Hereditary white matter disease is a series of progressive genetic diseases that mainly affect the white matter of the central nervous system. The development of molecular genetics enables the clinical diagnosis, carrier detection, and prenatal diagnosis of hereditary white matter disease. Here, we block the transmission of pathogenic variants in ABCD1 and NOTCH3 in a family with cerebral white matter disease via preimplantation genetic testing (PGT). Pathogenic genes were identified based on clinical manifestations, genetic background, and the results of targeted gene capture sequencing. A blastocyst biopsy was performed, and multiple annealing and looping-based amplification (MALBAC), next-generation sequencing (NGS), and single nucleotide polymorphism (SNP) arrays were used to analyze ploidy and the state of the gene mutations. The proband (III:1) had hemizygous mutations in ABCD1 (c.323C>A (p.Ser108 *) and c.775C>T (p.Arg259Trp)) and heterozygous mutations in NOTCH3 (c.1630C>T (p.Arg544Cys)), which were maternally inherited (II:2). After genetic analysis, a euploid blastocyst without ABCD1 and NOTCH3 variations was transferred. A healthy male baby was born at full term, and the results of prenatal diagnosis by amniocentesis in the second trimester verified the results of PGT. To our knowledge, this is the first report of simultaneously blocking the transmission of pathogenic variants in ABCD1 and NOTCH3 via PGT. This report highlights the feasibility and effectiveness of PGT in preventing cerebral adrenoleukodystrophy (cALD) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) and provides valuable insights for the diagnosis and treatment of similar cases.

Analytes within liquid biopsies have emerged as promising alternatives to traditional tissue biopsies for various malignancies, including lymphomas. This review explores the clinical applications of one such liquid biopsy analyte, circulating tumor DNA (ctDNA) in different types of lymphoma, focusing on its role in diagnosis, disease monitoring, and relapse detection. Advancements in next-generation sequencing (NGS) and machine learning have enhanced ctDNA analysis, offering a multi-omic approach to understanding tumor genetics. In lymphoma, ctDNA provides insights into tumor heterogeneity, aids in genetic profiling, and predicts treatment response. Recent studies demonstrate the prognostic value of ctDNA and its potential to improve patient outcomes by facilitating early disease detection and personalized treatment strategies Despite these advancements, challenges remain in optimizing sample collection, processing, assay sensitivity, and overall consensus workflows in order to facilitate integration into routine clinical practice.

Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

Tauroursodeoxycholic acid (TUDCA) increases the influx of primary bile acids into the gut. Results obtained on animal models suggested that Firmicutes and Proteobacteria phyla are more resistant to bile acids in rats. As part of a pilot study investigating the role of probiotics supplementation in elderly people with home enteral nutrition (HEN), a case of a 92-year-old woman with HEN is reported in the present study. She lives in a nursing home and suffers from Alzheimer\'s disease (AD); the patient had been prescribed TUDCA for lithiasis cholangitis. The aim of this case report is therefore to investigate whether long-term TUDCA administration may play a role in altering the patient\'s gut microbiota (GM) and the impact of an antibiotic therapy on the diversity of microbial species. Using next generation sequencing (NGS) analysis of the bacterial 16S ribosomal RNA (rRNA) gene a dominant shift toward Firmicutes and a remodeling in Proteobacteria abundance was observed in the woman\'s gut microbiota. Considering the patient\'s age, health status and type of diet, we would have expected to find a GM with a prevalence of Bacteroidetes phylum. This represents the first study investigating the possible TUDCA\'s effect on human GM.

One nucleotide substitution in codon 320 of A*03:01:01:01 results in the novel allele, HLA-A*03:478.

Background Despite advances in chronic myeloid leukemia (CML) genetics, the role of nitric oxide (NO) and hydrogen sulfide (H2S) gene mutations and their relationship to apoptotic genes is unclear. Therefore, this study investigated NO- and H2S-producing genes\' mutations and their interactions with apoptotic genes using Sanger sequencing and next-generation sequencing (NGS). Methodology A complete blood count (CBC) was carried out to measure the total number of white blood cells, while IL-6 levels were assessed in both control and CML patients using an ELISA technique. Sanger sequencing was used to analyze mutations in the CTH and NOS3 genes, whereas NGS was applied to examine mutations on all chromosomes. Results White blood cell (WBC) and granulocyte counts were significantly higher in CML patients compared to controls (p<0.0001), and monocyte counts were similarly higher (p<0.05). Interleukin-6 (IL-6) levels were significantly elevated in CML patients than controls (p<0.0001), indicating a possible link to CML etiology or progression. Multiple mutations have been identified in both genes, notably in CTH exon 12 and the NOS3 genes VNTR, T786C, and G894T. This study also measured IL-6 concentrations using IL-6 assays, identifying its potential as a CML prognostic diagnostic. WBC counts, granulocyte counts, and mid-range absolute counts, or MID counts, were significantly higher in CML patients than in normal control individuals. NGS identified 1643 somatic and sex chromosomal abnormalities and 439 actively expressed genes in CML patients. The findings imply a genomic landscape beyond the BCR-ABL1 mutation in CML development compared to other databases. Conclusion In conclusion, this study advances the understanding of the genetic characteristics of CML by identifying mutations in the NO- and H2S-producing genes and their complex connections with genes involved in apoptosis. The comprehensive genetic profile obtained by Sanger sequencing and NGS provides possibilities for identifying novel targets for therapy and personalized treatments for CML, therefore contributing to developments in hematological diseases.

OBJECTIVE: Genomic surveillance of positive SARS-CoV-2 samples is important to monitor the genetic changes occurring in virus, this was enhanced after the WHO designation of XBB.1.16 as a variant under monitoring in March 2023. From 5th February till May 6, 2023 all positive SARS-CoV-2 samples were monitored for genetic changes.
METHODS: A total of 1757 samples having Ct value <25 (for E and ORF gene) from different districts of Rajasthan were processed for Next Generation Sequencing (NGS). The FASTA files obtained on sequencing were used for lineage determination using Nextclade and phylogenetic tree construction.
CONCLUSIONS: Sequencing and lineage identification was done in 1624 samples. XBB.1.16 was the predominant lineage in 1413 (87.0%) cases while rest was other XBB (207, 12.74%) and other lineages (4, 0.2%). Of the 1413 XBB.1.16 cases, 57.47% were males and 42.53% were females. Majority (66.53%) belonged to 19-59 year age. 84.15% of XBB.1.16 cases were infected for the first time. Hospitalization was required in only 2.2% cases and death was reported in 5 (0.35%) patients. Most of the cases were symptomatic and the commonest symptoms were fever, cough and rhinorrhea. Co-morbidities were present in 414 (29.3%) cases. Enhanced genomic surveillance helped to rapidly identify the spread of XBB variant in Rajasthan. This in turn helped to take control measures to prevent spread of virus and estimate public health risks of the new variant relative to the previously circulating lineages. XBB variant was found to spread rapidly but produced milder disease.

Low-pass whole genome sequencing (LP-WGS) has been applied as alternative method to detect copy number variants (CNVs) in the clinical setting. Compared with chromosomal microarray analysis (CMA), the sequencing-based approach provides a similar resolution of CNV detection at a lower cost. In this study, we assessed the efficiency and reliability of LP-WGS as a more affordable alternative to CMA. A total of 1363 patients with unexplained neurodevelopmental delay/intellectual disability, autism spectrum disorders, and/or multiple congenital anomalies were enrolled. Those patients were referred from 15 nonprofit organizations and university centers located in different states in Brazil. The analysis of LP-WGS at 1x coverage (>50kb) revealed a positive testing result in 22% of the cases (304/1363), in which 219 and 85 correspond to pathogenic/likely pathogenic (P/LP) CNVs and variants of uncertain significance (VUS), respectively. The 16% (219/1363) diagnostic yield observed in our cohort is comparable to the 15%-20% reported for CMA in the literature. The use of commercial software, as demonstrated in this study, simplifies the implementation of the test in clinical settings. Particularly for countries like Brazil, where the cost of CMA presents a substantial barrier to most of the population, LP-WGS emerges as a cost-effective alternative for investigating copy number changes in cytogenetics.

The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurate genetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from hematoxylin and eosin (H&E)-stained images has been found to vary between pathologists, making it an important challenge to obtain an accurate TCR. In this study, three pathologists exhaustively labeled all cells in 41 regions from 41 lung cancer cases as either tumor, non-tumor or indistinguishable, thus establishing a \"gold standard\" TCR. We then compared the accuracy of the TCR estimated by 13 pathologists based on visual assessment and the TCR calculated by an AI model that we have developed. It is a compact and fast model that follows a fully convolutional neural network architecture and produces cell detection maps which can be efficiently post-processed to obtain tumor and non-tumor cell counts from which TCR is calculated. Its raw cell detection accuracy is 92% while its classification accuracy is 84%. The results show that the error between the gold standard TCR and the AI calculation was significantly smaller than that between the gold standard TCR and the pathologist\'s visual assessment (p<0.05). Additionally, the robustness of AI models across institutions is a key issue and we demonstrate that the variation in AI was smaller than that in the average of pathologists when evaluated by institution. These findings suggest that the accuracy of tumor cellularity assessments in clinical workflows is significantly improved by the introduction of robust AI models, leading to more efficient genetic testing and ultimately to better patient outcomes.