Background & aimsThe impact of maternal zinc-copper (Zn-Cu) homeostasis on offspring brain development remains underexplored. This study investigated the relationships between maternal zinc (Zn), copper (Cu), and the Cu/Zn ratio during mid-pregnancy and subsequent early childhood neurodevelopment, utilizing both a prospective birth cohort and an in vivo rodent model to elucidate the underlying biological mechanisms.MethodsThis prospective cohort study recruited 725 mother-child pairs from the Guangxi Birth Cohort Study (GBCS). Multivariable linear regression models, restricted cubic splines (RCS), and formal interaction tests with stratified analyses were employed to evaluate the non-linear dose-response relationships and metal-metal interactions influencing the child's developmental quotient (DQ). To explore the underlying mechanisms and provide biological plausibility for the epidemiological findings, pregnant rats were divided into four dietary groups with varying Cu/Zn ratios. Offspring spatial cognitive behaviors were assessed via the Morris Water Maze (MWM), followed by high-throughput hippocampal proteomics.ResultsMaternal Cu and the Cu/Zn ratio exhibited significant inverse non-linear associations with child neurodevelopment across multiple domains. Crucially, the neuroprotective potential of Zn was significantly moderated by maternal Cu status; Zn was positively associated with developmental scores under low Cu conditions, but this benefit was progressively attenuated at higher Cu levels. In the MWM test, despite comparable baseline spatial learning boundaries, the Medium Cu/Zn ratio group displayed significantly altered exploratory patterns during the spatial probe trials. Proteomic analysis revealed a universal downregulation of the glutamate transporter SLC1A3 and significant dysregulation of critical synaptic markers (including SYT1, KCC2, and MAP2) across all Cu/Zn imbalance groups.ConclusionsThe maternal mid-pregnancy Cu/Zn ratio is non-linearly associated with offspring neurodevelopment, potentially mediated by hippocampal synaptic protein alterations. These findings provide a preliminary conceptual framework advocating for personalized prenatal nutritional assessments, which should integrate dual-index monitoring and consider regional geochemical baselines to optimize intervention strategies.

BACKGROUND:Ciprofol comes from a structural modification of the propofol molecule. Approved in China for non-intubated procedures, the induction and maintenance of general anesthesia, and sedation in the intensive care unit, it is now transiting from studies into clinical practice. This review focuses on its pharmacological basis, comparative clinical profile, and the potential to shape the future of anesthesia and sedation. MAIN BODY:Ciprofol acts on γ-aminobutyric acid type A receptors. It has a predictable linear pharmacokinetic and pharmacodynamic relationship and well-characterized metabolic pathways. Compared to propofol, ciprofol demonstrates superior tolerability, causing significantly less injection pain, better hemodynamic stability, and a low potential for clinically relevant drug interactions. Ciprofol is likely to be a non-inferior alternative to propofol for achieving successful anesthesia and sedation that may translate into clinical benefits for various specialized patient groups. Beyond its hypnotic effects, ciprofol may possess additional therapeutic potentials. CONCLUSION:Ciprofol is transitioning from a novel structural analogue of propofol to a clinically relevant intravenous anesthetic and sedative agent with potential advantages. Future research should prioritize the clinical validation of promising preclinical and early clinical findings. These efforts will help define ciprofol's role in patient-centered anesthesia and sedation.

Background N6-methyladenosine (m6A) is the most common RNA modification and plays a key role in the initiation, progression, and relapse of multiple cancers, including hematologic malignancies. However, the role of m6A and m6A regulatory genes in myelodysplastic syndromes (MDS) remains unclear. This study aims to elucidate the function and molecular mechanism of methyltransferase METTL14 in MDS. Methods RT-qPCR was used to assess the expression of multiple m6A regulators, focusing on METTL14 in MDS patients and cell lines. METTL14 overexpressing and knockdown cell lines were established, and CCK-8, EdU, and flow cytometry assays were performed to explore the biological functions of METTL14.Dot blot, MeRIP-Seq, MeRIP-qPCR, RT-qPCR, and Western blot were employed to investigate the underlying molecular mechanism. Results Dysregulation of multiple m6A regulators was observed in MDS, among which METTL14 was upregulated. Elevated METTL14 expression increases MDS risk and adverse prognosis, emerging as a biomarker for poor prognosis. METTL14 promoted proliferation and cell-cycle progression of MDS cells while inhibiting apoptosis; corresponding changes were observed in cell cycle and apoptosis markers. METTL14 regulated cellular m6A levels. Downstream targets of METTL14 were enriched in cell cycle-related pathways, with CCNE1 identified as a critical target. Knockdown of METTL14, actinomycin D, or S-adenosylhomocysteine treatment reduced CCNE1 mRNA and protein levels. Furthermore, METTL14 activated MAPK-ERK and PI3K-AKT signaling via CCNE1 in an m6A-dependent manner, thereby promoting proliferative MDS cells' capacity. Conclusions This study delineates a METTL14/m6A/CCNE1 signaling axis in MDS progression and suggests that METTL14-mediated m6A modification may be a potential therapeutic target for MDS.

BACKGROUND:The association between organochlorine pesticides (OCPs)/synthetic pyrethroids (SPs) and thyroid cancer (TC) remains poorly understood, with metabolic mechanisms unexplored. METHODS:We conducted a 1:1 age- and sex-matched case-control study (n = 668). Serum levels of 27 target analytes (19 OCPs and 8 SPs) were quantified; subsequent analyses were restricted to 13 compounds (10 OCPs and 3 SPs) with detection frequencies ≥85%. Eight machine learning (ML) algorithms with Shapley Additive Explanations (SHAP) were used to identify key pollutants in the 334 case-control pairs. Untargeted metabolomics was performed in a subset of 50 age- and sex-matched case-control pairs. Mixture effects were assessed by Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression. Furthermore, the Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) model was employed to integrate exposure and metabolic data, enabling the identification of TC patient subgroups and the exploration of underlying metabolic mechanisms. RESULTS:Participants (mean age 45.2 years, 82.3% female) had serum OCPs at 0.007-0.333 ng/mL and SPs at 0.046-0.095 ng/mL. ML algorithms identified fenpropathrin, β-BHC, cyhalothrin, α-BHC, and p,p'-DDD as the top five contributors to TC. Elevated OCPs/SPs exposure was significantly associated with increased TC risk (WQS: adjusted OR = 1.45, 95%CI = 1.34-2.24, P = 0.019; LUCID: OR = 9.33). Fenpropathrin was the primary contributor (BKMR posterior inclusion probability = 1.00; WQS weight = 67.6%). A total of 45 significant differential metabolites (DMs) were identified (VIP ≥1, P < 0.05, and qualitative level 1). LUCID revealed a distinct TC cluster characterized by upregulated S-sulfo-L-cysteine/adenosine and downregulated 2-hydroxycaprylic acid. CONCLUSION:OCPs/SPs mixtures, driven by fenpropathrin, disrupt amino acid/nucleotide metabolism while suppressing organic acid metabolism, representing a potential TC-associated metabolic signature.

Frailty is a critical geriatric syndrome associated with disability and mortality, yet the associations and mechanisms between environmental metal co-exposure with frailty remain unclear. This cross-sectional study included 4484 adults aged ≥ 60 years from China. Metal concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS), and frailty was assessed using a frailty index (FI). Single-metal analyses used generalized linear models (GLMs). For multi-metal analyses, we applied machine-learning-based variable selection, followed by Bayesian kernel machine regression (BKMR) and quantile g-computation (qg-comp) model. Potential explanatory pathways involving inflammatory markers, as well as interactions with age, sex, and lifestyle, were also explored. Positive associations were observed for magnesium (Mg), molybdenum (Mo), and vanadium (V) with FI, whereas rubidium (Rb) showed an inverse association (P < 0.001). Significant effect modification by age, sex, and lifestyle was identified. Positive metal‑FI associations were stronger in older females, while the inverse Rb‑FI correlation was more evident in participants with unhealthy lifestyles (P for interaction < 0.05). Multi-metal models showed overall positive co-exposure effects on frailty, with Mo (positive) and Rb (negative) contributing the largest weights. In exploratory mediation analyses, the neutrophil-to-high-density lipoprotein ratio (NHR) accounted for 2.09

This study aimed to systematically investigate the relationship between mixed heavy metal exposure and serum C-reactive protein (CRP) level using an integrated multi-model statistical strategy. This study included 568 participants from the Manganese-Exposed Workers Healthy Cohort. Serum CRP and 20 blood metal concentrations were measured. Key metals were selected via LASSO regression and overall mixture effects and metal contributions were quantified by Quantile g-computation; and joint effects, nonlinearity, and interactions were evaluated using Bayesian Kernel Machine Regression (BKMR). LASSO regression identified 9 key metals (Calcium, Nickel, Copper, Titanium, Tin, Vanadium, Selenium, Arsenic, Rubidium). GLM revealed inverse linear associations for Ca, Se, Rb, and Ni, and a positive association for Cu with CRP. Quantile g-computation showed the overall mixture was significantly inversely associated with CRP (HR = 0.955, 95

The occurrence and development of atherosclerosis are fundamentally linked to the aging of blood vessels. Previous researches have found that exposure to metals in the environment is linked to atherosclerosis, yet the underlying biological mechanism remains unclear. Twelve blood metals, vascular age and brachial-ankle pulse wave velocity (baPWV) were quantified among the 431 individuals involved in Manganese-exposed workers healthy cohort in 2023. The generalized linear model (GLM) indicated that chromium (Cr) was negatively associated with baPWV (β = -0.041). The GLM, least absolute shrinkage and selection operator and weighted quantile sum (WQS) analysis indicated that lead (Pb) was positively associated with baPWV. Pb contributed the most to the positive association between metal mixtures (Pb, selenium, manganese, Cr, calcium) and baPWV, showing that for every unit increase in the WQS index of metal mixtures, baPWV increased by 0.014 m/s. Subsequently, positive associations were found between Pb and vascular age as well as between vascular age and baPWV. Mediation analysis revealed that vascular age partially mediated (42

Severe fever with thrombocytopenia syndrome (SFTS), primarily a tick-borne disease, can also cause fatal human-to-human transmission. This report analyzes a cluster of six SFTS cases identified in China in 2022, involving one index patient and five secondary infections, with an overall mortality of 83%. All secondary cases occurred in elderly individuals (aged 66-85 years) following unprotected exposure to the index patient's body fluids during bedside care or traditional postmortem rituals, without documented tick bites. The high fatality rate underscores the potential severity of secondary transmission, particularly among elderly adults. More critically, this outbreak exposes systemic delays in early diagnosis even within an endemic area, highlighting fundamental gaps in the clinical management of undifferentiated fever. Effective prevention, therefore, relies on establishing a clinical system for early detection, rapid diagnosis, and prompt isolation while implementing culturally adapted community interventions to reliably interrupt transmission.

Background: Exposure to per- and polyfluoroalkyl substances (PFAS) may linked to thyroid cancer (TC) risk, but inconsistent findings and a lack of studies on mixed exposures exist, especially regarding novel PFAS compounds. Additionally, little is known about the potential mechanisms underlying the association. Objectives: Explore the effects of PFAS exposure on the serum metabolome and its correlation with TC. Methods: A 1:1 age- and sex-matched case-control study was administered with 746 TC cases and healthy controls. Liquid chromatography-high resolution mass spectrometry determined serum 11 PFAS and untargeted metabolome profile. ENET and LightGBM models were used to explore the exposure patterns and perform variable selection. The mixed exposure effects were assessed using Weighted quantile sum regression and Bayesian kernel machine regression. Metabolome-wide association analyses were performed to assess metabolic dysregulation associated with PFAS, and a structural synthesis analysis was used to detect latent groups of individuals with TC based on PFAS levels and metabolite patterns. Results: Ten of the 11 PFAS were detected in > 80 % of the population. PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. A novel integrative analysis identified a cluster of TC patients characterized by increased PFHxA, PFDoA, PFHpA and decreased PFOA, PFHxS levels, and altered metabolite patterns highlighted by the upregulation of free fatty acids. Conclusions: PFAS exposure is linked to a higher risk of TC, possibly through changes in fatty acid metabolism. Larger, prospective studies are needed to confirm these findings, and the role of short-chain PFAS requires more attention.

BACKGROUND:Over the past three decades, there has been a significant increase in the incidence of thyroid cancer. Ultrasound serves as a non-invasive tool in differentiating between benign and malignant thyroid nodules. However, its reliance on manual input can often lead to subjective bias. PURPOSE:This study proposes a novel network architecture committed to diminishing subjective bias led by manual masks and enhancing the accuracy of the current models. It amalgamates multi-scale features for the effective classification of thyroid nodules. METHODS:The innovative model, deemed APSNet, finds inspiration from active and passive systems. It incorporates attention mechanisms to augment nodule recognition. The model underwent training on a localized ultrasound image dataset and was tested using an external datasets TDID and TN3K. The assessment of its performance involved metrics such as Dice, IoU, F1, Acc, Sen, Spe, Ppv, Npv, and AUC, followed by statistical tests including the Friedman and DeLong tests. RESULTS:APSNet outperformed existing models across multiple metrics, achieving an Acc of 0.9259, F1 score of 0.9540, and AUC of 0.9243 on the TDID dataset, and an Acc of 0.9287, F1 score of 0.9001, sensitivity of 0.9273, and AUC of 0.9290 on the TN3K dataset. The DeLong test confirmed its superiority, indicating statistically significant improvements over other models. Ablation Study confirms the effectiveness of Dual-System design and the potention of Transformer-based backbone. CONCLUSIONS:APSNet offers a remarkable stride forward in thyroid nodule diagnosis by effectively addressing subjectivity and amplifying feature extraction capabilities. It proffers a more accurate and dependable diagnostic tool to clinicians.

The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal function on gestational anemia remain inconclusive. The concentrations of 20 blood essential trace and nonessential metals and 7 serum kidney function indicators were measured among 2000 pregnant women from the Guangxi Birth Cohort Study. Maternal hemoglobin < 110 g/L and hematocrit < 0.33 were defined as gestational anemia. We utilized twelve machine learning (ML) algorithms to independently screen for effective metal mixtures, assess their combined impacts and dose-response relationships on gestational anemia, and estimate the mediating role of kidney function. In the total population, manganese (Mn), copper (Cu), rubidium (Rb), and iron (Fe) were identified as significant metals with independent effects on gestational anemia by seven ML algorithms. The results of the Mn-Cu-Rb-Fe metal mixture ML models revealed that Rb (P = 0.008) and Fe (P < 0.001) were linearly and nonlinearly negatively associated with gestational anemia, respectively, whereas Cu (P = 0.069) showed a borderline positive association. The results for the Mn-Cu-Rb-Fe metal mixtures from both the first and second trimesters were consistent with their significance in the total population. Moreover, the protective effect of Rb increased while that of Fe decreased as pregnancy progressed; simultaneously, the risk effect of Cu diminished. In the third trimester, linear positive combined effects of tin (Sn) (P = 0.002) and cadmium (Cd) (P = 0.004) on gestational anemia were observed in the Sn-Rb-vanadium-Cd-Fe metal mixture ML models. Furthermore, we found that creatinine mediated the association between Fe and gestational anemia in the second trimester (mediated proportion = 3.7 %, P = 0.032). Hence, exposure to Mn-Cu-Rb-Fe mixtures inversely correlated with gestational anemia, which is mediated by creatinine. Sustained Rb and Fe supplementation may avert anemia and ameliorate metal toxicity.

Fospropofol disodium is comparable to propofol in maintaining mild-to-moderate sedation for mechanically ventilated patients in intensive care unit (ICU). However, its efficacy for deep sedation remains unclear. Therefore, we conducted a randomized-controlled trial comparing the efficacy and safety of fospropofol disodium with propofol for deep sedation of mechanically ventilated patients in ICU. In this randomized pilot study, critically ill adult patients requiring deep sedation were randomized to receive fospropofol disodium or propofol. The study drug was titrated to maintain a Richmond Agitation-Sedation Scale score (RASS) of−5 or−4. Narcotrend Index (NI) value was monitored during the whole study period. The primary outcome was the percentage of time in the target sedation range without rescue sedation. The secondary outcomes were successful extubation, ventilator-free days at day 7, ventilator-free days at day 28, 28-day all-cause mortality and adverse events. Thirty patients were included in each group. The fospropofol disodium infusion lasted for 47.50 (IQR 31.75 to 48.00) hours at a dose of 8.19 ± 2.36 mg/kg/h, while propofol infusion for 48.00 (IQR 30.88 to 48.00) hours at 2.73 ± 0.83 mg/kg/h. The proportion of time within the target RASS range without rescue sedation was 96.78

Uveal melanoma (UVM) is the second most common type of malignant melanoma occurring in the eye, which arises from the interstitial melanocytes in the uveal tract. This study aims to identify a highly efficient biomarker for the immunotherapy against UVM. Initially, a comprehensive analysis was conducted using the transcriptional and clinical data from The Cancer Genome Atlas (TCGA) database through the immune and stromal scores to assess the composition of infiltrating immune cells in the tumor microenvironment. Further, the expression of BCL2-Associated X, Apoptosis Regulator (BAX), and its co-expression gene networks were analyzed using the weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules and hub genes. The immunohistochemistry (IHC) analysis was carried out to confirm the influence of BAX on immune infiltration. In addition, the survival analysis on the hub genes, including BAX, was performed using an external dataset from the Gene Expression Omnibus (GEO) to corroborate the prognostic significance of these genes in an independent patient cohort. A nomogram integrating patients' clinical features was developed to predict the survival outcomes. Our investigations revealed that high BAX expression was associated with severe clinical characteristics and poor prognosis in UVM. Our analyses identified 12 hub genes at the intersection of differentially expressed genes categorized by BAX expression levels and a co-expression gene model. Further, the GEO database validated the prognostic significance of these hub genes. The IHC analysis established a significant correlation between BAX expression and immune infiltration. This nomogram model demonstrated robust predictive efficiency with a concordance index (C-index) of 0.909 (95% CI: 0.846-0.971), indicating excellent discriminative ability. The calibration curves for 1-year, 3-year, and 5-year overall survival (OS) rates confirmed the nomogram's accuracy, closely reflecting the actual patient outcomes. Finally, the Decision Curve Analysis (DCA) revealed that this nomogram could accurately predict OS for a majority of patients, covering a probability range of 25-95%. Our research may provide a new therapeutic regimen to benefit the UVM patients.

Metal exposure is a critical driver for dyslipidemia, yet the associations and underlying mechanisms between them remain uncertain. This study aimed to investigate the relationships between metal exposure, lipid profile, and total testosterone (TT) and to explore the mediating role of TT in 548 manganese-exposed male workers. We quantified 15 blood metals alongside serum lipid profiles [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C)] and TT levels. Least absolute shrinkage and selection operator (LASSO) was used to select key metals. Bayesian kernel machine regression (BKMR) and generalized linear regression (GLM) were performed to explore the associations among key metals, lipid profiles and TT. Mediation analysis was used to evaluate mediating role of TT between metal and lipid profile. BKMR indicated that Pb-dominated (PIP = 0.72) mixtures (Pb, Cu, As, Se) elevated HDL-C, whereas Cu-dominated (PIP = 0.42) mixtures (Mg, Cu, As, Se) reduced TT. GLM revealed that Pb (β = 0.05) and Se (β = 0.18) were positively associated with HDL-C, whereas Cu was inversely correlated with TC (β = − 0.16), HDL-C (β = − 0.20) and LDL-C (β = − 0.34). Mg (β = − 0.31) and As (β = 0.09) were significantly associated with TT. Mediation analysis revealed that TT mediated 8

Few repeated-measures studies with multiple time points have evaluated the sex-specific association between mitochondrial DNA copy number (mtDNAcn) and long-term exposure to metal mixtures. We conducted a repeated-measures study with three time points and 2550 observations in the manganese-exposed workers healthy cohort. Specifically, the blood concentrations of 15 metals and mtDNAcn were measured in 2012, 2017, and 2021. We employed a machine learning approach (GLMMLASSO) to select the metals most associated with mtDNAcn and used Bayesian kernel machine regression to examine their joint effects. In cross-sectional analyses (2550 visits), statistical methods consistently showed Calcium (Ca) was positively associated with mtDNAcn among overall visits (β = 0.292 in the linear mixed-effects model (LMM)) and dominated the positive overall effects of magnesium, Ca, titanium, iron, nickel, rubidium on mtDNAcn. An inverted "U"-shaped exposure-response curve between Ca and mtDNAcn appeared in males but not in females. Interaction analysis showed the association between Ca and mtDNAcn was significantly modified by gender. In repeated-measures analyses (807 visits), we explored the tendency for metals and mtDNAcn to change over three time points; the results confirmed the cross-sectional analyses. In overall visits, the positive association between Ca and mtDNAcn remained significant (β = 0.207 in LMM). For metal selection, Ca was identified as the predictor for mtDNAcn, and an inverted "U"-shaped exposure-response curve was found with mtDNAcn in males. Our findings reveal a consistent positive association between Ca and mtDNAcn with sex-dependent heterogeneity and suggest Ca may mitigate mitochondrial dysfunction induced by other metals.

BACKGROUND:Metals are significantly associated with the risk of gestational diabetes mellitus (GDM). However, the effects of liver function on the relationships between metals and GDM risk remain unexplored. This study aimed to investigate whether maternal liver function mediates the association between exposure to multiple metals and GDM among pregnant women. METHODS:This cross-sectional study included 1321 pregnant women from the Guangxi Birth Cohort Study. The concentrations of 22 metals in the blood and the levels of 12 liver function biomarkers in the serum were measured in pregnant women at less than 24 gestational weeks. GDM was diagnosed by an oral glucose tolerance test (OGTT) at 24-28 weeks of gestation. A total of 292 pregnant women with GDM and 1029 pregnant women without GDM were included. After least absolute shrinkage and selection operator (LASSO) regression screening, a Bayes kernel machine regression (BKMR) model was used to study the combined effect of maternal blood polymetallic exposure on the risk of GDM, and restricted cubic spline analysis, a quantile g-computation model and a generalized linear regression model were used to evaluate the associations between metals and GDM. Moreover, mediation analyses were performed to determine whether liver function biomarkers mediate the associations between metals and GDM risk. RESULTS:LASSO regression analysis revealed that 10 metals were associated with GDM risk, and BKMR analysis suggested a positive association between combined exposure to multiple metals and GDM risk (OR: 1.275, 95 % CI: 1.004-1.618). Blood chromium (Cr) and cobalt (Co) were protective factors for GDM (OR: 0.575, 95 % CI: 0.394-0.835; OR: 0.838, 95 % CI: 0.705-0.995), whereas blood lead (Pb) and nickel (Ni) were risk factors for GDM (OR: 1.695, 95 % CI: 1.110-2.257; OR: 1.444, 95 % CI: 1.080-1.930) in the generalized linear model. According to the mediation analysis, albumin mediated the association between blood Pb exposure and GDM risk by 13.2 % (P = 0.039) and the association between blood Co exposure and GDM risk by 8.3 % (P = 0.027). CONCLUSION:The results suggest that exposure to Pb and Ni during pregnancy increases the risk of GDM, whereas exposure to the trace elements Cr and Co can reduce the risk of GDM. Albumin may play a key role in the association of Pb and Co exposure with GDM.

Our study aimed to assess the effects of inhaled nitric oxide (iNO) on ventilation/perfusion mismatch, and individual variability in patients with acute respiratory distress syndrome (ARDS) by electrical impedance tomography (EIT). This single-center, prospective physiological study enrolled mechanically ventilated ARDS patients. All patients initially received 5 ppm iNO; responders (≥ 20