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Background: Stool-based molecular tests are a noninvasive option for pediatric tuberculosis (TB) diagnosis, but have lower sensitivity compared to sputum-based tests. Untargeted metagenomic sequencing (mNGS) on stool could improve sensitivity and identify new gene targets for molecular testing.

Methods: We performed shotgun mNGS on DNA isolated from stool samples of children undergoing assessment for pulmonary TB in Uganda. We defined the performance of mNGS to identify Mycobacterium tuberculosis ( Mtb ) against a microbiological reference standard (MRS, TB if sputum Xpert Ultra or culture positive) and a composite reference standard (TB if confirmed or unconfirmed TB). We also compared accuracy of mNGS against the stool-based Xpert Ultra test. Finally, we identified enriched genomic loci among Mtb classified reads.

Results: We analyzed 176 stool samples of children with a median age of 3.6 years (IQR, 1-6 years). !"#$%&'(')*(+,-. (')*(&*%&$'$/$'$*&(01(234-(5$')(60&$'$/*(78(9*1$%*9(as ≥ 1, 2, or 5 sequence fragments were 35.5% (95% CI 19%:;;<=.(>;?@<(AB>< : 45%), and 19.4% (13%-25%) respectively, and specificities 92.64% (87%-96%), 97% (93%-99%), and 99.3% (96%-100%). Stool Xpert Ultra had similar sensitivity (22.6%) to stool mNGS considering all samples tested. In a head-to-head comparison, stool mNGS had lower sensitivity than stool Xpert Ultra (38.5% vs. 53.8%, difference -15.3%, 95% CI 14-68 to 25-81). mNGS utilized rRNA, virulence proteins and membrane proteins not targeted in current PCR-based platforms.

Conclusions: Metagenomic sequencing of stool DNA did not increase sensitivity of TB detection, but identified novel targets for molecular testing that may support development of more sensitive tests.

Several lines of evidence suggest that normal-range facial features and nonsyndromic orofacial clefts (OFCs) exhibit a shared genetic basis. Approaches designed to leverage this relationship hold the possibility of revealing new OFC risk loci by boosting discovery power. To test this idea, we applied a pleiotropy-informed GWAS method (cFDR-GWAS) with summary statistics from large, independent European GWASs of normal facial shape (n=4,680; n=3,566) and nonsyndromic cleft lip with or without cleft palate (nsCL/P, n=3,969). The cFDR approach identified 21 independent genomic loci significantly associated with nsCL/P, providing further evidence of the interconnected genetic architecture between these traits. The five original nsCL/P GWAS signals were detected and joined by nine additional loci previously implicated in other OFC association studies. The remaining seven loci represent new nsCL/P genomic regions, and three of these replicated (P < 0.05) in an independent nsCL/P cohort: ASPSCR1, MSX2, and RALYL. A relaxed 10% cFDR-GWAS threshold identified 15 more independent loci with comparable effect sizes to those detected at the strict 5% threshold, two of which replicated: FHOD3 and SMARCA2. Gene expression patterns in major cell types and spatial transcriptomics data highlighted our gene candidates' roles in craniofacial development. In conclusion, the application of an empirical Bayesian strategy to draw on association signals from genetically related traits can boost the power to identify and prioritize OFC risk loci missed by agnostic gene mapping approaches. These results hold promise that the cFDR-GWAS approach may be able to enhance our understanding of the genetic architecture of other structural birth defects.

Cerebrospinal fluid (CSF) biomarkers are central to Alzheimer's disease (AD) diagnosis and research. However, CSF composition is shaped not only by neurodegeneration, but also by underlying physiological and pathological processes that remain poorly characterized. By integrating multi-omics data from the deeply characterized memory-clinic ACE CSF cohort (N=1,372), the Global Neurodegeneration Proteomics Consortium (N=1,863), and publicly available quantitative trait loci data, we reveal that 73.2-85.9% of the molecular variance in CSF omics data is driven by two main factors: one reflecting CSF turnover rate, and another representing blood-brain barrier (BBB) integrity. CSF turnover mainly determines brain-derived molecules, while BBB damage leads to increased blood-derived protein abundance. CSF turnover/clearance severely impacted core AD biomarker levels, affecting the classification of subjects in the A/T framework. Adjusting biomarker levels for OPCML, a novel reference marker, improved biomarker-based prediction of AD progression and removed confounded associations, revealing a proteomic signature of sporadic AD pathology that closely resembles that of autosomal dominant AD. Finally, using the ACE CSF cohort as discovery (N=1,221) and Knight ADRC as replication (N=1,073), we report a curated AD signature comprising 446 unique proteins. Our findings identify CSF dynamics as a major source of molecular variation, reshaping the interpretation of CSF biomarkers.

Prenatal alcohol exposure (PAE) can lead to a range of deficits falling under the umbrella of Fetal Alcohol Spectrum Disorder (FASD), which included higher risk for adverse neurodevelopmental and mental health outcomes. Although the biological mechanisms underlying the link between PAE and mental health remain unclear, DNA methylation (DNAm), an epigenetic modification responsive to environmental exposures, may explain these relationships. Here, we applied a two-sample Mendelian randomization (MR) framework to assess whether DNAm loci previously associated with PAE or FASD are linked to 11 psychiatric outcomes. Using summary statistics from the Genetics of DNA Methylation Consortium (GoDMC) mQTL database and large-scale GWAS, we analyzed DNAm loci from two epigenome-wide association studies: one examining FASD by Lussier et al. (2018) and one examining PAE patterns by Sharp et al. (2018). A total of 106 associations (Lussier) and 28 associations (Sharp) reached nominal significance (p<0.05) and passed sensitivity tests, with several surviving multiple testing correction. Notably, schizophrenia and bipolar disorder had the highest number of associated loci across both studies. Functional analysis showed that DNAm loci were enriched in signaling pathways, embryonic development, and neuron differentiation. Regional enrichment analysis revealed that FASD-related loci were more likely to occur in enhancer and south shore, implicating distal regulatory elements. PAE patterns conferred heterogeneous effects on DNAm and mental health risk, underscoring the complexity of timing-specific epigenetic vulnerability. These findings offer novel insights into the potential mechanism of DNAm linking PAE to mental health, and demonstrate the utility of MR in epigenetic epidemiology.

Purpose: To determine whether significant changes in best-corrected visual acuity (BCVA) precede or coincide with increases in central retinal thickness (CRT) in diabetic macular edema (DME) during a treat-and-extend (T&E) regimen following initial edema resolution.

Methods: This post-hoc analysis included 60 eyes (60 participants) from the READ-3 clinical trial that achieved CRT <250 µm and were followed until edema recurrence. Following a six-month ranibizumab loading phase, patients were monitored through 24 months with as-needed retreatment. Significant changes were defined as ≥4 Early Treatment Diabetic Retinopathy Study (ETDRS) letters and ≥30 µm on time-domain optical coherence tomography (TD-OCT). The temporal relationship between functional (BCVA) and anatomical (CRT) changes was analyzed.

Results: Median time to edema resolution was 10 months (IQR: 6-16) and to recurrence was 3 months (IQR: 2-3). 52 eyes (86.7%) had functional worsening and 43 (71.7%) had anatomical worsening. In 39 eyes exhibiting both types of deterioration, changes were concurrent in 24 (61.5%). Vision loss preceded anatomical recurrence (BCVA-led) in 23.1% of eyes, with a lead time of 1-4 months. Conversely, anatomical thickening preceded vision loss (OCT-led) in 15.4% of eyes, by a maximum of 2 months.

Conclusions: BCVA fluctuations frequently mirror CRT changes and can precede structural relapse, suggesting that BCVA is a sensitive indicator of DME activity. In resource-limited settings, BCVA may allow for earlier detection of recurrence than OCT alone.

Translational relevance: Functional vision loss can precede structural edema recurrence, supporting the potential for home-based BCVA monitoring as a validated bridge for timely clinical intervention in DME.

To evaluate the impact of sex on acute SARS-CoV-2 infection, 668 participants from the ACTIV-2/A5401 study were followed over a 28-day period. A primary analysis was performed on the 469 participants who had quantifiable viral loads at baseline. Male and female participants had comparable nasal SARS-CoV-2 RNA levels at study entry and throughout follow-up. However, sex-specific differences in viral shedding emerged when stratified by duration of symptoms. In the first three days from symptom onset, female participants exhibited higher nasal SARS-CoV-2 RNA levels than males, but lower viral RNA levels thereafter. The higher viral RNA levels in females during the earliest phase of acute COVID-19 was seen even after adjusting for age, race and region of enrollment. Female participants also tended to have higher symptom scores across days since symptom onset but no significant correlation was observed between nasal SARS-CoV-2 RNA levels and symptom score regardless of sex. These findings highlight the impact of sex on both viral shedding and symptom dynamics and underscore the importance of considering time since symptom onset when evaluating respiratory virus antiviral therapies in clinical trials.

In death certificates Entity Axis reflects reported death causes in their original order, and Record Axis reflects standardized re-classifications processed with expert rules. Additionally, while conventional mortality statistics consider a single underlying cause ignoring multiple contributing conditions, weighting schemes may consider all listed causes. We evaluated the impact of re-classification and weighting schemes across all 56,986,831 US death certificates from 2003-2023. ICD-10 codes were mapped to 14 broad disease categories. We recorded the frequency of changes and concordance in reported underlying cause of death between Entity and Record Axes. We considered weighting schemes for attributing mortality burden with Record Axis data: W1 (50% weight to underlying cause, 50% distributed equally among contributing causes), W2 (equal weighting across all causes) and W2A (equal weighting across all causes at ICD-10 level). Entity and Record Axes agreed on underlying cause category in 84.8% and on specific ICD-10 code in 68.9%. Reclassification from Entity Axis to Record Axis markedly increased COVID-19 (+92%) and Transport (+44%) and markedly decreased deaths from Other External Causes (-54%). Weighting schemes substantially altered death burden attribution: e.g. they reduced COVID-19 (-44-63%) and Falls (-46-66%), and changes tended to be more prominent with W2 and W2A than with W1 weighting. Weighting brought death counts per disease category closer to the Entity Axis. Weighting also restored Respiratory seasonality patterns. Systematic differences between reported and re-classified causes of death and weighting schemes for multiple causes profoundly change some disease burden estimates with major implications for resource allocation and public health priorities.

Significance statement: Standardized re-classification processes using expert rules recast the selected causes of death in many death certificates. Moreover, vital statistics typically isolate a single underlying cause, while for many deaths multiple causes jointly lead to demise. Analysis of ∼57 million deaths in the USA (2003-2023) shows that a large proportion of deaths are re-classified by expert rules to different causes than those filled by original certifiers. Analyses that give weight not only the recorded underlying cause but also the other listed causes lead to markedly different estimates of deaths from several diseases. For example, the footprint of COVID-19 fatalities during the pandemic years decreases by 44-63%. Re-classification and weighting schemes may have profound impact on disease burden estimates and policy decisions.

Background: Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and platelet-derived growth factor receptor A (PDGFRA) mutations. Specific variants, such as KIT exon 11 deletions, carry prognostic and therapeutic implications, whereas wild-type (WT) variants derive limited benefit from tyrosine kinase inhibitors (TKIs). Given the limited reproducibility of established clinicopathological risk models, deep learning (DL) applied to whole-slide images (WSIs) emerged as a promising tool for molecular classification and prognostic assessment.

Patients and methods: We analyzed 8398 GIST cases from 21 centers in 7 countries, including 7238 with molecular data and 2638 with clinical follow-up. DL models were trained on WSIs to predict mutations, treatment sensitivity, and recurrence-free survival (RFS).

Results: DL predicted mutational status in GIST from WSIs, with area under the curve (AUC) of 0.87 for KIT , 0.96 for PDGFRA . High performance was observed for subtypes, including KIT exon 11 delinss 557-558 (0.67) and PDGFRA exon 18 D842V (0.93). For therapeutic categories, performance reached 0.84 for avapritinib sensitivity, 0.81 for imatinib sensitivity. DL models predicted RFS, with hazard-ratios (HR) of 8.44 (95%CI 6.14-11.61) in the overall cohort and 4.74 (95%CI 3.34-6.74) in patients receiving adjuvant therapy. Prognostic performance was comparable to pathology-based scores, with highest discrimination in the overall cohort and in patients without adjuvant therapy (9.44, 95%CI (5.87-15.20)).

Conclusion: DL applied to WSIs enables prediction of molecular alterations, treatment sensitivity, and RFS in GIST, performing comparably to established risk scores across international cohorts, providing a baseline for future multimodal predictors.

Highlights: Deep learning on histology predicts KIT and PDGFRA mutations in a large international cohort of GISTs from multiple centers Whole-slide image models stratify recurrence-free survival comparable to pathology-based risk scores Prognostic value of deep learning is preserved in adjuvant therapy subgroups, supporting treatment duration decisions.