👤 Constantin Adrian Oprea

The relationship between observed clinical phenotypes and underlying genotypes is blended or skewed in multiple molecular diagnoses, complicating a comprehensive molecular genetic diagnosis. We report two families with dual diagnoses, using the deafness-associated gene, COL4A6, to exemplify its contribution to blended, complex clinical presentations. This is an observational study within a large, ethnically diverse rare disease cohort, focusing on families with hearing loss and suspected dual diagnoses, followed by functional and structural studies of novel variants. Families were identified through a large rare disease sequencing initiative. Exome or genome sequencing was performed, with follow-up RNA studies for a synonymous COL4A6 variant. Spatial and temporal expression analysis in zebrafish traced col4a6 expression in the otic vesicle and ear from 1 to 5 days post-fertilization. Structural modeling was used to estimate variant impact on protein structure. We identified two families affected by multiple genetic disorders. The first family presented a missense COL4A6 variant (NM₀₃₃₆₄₁.4: c.1480G>A p.(Gly494Arg)), accounting for hearing loss, while a likely pathogenic HEXA variant (NM₀₀₀₅₂₀.6: c.902T>G p.(Met301Arg)) explained Tay-Sachs disease features. The second family exhibited a synonymous COL4A6 variant (NM₀₃₃₆₄₁.4: c.1767G>A p.(Pro589=)), leading to partial exon skipping and hearing loss, along with a pathogenic splice-site variant in DYM (NM₀₀₁₃₅₃₂₁₄.3: c.1125 + 1G>T p.?), causing the Dyggve-Melchior-Clausen disease. Our findings highlight the importance of recognizing dual molecular diagnoses to untangle blended phenotypes, as well as the diagnostic relevance of synonymous variants with predicted splicing effects. Show less

As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like "system" and "energy" are among the most frequent, signaling that Topic 1 revolves around Show less

SARS-CoV-2 infection is a significant health concern that needs to be addressed not only during the initial phase of infection but also after hospitalization. This is the consequence of the various pathologies associated with long COVID-19, which are still being studied and researched. Lung fibrosis is an important complication after COVID-19, found in up to 71% of patients after discharge. Our research is based on scientific articles indexed in PubMed; in the selection process, we used the following keywords: "lung fibrosis", "fibrosis mediators", "fibrosis predictors", "COVID-19", "SARS-CoV-2 infection", and "long COVID-19". In this narrative review, we aimed to discuss the current understanding of the mechanisms of initiation and progression of post-COVID-19 lung fibrosis (PC-19-LF) and the risk factors for its occurrence. The pathogenesis of pulmonary fibrosis involves various mediators such as TGF-β, legumain, osteopontin, IL-4, IL-6, IL-13, IL-17, TNF-α, Gal-1, Gal-3, PDGF, and FGFR-1. The key cellular effectors involved in COVID-19 lung fibrosis are macrophages, epithelial alveolar cells, neutrophils, and fibroblasts. The main fibrosis pathways in SARS-CoV-2 infection include hypoxemia-induced fibrosis, macrophage-induced fibrosis, and viral-fibroblast interaction-induced fibrosis. Show less

Proximal spinal muscular atrophy (SMA) is a neuromuscular disorder caused by homozygous mutations of the SMN1 gene. SMN1 interacts with multiple proteins with functions in snRNP biogenesis, pre-mRNA splicing and presumably neural transport. SMN2, a nearly identical copy of SMN1, produces predominantly exon 7-skipped transcripts, whereas SMN1 mainly produces full-length transcripts. The SR-like splicing factor Htra2-beta1 facilitates correct splicing of SMN2 exon 7 through direct interaction with an exonic splicing enhancer within exon 7. In rare cases, siblings with identical 5q13-homologues and homozygous absence of SMN1 show variable phenotypes, suggesting that SMA is modified by other factors. By analysing nine SMA discordant families, we demonstrate that in all families unaffected siblings produce significantly higher amounts of SMN, Gemin2, Gemin3, ZPR1 and hnRNP-Q protein in lymphoblastoid cell lines, but not in primary fibroblasts, compared with their affected siblings. Protein p53, an additional SMN-interacting protein, is not subject to an SMN-dependent regulation. Surprisingly, Htra2-beta1 is also regulated by this tissue-specific mechanism. A similar regulation was found in all type I-III SMA patients, although at a different protein level than in discordant families. Thus, our data show that reduced SMN protein levels cause a reduction in the amount of its interacting proteins and of Htra2-beta1 in both discordant and non-discordant SMA families. We provide evidence that an intrinsic SMA modifying factor acts directly on the expression of SMN, thus influencing the SMA phenotype. Further insights into the molecular pathway and the identification of SMA modifying gene(s) may help to find additional targets for a therapy approach. Show less