Our aim was to evaluate the concordance between the Myriad MyChoice and two alternative homologous recombination deficiency (HRD) assays (AmoyDx HRD Focus NGS Panel and OncoScan™) in patients with epithelial ovarian cancer (EOC). Tissue samples from 50 patients with newly diagnosed EOC and known Myriad MyChoice HRD status were included. DNA aliquots from tumor samples, previously evaluated with Myriad MyChoice and centrally reassessed, were distributed to laboratories to assess their HRD status using the two platforms, after being blinded for the Myriad MyChoice CDx HRD status. The primary endpoint was the concordance between Myriad MyChoice and each alternative assay. Tumor samples were evaluated with an AmoyDx^® HRD Focus Panel (n = 50) and with OncoScan™ (n = 43). Both platforms provided results for all tumors. Analysis showed that correlation was high for the Myriad MyChoice GI score and AmoyDx^® HRD Focus Panel (r = 0.79) or OncoScan™ (r = 0.87) (continuous variable). The overall percent agreement (OPA) between Myriad MyChoice GI status (categorical variable) and each alternative assay was 83.3% (68.6–93.3%) with AmoyDx and 77.5% (61.5–89.2%) with OncoScan™. The OPA in HRD status between Myriad MyChoice and AmoyDx was 88.6% (75.4–96.2). False-positive rates were 31.6% (6/19) for AmoyDx GI status and 31.9% (7/22) for OncoScan™, while false-negative rates were 0% (0/28, AmoyDx) and 11.1% (2/18, OncoScan™) compared with the Myriad MyChoice GI status. While substantial concordance between Myriad MyChoice and alternative assays was demonstrated, prospective validation of the analytical performance and clinical relevance of these assays is warranted.

Objective: Despite the significant advances in healthcare, people living with HIV still face challenges that affect their quality of life (QoL), both in terms of their physical state as represented by frailty and of their illness perceptions (IP). The aim of this study was to unravel the associations between these constructs (QoL, frailty, IP). Methods: This multicenter, cross-sectional study included 477 people living with HIV (93% male; median age = 43 years, IQR = 51.7) from six HIV clinics in Greece. Frailty phenotype, QoL and IP were assessed using Fried’s criteria, EuroQoL (EQ-5D-5L) and Brief Illness Perception Questionnaire (BIPQ), respectively. Network analysis model was utilized. Results: Among frailty criteria, exhaustion had the highest expected influence, while the strongest correlation concerns exhaustion and weak grip strength (pr = 0.14). Regarding the QoL items, usual activities displayed the highest expected influence. The correlations of pain/discomfort with mobility (pr = 0.31), and usual activities with self-care (pr = 0.34) were the strongest. For the BIPQ items, the strongest correlation was found between illness concern and emotional response (pr = 0.45), whereas the latter item was the one that displayed the highest expected influence. Three communities were formed: 1) personal control, treatment control and coherence, 2) the frailty items with mobility, self-care, usual activities, and pain/discomfort, and 3) the rest BIPQ items with anxiety/depression. Identity displayed the highest bridge strength, followed by pain/discomfort, usual activities and consequences. Conclusions: The interplay between QoL, frailty, and IP in people living with HIV requires clinical attention. Self-reported exhaustion, slow walking speed, and low physical activity affect the physical QoL dimensions, while anxiety/depression is strongly associated with illness-related concern and perceived emotional effects, leading to psychological distress. Symptom management can improve QoL, and information on the disease and treatment can enhance control over the disease. Developing interventions to address QoL, frailty, and IP is crucial.

Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests co-expression and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. Availability and Implementation: APNet’s R, Python scripts and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet Contact: ggeorav@certh.gr Supplementary information: Supplementary information can be accessed in Zenodo (10.5281/zenodo.10438830).