Background: Spinal muscular atrophy (SMA) is an autosomal recessive disease caused by mutations and deletions in SMN1 at exon 7. The carrier frequency for SMN1 mutations ranges from 2 to 4% in the general population.Methods: We examined allelic, genotypic relatedness and copy number (CN) variations and frequencies of SMN1 and SMN2, in 13,426 samples from Qatar biobank (QBB) to provide a precise estimation of SMA carrier frequency in Qatar in comparison to other populations.Results: The SMA carrier frequency was found to be (2.8%) and the rs143838139 was found in 491/13426 (3.66%) of individuals. The SNP rs121909192, which is a pathogenic risk factor, was found in 321/13500 (2.38%). In Addition 242/11379 (2.13%) had two copies of SMN1 and the rs143838139, which may explain the (2 + 0) silent carrier. Additionally, two participants were found to be SMA type 4 with 0 and 4 copy numbers in SMN1 and SMN2, respectively.Conclusion: The SMA carrier frequency in Qatar was found to be comparable to Saudi Arabia and Caucasians. The likely pathogenic variant, rs121909192, was found to be significantly higher when compering with other in our study. The rs143838139 variant, which has a strong association with the silent carrier genotype, has been found. Consequently, testing for this SNP may enhance the precision of evaluating the likelihood of a patient having an affected child. We conclude that the frequency of SMA carriers varies within the Qatar population and other ethnic groups.

Precision Medicine (PM) is a transformative clinical medicine strategy that aims to revolutionize healthcare by leveraging biological information and biomarkers. In the context of maternal and neonatal health, PM enables personalized care from preconception through the postnatal period. Qatar has emerged as a key player in PM research, with dedicated programs driving advancements and translating cutting-edge research into clinical applications. This article delves into neonatal and maternal health in Qatar, emphasizing PM programs and initiatives that have been implemented. It also features noteworthy clinical cases that demonstrate the effectiveness of precision interventions. Furthermore, the article highlights the role of pharmacogenomics in addressing various maternal health conditions. The review further explores potential advancements in the application of PM in maternal and neonatal healthcare in Qatar.

Transition probabilities of intermediate-spin yrast and non-yrast excitations in ${}^{80,82}\mathrm{Se}$ were investigated in a recoil distance Doppler-shift (RDDS) experiment performed at the Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Legnaro. The Cologne Plunger device for deep inelastic scattering was used for the RDDS technique and was combined with the AGATA Demonstrator array for the $\gamma$-ray detection and coupled to the PRISMA magnetic spectrometer for an event-by-event particle identification. In ${}^{80}\mathrm{Se}$, the level lifetimes of the yrast ($6_1^{+}$) and ($8_1^{+}$) states and of a non-yrast band feeding the yrast $4_1^{+}$ state are determined. A spin and parity assignment of the head of this sideband is discussed based on the experimental results and supported by large-scale shell-model calculations. In ${}^{82}\mathrm{Se}$, the level lifetimes of the yrast $6_1^{+}$ state and the yrare $4_2^{+}$ state and lifetime limits of the yrast (${10}_1^{+}$) state and of the $5_1^{-}$ state are determined. Although the experimental results contain large uncertainties, they are interpreted with care in terms of large-scale shell-model calculations using the effective interactions JUN45 and jj44b. The excited states' wave functions are investigated and discussed with respect to the role of the neutron $g_{9/2}$ orbital.

METHODOLOGICAL APPROACH AND THEORETICAL CONCEPT FOR MEASURING HOSPITAL TECHNICAL EFFICIENCY

Background: Biotinidase deficiency (BD) is a common autosomal recessive metabolic disorder in Qatar and the Arab world. It is treatable if detected early, making it essential to understand the genetic variants involved. This study aimed to investigate the carrier frequency of BD-related variants in a healthy Qatari population, reflecting the genetic landscape of the broader Middle Eastern region; classify them using bioinformatics tools; and compare findings with global datasets. Methods: Whole-genome sequencing data from 14,669 participants in the Qatar Genome Program (QGP), a multiethnic cohort including Qatari nationals and long-term residents (≥15 years), were analyzed to identify BTD variants. A total of 723, including 653 single-nucleotide polymorphisms (SNPs) and 70 structural variants (SVs) in BTD associated with BD, were screened against the Qatari cohort and compared with international data. In silico tools were used to assess variant pathogenicity, conservation, and protein stability. Molecular dynamics (MD) simulations were performed to evaluate structural and functional changes in the BTD. Results: A total of 80 SNPs and 3 SVs were identified, among which 21 variants (19 SNPs and 2 SVs) were classified as pathogenic or likely pathogenic, according to ClinVar. The carrier frequency of BTD-related variants in Qatar was 1:20, primarily driven by rs13078881 (D444H). Molecular dynamics (MD) simulations revealed significant conformational changes with H323R, D444H, and P497S, which demonstrated increased flexibility (higher RMSD/RMSF and PCA trace values). Additionally, R209C and D444H showed reduced compactness (higher Rg) and distinct energy minima, suggesting altered conformational states. Conclusions: This study demonstrates a high carrier frequency of pathogenic BTD variants in the Qatari population, underscoring the need to integrate these SNPs and SVs into the national genomic neonatal screening program (gNBS) for enhanced early detection and treatment strategies. The mild structural deviations observed in the D444H mutant through MD simulations may explain its association with milder clinical phenotypes of BTD, offering valuable insights for personalized therapeutic approaches.

Background: Variants of uncertain significance (VUS) in the LDLR gene remain a major barrier to the molecular diagnosis of familial hypercholesterolemia. Although computational approaches offer scalable prioritization, their clinical utility is limited by predictor discordance, incomplete annotations, and inflated performance arising from variant-type imbalance. We aimed to develop a calibrated, uncertainty-aware machine learning framework for LDLR VUS triage. Methods: We developed an end-to-end pipeline integrating multi-source variant annotations and 45 engineered features (expanded to 68 encoded features) to train RandomForest, XGBoost, and support vector machine models. The framework incorporates predictor concordance analysis, splice-aware annotation, and explicit uncertainty modeling. Models were trained using nested cross-validation and evaluated on an independent ClinGen FH-VCEP expert-panel dataset. Results: The XGBoost model achieved high discriminative performance (ROC-AUC up to 0.99 for all variants and ~0.97 for missense variants). Among 1092 LDLR VUS, 69% were assigned a directional classification, including 485 (44.4%) pathogenic-leaning and 268 (24.5%) benign-leaning variants. The remaining 31% were explicitly categorized as predictor-discordant (n = 170, 15.6%) or unresolved (n = 169, 15.5%), preserving uncertainty. Predictor discordance was particularly enriched among missense variants, indicating that naive aggregation of in silico predictors may lead to overclassification. Conclusions: This framework provides a scalable, transparent, and clinically aligned approach for LDLR VUS prioritization, generating ACMG/AMP-compatible computational evidence to support expert curation and downstream functional validation.