BACKGROUND:Multiple myeloma (MM) remains incurable, with drug resistance being a key clinical challenge. Impaired natural killer T (NKT) cell function may contribute to MM immune escape, while the significance of the inhibitory receptor CD161 expression on NKT cells is unclear. This study investigated the association between the peripheral blood CD3⁺CD56⁺CD161⁺ NKT cell proportion and response to bortezomib plus dexamethasone therapy in newly diagnosed MM (NDMM) patients. METHODS:Seventy-two NDMM patients receiving bortezomib plus dexamethasone and 37 healthy controls (HCs) were enrolled. Flow cytometry assessed the peripheral blood CD3⁺CD56⁺CD161⁺ cell proportion before and after treatment. Treatment response was evaluated according to IMWG criteria (responders: ≥ partial response [PR]; non-responders: ≤ stable disease [SD]). Receiver operating characteristic (ROC) curve analysis evaluated predictive value. Correlation with clinical parameters (ISS stage, LDH, β₂-MG, etc.) was analyzed. RESULTS:The baseline CD3⁺CD56⁺CD161⁺ proportion was significantly lower in NDMM patients than in HCs (2.25% vs. 4.20%, p < 0.05). After treatment, it increased to 3.10% (p < 0.05). Responders had a significantly higher baseline proportion than non-responders (3.40% vs. 1.60%, p < 0.0001). ROC analysis showed the baseline proportion predicted treatment response with an AUC of 0.789 (95% CI: 0.675 - 0.903). At the optimal cutoff of 1.85%, sensitivity was 87.9% and specificity was 71.8%. Patients with low proportions (< 1.85%) had a higher frequency of ISS stage III (p < 0.05) and significantly elevated LDH and β₂-MG levels (both p < 0.05). CONCLUSIONS:Low expression of peripheral blood CD3⁺CD56⁺CD161⁺ NKT cells is associated with increased tumor burden and bortezomib resistance in NDMM, suggesting its potential as a predictive biomarker for treatment response.

Nitrite (NO2-) in urine is usually recognized as a disease marker for urinary tract infections (UTI), in this work, a flavylium-based colorimetric and fluorescent probe Fla-NH2 for NO2- detection was designed and synthesized to explore its potential for fast and accurate diagnosis of UTI. To facilitate further application in urine, artificial urine (pH = 1.0) rather than acid solution in water or buffer solution was used as the solvent for spectral measurements. The experimental results revealed that Fla-NH2 selectively responded to NO2- via the classical diazotization reaction under acidic conditions with a limit of detection as low as 0.24 mu M. Taking advantage of the distinct color changes of Fla-NH2 upon the addition of NO2- under natural light or UV radiation (365 nm), a fast detection procedure based on RGB recognition was developed using smart phone. The results of spikerecovery experiments in artificial urine sample indicated that accurate detection of NO2- was achieved based on the fluorescence changes of Fla-NH2. Moreover, the RGB-based method could quantify NO2- in real urine samples rapidly and conveniently, providing a practical tool for diagnosing UTI in both clinic and home healthcare.

Immune cells are those involved in or related to immune responses, found throughout immune organs and the body. The discipline of immunometabolism, which merges immunology with metabolism, has gained significant attention in recent years. This emerging field focuses on the metabolic processes and mechanisms of various immune cells, aiming to uncover how these cells’ metabolism influences disease onset and progression. Research in immunometabolism spans several diseases, including chronic inflammatory conditions, infectious diseases, cardiovascular disorders, and cancer, highlighting the critical role of immune cell metabolism in these diseases. Mental illnesses, characterized by brain function abnormalities due to biological, psychological, and environmental factors, lead to impairments in cognitive, emotional, volitional, and behavioral functions. Conditions such as schizophrenia, neurodegenerative diseases (Alzheimer’s disease AD, Parkinson’s disease PD), anxiety, and depression are associated with significant metabolic changes. The intersection of neuroimmunology and immunometabolism has become a focal point for understanding the regulation of mental illnesses by immune cells’ metabolic alterations. This review systematically examines how metabolic reprogramming of central and peripheral immune cells contributes to the pathogenesis of mental disorders, and critically evaluates emerging therapeutic strategies targeting these immunometabolic pathways—including pharmacological modulators (HK2 inhibitors, kynurenine pathway modulators, CD38 checkpoint targeting), lifestyle interventions (ketogenic diet, exercise), and their translational challenges. By integrating mechanistic insights with therapeutic perspectives, this review aims to provide fresh insights into disease mechanisms and inform the development of precision diagnostic and therapeutic approaches for mental disorders.

Introduction:Previous studies primarily focused on baseline sarcopenia status, neglecting changes over time. This study aims to explore the association between changes in sarcopenia status and frailty progression, hypothesizing that transitions to possible sarcopenia or sarcopenia increase frailty risk, while recovery to non-sarcopenia reduces this risk. Methods:Using data from the China Health and Retirement Longitudinal Study (CHARLS), sarcopenia was evaluated at baseline and after 2 years using the AWGS 2019 criteria. Frailty was assessed with a 32-item frailty index. Cox regression and linear mixed models analyzed the association between sarcopenia transitions and frailty progression. Results:Participants who developed possible sarcopenia or sarcopenia faced a 56% higher frailty risk (HR 1.56, 95% CI 1.21-2.01) and a faster frailty index increase (β = 0.007/year, 95% CI 0.005-0.010) compared to those remaining non-sarcopenic. In contrast, those recovering from sarcopenia to non-sarcopenia or possible sarcopenia had a 46% lower frailty risk (HR 0.54, 95% CI 0.32-0.89) and a slower frailty index rise (β = -0.008/year, 95% CI -0.015 to -0.002). Among individuals with possible sarcopenia at baseline, recovery to non-sarcopenia reduced frailty risk by 48% (HR 0.52, 95% CI 0.39-0.68), while progression to sarcopenia increased it by 77% (HR 1.77, 95% CI 1.07-2.94). Conclusion:Transitions in sarcopenia status significantly affect frailty risk and progression. Worsening sarcopenia heightens frailty risk, whereas recovery diminishes it. These findings highlight the value of monitoring and addressing sarcopenia in middle-aged and older adults, offering potential to enhance quality of life and lower healthcare costs through targeted interventions.

β-Lactams remain central to the treatment of pneumococcal infections, and whole-genome sequencing increasingly enables pneumococcal β-lactam susceptibility to be inferred from the combined transpeptidase-domain sequences of PBP1a, PBP2b, and PBP2x. PBP-profile lookup, statistical models, and integrated genomic pipelines can predict drug-specific minimum inhibitory concentrations with high overall agreement with phenotypic antimicrobial susceptibility testing. However, technical prediction accuracy does not by itself establish direct clinical use. Novel or sparsely represented PBP profiles, interspecies recombination within the mitis-group gene pool, lineage and geographical structure, non-PBP genetic effects, and uncertainty in reference MIC measurements can limit model transportability. Furthermore, a predicted MIC cannot be converted into a clinically meaningful susceptibility interpretation without considering the antimicrobial agent, infection site, dosing or exposure context, interpretive standard, and breakpoint version. This mini review summarizes the PBP-centered genetic architecture of pneumococcal β-lactam susceptibility, evaluates current approaches for genome-based MIC prediction, and examines the factors that constrain their generalizability. We propose a three-layer reporting framework that separates genomic findings, predicted phenotypes, and potential clinical interpretation, while explicitly communicating prediction confidence and identifying circumstances requiring confirmatory phenotypic MIC testing. Genome-based PBP profiling is already well positioned to strengthen pneumococcal surveillance and may inform potential clinical interpretation, but patient-level reporting will require continuously curated phenotype-linked databases, external validation in intended-use populations, and an uncertainty-aware interpretive layer connecting genomic evidence to treatment-specific breakpoints.

Ultra short wave (USW) therapy, known for its deep tissue penetration in electromagnetic waves, is clinically used to treat local inflammation and alleviate pain, yet its role in intestinal inflammation via macrophage regulation remains unclear. We investigated whether USW exerts non-thermal effects on macrophages to alleviate colitis using dextran sulfate sodium (DSS)-induced mouse models and LPS-stimulated BMDMs/THP-1 cells. Results demonstrated that USW significantly ameliorated intestinal inflammation by directly targeting macrophages, as macrophage depletion completely abrogated its therapeutic effects. Mechanistically, USW induced distinct shifts in intracellular calcium flux and modulated cytokine expression through the TRPV2/CaMKII/Nrf2 signaling pathway. This study confirms that USW alleviates colitis by regulating macrophage polarization and calcium signaling, uncovering a distinct therapeutic pathway and highlighting the potential of non-invasive USW treatment as an effective strategy for improving colitis prognosis.

BackgroundStaphylococcus aureus is one of the most common pathogens that colonizes human skin/mucous membranes, where it causes local infection that can progress to invasive infection, resulting in high morbidity and mortality worldwide. This study aimed to investigate the antibiotic susceptibility and molecular characteristics of invasive S. aureus in children and women in Southwest China from 2018 to 2023 to provide novel insights helpful in preventing and treating S. aureus infections.MethodsThe demographic and clinical characteristics of patients with invasive S. aureus infection were collected and analyzed. Next-generation sequencing (NGS) and sequence analysis techniques were used to determine the molecular epidemiological characteristics of the S. aureus isolates, and the microdilution broth method was used for antimicrobial susceptibility testing.ResultsA total of 108 invasive S. aureus isolates, 29 methicillin-resistant S. aureus (MRSA) isolates and 79 methicillin- susceptible S. aureus (MSSA) isolates, were included. The isolates had the highest rate of resistance to PEN, at 91.67%, with all the MRSA isolates being resistant; the next highest resistance was to ERY and CLI, both at 65.74%. A total of 32 STs (including 8 novel STs) were detected and divided into 10 CCs. Moreover, 45 spa types were also detected. The main STs were ST22 (17.59%) and ST59 (15.74%), and the main CCs were CC59 (21.30%) and CC22 (19.44%). The most prevalent spa types were t309 and t437, both at 14.81%, and the SCCmec type could be assigned to two categories: IV (62.07%) and V (34.48%). Among the 29 MRSA isolates tested, CC59-IV-t437 (34.48%) and CC59-V-t437 (13.79%) were the main lineages, and among the 79 MSSA isolates, CC22-t309 (18.99%), CC1-t189 (10.13%), and CC5-t002 (7.60%) were the main lineages. Except for SXT, the resistance rates of the 29 MRSA isolates were greater than those of the MSSA isolates. Most isolates carried common virulence genes, among which the carriage rate of pvl reached 33.33%.ConclusionsThis study provides valuable information, including the prevalence, molecular characteristics and antimicrobial resistance of S. aureus isolates that cause invasive infectious diseases in Southwest China, and the findings may advance the prevention and treatment of S. aureus infections.

Lactylation, an emerging form of post-translational modification derived from lactate, plays a pivotal role in numerous cellular processes such as tumor proliferation, metabolism, inflammation, and embryonic development. However, the precise molecular mechanisms by which lactylation controls these biological functions in both physiological and pathological contexts remain elusive. This review summarizes the latest reported regulatory mechanisms of protein lactylation in various diseases since 2024, introducing the latest research progress regarding the regulatory functions of protein lactylation in pathological processes, with particular attention to the regulatory mechanisms of non-histone lactylation modification in diseases. Finally, it outlines the potential of targeted lactylation therapy, proposes the main directions for future research, and emphasizes its scientific significance for future studies.

This study aimed to explore the association of red cell distribution width to albumin ratio (RAR) with all-cause mortality in ICU-NAFLD patients, to evaluate RAR as a prognostic tool. Patients were stratified into four groups, with 365- and 28-day all-cause mortality as primary and secondary endpoints. The Kaplan-Meier analysis compared survival across groups, while Cox regression and restricted cubic spline (RCS) evaluated associations between red cell distribution width-to-albumin ratio (RAR) and outcomes. The predictive performance of RAR was quantified using receiver operating characteristic (ROC) curves. A nomogram predicting treatment response was developed and internally validated via bootstrap resampling. Calibration and clinical utility were further assessed by Hosmer-Lemeshow test and decision curve analysis (DCA). A total of 590 patients with NAFLD were enrolled. Higher levels of the RAR index were correlated with an increased risk of 28- and 365-day all-cause mortality, as indicated by the K-M curves (log-rank P < 0.001). Multivariate Cox proportional risk analysis, after adjusting for confounding factors, displayed that high RAR levels were associated with an increased risk of 28-day mortality (HR 1.82[95

AKR1Cs, as a reductase enzyme family, play a pro-carcinogenic role in various types of cancers, including hormone-related malignancies and non-hormonal tumors. However, there exists a notable scarcity of literature concerning AKR1Cs expression in pancreatic cancer and the subsequent impacts on its progression. Analyzing pancreatic cancer database information by employing advanced bioinformatics techniques to unravel AKR1Cs’ intricate involvement in cancer malignancy, their correlation with clinical pathology, prognostic implications, as well as their responsiveness to conventional and immune-based therapies. Furthermore, the role of AKR1C1 in promoting the malignant progression of pancreatic cancer cell lines was validated using cell proliferation assays (EdU labeling and colony formation), and cell migration and invasion experiments including scratch wound healing and Transwell migration/invasion assays. AKR1Cs are not only significantly overexpressed in pancreatic cancer, but also closely associated with poor clinical grading, clinical chemoresistance and poor immune response in pancreatic cancer.Moreover, regulating the expression of AKR1C1 in pancreatic cancer cells will affect its proliferation, migration, invasion and the occurrence of epithelial-mesenchymal transformation (EMT). Our findings are expected to establish AKR1Cs, especially AKR1C1 as a promising therapeutic target for the clinical treatment of pancreatic cancer.

Objectives: This study aimed to explore the mediating effects of iron homeostasis biomarkers linking central obesity with metabolic dysfunction-associated steatotic liver disease (MASLD) and primary liver cancer (PLC) via Mendelian randomization (MR) analysis. Methods: Two-sample bidirectional MR, multivariable MR, and mediation analyses were used to investigate the causal associations among obesity-related traits, iron homeostasis biomarkers, MASLD, and PLC. For the discovery and replication analyses, GWAS summary data for iron homeostasis biomarkers, MASLD, and PLC were extracted from two datasets, and the combined effects were pooled to corroborate the conclusions. Results: BMI and waist circumference were associated with a risk of MASLD in their combined effects (OR = 1.83, 95% CI = 1.33–2.52 for BMI; OR = 1.98, 95% CI = 1.63–2.41 for waist circumference). Waist circumference but not BMI had significant causal effects on the risk of PLC in the discovery dataset (OR = 1.71, 95% CI = 1.01–2.89 for BMI; OR = 2.72, 95% CI = 1.37–5.39 for waist circumference). In both of the iron homeostasis datasets, genetically predicted increased ferritin was associated with increased risk of MASLD by multivariable MR. We only observed that genetic liability to increased ferritin was associated with increased risk of PLC in iron homeostasis dataset 1 after adjusting for waist circumference. By two-step MR analysis, we found that genetic liability to ferritin mediated 3.34% (95% CI: 0.17–8.08%) of waist circumference effects on MASLD risk and 18.84% (95% CI: 3.01–40.51%) of its effects on PLC risk. Conclusions: Waist circumference and iron homeostasis biomarkers were causally associated with increased risks of MASLD and PLC. Central obesity may contribute to the development of MASLD and PLC by increasing ferritin levels.

ObjectivesVariability in biomarkers is crucial for clinical decision-making in individuals with type 2 diabetes mellitus (T2DM). The biological variation (BV) of biomarkers associated with thyroid function, iron metabolism, and bone metabolism may show population-specific differences. This study aims to evaluate the biological variation of sixteen biomarkers in T2DM patients and compare these with variations observed in a healthy population.MethodsTwenty-four T2DM patients, aged 43 to 67 and in stable condition, were enrolled. Blood samples were collected biweekly for three months. Analysis of variance models were used to assess the BV, including within-subject BV (CVI), between-subject BV (CVG), analytical variation, reference change value (RCV), index of individuality (II), the number of samples required for steady-state set points (NHSP), and analytical performance specifications for all biomarkers.ResultsFemales exhibited lower CVI estimates for thyroid-stimulating hormone, parathyroid hormone, and phosphate compared to males. No significant differences in CVI estimates were observed between T2DM patients and healthy individuals across the study. However, the CVG estimates for cortisol and iron were significantly lower in T2DM patients compared to the healthy individuals.ConclusionsBV data is critical for the precise interpretation of serial biomarker level changes in T2DM patients. It is deemed reasonable to use RCVs for four bone metabolism markers and five thyroid biomarkers, derived from a healthy population, as a reference for monitoring T2DM patients.

ABSTRACT Sarcopenia is a known risk factor for cardiovascular disease (CVD) in individuals with diabetes or prediabetes, but the impact of changes in sarcopenia status on CVD risk remains unclear. This study aimed to examine how changes in sarcopenia status between baseline and the second follow‐up survey, conducted 2 years later, influence the risk of developing incident CVD. Incident CVD was identified based on self‐reported physician diagnoses of heart disease, such as angina, myocardial infarction, heart failure, or stroke. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for potential confounders. The results showed that participants who progressed from non‐sarcopenia to possible sarcopenia or sarcopenia had a higher risk of developing CVD. Their risk was significantly greater compared to those who remained non‐sarcopenic (HR 1.37, 95% CI 1.08–1.73). Conversely, individuals who recovered from sarcopenia to non‐sarcopenia or possible sarcopenia had a lower risk of CVD. Their risk was lower than those who remained sarcopenic (HR 0.40, 95% CI 0.20–0.82). Among individuals with possible sarcopenia at baseline, those who recovered to non‐sarcopenia had a reduced CVD risk. This reduction was significant compared to those who remained in possible sarcopenia (HR 0.62, 95% CI 0.46–0.84). These findings suggest that changes in sarcopenia status have a significant impact on CVD risk, with worsening sarcopenia increasing the likelihood of CVD and recovery lowering the risk in individuals with diabetes or prediabetes.

BACKGROUND AND OBJECTIVE:Serum protein electrophoresis (SPEP) plays a critical role in diagnosing diseases associated with M-proteins. However, its clinical application is limited by a heavy reliance on experienced experts. METHODS:A dataset comprising 85,026 SPEP outcomes was utilized to develop artificial intelligence diagnostic models for the classification and localization of M-proteins. These models were trained and validated using three data features, and their performance was evaluated using comprehensive metrics, including sensitivity, positive predictive value (PPV), specificity, negative predictive value (NPV), F1 score, accuracy, area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), and Intersection over Union (IoU). The best-performing machine learning (ML) and deep learning (DL) models were further tested on a separate dataset of 1,079 samples. The localization ability of the DL model was compared against three clinical experts. RESULTS:Among the four ML models, the extreme gradient boosting (XGB) model achieved the best performance, with MCC, AUC, F1 score, sensitivity, specificity, accuracy, PPV, and NPV of 0.847, 0.903, 0.875, 0.822, 0.985, 0.951, 0.934, and 0.955, respectively. Different feature extraction methods significantly influenced model performance. The DL models outperformed the ML models in comprehensive performance. The U-Net combined with Transformer model demonstrated localization ability comparable to that of clinical experts, achieving sensitivity, specificity, accuracy, PPV, NPV, F1 score, AUC, MCC, and IoU of 0.947, 0.984, 0.976, 0.938, 0.986, 0.942, 0.966, 0.927, and 0.877, respectively. CONCLUSION:The U-Net combined with the Transformer model demonstrated expert-level performance in M-protein classification and localization, achieving an accuracy of 0.976 and an IoU of 0.877. This exceptional performance highlights the potential of this combined model for automating clinical SPEP workflows.

In recent years, the abuse of ketamine as a recreational drug has been growing, and has become one of the most widely abused drugs. Continuous using ketamine poses a risk of drug addiction and complications such as attention deficit disorder, memory loss and cognitive decline. Ketamine-induced neurotoxicity is thought to play a key role in the development of these neurological complications. In this paper, we focus on the molecular mechanisms of ketamine-induced neurotoxicity. According to our analyses, drugs in causing neurotoxicity are closely associated with programmed cell death (PCD) such as apoptosis, autophagy, necroptosis, pyroptosis, and Ferroptosis. Therefore, this review will collate the existing mechanisms of programmed death in ketamine-induced neurotoxicity as well as explore the possible mechanisms by outlining the mechanisms of programmed death in other drug-induced neurotoxicity, which may be helpful in identifying potential therapeutic targets for neurotoxicity induced by ketamine abuse.

Ketamine, a psychoactive substance strictly regulated by international drug conventions, is classified as a "new type drug" due to its excitatory, hallucinogenic, or inhibitory effects. The etiology of ketamine-induced psychiatric symptoms is multifaceted, with the immune regulatory mechanism being the most prominent among several explanatory theories. In recent years, the interaction between the immune system and nervous system have garnered significant attention in neuropsychiatric disorder research. Notably, the infiltration of peripheral lymphocytes into the central nervous system has emerged as an early hallmark of certain neuropsychiatric disorders. However, a notable gap exists in the current literature, regarding the immune regulatory mechanisms, specifically the peripheral immune alterations, associated with ketamine-induced psychiatric symptoms. To address this void, this article endeavors to provide a comprehensive overview of the pathophysiological processes implicated in psychiatric disorders or symptoms, encompassing those elicited by ketamine. This analysis delves into aspects such as nerve damage, alterations within the central immune system, and the regulation of the peripheral immune system. By emphasizing the intricate crosstalk between the peripheral immune system and the central nervous system, this study sheds light on their collaborative role in the onset and progression of psychiatric diseases or symptoms. This insight offers fresh perspectives on the underlying mechanisms, diagnosis and therapeutic strategies for mental disorders stemming from drug abuse.

Long non-coding RNAs (lncRNAs) are emerging as critical epigenetic regulators within the gene-environment interaction networks and have been implicated in the progression of rheumatoid arthritis (RA). In the present study, we employed high-throughput RNA sequencing to elucidate the differential expression profiles of lncRNAs in peripheral blood mononuclear cells (PBMCs) from a discovery cohort (3 RA patients vs. 3 healthy controls). Following comprehensive sequencing analysis, 8 lncRNAs were identified as potential biomarkers for RA. Through reverse transcription quantitative polymerase chain reaction (RT-qPCR) validation, we confirmed that LINC01881 (p < 0.01) and MIR3142HG (p < 0.01) exhibited expression patterns consistent with our sequencing results. Both showed moderate diagnostic performance (AUC = 0.713 and 0.723, respectively), and their combination improved diagnostic efficacy (AUC = 0.786). LINC01881 expression was negatively correlated with CRP and ESR, while MIR3142HG correlated positively with platelet count. Bioinformatic analysis suggested that LINC01881 may influence the PI3K-Akt pathway through interactions with PTEN-targeting miRNAs, and MIR3142HG may be involved in hematopoietic and platelet-related processes. In summary, MIR3142HG and LINC01881 are potential diagnostic biomarkers for RA. LINC01881 may act as a compensatory regulator of inflammation via PI3K-Akt signaling, while MIR3142HG may influence platelet biology. Further functional studies are warranted to confirm these mechanisms and explore their clinical utility in multi-marker diagnostic panels.

Kawasaki disease (KD), a pediatric systemic vasculitis, lacks reliable diagnostic biomarkers and exhibits immune heterogeneity, complicating clinical management. Current therapies face challenges in targeting specific immune pathways and predicting treatment responses. Multi-cohort transcriptomic data were integrated to identify inflammation-related genes (IRGs). Differential analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (LASSO, Boruta, SVM-RFE, Random Forest) were applied to screen diagnostic biomarkers. Immune infiltration and molecular subtyping based on diagnostic biomarkers were analyzed, complemented by regulatory network analysis to explore transcriptional, pharmacological, and miRNA interactions. Six robust diagnostic biomarkers (ADM, ALPL, FCGR1A, HP, S100A12, SLC22A4) were identified, achieving AUC > 0.9 in cohorts. KD exhibited elevated neutrophils, monocytes, and Tregs but reduced CD8 + T cells and cytolytic activity. Consensus clustering stratified KD into two immune-heterogeneous subtypes: Cluster1 (neutrophil/Treg-dominant, enriched in TLR signaling) and Cluster2 (B cell/CD8 + T cell-dominant, linked to cytolytic activity). Regulatory networks revealed subtype-specific transcriptional regulators and therapeutic agents. This study establishes inflammation-related diagnostic biomarkers and immune-stratified subtypes for KD, offering a framework for precision immunomodulatory therapies. Multi-algorithm integration identifies six inflammation-related diagnostic biomarkers with high AUC values across cohorts. Stratification of KD patients into two subtypes with distinct inflammatory signatures. Regulatory networks link biomarkers to transcriptional regulators, miRNAs, and therapeutic agents.

Ketamine (Ket) is a globally widely used injectable anesthetic and recreational drug that can lead to persistent behavioral deficits and induce psychotic states. Immune pathogenesis is believed to play a pivotal role in psychological symptoms and abnormal behavior. However, the role of the immune system, particularly peripheral immune changes, in ketamine-induced behavioral deficits and even psychotic symptoms remains largely elusive. This study aimed to explore the potential role of the peripheral immune system in ketamine-induced behavioral abnormalities in mice. Continuous administration of high-dose ketamine in C57/B6J mice induced abnormalities representative of anxiety-depressive-like behavior or memory-cognitive behavior, accompanied by morphological changes, elevated levels of inflammatory cytokines, and enhanced expression of markers representing astrocyte activity in the hippocampus and prefrontal cortex. Furthermore, flow cytometry was used to analyze changes in the number and composition of immune cells in the peripheral blood of mice after high-dose ketamine administration. The results showed a significant increase in peripheral T lymphocytes, especially CD4+ lymphocytes, while NK cells and B lymphocytes did not exhibit significant changes. Additionally, there was a significant increase of CD4+ lymphocytes in the hippocampus and prefrontal cortex of the mice. Based on these findings, in vivo neutralization of CD4+ lymphocytes surprisingly reversed the anxiety-depressive-like behavior or memory-cognitive behavior of the mice and partially or fully restored brain tissue morphology and the expression of astrocyte activity molecules. Our results indicate that peripheral CD4+ lymphocytes play a crucial role in ketamine-induced behavioral abnormalities, and the presence of CD4+ lymphocytes may participate in and promote ketamine-induced anxiety, depressive-like behavior, and memory-cognitive dysfunction.

OBJECTIVE:The purpose of this study was to explore the correlation between inflammatory indicators and blood lipids and to further provide a theoretical basis for the diagnosis and treatment of clinical polycystic ovary syndrome (PCOS).METHODS:Whole-blood cell counts and hormone and blood lipid levels were measured in 110 patients with PCOS and 126 healthy women. The differences in the above levels and the correlation between inflammation and blood lipid levels in the two groups were determined, and classified according to BMI. Differences in inflammatory indices were also analyzed. The independent risk factors for PCOS were analyzed by binary logistic regression.RESULTS:The PCOS group had greater BMI and greater body weight than the control group. The inflammatory indicators WBC, neutrophil, lymphocyte, monocyte counts and the NLR were significantly higher than those of the control group. It had higher testosterone (TSTO), triglyceride (TG) and total cholesterol (TC) levels. Correlation analysis showed that leukocyte and neutrophil counts were positively correlated with TSTO and TG levels and negatively correlated with HDL. In the BMI ≥ 24 and BMI < 24 groups, WBC was higher in PCOS patients than in healthy controls. Logistic regression showed that TSTO, TG and FSH were independent risk factors for PCOS.CONCLUSION:Inflammatory markers are correlated with blood lipids in PCOS. During the treatment of PCOS, blood lipids and serum inflammatory factors should be monitored.