Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

The Application of Pneumatic Arm in Neuroendoscopic Transsphenoidal Pituitary Adenoma Resection.

OBJECTIVE: To summarize the application experience of the pneumatic arm in transnasal sphenoidal pituitary adenoma resection under neuroendoscope. METHODS: A retrospective analysis was conducted on the clinical data of 52 patients with pituitary adenoma who underwent endoscopic transsphenoidal surgery with pneumatic arm fixation in the Neurosurgery Department of the First Affiliated Hospital of Anhui Medical University from July 2021 to March 2024. Among them, there were 5 cases of pituitary microadenoma, 35 cases of macroadenoma, and 12 cases of giant adenoma. Head CT and a full set of hormones were re-examined within 24 hours after surgery to evaluate the surgical effect. Follow-up was conducted by the outpatient department after surgery to assess the clinical symptoms, hormone level, and imaging of all patients. RESULTS: Among 52 patients, gross total resection was achieved in 48 cases (92.3%), subtotal resection in 3 cases (5.8%), and partial resection in 1 case (1.9%). Preoperatively, 43 patients had diminished vision, with 40 showing improvement postoperatively, 1 worsening, and 2 having no significant improvement. Thirty-eight patients had headaches preoperatively, and all showed varying degrees of improvement postoperatively. Routine hormone examination within 24 hours after surgery showed that all 20 prolactinoma patients had restored normal hormone levels, 10 of 12 growth hormone-secreting adenoma patients normalized, and 4 of 6 cases of adrenocorticotropic hormone-secreting adenoma immediately relieved after surgery. Postoperative complications included intracranial hematoma in 1 case, cerebrospinal fluid leakage in 2 cases, transient diabetes insipidus in 6 cases, intracranial infection in 1 case, and no death cases. The median follow-up time of 52 patients was 18.6 months (range: 1-32 mo). During the follow-up period, the initial clinical symptoms of all patients improved to varying degrees, and they were able to work and live normally. At the last follow-up, 1 patient had recurrent tumor and 1 patient had progression. CONCLUSION: Transnasal sphenoidal resection of pituitary adenoma using a pneumatic arm-fixed neuroendoscope allows the operator to perform the surgery with both hands, resulting in satisfactory overall tumor resection and fewer surgical complications. This technique has good clinical value for promotion.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

Tracing the vertical migration of exogenous cadmium in soil by seasonal freeze-thaw event using rare earth elements.

Environmental behaviors of heavy metal in soil are strongly influenced by seasonal freeze-thaw events at the mid-high altitudes. However, the potential impact mechanisms of freeze-thaw cycles on the vertical migration of heavy metal are still poor understood. This study aimed to explore how exogenous cadmium (Cd) migrated and remained in soil during the in-situ seasonal freeze-thaw action using rare earth elements (REEs) as tracers. As a comparison, soil which was incubated in the controlled laboratory (25 °C) was employed. Although there was no statistically significant difference in the Cd levels of different soil depths under different treatments, the original aggregate sources of Cd in the 5-10 cm and 10-15 cm soil layers differed. From the distributions of REEs in soil profile, it can be known that Cd in the subsurface of field incubated soil was mainly from the breakdown of >0.50 mm aggregates, while it was mainly from the <0.106 mm aggregates for the laboratory incubated soil. Furthermore, the dissolved and colloidal Cd concentrations were 0.47 µg L-1 and 0.62 µg L-1 in the leachates from field incubated soil than those from control soil (0.21 µg L-1 and 0.43 µg L-1). Additionally, the colloid-associated Cd in the leachate under field condition was mainly from the breakdown of >0.25 mm aggregates and the direct migration of <0.106 mm aggregates, while it was the breakdown of >0.50 mm and the direct migration of <0.106 mm aggregates for the soil under laboratory condition. Our results for the first time provided insights into the fate of exogenous contaminants in seasonal frozen regions using the rare earth element tracing method.

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.

Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution.

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-ß (Aß), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aß clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aß plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

[Study on mechanism of inhibiting proliferation of head and neck cancer cells by citral, active ingredient of lemon essential oil].

To explore the active substances exerting anti-tumour effect in lemon essential oil and the molecular mechanism inhibiting the proliferation of head and neck cancer cells SCC15 and CAL33, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay(MTT) was utilized to identify the active component inhibiting the proliferation of head and neck cancer cells, namely citral. The IC_(50) of citral inhibiting the proliferation of head and neck cancer cells and normal cells were also determined. In addition, a 5-ethynyl-2'-deoxyuridine(EdU) staining assay was used to detect the effect of citral on the proliferation rate of head and neck cancer cells, and a colony formation assay was used to detect the effect of citral on tumor sphere formation of head and neck cancer cells in vitro. The cell cycle arrest and apoptosis induction of head and neck cancer cells by citral were evaluated by flow cytometry, and Western blot was used to detect the effect of citral on the expression levels of cell cycle-and apoptosis-related proteins in head and neck cancer cells. The findings indicated that citral could effectively inhibit the proliferation and growth of head and neck cancer cells, with anti-tumor activity, and its half inhibitory concentrations for CAL33 and SCC15 were 54.78 and 25.23 µg·mL~(-1), respectively. Furthermore, citral arrested cell cycle at G_2/M phase by down-regulating cell cycle-related proteins such as S-phase kinase associated protein 2(SKP2), C-MYC, cyclin dependent kinase 1(CDK1), and cyclin B. Moreover, citral increased the cysteinyl aspartate-specific proteinase-3(caspase-3), cysteinyl aspartate-specific proteinase-9(caspase-9), and cleaved poly ADP-ribose polymerase(PARP). It up-regulated the level of autophagy-related proteins including microtubule associated protein 1 light chain 3B(LC3B), sequestosome 1(P62/SQSTM1), autophagy effector protein Beclin1(Beclin1), and lysosome-associate membrane protein 1(LAMP1), suggesting that citral could effectively trigger cell apoptosis and cell autophagy in head and neck cancer cells. Furthermore, the dual-tagged plasmid system mCherry-GFP-LC3 was used, and it was found that citral impeded the fusion of autophagosomes and lysosomes, leading to autophagic flux blockage. Collectively, our findings reveal that the main active anti-proliferation component of lemon essential oil is citral, and this component has a significant inhibitory effect on head and neck cancer cells. Its underlying molecular mechanism is that citral induces apoptosis and autophagy by cell cycle arrest and ultimately inhibits cell proliferation.

Mitochondrial transfer mediates endothelial cell engraftment through mitophagy.

Ischaemic diseases such as critical limb ischaemia and myocardial infarction affect millions of people worldwide1. Transplanting endothelial cells (ECs) is a promising therapy in vascular medicine, but engrafting ECs typically necessitates co-transplanting perivascular supporting cells such as mesenchymal stromal cells (MSCs), which makes clinical implementation complicated2,3. The mechanisms that enable MSCs to facilitate EC engraftment remain elusive. Here we show that, under cellular stress, MSCs transfer mitochondria to ECs through tunnelling nanotubes, and that blocking this transfer impairs EC engraftment. We devised a strategy to artificially transplant mitochondria, transiently enhancing EC bioenergetics and enabling them to form functional vessels in ischaemic tissues without the support of MSCs. Notably, exogenous mitochondria did not integrate into the endogenous EC mitochondrial pool, but triggered mitophagy after internalization. Transplanted mitochondria co-localized with autophagosomes, and ablation of the PINK1-Parkin pathway negated the enhanced engraftment ability of ECs. Our findings reveal a mechanism that underlies the effects of mitochondrial transfer between mesenchymal and endothelial cells, and offer potential for a new approach for vascular cell therapy.

Safety, pharmacokinetics, and food-effect of pivmecillinam after single- and multiple-dose in healthy Chinese subjects: a phase I study.

Urinary tract infection (UTI) is one of the most prevalent bacterial infectious diseases worldwide. However, the resistance of urinary pathogens to other UTI antibiotics such as trimethoprim and trimethoprim/sulphamethoxazole increased. Pivmecillinam is a prodrug of mecillinam, which is effective for the treatment of urinary tract infections. The purpose of this study was to assess the safety, and pharmacokinetics of pivmecillinam and mecillinam after single- and multiple-dose oral administration of pivmecillinam tablets in healthy Chinese subjects. The study also investigated the profile of urinary excretion of mecillinam, as well as the effect of food and gender on the pharmacokinetics of pivmecillinam and mecillinam. This study was a single-center, open-label phase I study carried out in three groups. In total, 34 subjects were included in the study: group 1-food effect study with pivmecillinam 200 mg (n = 12); group 2-single- and multiple-dose study with pivmecillinam 400 mg (n = 12); group 3-single dose study with pivmecillinam 600 mg (n = 10). The plasma and urine concentrations of pivmecillinam and mecillinam were measured, and their pharmacokinetics were calculated. Treatment-emergent adverse events were evaluated and recorded in safety assessments for three groups. No severe adverse events were found in this study. After a single dose of pivmecillinam was taken orally, the maximum plasma concentration (Cmax) and the area under the concentration-time curve (AUC) of pivmecillinam increased in a dose-proportional manner, nor did mecillinam. Food had significant effects on Cmax and AUC0-t of pivmecillinam and Cmax of mecillinam. The mean cumulative percentage of urine excretion of mecillinam at 0 to 24 h ranged from 35.5 to 44.0%. Urinary cumulative excretion is relative to the drug dose, but the diet and multiple-dose administration did not affect the urinary cumulative excretion rate. The safety and pharmacokinetics of pivmecillinam and mecillinam after single- (200/400/600 mg) or multiple-dose (400 mg) administration were demonstrated in healthy Chinese subjects. Food affected the pharmacokinetics of pivmecillinam and mecillinam.

Evolution Characterization and Pathogenicity of an NADC34-like PRRSV Isolated from Inner Mongolia, China.

Porcine reproductive and respiratory syndrome virus (PRRSV) is a pathogen that causes severe abortions in sows and high piglet mortality, resulting in huge economic losses to the pig industry worldwide. The emerging and novel PRRSV isolates are clinically and biologically important, as there are likely recombination and pathogenic differences among PRRSV genomes. Furthermore, the NADC34-like strain has become a major epidemic strain in some parts of China, but the characterization and pathogenicity of the latest strain in Inner Mongolia have not been reported in detail. In this study, an NADC34-like strain (CHNMGKL1-2304) from Tongliao City, Inner Mongolia was successfully isolated and characterized, and confirmed the pathogenicity in pigs. The phylogenetic tree showed that this strain belonged to sublineage 1.5 and had high homology with the strain JS2021NADC34. There is no recombination between CHNMGKL1-2304 and any other domestic strains. Animal experiments show that the CHNMGKL1-2304 strain is moderately virulent to piglets, which show persistent fever, weight loss and high morbidity but no mortality. The presence of PRRSV nucleic acids was detected in both blood, tissues, nasal and fecal swabs. In addition, obvious pathological changes and positive signals were observed in lung, lymph node, liver and spleen tissues when subjected to hematoxylin-eosin (HE) staining and immunohistochemistry (IHC). This report can provide a basis for epidemiological investigations and subsequent studies of PRRSV.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Efficacy and safety of herbal medicine combined with acupuncture in pediatric epilepsy treatment: A meta-analysis of randomized controlled trials.

OBJECTIVE: To evaluate the efficacy and safety of herbal medicine and acupuncture combination for pediatric epilepsy treatment. METHODS: Databases were searched from their interception until October 2023 to identify randomized controlled trials focusing on the therapeutic efficacy of herbal medicine-acupuncture combination (intervention group) for pediatric epilepsy. The primary outcome was the risk of treatment failure, whereas the secondary outcomes included the risk of post-treatment electroencephalogram (EEG) abnormalities and adverse events. Subgroup analyses were conducted based on the type of herbal compound formulas. Meta-regression analysis was conducted to examine the influence of patient demographics and clinical history on the therapeutic efficacy of herbal medicine-acupuncture combination for pediatric epilepsy. To assess the cumulative evidence, trial sequential analysis (TSA) was performed. RESULTS: The analysis included 10 trials involving a total of 882 pediatric patients. Meta-analysis revealed that the intervention group had a lower risk of treatment failure than the control group (risk ratio [RR] = 0.3, 95% confidence interval [CI]: 0.19-0.47, P<0.00001, I2 = 0%, 10 trials). Subgroup analyses showed that therapeutic efficacy was consistent among the different herbal compound formulas. Meta-regression analysis revealed that the efficacy of the treatments did not significantly vary with patient age, male sex, and duration of seizure history. TSA suggested that herbal medicine-acupuncture combination exerted a robust and conclusive effect on seizure treatment. Although the combined used of herbal medicine and acupuncture was not associated with a lower risk of post-treatment EEG abnormalities (RR = 0.82, 95%CI:0.6-1.11, P = 0.2, 3 trials), the risk of adverse events was reduced (RR = 0.27, 95%CI:0.18-0.41, P<0.00001, 4 trials). CONCLUSION: The meta-analysis suggested that combined use of herbal medicine and acupuncture is a promising and safe clinical approach for pediatric epilepsy treatment. Further large-scale studies are necessary to conclusively determine the efficacy and safety of herbal medicine and acupuncture in pediatric epilepsy treatment.

Unveiling the link between physical activity levels and dementia risk: Insights from the UK Biobank study.

BACKGROUND: There is limited information on the mixture effect and weights of light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) on dementia risk. METHODS: A prospective cohort study was conducted based on the UK Biobank dataset. We included participants aged at least 45 years old without dementia at baseline between 2006-2010. The weighted quantile sum regression was used to explore the mixture effect and weights of three types of physical activity on dementia risk. RESULTS: This study includes 354,123 participants, with a mean baseline age of 58.0-year-old and 52.4 % of female participants. During a median follow-up time of 12.5 years, 5,136 cases of dementia were observed. The mixture effect of LPA, MPA, and VPA on dementia was statistically significant (ß: -0.0924, 95 % Confidence Interval (CI): (-0.1402, -0.0446), P < 0.001), with VPA (weight: 0.7922) contributing most to a lower dementia risk, followed by MPA (0.1939). For Alzheimer's disease, MPA contributed the most (0.8555); for vascular dementia, VPA contributed the most (0.6271). CONCLUSION: For Alzheimer's disease, MPA was identified as the most influential factor, while VPA stood out as the most impactful for vascular dementia.

Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach: We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results: We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p<10-15) that occurs in male, female, older subject (>65 years old), and younger subject (<55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.

Conservative versus liberal oxygen therapy for intensive care unit patients: meta-analysis of randomized controlled trials.

BACKGROUND: It remains unclear whether conservative oxygen therapy (COT) or liberal oxygen therapy (LOT) is more beneficial to the clinical outcomes of intensive care unit (ICU) patients. We systematically reviewed the efficacy and safety of conservative versus liberal oxygen therapy for ICU patients. METHODS: We systematically searched PubMed, Embase, Web of Science, Scopus, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, MedRxiv, and BioRxiv for reports on randomized controlled trials (RCTs) that compared the effects of COT versus LOT on the clinical outcomes of ICU patients published in English before April 2024. The primary outcome was the mortality rate, secondary outcomes included ICU and hospital length of stay, days free from mechanical ventilation support (MVF), vasopressor-free time (VFT), and adverse events. RESULTS: In all, 13 RCTs involving 10,632 patients were included in analyses. Meta-analysis showed COT did not reduce mortality at 30-day (risk ratio [RR] = 1.01, 95% confidence interval [CI] 0.94 to 1.09, I2 = 42%, P = 0.78), 90-day (RR = 1.01, 95% CI 0.95 to 1.08, I2 = 9%, P = 0.69), or longest follow-up (RR = 1.00, 95% CI 0.95 to 1.06, I2 = 22%, P = 0.95) compared to LOT in ICU patients. In subgroup analyses, no significant difference was observed between the two groups in terms of the different ICU, baseline P/F, and actual PaO2. In addition, COT did not affect ICU length of stay, hospital length of stay, or VFT, it only affected MVF days. CONCLUSIONS: COT did not reduce all-cause mortality in ICU patients. Further RCTs are urgently needed to confirm the impact of COT strategy on specific populations.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. SIGNIFICANCE: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Research on the tilt-to-length coupling noise suppression method inside the gravitational wave detection telescope.

As an integral component of the laser interferometry measurement system, the tilt-to-length (TTL) coupling noise inside the telescope stands out as a critical noise factor that requires meticulous consideration. In the TianQin project, the non-geometric TTL-coupled noise inside the telescope should be less than 0.22 pm/Hz1/2. Additionally, the wavefront aberration RMS at the small pupil of the telescope needs to be better than 0.0065 λ. These requirements set for the telescope are exceptionally stringent. To address this challenge, this study aims to relax the wavefront aberration requirements by mitigating non-geometric TTL coupling noise, while ensuring the non-geometric TTL coupling noise remains below 0.22 pm/Hz1/2. By controlling the coupling aberration proportion, the wavefront aberration RMS at the small pupil of the telescope can be relaxed to 0.014 λ. Alternatively, optimizing the Gaussian beam waist radius can relax the wavefront aberration RMS to 0.016 λ. By simultaneously utilizing two optimization methods, the wavefront aberration at the small pupil of the telescope can be reduced to 0.033 λ, resulting in an impressive success rate of 91.15% in meeting the noise requirements.

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis.

Background: The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data. Methods: The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results. Results: Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA. Conclusion: The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.