The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

A retrospective study on the clinical and molecular outcomes of calpainopathy in a Turkish patient cohort.

Background and aim: Calpainopathy, also known as limb-girdle muscular dystrophy recessive type 1, is a progressive muscle disorder that impacts the muscles around the hips and shoulders. The disease is caused by defects in the CAPN3 gene and can be inherited in both recessive and dominant forms. In this retrospective study, we aimed to evaluate the clinical and molecular results of our patients with calpainopathy and to examine the CAPN3 variants in Turkish and global populations. Materials and methods: Molecular analyses were performed using the next-generation sequencing (NGS) method. CAPN3 variants were identified through the examination of various databases. Results: In this retrospective study, the cohort consisted of seven patients exhibiting the CAPN3 (NM_000070.3) mutation and a phenotype compatible with calpainopathy at a single center in Türkiye. All patients displayed high CK levels and muscle weakness. We report a novel missense c.2437G>A variant that causes the autosomal dominant form of calpainopathy. Interestingly, the muscle biopsy report for the patient with the novel mutation indicated sarcoglycan deficiency. Molecular findings for the remaining individuals in the cohort included a compound heterozygous variant (frameshift and missense), one homozygous nonsense, one homozygous intronic deletion, and three homozygous missense variants. The most common variant in the Turkish population was c.550del. In both populations, pathogenic variants were most frequently located in exon 21, according to exon length. Variants were stochastically distributed based on consequences in CAPN3 domains. Conclusion: Therefore, the NGS method proves highly effective in diagnosing rare diseases characterized by clinical heterogeneity. Assessing variants based on ethnicity holds significance in the development of precise therapies.

Evaluation of chromosomal abnormalities in the postnatal cohort: A single-center study on 14,242 patients.

BACKGROUND AND AIM: Chromosomal analysis is a laboratory technique used to examine the chromosomes of an individual, offering insights into chromosome numbers, structures, and arrangements to diagnose and comprehend genetic diseases. This retrospective study provides a comprehensive understanding of the distribution by indications in a large cohort of 14,242 patients and the frequency of chromosomal abnormalities in different clinical populations. METHOD: The study examined various indications for karyotype evaluation, with recurrent pregnancy loss being the most common indication, followed by intellectual disability, dysmorphic features, congenital anomalies, and developmental delay. RESULTS: The overall chromosomal abnormality rate was found to be 5.4%, with numerical abnormalities accounting for the majority of cases (61.7%). Trisomies, particularly trisomy 21, were the most frequent numerical abnormalities. In terms of structural abnormalities, inversions and translocations were the most commonly identified. The rates of chromosomal anomalies varied in specific indications such as amenorrhea, disorders of sex development, and Turner syndrome. The study also highlighted significant differences between males and females in the presence of chromosomal abnormalities across certain indications. Males exhibited a higher incidence of chromosomal abnormalities in cases of Down syndrome and infertility, whereas females showed higher abnormalities in terms of recurrent pregnancy loss. CONCLUSION: While this study provides valuable insights into the frequency and distribution of chromosomal abnormalities, it has limitations, including its retrospective design and reliance on data from a single medical genetics department. Nevertheless, the findings emphasize the importance of karyotype analysis in diagnosing chromosomal disorders and providing appropriate management, while also pointing to potential gender-related variations in chromosomal abnormalities that warrant further investigation.