The primary treatment for autoimmune Diabetes Mellitus (Type 1 Diabetes Mellitus-T1DM) is insulin therapy. Unfortunately, a multitude of clinical cases has demonstrated that the use of insulin as a sole therapeutic intervention fails to address all issues comprehensively. Therefore, non-insulin adjunct treatment has been investigated and shown successful results in clinical trials. Various hypoglycemia-inducing drugs such as Metformin, glucagon-like peptide 1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 (DPP-4) inhibitors, amylin analogs, and Sodium-Glucose Cotransporters 2 (SGLT-2) inhibitors, developed good outcomes in patients with T1DM. Currently, SGLT-2 inhibitors have remarkably improved the treatment of patients with diabetes by preventing cardiovascular events, heart failure hospitalization, and progression of renal disease. However, their pharmacological potential has not been explored enough. Thus, the substantial interest in SGLT-2 inhibitors (SGLT-2is) underlines the present review. It begins with an overview of carrier-mediated cellular glucose uptake, evidencing the insulin-independent transport system contribution to glucose homeostasis and the essential roles of Sodium-Glucose Cotransporters 1 and 2. Then, the pharmacological properties of SGLT-2is are detailed, leading to potential applications in treating T1DM patients with automated insulin delivery (AID) systems. Results from several studies demonstrated improvements in glycemic control, an increase in Time in Range (TIR), a decrease in glycemic variability, reduced daily insulin requirements without increasing hyperglycemic events, and benefits in weight management. However, these advantages are counterbalanced by increased risks, particularly concerning Diabetic Ketoacidosis (DKA). Several clinical trials reported a higher incidence of DKA when patients with T1DM received SGLT-2 inhibitors such as Sotagliflozin and Empagliflozin. On the other hand, patients with T1DM and a body mass index (BMI) of ≥27 kg/m² treated with Dapagliflozin showed similar reduction in hyperglycemia and body weight and insignificantly increased DKA incidence compared to the overall trial population. Additional multicenter and randomized studies are required to establish safer and more effective long-term strategies based on patient selection, education, and continuous ketone body monitoring for optimal integration of SGLT-2 inhibitors into T1DM therapeutic protocol.

Thyroid Cancer (TC) is one of the most prevalent endocrine malignancies, with early detection being critical for patient management. The motivation for integrating Machine Learning (ML) in thyroid cancer research stems from the limitations of conventional diagnostic and monitoring approaches, as ML offers transformative potential for reducing human errors and improving prediction outcomes for diagnostic accuracy, risk stratification, treatment options, recurrence prognosis, and patient quality of life. This scoping review maps existing literature on ML applications in TC, particularly those leveraging clinical data, Electronic Medical Records (EMRs), and synthesized findings. This study analyzed 1231 papers, evaluated 203 full-text articles, selected 21 articles, and detailed three themes: (1) malignancy prediction and nodule classification; (2) other metastases derived from TC prediction; and (3) recurrence and survival prediction. This work examined the case studies’ characteristics and objectives and identified key trends and challenges in ML-driven TC research. Finally, this scoping review addressed the limitations of related and highlighted directions to enhance the clinical potential of ML in this domain while emphasizing its capability to transform TC patient care into advanced precision medicine.

As the burden of type 2 diabetes (T2D) continues to escalate globally, there is a growing need for novel, less-invasive biomarkers capable of early diabetes detection and monitoring of disease progression. Liquid biopsy, recognized for its minimally invasive nature, is increasingly being applied beyond oncology, and nevertheless shows its potential when the collection of the tissue biopsy is not possible. This diagnostic approach involves utilizing liquid biopsy markers such as cell-free nucleic acids, extracellular vesicles, and diverse metabolites for the molecular diagnosis of T2D and its related complications. In this context, we thoroughly examine recent developments in T2D liquid biopsy research. Additionally, we discuss the primary challenges and future prospects of employing liquid biopsy in the management of T2D. Prognosis, diagnosis and monitoring of T2D through liquid biopsy could be a game-changing technique for personalized diabetes management.

The bidirectional relationship between obstructive sleep apnea (OSA) and type 2 diabetes mellitus (T2DM) represents a critical intersection in metabolic medicine. Therefore, the present review examines the most recent data regarding molecular mechanisms linking OSA and T2DM, analyzing key biomarkers including hypoxia-inducible factors (HIF 1α), inflammatory mediators, adipokines, microRNAs, hormones, and neuropeptides that serve as both diagnostic indicators and potential therapeutic targets. Key molecular findings from the scientific literature report elevated HIF-1α promoting insulin resistance, decreased SIRT1 levels, dysregulated microRNA-181a and microRNA-199a, increased inflammatory cytokines (TNF-α, IL-6, CRP), and altered adipokine profiles with reduced adiponectin and elevated leptin and resistin. Current clinical evidence reveals significant therapeutic potential for modern antidiabetic medications in the management of OSA. GLP-1 receptor agonists, particularly tirzepatide, received FDA approval as the first medication for moderate-to-severe OSA in obese adults, showing a 55–63% AHI reduction. SGLT2 inhibitors also demonstrate promising results through weight loss and cardiovascular protection mechanisms. This integrated approach represents the evolution toward comprehensive OSA management beyond traditional mechanical ventilation strategies. Future research should focus on developing personalized treatment algorithms based on individual molecular biomarker profiles, investigating combination therapies, and exploring novel targets, including chronotherapy agents.

The present review examines the most recent data regarding molecular mechanisms linking OSA and T2DM, analyzing key biomarkers including hypoxia-inducible factors (HIF 1α), inflammatory mediators, adipokines, microRNAs, hormones, and neuropeptides that serve as both diagnostic indicators and potential therapeutic targets. Key molecular findings from the scientific literature report elevated HIF-1α promoting insulin resistance, decreased SIRT1 levels, dysregulated microRNA-181a and microRNA-199a, increased inflammatory cytokines (TNF-α, IL-6, CRP), and altered adipokine profiles with reduced adiponectin and elevated leptin and resistin. Current clinical evidence reveals significant therapeutic potential for modern anti-diabetic medications in the management of OSA. GLP-1 receptor agonists, particularly tirzepatide, that received FDA approval as the first medication for moderate-to-severe OSA in obese adults, showing 55-63% AHI reduction. SGLT2 inhibitors also demonstrate promising results through weight loss and cardiovascular protection mechanisms. This integrated approach represents the evolution toward comprehensive OSA management beyond traditional mechanical ventilation strategies. Future research should focus on developing personalized treatment algorithms based on individual molecular biomarker profiles, investigating combination therapies, and exploring novel targets, including chronotherapy agents.

Background/Objectives: Thyroid cancer (TC) is among the most common endocrine malignancies, with incidence rates increasing worldwide. However, careful inferential analysis based on refined data is needed to provide a sharper clinical and epidemiological description of this serious condition in a biologically and technologically evolving society. This study presents an exploratory statistical analysis of data from 1470 patients who underwent thyroid surgery for differentiated TC. Methods: The analysis combines bivariate exploration of associations between variables with univariate and multivariate analyses stratified by histological subtype. We examined pathological characteristics (tumor location, nodal metastases, distant metastases, margin involvement, lymphovascular invasion, vascular invasion, and perineural invasion), clinical characteristics (clinical stage), biochemical markers (thyroglobulin, anti-thyroglobulin antibodies, and thyroid-stimulating hormone), and demographic variables (sex assigned at birth and age). In addition, exploratory multivariable models were used to investigate factors associated with lymph node metastasis and margin involvement in papillary microcarcinoma, the diffuse sclerosing variant, and the classical variant of papillary thyroid carcinoma. Results: Notably, moderate to high effect size correlations highlight the interdependence of invasive histopathological features in thyroid cancer and their collective link to adverse surgical outcomes and prognosis. Conclusions: This study provides an analysis of associations between the variables and subtype-specific descriptive estimates, serving as a foundation for future work in tailoring personalized medicine.