AI News Archive: July 6, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- Genetic dose-response modelling predicts drug mechanisms, dosing, and adverse events
Understanding how changes in gene function affect disease risk is central to drug development. Genetic variants are natural perturbations of gene activity and provide an opportunity to systematically model dose-response relationships between genes and phenotypes. Here, we present Variant-Informed Dose-Response Analysis (VIDRA), a computational framework that integrates trait-associated variants spanning a spectrum of allele frequencies and functional consequences within a hierarchical Bayesian regression model to systematically infer genetic dose-response relationships. Using 1,607,115 phenotypically associated germline variants available through Open Targets data (including common trait, rare disease, and gene burden associations), we systematically modelled how genetically driven alterations in gene function relate to disease risk, generating 148,350 dose-response-like gene-phenotype relationships across 7,681 phenotypes. Incorporating rare variant information alongside common variants resulted in a 7.23-fold increase in unique phenotypes, and a 51% increase in gene-disease pair associations. We calibrated our model against known drug targets to derive VIDRA Therapeutic Potential Score to rank genes based on their likelihood of succeeding as therapies, and identified 1,860 significant genes, 87% of which are currently therapeutically not targeted. VIDRA framework captures the direction and magnitude of gene-phenotype dependencies, enabling insights beyond target identification. Sixty-two percent of high-scoring targets are predicted to benefit from agonistic modulation, highlighting untapped potential for therapeutic activation. Furthermore, VIDRA framework extends to modelling relationships between genes, intermediate phenotypes, and diseases, enabling identification of biomarker-disease correlations. Applied to blood-cell and cardiovascular traits, VIDRA recovered known genetic links, suggesting a capacity to identify novel biomarker-disease relationships. Lastly, we observed a positive correlation by comparing VIDRA slopes with drug dose-response data from clinical trials, supporting the use of genetic data as a proxy for pharmacological titration. Together, VIDRA framework provides a generalizable, interpretable approach to inform multiple stages of drug development, from target prioritization and therapeutic direction of modulation prediction to biomarker identification, dose guidance, and safety risk assessment.
- cfDNA concentration as an independent determinant of multi-cancer early detection sensitivity: evidence from a large Indian case-control cohort
Background The relationship between total cell-free DNA (cfDNA) concentration and multi-cancer early detection (MCED) sensitivity is non-obvious on account of competing considerations. On the one hand, this concentration is elevated in cancer and increases in advanced disease, suggesting higher concentrations may be associated with more biologically active tumors that are easier to detect. On the other hand, this elevation is known to be largely leukocyte-derived, which may dilute tumor-derived DNA (ctDNA) and make detection harder. The net direction of these competing effects on detection sensitivity has not been systematically examined. Methods EMERGE is an observational case-control study conducted at 43 Indian sites from June 2022-February 2025. It prospectively enrolled and analyzed 1,030 treatment-naive participants with malignant or benign conditions, most presenting symptomatically, along with 450 controls aged [≥]50 years without prior malignancy. Plasma cfDNA underwent targeted hybrid-capture enzymatic methylation sequencing. Classifiers were trained for cancer detection and tissue-of-origin prediction, and tested on the independent validation set. Primary outcomes were the associations between total cfDNA concentration and (i) detection sensitivity and (ii) tissue-of-origin accuracy, evaluated in an independent validation cohort. Results After adjustment for cancer type, stage, demographic and technical covariates, cfDNA concentration was significantly associated with detection sensitivity (p=6x10-4) but not with tissue-of-origin accuracy (p=0.67). At 0.986 specificity (95% CI: 0.968-1.000), stage I sensitivity rose monotonically from 0.52 (95% CI: 0.34-0.69) in the lowest cfDNA concentration tertile to 0.85 (95% CI: 0.73-0.97) in the highest. This association was mechanistically supported by a region-specific increase in hypermethylation scores within regions identified as differentially hypermethylated in TCGA tumor tissue, while panel-wide scores declined. The dissociation between the concentration-sensitivity and concentration-tissue-of-origin associations, together with inverse or insignificant correlations between ctDNA fraction and cfDNA concentration at early stages in published datasets, suggests that the concentration-sensitivity association is partly independent of ctDNA fraction. Conclusions Total cfDNA concentration is a routinely measured determinant of MCED assay sensitivity, reflecting enrichment of tumor-associated aberrant methylation partly independent of ctDNA fraction, an association likely most pronounced in symptomatic cohorts. Standardized reporting of cfDNA concentration could improve cross-study benchmarking. Study Registration Clinical Trials Registry, India: CTRI2022/05/042936 Keywords Cell-free DNA (cfDNA), Tumor Fraction, Circulating Tumor DNA (ctDNA), Circulating Mutant Allele Frequency (cMAF), Multi-cancer early detection (MCED), cfDNA Concentration, Tissue-of-Origin (TOO), Methylation, Epigenomics
- Correlation Between Clinical Presentation and Brain CT Findings in Acute Dizziness: A Retrospective Cross-Sectional Analysis at a Tertiary Referral Center
Background: Dizziness is a frequent presenting complaint in the emergency department (ED), prompting extensive diagnostic evaluation. Non-contrast brain computed tomography (CT) is often utilized to rule out serious central pathologies, but its diagnostic yield is debated, leading to concerns about overuse. This study aimed to identify clinical predictors associated with abnormal brain CT findings in patients with acute dizziness to help refine imaging selection criteria. Methods: We conducted a retrospective analysis of 291 consecutive adult patients who presented with new-onset dizziness and underwent a non-contrast brain CT scan at Namazi Hospital, a tertiary referral center, between January 2019 and 2021. Patient data, including demographics, comorbidities, clinical symptoms, and hospital outcomes, were extracted from medical records. Statistical analyses were performed to determine associations between clinical variables and CT findings, with odds ratios (OR) and 95% confidence intervals (CI) calculated. Results: The diagnostic yield of brain CT was low, with a significant majority of scans (72.2%, n=210) revealing no acute pathology. Key clinical factors predicting abnormal CT findings included a history of diabetes mellitus, the presence of ataxic gait, and headache. Conversely, nausea and vomiting were significant predictors of normal findings, being associated with lower odds of central pathology. Conclusion: The diagnostic yield of routine brain CT in patients with acute dizziness is low. However, specific clinical indicators can effectively stratify risk. The presence of focal neurological signs like ataxia, headache, and certain comorbidities such as diabetes should heighten suspicion for central pathology and support the use of neuroimaging. In contrast, isolated vestibular symptoms like nausea and vomiting are associated with a lower probability of abnormal findings. These results could inform the development of clinical decision rules to optimize CT utilization, thereby reducing unnecessary radiation exposure and healthcare costs.
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- Expanding the Pediatric Heart Donor Pool: National Outcomes of Donation After Circulatory Death Versus Donation After Brain Death Heart Transplantation
Background Donation after circulatory death (DCD) is an increasingly accepted strategy to expand the adult heart donor pool, but its use in children remains limited and incompletely characterized. We compared national characteristics and post-transplant outcomes of pediatric DCD versus donation after brain death (DBD) heart transplantation. Methods We performed a retrospective cohort study of the Organ Procurement and Transplantation Network (OPTN) registry, including patients younger than 18 years who underwent primary isolated heart transplantation between January 1993 and March 2025. Recipients were stratified by donor type (DCD vs DBD). Continuous variables were compared with the Mann Whitney U test and categorical variables with the Fisher exact test. Survival was estimated by the Kaplan Meier method and compared using the log-rank test and Cox proportional hazards regression. Results Of 10,671 pediatric heart transplant recipients, 33 (approximately 0.3%) received DCD allografts. The first DCD transplant was recorded in 2004, with a marked increase in 2023 to 2024. Compared with DBD recipients, DCD recipients were more frequently infants (<1 year, 51.5% vs 28.4%) and more often had congenital heart disease (69.7% vs 47.6%; P=0.033); DCD donors were younger (median 0 vs 6 years; P=0.038) and more frequently died of anoxia (72.7% vs 37.0%; P<0.001). Donor and recipient left ventricular mass were lower in the DCD group (P<0.05), but predicted left ventricular mass matching was similar. DCD recipients had longer hospital stays (median 31.5 vs 19 days; P=0.023); rates of treated rejection, dialysis, stroke, and pacemaker implantation were comparable. Early survival did not differ (30-day, 90-day, and 1-year), and Kaplan Meier survival through 5 years was not significantly different (hazard ratio 1.17; 95% CI 0.49 to 2.81; log-rank P=0.73). More than 90% of DCD transplants were performed in four UNOS regions (11, 4, 5, and 8). Conclusions In this national analysis, pediatric DCD heart transplantation was uncommon but expanding rapidly, concentrated in a few regions, and used preferentially in infants and children with congenital heart disease. Early post-transplant outcomes were not significantly different from DBD, supporting cautious expansion of DCD as a means of enlarging the pediatric donor pool. The small number of DCD recipients and limited followup warrant confirmation in larger, longer-term studies. Keywords: pediatric heart transplantation; donation after circulatory death; donor pool; congenital heart disease; OPTN registry; organ allocation.
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