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Background: Reproductive tract infections (RTIs) including sexually transmitted infections (STIs), endogenous and iatrogenic infections are a major health concern globally, particularly among young women in developing nations. If untreated, they can lead to infertility, increased HIV susceptibility, and cervical cancer. This study examines RTI symptoms prevalence, risk factors, and hesitancy toward STI vaccination.

Methods: A cross-sectional study was conducted among 1,920 urban women (predominantly aged 18-25) in Delhi/NCR using a structured questionnaire. Data on demographics, reproductive health, RTI symptoms, and vaccine awareness were analyzed using SPSS 20. Chi-square tests, Fisher's exact tests, and Bonferroni corrections were applied, with multinomial logistic regression identifying RTI risk predictors.

Results: Among respondents, 1,240 (64.6%) reported RTI symptoms, with 1,029 (83%) experiencing vaginal discharge and 974 (78.5%) vulval itching. Significant risk factors included early menarche, irregular menstrual cycles, poor menstrual hygiene, and prior RTI medication use. Additionally, 1,033 (53.8%) were hesitant about STI vaccination, citing safety concerns (36%, 372) and cost (18.6%, 192). However, 826 (43%) reported that healthcare provider recommendations positively influenced their decision to vaccinate.

Conclusion: The high prevalence of RTI symptoms highlights the urgent need for improved awareness, accessible healthcare services, and stronger vaccination promotion efforts to enhance RTI prevention and management.

Introduction: Little is known about how Alzheimer's disease (AD) plasma biomarkers relate to cerebral small vessel disease (cSVD) neuroimaging biomarkers.

Methods: The study involved 251 Wake Forest Multi-Ethnic Study of Atherosclerosis (MESA) Exam 6 participants with plasma AD biomarkers, MRI, amyloid PET, and adjudicated cognitive status. Multivariable models examined cross-sectional relationships between plasma and neuroimaging biomarkers, considering comorbidities.

Results: Lower Aβ42/Aβ40, and higher GFAP, NfL, and p-tau217 were associated with greater neurodegeneration. Lower plasma Aβ42/Aβ40 and higher p-tau217 and p-tau231 were associated with greater Aβ PET deposition. NfL was positively associated with WMH and WM Free Water. P-tau measures were positively associated with WM Free Water. Lower Aβ42/Aβ40 was associated with presence of microbleeds. GFAP was positively associated with WMH.

Discussion: We observed expected associations of plasma biomarkers with cognitive status and imaging biomarkers. GFAP, NfL, p-tau181, p-tau217, and p-tau231 are associated with cSVD in addition to AD-related pathology.

Mutations and multiplications in the SNCA gene, encoding alpha-synuclein (aSyn), are associated with familial forms of Parkinson's disease (PD). We report the identification of a novel SNCA missense mutation (NM_000345.4, cDNA 174G>C; protein K58N) in a PD patient using whole exome sequencing, and describe comprehensive molecular and cellular analysss of the effects of this novel mutation. The patient exhibited typical sporadic PD with early onset and a benign disease course. Biophysical studies revealed that the K58N substitution causes local structural effects, disrupts binding to membranes, and enhances aSyn in vitro aggregation. K58N aSyn produces fewer inclusions per cell, and fails to undergo condensate formation. The mutation increases the cytoplasmic distribution of the protein, and has minimal effect on the dynamic reversibility of serine-129 phosphorylation. In total, the identification of this novel mutation advances our understanding of aSyn biology and pathobiology.

Background: Osteoradionecrosis of the jaw (ORNJ) is a debilitating complication that affects up to 15% of head and neck cancer patients who undergo radiotherapy. The ASCO/ISOO/MASCC-endorsed ClinRad severity classification system was recently proposed (and recommended in the latest ASCO guidelines) to incorporate radiographic findings for determining ORNJ severity based on the vertical extent of bone necrosis. However, variability in imaging modalities and specialty-specific knowledge may contribute to disparities in diagnosing and classifying ORNJ. This study aims to evaluate and benchmark multi-specialty physician performance in diagnosing and severity classification of ORNJ using different radiographic imaging.

Methods: A single institution retrospective diagnostic validation study was conducted at The University of Texas MD Anderson Cancer Center involving 20 healthcare providers across varying specialties including oral oncology, radiation oncology, surgery, and neuroradiology. Participants reviewed 85 de-identified imaging sets including computed tomography (CT) and orthopantomogram (OPG) images from 30 patients with confirmed ORN, with blinded replicates (n=10) for assessment of intra-observer variability and asked to diagnose and stage ORNJ using the ClinRad system. Diagnostic performance was assessed using ROC curves; intra- and inter-observer agreement were measured with Cohen's and Fleiss kappa, respectively. Sub-analyses considered physician specialty, years of clinical experience and level of confidence.

Results: Paired CT-OPG imaging improved ORNJ diagnostic performance across all specialties, with AUC values ranging from 0.79 (residents) to 0.98 (surgeons). Inter- and intra-rater agreements for ORNJ detection were limited, with median (IQR) Fleiss and Cohen's kappa values of 0.38 (0.22) and 0.08 (0.17), respectively. Slight to fair inter-rater agreement in severity classification ORNJ was observed with median (IQR) Fleiss kappa values of 0.22, 0.13, and 0.05 for stages 0/1, 2, and 3, respectively. The most commonly reported radiographic features for confirmed ORNJ cases staged as ClinRad grade 1 or 2 were "bone necrosis confined to alveolar bone" (22.7%), "bone necrosis involving the basilar bone or maxillary sinus" (14.8%), and "bone lysis/sclerosis" (20.0%).

Conclusion: This study establishes an essential benchmark for physician detection of radiographic ORNJ. The significant variability in diagnostic and severity classification observed across specialties emphasizes the need for standardized imaging protocols and specialist training as well as highlights the value of multimodality imaging.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and healthcare utilization, with frequent exacerbations contributing to emergency department visits and hospitalizations. This study evaluates a multimodal, community-based digital health intervention's association with changes in acute care utilization among patients with COPD to develop preliminary estimates of intervention effects.

Methods: In this decentralized, nonrandomized pilot clinical trial, participants with moderate to severe COPD were offered biometric monitoring, symptom tracking, on-demand MIH services, and a digital pulmonary rehabilitation program. Outcomes were compared between intervention participants and a weighted synthetic control group using full optimal matching. Weighted odds ratios derived from regression models were used to estimate intervention effect size. The primary outcome was hospitalization during the study period. Secondary outcomes included 30 and 90-day readmission rates, emergency department visits, and hospital length of stay.

Results: In total, 88 participants from the intervention arm (mean age 67, 50% female) were compared to a weighted synthetic control of 14,492 participants (weighted mean age 66, 48.7% female). We observed that participants in the intervention arm had a trend toward decreased hospitalization with an OR of 0.69 (CI 0.44-1.03, p=0.066). The intervention was also associated with 61% decreased odds of 30-day readmission after an index admission compared to controls (OR: 0.39, 95% CI: 0.16-0.95, p = 0.04). Trends toward reductions in ED visits and hospital length of stay were also observed.

Conclusions: A combined digital and mobile health approach to COPD management was associated with reductions in acute care utilization. These findings support further investigation into hybrid care models to enhance COPD self-management and improve patient outcomes. Future research should evaluate scalability, cost-effectiveness, and long-term clinical impact.

To identify biomarkers and pathways to Type-2 diabetes (T2D), a major global disease, we completed array-based epigenome-wide association in whole blood in 5,709 Asian people. We found 323 Sentinel CpGs (from 314 genetic loci) that predict future T2D. The CpGs reveal coherent, nuclear regulatory disturbances in canonical immune activation pathways, as well as metabolic networks involved in insulin signalling, fatty acid metabolism and lipid transport, which are causally linked to development of T2D. The CpGs have potential clinical utility as biomarkers. An array-based composite Methylation Risk Score (MRS) is predictive for future T2D (RR: 5.2 in Q4 vs Q1; P=7×10^-25), and is additive to genetic risk. Targeted methylation sequencing revealed multiple additional CpGs predicting T2D, and synthesis of a sequencing-based MRS that is strongly predictive for T2D (RR: 8.3 in Q4 vs Q1; P=1.0×10^-11). Importantly, MRS varies between Asian ethnic groups, in a way that explains a large fraction of the difference in T2D risk between populations. We thus provide new insights into the nuclear regulatory disturbances that precede development of T2D, and reveal the potential for sequence-based DNA methylation markers to inform risk stratification in diabetes prevention.

While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects. Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet's value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.

Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents another variant from a different locus from manifesting its effects. The potential effects of genetic variant interactions on complex diseases have been recognized for the past decades. Moreover, It has been studied and demonstrated that leveraging the combined SNP effects within the genetic block can significantly increase calculation power, reducing background noise, ultimately leading to novel epistasis discovery that the single SNP statistical epistasis study might overlook. However, it is still an open question how we can best combine gene structure representation modelling and interaction learning into an end-to-end model for gene interaction searching. Here, in the current study, we developed a neural genetic block interaction searching model that can effectively process large SNP chip inputs and output the potential genetic block interaction heatmap. Our model augments a previously published hierarchical transformer architecture (Liu and Lapata, 2019) with the ability to model genetic blocks. The cross-block relationship mapping was achieved via a hierarchical attention mechanism which allows the sharing of information regarding specific phenotypes, as opposed to simple unsupervised dimensionality reduction methods e.g. PCA. Results on both simulation and UK Biobank studies show our model brings substantial improvements compared to traditional exhaustive searching and neural network methods.

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data from the multicenter Enhancing Neuro-Imaging and Genetics through Meta-Analysis (ENIGMA)-OCD consortium. Baseline clinical and resting-state functional magnetic imaging (rs-fMRI) data from 159 adult patients aged 18-60 years (88 female) with OCD who received CBT at four treatment/neuroimaging sites were included. Fractional amplitude of low frequency fluctuations, regional homogeneity and atlas-based functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p=0.001). Lower baseline symptom severity, younger age, an absence of cleaning obsessions, unmedicated status, and higher education had the highest model impact in predicting remission. The best predictive performance using only rs-fMRI was obtained with regional homogeneity for remission (AUC=0.59). Predicting response with rsf-MRI generally did not exceed chance level. Machine learning models based on clinical data may thus hold promise in predicting remission after CBT for OCD, but the predictive power of multicenter rs-fMRI data is limited.

Background: Existing polygenic scores (PGS) are derived primarily from studies performed in European populations. It is still unclear how these perform in improving risk predictions in East-Asians.

Methods: We generated 2,173 PGSs from 519 traits and assessed their associations with 58 baseline phenotypes in the Singapore Chinese Health Study (SCHS), a prospective cohort of 23,622 middle-aged and older Chinese residing in Singapore. We used linear regression to evaluate PGS performances for quantitative traits by calculating the explained variance (r²). For dichotomized phenotypes, we employed logistic regression to estimate the area under the receiver operating characteristic curve (AUC) in predictive models.

Results: Overall, traits with higher heritability scores exhibited stronger associations with PGSs, while behavioural traits, for example sleep duration and hours spent watching TV, showed weaker associations. Height and type 2 diabetes (T2D) exhibited the largest SNP-based heritability estimates with the largest increments in explained variance and AUC, respectively, compared to phenotypic models. We explored the effect of T2D risk factors on the association between the T2D PGS (PGS003444) and incident T2D. The PGS association was significantly mediated and modified by hypertension ( P _indirect =1.56×10 ^-18 , P _interaction =1.11×10 ^-6 ) and body mass index (BMI, P _indirect =1.25×10 ^-36 , P _interaction =2.10×10 ^-3 ). The prediction ability of PGS003444 for incident T2D was stronger was stronger among individuals who were non-overweight without hypertension (AUC=0.774) than in overweight individuals with hypertension (AUC=0.709).

Conclusions: In conclusion, our study demonstrated the divergent ability of PGSs in predictions of complex traits, and showed that for certain traits, such as T2D, PGSs may have the potential for improving risk prediction and personalized healthcare.