-term influenza trend in Beijing. The influenza trend peak was predicted to occur in the second week of 2025, but lower than that in previous influenza season.

OBJECTIVES: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy, with disease progression driven by cytogenetic abnormalities and a complex bone marrow microenvironment. This study aims to construct a prognostic model for MM based on transcriptomic data and lipid metabolism related genes (LRGs), and to identify potential drug targets for high-risk patients to support clinical decision-making. METHODS: In this study, 2 transcriptomic datasets covering 985 newly diagnosed MM patients were retrieved from the Gene Expression Omnibus (GEO) database. Univariate Cox regression and 101 machine learning algorithms were used for gene selection. An LRG-based prognostic model was constructed using Stepwise Cox (both directions) and random survival forest (RSF) algorithms. The association between the prognostic score and clinical events was evaluated, and model performance was assessed using time-dependent receiver operating characteristic (ROC) curves and the C-index. The added predictive value of combining prognostic scores with clinical variables and staging systems was also analyzed. Differentially expressed genes between high- and low-risk groups were identified using limma and clusterProfiler and subjected to pathway enrichment analysis. Drug sensitivity analysis was conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database and oncoPredict to identify potential therapeutic targets for high-risk patients. The functional role of key LRGs in the model was validated via in vitro cell experiments. RESULTS: An LRG-based prognostic model (LRG17) was successfully developed using transcriptomic data and machine learning. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.962, 0.912, and 0.842 for 3-, 5-, and 7-year survival, respectively. Patients were stratified into high- and low-risk groups, with high-risk patients showing significantly shorter overall survival (OS) and event-free survival (EFS) (both P<0.001) and worse clinical profiles (e.g., lower albumin, higher β2-microglobulin and lactate dehydrogenase levels). Enrichment analysis revealed that high-risk patients were significantly enriched for pathways related to chromosome segregation and mitosis, whereas low-risk patients were enriched for immune response and immune cell activation pathways. Drug screening suggested that AURKA inhibitor BMS-754807 and FGFR3 inhibitor I-BET-762 may be more effective in high-risk patients. Functional assays demonstrated that silencing of key LRG PLA2G4A significantly inhibited cell viability and induced apoptosis. CONCLUSIONS LRGs serve as promising biomarkers for prognosis prediction and risk stratification in MM. The overexpression of chromosomal instability-related and high-risk genetic event-associated genes in high-risk patients may explain their poorer outcomes. Given the observed resistance to bortezomib and lenalidomide in high-risk patients, combination therapies involving BMS-754807 or I-BET-762 may represent effective alternatives.
Humans ; Multiple Myeloma/mortality* ; Prognosis ; Lipid Metabolism/genetics* ; Transcriptome ; Machine Learning ; Male ; Female ; Gene Expression Profiling ; Algorithms