Association of Subclinical Hypothyroidism with Type 2 Diabetes Mellitus in Qatar: A Cross-Sectional Study.

Purpose: The relationship between subclinical hypothyroidism and type 2 diabetes mellitus (T2DM) in Qatar is under-studied, despite the high prevalence of diabetes in the region. This study evaluates the potential association between subclinical hypothyroidism and T2DM in Qatar. Patients and Methods: A cross-sectional study used participants with and without T2DM from the Qatar Biobank (QBB). Logistic regression analysis was used to assess the association between subclinical hypothyroidism and T2DM, with multivariable logistic regression used to adjust for potential confounders. Results: The study found that subclinical hypothyroidism was significantly associated with a 2.82 increase in the odds of having T2DM (OR=2.82, 95% CI (1.13, 7.02), p=0.026) after adjusting for potential confounders. The proportion of subclinical hypothyroidism among individuals with T2DM in Qatar was 4.6%, significantly higher than in those without T2DM (2.8%, p=0.18). Conclusion: This study demonstrates a significant association between subclinical hypothyroidism and T2DM in Qatar. Further research is required to investigate the directionality of this association and its clinical implications.

Proteomic Profiling of Small-Cell Lung Cancer: A Systematic Review.

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects.

Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.