medRxiv : the preprint server for health sciences最新文献

Chronic occupational exposure to metals in welding fumes has been implicated in the etiology of neurodegenerative diseases (NDDs), including Parkinson's disease (PD) and Alzheimer's disease (AD). Changes in microRNA (miRNA) expression have been associated with various neurodegenerative conditions. Circulating miRNAs, in particular, have emerged as promising, minimally invasive biomarkers for diagnosing and monitoring disease progression. This study was designed to characterize the expression of miRNAs in neuronally-enriched serum extracellular vesicles (EVs) among welders and non-welders to explore their potential link to metal concentrations and welding exposure measures and their potential as early diagnostic biomarkers for neurodegeneration. Serum samples from 39 welders and 27 healthy individuals were collected, and EV-enclosed miRNAs were extracted and analyzed. Also, whole blood metal concentrations and welding exposure measurements were obtained. Fifty miRNAs were found to be dysregulated in welders vs. non-welders, of which three (miR-16-5p, miR-93-5p, miR-486-5p) showing reduced expression and two (miR-4281 and miR-4417) exhibiting positive correlations with blood metal concentrations as well as with long- and short-term welding exposure measures. The dysregulation of these miRNAs suggests that exposure to metals could disrupt important biological processes, possibly contributing to an elevated risk of NDDs. These findings highlight the need for further research to validate the causal relationship between exposure to metals in welding fumes, the dysregulation of circulating miRNAs, and their role in neurodegenerative disease development, with implications for miRNA-based biomarkers in early disease detection and prevention.

Patients who have radiographically detectable lesions in their brain or other symptoms compatible with brain tumors pose challenges for diagnosis. The only definitive way to diagnose such patients is through brain biopsy, an obviously invasive and dangerous procedure. Here we present a new workflow termed "CSF-BAM" that simultaneously identifies B cell or T cell receptor rearrangements, A neuploidy, and M utations using PCR-mediated amplification of both strands of the DNA from CSF samples. We first describe the details of the molecular genetic assessments and then establish thresholds for positivity using training sets of libraries from patients with or without cancer. We then applied CSF-BAM to an independent set of 206 DNA samples from patients with common, aggressive cancer types as well as other forms of brain cancers. Among the 126 samples from patients with the most common aggressive cancer types (high grade gliomas, medulloblastomas, or metastatic cancers to the brain), the sensitivity of detection was >81%. None of 33 CSF-BAM assays (100% specificity, 90% to 100% credible interval) were positive in CSF samples from patients without brain cancers. The sensitivity of CSF-BAM was considerably higher than that achieved with cytology. CSF-BAM provides an integrated multi-analyte approach to identify neoplasia in the central nervous system, provides information about the immune environment in patients with or without cancer, and has the potential to inform the subsequent management of such patients.

Statement of significance: There is a paucity of technologies beyond surgical biopsy that can accurately diagnose central nervous system neoplasms. We developed a novel, sensitive and highly specific assay that can detect brain cancers by comprehensively identifying somatic mutations, chromosomal copy number changes, and adaptive immunoreceptor repertoires from samples of cerebrospinal fluid.

As COVID-19 transitions into endemicity and vaccines are annually updated to circulating SARS-CoV-2 lineages such as JN.1, exposure intervals and immune imprinting become critical considerations for vaccination strategy. Imprinting by the ancestral spike protein has been observed with the bivalent Wuhan-Hu-1/BA.4-5 vaccine and its persistence can be further evaluated in the context of the more recent XBB.1.5 monovalent vaccine. We assessed antibody responses in individuals who received three to four doses of Wuhan-Hu-1, one dose of bivalent Wuhan-Hu-1/BA.4-5, and one dose of XBB.1.5 vaccine (bivalent recipients). We compared these to individuals who received three to four doses of Wuhan-Hu-1 and one dose of XBB.1.5 vaccine without prior bivalent vaccination (bivalent non-recipients). Before XBB.1.5 vaccination, bivalent non-recipients demonstrated decreased breadth and potency of neutralizing antibodies compared to recipients, but at post-vaccination exhibited greater boosting of neutralizing antibodies against XBB.1.5 (18.4X versus 6.2X), EG.5.1 (30.9X versus 7.0X), and JN.1 (9.2X versus 3.7X) variants with trends toward higher neutralizing titers and comparable variant cross-neutralization. Increased boosting in non-recipients were similarly observed for IgA and total IgG/A/M isotypes binding the spike receptor-binding domain but not IgG nor IgM. Bivalent non-recipients had longer intervals between exposures, which has been reported to enhance antibody boosting; however, bivalent receipt and interval were tightly linked variables, preventing the isolation of individual contributions to boosting. Nonetheless, significant "back-boosting" of ancestral SARS-CoV-2 titers upon XBB.1.5 vaccination in both participant groups indicate that immune imprinting continues to affect contemporary vaccines. Altogether, our findings highlight imprinting and exposure intervals as important phenomena underlying variant-adapted COVID-19 vaccine immunogenicity.

Early identification of cerebral palsy (CP) remains a major challenge due to the reliance on expert assessments that are time-intensive and not scalable. Consequently, a range of studies have aimed at using machine learning to predict CP scores based on motion tracking, e.g. from video data. These studies generally predict clinical scores which are a proxy for CP risk. However, clinicians do not REALLY want to estimate scores, they want to estimate the patients' risk of developing clinical symptoms. Here we present a data-driven machine-learning (ML) pipeline that extracts movement features from infant video based motion tracking and estimates CP risk using AutoML. Using AutoSklearn, our framework minimizes risk of overfitting by abstracting away researcher-driver hyperparameter optimization. Trained on movement data from 3- to 4-month-old infants, our classifier predicts a highly indicative clinical score (General Movements Assessment [GMA]) with an ROC-AUC of 0.78 on a held-out test set, indicating that kinematic movement features capture clinically relevant variability. Without retraining, the same model predicts the risk of cerebral palsy outcomes at later clinical follow-ups with an ROC-AUC of 0.74, demonstrating that early motor representations generalize to long-term neurodevelopmental risk. We employ pre-registered lock-box validation to ensure rigorous performance evaluation. This study highlights the potential of AutoML-powered movement analytics for neurodevelopmental screening, demonstrating that data-driven feature extraction from movement trajectories can provide an interpretable and scalable approach to early risk assessment. By integrating pre-trained vision transformers, AutoML-driven model selection, and rigorous validation protocols, this work advances the use of video-derived movement features for scalable, data-driven clinical assessment, demonstrating how computational methods based on readily available data like infant videos can enhance early risk detection in neurodevelopmental disorders.

Background: Prophylactic steroids are often used to reduce the systemic inflammatory response to cardiopulmonary bypass in infants undergoing heart surgery. The STRESS trial found that the likelihood of a worse outcome did not differ between infants randomized to methylprednisolone (n=599) versus placebo (n=601) in a risk-adjusted primary analysis (adjusted odds ratio [OR], 0.86; 95% CI, 0.71 to 1.05; P=0.14). However, secondary analyses showed possible benefits with methylprednisolone. To ensure that a potentially efficacious therapy is not unnecessarily avoided, we re-analyzed the STRESS trial using Bayesian analytics to assess the probability of benefit.

Methods: Our Bayesian analysis used the original STRESS trial primary outcome measure, a hierarchically ranked composite of death, transplant, major complications and post-operative length of stay. We evaluated probability of benefit (OR<1) versus harm (OR>1) by comparing the posterior distribution of the OR assuming a neutral probability of benefit versus harm with weak prior belief strength (nearly non-informative prior distribution). Reference results were calculated under the vague prior distribution. To convey magnitude of effect we used model parameters to calculate a predicted risk of death, transplant or major complications for methylprednisolone and placebo. Analyses consisted of 10 Markov Chain Monte Carlo simulations, each consisting of 2000 iterations with a 1000 iteration burn-in to ensure proper posterior convergence. Sensitivity analyses evaluated pessimistic (5%-30% prior likelihood of benefit), neutral and optimistic (70%-95%) prior beliefs, and controlled strength of prior belief as weak (30% variance), moderate (15%) and strong (5%).

Results: In primary analysis, the posterior probability of benefit from methylprednisolone was 91% and probability of harm was 9%. Composite death or major complication occurred in 18.8% of trial subjects with an absolute risk difference of -2% (95% CI -3%, +1%) associated with methylprednisolone. Each of 9 sensitivity analyses demonstrated greater probability of benefit than harm in the methylprednisolone group with 8 of 9 demonstrating >80% probability of benefit and ≥1% absolute difference in risk of death, transplant or major complications.

Conclusion: Probability of benefit with prophylactic methylprednisolone is high and harm is unlikely. This more in-depth analysis of the data expands the initial clinical evaluation of methylprednisolone provided by the STRESS trial.

Objective: This study aims to evaluate the short form International Physical Activity Questionnaire (IPAQ) for use in women with chronic pelvic pain disorders (CPPDs) by comparing its scores against objectively-estimated physical activity (PA) outcomes. We investigated IPAQ components that are most consistently predictive of habitual PA behavior.

Method: The study sample included 966 weeks of data from 112 women with CPPDs who enrolled in a 14-week mHealth-based self-tracking study. Participants wore Fitbit devices and completed the IPAQ every week. We compared the IPAQ-reported minutes of walking, total activity, sitting, light-, moderate-, and vigorous intensity PA for concordance and divergence against their corresponding Fitbit estimates. We used linear mixed-effects regression models (MLMs) for all analyses and quantified the between-participant variance in the magnitude of agreement between the two methods via random slope terms. We further evaluated temporal consistency in scores using intraclass correlation coefficients (ICCs).

Results: IPAQ-reported walking minutes were strongly associated with Fitbit step counts (B = 3952.36; p = 0.006), minutes of moderate PA (B = 15.498; p = 0.0113), and moderate-to-vigorous PA (MVPA; B = 28.973; p = 0.007). IPAQ total activity minutes were associated with Fitbit minutes of vigorous PA (B = 15.183; p = 0.007) and MVPA (B = 25.658; p = 0.010). IPAQ moderate activity minutes were predictive of Fitbit vigorous PA minutes (B = 9.060; SE = 3.719; p = 0.0151). There was substantial between-individual variance in these point estimates based on the significant random-effect terms, and average weekly PA level was a significant moderator of the association between IPAQ-reported and Fitbit-estimated scores for these variables. IPAQ-reported sitting minutes were inversely associated with Fitbit step counts (B = -3125.61; p = 0.004), and minutes of MVPA (B = -21.848; p = 0.007), vigorous AP (B = -10.854; p = 0.042), and moderate PA (B = -10.985; p = 0.004).

Conclusion: These findings provide support for using IPAQ-reported walking and total activity minutes to monitor several PA domains in women with CPPDs, given their concordance with several tracker-estimated PA outcomes. However, the item on "sitting time" may not be a suitable for assessing sedentary time.

Epigenome-wide association studies (EWAS) profile DNA methylation across the human genome to identify associations with diseases and exposures. Most employ Illumina methylation arrays; this platform, however, under-samples interindividual epigenetic variation. The systemic and stable nature of epigenetic variation at correlated regions of systemic interindividual variation (CoRSIVs) should be advantageous to EWAS. Here, we analyze 2,203 published EWAS to determine whether Illumina probes within CoRSIVs are over-represented in the literature. Enrichment of CoRSIV-overlapping probes was observed for most classes of disease, particularly for neurodevelopmental disorders and type 2 diabetes, indicating an opportunity to improve the power of EWAS by over 200- and over 100-fold, respectively. EWAS targeting all known CoRSIVs should accelerate discovery of associations between individual epigenetic variation and risk of disease.

Pregnant women are at heightened risk for severe outcomes from many infectious diseases, including COVID-19. However, they were not included in the initial COVID-19 vaccine clinical trials and current rates of vaccine uptake among pregnant women remain below 15%. We explored the serological and cellular responses to COVID-19 mRNA booster vaccines (i.e., ancestral and BA.5) in pregnant and age-matched non-pregnant females to identify how pregnancy affects immunity against vaccine viruses in addition to novel variants. Blood was collected from participants prior to and 3-5 weeks post booster vaccine. Post-vaccination antibodies from pregnant women were less cross-reactive to non-vaccine antigens, including XBB1.5 and JN.1, than antibodies from non-pregnant females. Antibodies from non-pregnant females showed strong correlations between IgG1 and FcR binding and greater IgG1:IgG3 ratios and live virus neutralization against all variants. In contrast, antibodies from pregnant women had lower IgG1:IgG3 ratios and neutralization and greater antibody-dependent NK cell cytokine production and neutrophil phagocytosis, particularly against variants. CD4+ T cells from pregnant patients exhibited reduced polyfunctionality, with IFNγ+ monofunctional dominance. In contrast, among non-pregnant women, CD4+ T cells displayed greater polyfunctionality, with more IL-21+ cells. Pregnancy may reduce the breadth, composition, and magnitude of humoral and cellular immunity, particularly in response to novel variants.

Despite considerable advances in identifying risk factors for obesity development, there remains substantial gaps in our knowledge about its etiology. Variation in obesity (defined by BMI) is thought to be due in part to heritable factors; however, obesity-associated genetic variants only account for a small portion of heritability. Epigenetic regulation defined by genetic and/or environmental factors with changes in gene expression, may account for some of this "missing heritability". Epigenetic studies of obesity have largely been conducted in populations of European ancestry, despite the disproportionate burden of obesity in African Americans (AAs). To address race/ethnic (RE)-differences in obesity, we conducted a BMI epigenome-wide association study (EWAS) meta-analysis using AA participants from the Jackson Heart Study (JHS, n=1604) and the Multi-Ethnic Study of Atherosclerosis (MESA, n=179). Analyses using a linear regression model with methylation as the outcome and continuous BMI as the predictor were stratified by study and sex, then meta-analyzed. There were 208 methylation sites (CpGs) that reached epigenome-wide significance (p< 8.72x10 ^-8 ); 151 of these were novel. Of the novel CpGs, 29 CpGs were available for replication testing in a separate sample of AA and 20 replicated. Differentially methylated region (DMR) analysis resulted in 54 DMRs significantly associated with BMI. Several regions are proximal to, or include, genes previously associated with obesity traits (e.g., SOCS3 , ABCG1 , and TGFB1 ) in GWAS. Gene and trait enrichment and pathway analysis showed enrichment for genes in immune system and inflammation related pathways (e.g., the IL-6/JAK/STAT pathway). In conclusion, EWAS of BMI in AAs replicated previously known associations identified in European and multi-ethnic EWAS and identified novel obesity-associated CpGs.