Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management.

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

A Splicing Variant in RDH8 Is Associated with Autosomal Recessive Stargardt Macular Dystrophy.

Stargardt macular dystrophy is a genetic disorder, but in many cases, the causative gene remains unrevealed. Through a combined approach (whole-exome sequencing and phenotype/family-driven filtering algorithm) and a multilevel validation (international database searching, prediction scores calculation, splicing analysis assay, segregation analyses), a biallelic mutation in the RDH8 gene was identified to be responsible for Stargardt macular dystrophy in a consanguineous Italian family. This paper is a report on the first family in which a biallelic deleterious mutation in RDH8 is detected. The disease phenotype is consistent with the expected phenotype hypothesized in previous studies on murine models. The application of the combined approach to genetic data and the multilevel validation allowed the identification of a splicing mutation in a gene that has never been reported before in human disorders.

Analysis of Genetic Variants Associated with COVID-19 Outcome Highlights Different Distributions among Populations.

The clinical spectrum of SARS-CoV-2 infection ranges from asymptomatic status to mild infections, to severe disease and death. In this context, the identification of specific susceptibility factors is crucial to detect people at the higher risk of severe disease and improve the outcome of COVID-19 treatment. Several studies identified genetic variants conferring higher risk of SARS-CoV-2 infection and COVID-19 severity. The present study explored their genetic distribution among different populations (AFR, EAS, EUR and SAS). As a result, the obtained data support the existence of a genetic basis for the observed variability among populations, in terms of SARS-CoV-2 infection and disease outcomes. The comparison of ORs distribution for genetic risk of infection as well as for disease outcome shows that each population presents its own characteristics. These data suggest that each country could benefit from a population-wide risk assessment, aimed to personalize the national vaccine programs and the preventative measures as well as the allocation of resources and the access to proper therapeutic interventions. Moreover, the host genetics should be further investigated in order to realize personalized medicine protocols tailored to improve the management of patients suffering from COVID-19.

Identification of Genetic Networks Reveals Complex Associations and Risk Trajectory Linking Mild Cognitive Impairment to Alzheimer's Disease.

Amnestic mild cognitive impairment (aMCI) and sporadic Alzheimer's disease (AD) are multifactorial conditions resulting from a complex crosstalk among multiple molecular and biological processes. The present study investigates the association of variants localized in genes and miRNAs with aMCI and AD, which may represent susceptibility, prognostic biomarkers or multi-target treatment options for such conditions. We included 371 patients (217 aMCI and 154 AD) and 503 healthy controls, which were genotyped for a panel of 120 single nucleotide polymorphisms (SNPs) and, subsequently, analyzed by statistical, bioinformatics and machine-learning approaches. As a result, 21 SNPs were associated with aMCI and 13 SNPs with sporadic AD. Interestingly, a set of variants shared between aMCI and AD displayed slightly higher Odd Ratios in AD with respect to aMCI, highlighting a specific risk trajectory linking aMCI to AD. Some of the associated genes and miRNAs were shown to interact within the signaling pathways of APP (Amyloid Precursor Protein), ACE2 (Angiotensin Converting Enzyme 2), miR-155 and PPARG (Peroxisome Proliferator Activated Receptor Gamma), which are known to contribute to neuroinflammation and neurodegeneration. Overall, results of this study increase insights concerning the genetic factors contributing to the neuroinflammatory and neurodegenerative mechanisms underlying aMCI and sporadic AD. They have to be exploited to develop personalized approaches based on the individual genetic make-up and multi-target treatments.

Tracking the Initial Diffusion of SARS-CoV-2 Omicron Variant in Italy by RT-PCR and Comparison with Alpha and Delta Variants Spreading.

The emergence of the Omicron SARS-CoV-2 variant caused public health concerns worldwide, raising the need for the improvement of rapid monitoring strategies. The present manuscript aimed at providing evidence of the utility of a diagnostic kit for the routine testing of SARS-CoV-2 infection as a cost-effective method for tracking the Omicron variant in Italy. The study was conducted on patients' naso-oropharyngeal-swab-derived RNA samples. These samples were subjected to RT-PCR using the TaqPath COVID-19 RT PCR CE IVD kit. Nonparametric testing and polynomial models fitting were used to compare the spreading of Alpha, Delta and Omicron variants. The samples of interest were correctly amplified and displayed the presence of S gene-target failure, suggesting that these patients carry the Omicron variant. The trend of diffusion was found to be significantly different and more rapid compared with that of the Alpha and Delta variants in our cohorts. Overall, these results highlight that the S gene target failure was a very useful tool for the immediate and inexpensive tracking of Omicron variant in the three weeks from the first detection. Thus, our approach could be used as a first-line screening to reduce the time and costs of monitoring strategies, facilitating the management of preventive and counteracting measures against COVID-19.

Age and Sex Modulate SARS-CoV-2 Viral Load Kinetics: A Longitudinal Analysis of 1735 Subjects.

The COVID-19 pandemic caused by SARS-CoV-2 represents a public health emergency, which became even more challenging since the detection of highly transmissible variants and strategies against COVID-19 were indistinctly established. We characterized the temporal viral load kinetics in individuals infected by original and variant strains. Naso-oropharyngeal swabs from 33,000 individuals (admitted to the IRCCS Santa Lucia Foundation Drive-in, healthcare professionals and hospitalized patients who underwent routinary screening) from November 2020 to June 2021 were analyzed. Of them, 1735 subjects were selected and grouped according to the viral strain. Diagnostic analyses were performed by CE-IVD RT-PCR-based kits. The subgenomic-RNA component was assessed in 36 subjects using digital PCR. Infection duration, viral load decay speed, effects of age and sex were assessed and compared by extensive statistical analyses. Overall, infection duration and viral load differed between the groups (p < 0.05). Male sex was more present among both original and variant carriers affected with high viral load and showing fast decay speed, whereas original strain carriers with slow decay speed resulted in older (p < 0.05). Subgenomic-RNA was detected in the positive samples, including those with low viral load. This study provides a picture of the viral load kinetics, identifying individuals with similar patterns and showing differential effects of age and sex, thus providing potentially useful information for personalized management of infected subjects.

Comparative analysis of antigen and molecular tests for the detection of Sars-CoV-2 and related variants: A study on 4266 samples.

OBJECTIVES: The present study compared the performance of the Lumipulse G Sars-CoV-2 Ag kit with the TaqPath COVID-19 RT-PCR CE IVD kit. METHODS: The study was conducted on 4266 naso-oropharyngeal swabs. Samples were subjected to antigen RT-PCR tests for the detection of Sars-CoV-2 and related variants. Statistical analyses were conducted in R software. RESULTS: We found 503 positives (including 138 H69-V70 deletion carriers) and 3763 negatives by RT-PCR, whereas 538 positives and 3728 negatives were obtained by antigen testing. We achieved empirical and binormal AU-ROCs of 0.920 and 0.990, accuracy of 0.960, sensitivity of 0.866, specificity of 0.973, positive and negative predictive values of 0.810 and 0.980. We obtained a positive correlation between viral loads and antigen levels (R2 = 0.81), finding a complete concordance for high viral loads (log10 copies/mL > 5.4). Antigen levels > 222 pg/mL were found to be reliable in assigning positive samples (p < 0.01). Concerning variant carriers, antigen test detected them with the same accuracy as other positive samples. CONCLUSIONS: Molecular and antigen tests should be evaluated regarding the prevalence of the area. In case of low prevalence, antigen testing can be employed as a first-line screening for the timely identification of affected individuals with high viral load, also if carriers of Sars-CoV-2 variants.