Type 1 diabetes (T1D) is a chronic disease characterized by inadequate or absent insulin production due to the autoimmune destruction of beta (β) cells in the pancreas. It was once called "juvenile diabetes" since the disease frequently occurs in children, but it can also develop in adults. According to the International Diabetes Federation, an estimated 700 million adults will suffer from diabetes by 2045. Although the exact cause of diabetes remains unknown, it is hypothesized that genetic factors, environmental factors, and exposure to certain viruses play a role in the development of T1D. To date, exogenous insulin is the most common treatment for T1D. However, it is not a cure for the disease. Islet cell transplantation and pancreatic transplantation are two additional treatments that have gained popularity in recent years, but their clinical application may be limited by the need for high doses of immunosuppressants, the rarity of human cadaveric islets, and the need for extensive surgery in pancreatic transplantation. Mesenchymal stem cells (MSCs) are a highly promising novel treatment for T1D and their discovery has advanced biological sciences by allowing for modification of cell fate and the development of higher-order cellular structures. They play an essential role in lowering levels of fasting blood sugar, hemoglobin A1c, and C-peptide, and in treating microvascular complications associated with T1D. However, some of the disadvantages of its use in clinical practice are limited to its method of collection, proliferation rate, cell activity with age, and the risk of tumour formation identified in some studies. Large-scale studies are required to discover the mechanism of action of MSCs after administration as well as the optimal route, dose, and timing to maximize the benefits to patients. This article focuses primarily on the role of MSCs in the treatment of T1D and compares the feasibility, benefits, and drawbacks of MSCs in the treatment of T1D.

Trigeminal neuralgia (TN) is a unilateral, paroxysmal, sharp, shooting, or jabbing pain that occurs in the trigeminal nerve divisions, including the ophthalmic (V1), maxillary (V2), and mandibular (V3) nerves. Typically, an episode is triggered by anything touching the face or teeth. TN is a clinical diagnosis with no specific diagnostic test; it is determined by the patient's medical history and pain description. Imaging is necessary to exclude secondary causes. The precise reason for TN is uncertain, but it is commonly believed to result from vascular compression of the trigeminal nerve root, typically near its origin in the pons. There are numerous surgical and medical treatment options available. The most frequently applied medical treatment therapies are carbamazepine and oxcarbazepine. Surgical alternatives are reserved for patients who do not respond to medical treatment. Botulinum toxin A (BTX-A) has emerged as a novel and promising alternative to surgery for individuals whose pain is unresponsive to medication. Multiple studies have established the safety and usefulness of BTX-A in treating TN, with the most significant benefits occurring between six weeks and three months after the surgery. This article reviews various studies published in the last 10 years regarding the therapeutic use of BTX-A in TN. These studies include various observational, clinical, pilot, and animal studies.

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption.

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, accounting for approximately 10% of all cancer cases. Despite the proven effectiveness of conventional screening modalities such as colonoscopy and fecal immunochemical testing (FIT), their invasive nature, high cost, and limited patient compliance hinder widespread adoption. Recent advancements in artificial intelligence (AI) and bowel sound-based signal processing have enabled non-invasive approaches for gastrointestinal diagnostics. Among these, bowel sound analysis—historically considered subjective—has reemerged as a promising biomarker using digital auscultation and machine learning. Objective: This review explores the potential of AI-powered bowel sound analytics for early detection, screening, and characterization of colorectal cancer. It aims to assess current methodologies, summarize reported performance metrics, and highlight translational opportunities and challenges in clinical implementation. Methods: A narrative review was conducted across PubMed, Scopus, Embase, and Cochrane databases using the terms colorectal cancer, bowel sounds, phonoenterography, artificial intelligence, and non-invasive diagnosis. Eligible studies involving human bowel sound-based recordings, AI-based sound analysis, or machine learning applications in gastrointestinal pathology were reviewed for study design, signal acquisition methods, AI model architecture, and diagnostic accuracy. Results: Across studies using convolutional neural networks (CNNs), gradient boosting, and transformer-based models, reported diagnostic accuracies ranged from 88% to 96%. Area under the curve (AUC) values were ≥0.83, with F1 scores between 0.71 and 0.85 for bowel sound classification. In CRC-specific frameworks such as BowelRCNN, AI models successfully differentiate abnormal bowel sound intervals and spectral patterns associated with tumor-related motility disturbances and partial obstruction. Distinct bowel sound-based signatures—such as prolonged sound-to-sound intervals and high-pitched “tinkling” proximal to lesions—demonstrate the physiological basis for CRC detection through bowel sound-based biomarkers. Conclusions: AI-driven bowel sound analysis represents an emerging, exploratory research direction rather than a validated colorectal cancer screening modality. While early studies demonstrate physiological plausibility and technical feasibility, no large-scale, CRC-specific validation studies currently establish sensitivity, specificity, PPV, or NPV for cancer detection. Accordingly, bowel sound analytics should be viewed as hypothesis-generating and potentially complementary to established screening tools, rather than a near-term alternative to validated modalities such as FIT, multitarget stool DNA testing, or colonoscopy.

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND), is a progressive neurodegenerative disease characterized by the weakness of voluntary muscles due to the loss of motor neurons. Symptoms ultimately culminate in the form of respiratory failure due to the involvement of the diaphragm. Unfortunately, there is no known cure for this disease. Hence, supportive therapy is the only available option in most terminal cases. However, Riluzole and Edaravone (EDA) are the only two known drugs approved by the U.S. Food and Drug Administration (FDA) for treating this condition. In 2017, EDA was approved for the treatment of ALS. It is hypothesized that Riluzole and EDA work via a mechanism involving antioxidants, which nullifies the oxidative stress believed to be involved in ALS. However, most studies in several countries have found a wide range of disparities in the efficacy of this drug. In this review, we aim to summarize the differences in results from epidemiological studies across 10 different countries and hypothesize the potential causes of these differences.

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is a life-threatening vascular complication of SSc, marked by high morbidity and mortality. Early diagnosis remains a major challenge due to nonspecific symptoms and the limitations of conventional tools such as echocardiography (ECHO), pulmonary function tests (PFTs), and serum biomarkers. This review evaluates the emerging role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in improving the diagnostic landscape of SSc-PAH. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Embase and Google Scholar to identify studies involving AI applications in SSc, pulmonary arterial hypertension (PAH), and their intersection. Evidence indicates that AI models can assist interpretation across modalities, including heart sounds, ECGs, chest X-rays (CXRs), ECHOs, CT pulmonary angiography (CTPA), and omics-based biomarkers. While several models show encouraging diagnostic performance, their accuracy varies by dataset and modality, and most require external validation against right heart catheterization (RHC)-confirmed cohorts. Integrating multimodal data through AI frameworks may enhance early recognition and individualized risk stratification; however, these tools remain exploratory. Future work should emphasize harmonized hemodynamic definitions, transparent validation protocols, and SSc-specific datasets to ensure clinical applicability and reproducibility.