A novel missense variant located within the zinc finger domain of the GLI3 gene was identified in a Vietnamese pedigree with index finger polydactyly.

BACKGROUND: Polydactyly, particularly of the index finger, remains an intriguing anomaly for which no specific gene or locus has been definitively linked to this phenotype. In this study, we conducted an investigation of a three-generation family displaying index finger polydactyly. METHODS: Exome sequencing was conducted on the patient, with a filtration to identify potential causal variation. Validation of the obtained variant was conducted by Sanger sequencing, encompassing all family members. RESULTS: Exome analysis uncovered a novel heterozygous missense variant (c.1482A>T; p.Gln494His) at the zinc finger DNA-binding domain of the GLI3 protein within the proband and all affected family members. Remarkably, the variant was absent in unaffected individuals within the pedigree, underscoring its association with the polydactyly phenotype. Computational analyses revealed that GLI3 p.Gln494His impacts a residue that is highly conserved across species. CONCLUSION: The GLI3 zinc finger DNA-binding region is an essential part of the Sonic hedgehog signaling pathway, orchestrating crucial aspects of embryonic development through the regulation of target gene expression. This novel finding not only contributes valuable insights into the molecular pathways governing polydactyly during embryonic development but also has the potential to enhance diagnostic and screening capabilities for this condition in clinical settings.

Operando Revealing the Crystal Phase Transformation and Electrocatalytic Activity Correlation of MnO2 toward Glycerol Electrooxidation.

In this work, we report for the first time a comprehensive operando investigation of the intricate correlation between dynamic phase evolution and glycerol electrooxidation reaction (GEOR) performance across three primary MnO2 crystallographic phases (α-, ß-, and γ-MnO2). The results showed that all three electrocatalysts exhibited comparable selectivity toward three-carbon products (∼90%), but γ-MnO2 exhibited superior performance, with a low onset potential of ∼1.45 VRHE, the highest current density of ∼1.9 mA cm-2 at 1.85 VRHE, and reasonable stability. Operando Raman spectroscopy revealed the potential-induced surface reconstruction of different MnO2 structures from which a correlation among the applied potential, electrocatalytic activity, and product distribution was identified. The higher the applied potential, the greater conversion from the original structure to δ-MnO2, resulting in lower C-C cleavage and higher 3C product selectivity. This study not only provides a systematic understanding of structure-controlled electrocatalytic activity for high selectivity toward 3C products of MnO2 but also contributes to the development of a non-noble and environmentally friendly catalyst for valorizing glycerol.

The Effects of One-Point Mutation on the New Delhi Metallo Beta-Lactamase-1 Resistance toward Carbapenem Antibiotics and ß-Lactamase Inhibitors: An In Silico Systematic Approach.

Antibiotic resistance has been becoming more and more critical due to bacteria's evolving hydrolysis enzymes. The NDM-1 enzyme could hydrolyze not only carbapenems but also most of ß-lactam's antibiotics and inhibitors. In fact, variant strains could impose a high impact on the resistance of bacteria producing NDM-1. Although previous studies showed the effect of some variants toward antibiotics and inhibitors binding, there has been no research systematically evaluating the effects of alternative one-point mutations on the hydrolysis capacity of NDM-1. This study aims to identify which mutants could increase or decrease the effectiveness of antibiotics and ß-lactamase inhibitors toward bacteria. Firstly, 35 different variants with a high probability of emergence based on the PAM-1 matrix were constructed and then docked with 5 ligands, namely d-captopril, l-captopril, thiorphan, imipenem, and meropenem. The selected complexes underwent molecular dynamics simulation and free energy binding estimation, with the results showing that the substitutions at residues 122 and 124 most influenced the binding ability of NDM-1 toward inhibitors and antibiotics. The H122R mutant decreases the binding ability between d-captopril and NDM-1 and diminishes the effectiveness of this antibiotic toward Enterobacteriaceae. However, the H122R mutant has a contrary impact on thiorphan, which should be tested in vitro and in vivo in further experiments.

Chalcone Derivatives as Potential Inhibitors of P-Glycoprotein and NorA: An In Silico and In Vitro Study.

The human P-glycoprotein (P-gp) and the NorA transporter are the major culprits of multidrug resistance observed in various bacterial strains and cancer cell lines, by extruding drug molecules out of the targeted cells, leading to treatment failures in clinical settings. Inhibiting the activity of these efflux pumps has been a well-known strategy of drug design studies in this regard. In this manuscript, our earlier published machine learning models and homology structures of P-gp and NorA were utilized to screen a chemolibrary of 95 in-house chalcone derivatives, identifying two hit compounds, namely, F88 and F90, as potential modulators of both transporters, whose activity on Staphylococcus aureus strains overexpressing NorA and resistant to ciprofloxacin was subsequently confirmed. The findings of this study are expected to guide future research towards developing novel potent chalconic inhibitors of P-gp and/or NorA.

Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.

Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule. Focal loss function is then applied to the training process to boost classification accuracy of the model. We evaluated our method on the LIDC/IDRI dataset extracted by the LUNA16 challenge. The experiments showed that our deep learning method with focal loss is a high-quality classifier with an accuracy of 97.2%, sensitivity of 96.0%, and specificity of 97.3%.