Guven Medical and Health Sciences
Articles
Menopause: biological causes, cinical consequences, and evidence-based management
Authors: Yetkin Karasu
Abstract
Menopause is a physiological life stage defined by at least 12 consecutive months of amenorrhea, most commonly occurring between 45 and 55 years of age (median 51 years). With increasing life expectancy, this post-reproductive period may extend to one-third of a woman’s life, amplifying its clinical and societal impact. Biologically, the process is driven by lifelong atresia and depletion of a fixed, non-renewable oocyte/follicle pool established in fetal life. This mechanism makes reproductive aging earlier and steeper than somatic aging. Although evolutionary biology suggests several potential population-level advantages of menopause, it remains a challenging period for many women. In clinical practice, the main concerns cluster around vasomotor symptoms, sleep and mood disturbances, cognitive complaints, genitourinary syndrome of menopause (GSM), bone loss/osteoporosis, and increased cardiometabolic risk. For management, hormone replacement therapy (HRT) is the most effective option for vasomotor symptoms in appropriately selected patients; when HRT is contraindicated or not preferred, SSRIs/SNRIs, gabapentinoids, and behavioral interventions are reasonable alternatives.
DOI: 10.62351/gmhs.2025.0028
Artificial intelligence in Emergency Medicine
Authors: Mehmet Ali Çalışkan, Onur Polat
Abstract
This review summarizes current evidence on the impact of artificial intelligence and machine learning on triage, hospital admission decisions, and medical imaging in emergency departments. In triage, electronic systems and deep learning models outperform manual assessments in predicting the need for critical care and hospitalization. In admission decision support, natural language processing and neural network–based models improve early capacity planning and optimize resource utilization. In medical imaging, deep learning applications accelerate the detection of urgent findings on computed tomography and radiography, thereby enhancing diagnostic accuracy. Overall, artificial intelligence– and machine learning–based decision support systems have the potential to improve speed, accuracy, and patient safety in emergency care; however, multicenter prospective validation studies and effective integration into clinical workflows are essential for widespread implementation.
DOI: 10.62351/gmhs.2025.0027
Liver cancer and machine perfusion: Next generation transplant strategies
Authors: Fuat Aksoy, İrem Kumru Öz, Beyza İsmailoğlu, Esma Özdemir
Abstract
The number of liver transplants worldwide continues to increase each year, while the shortage of suitable donor organs persists at a similar rate. In recent years, the dramatic rise in obesity and liver steatosis has further reduced donor quality globally, making it increasingly difficult to identify healthy donors. In response to this growing demand, the use of marginal donor livers has become more common. However, these grafts are generally more susceptible to ischemic injury and are associated with serious post-transplant complications, including primary graft dysfunction, early graft failure, and biliary complications. To mitigate these adverse outcomes, machine perfusion (MP) technology has been developed to preserve grafts under more physiological conditions, thereby reducing ischemic injury and enabling assessment of organ function prior to transplantation. This approach particularly supports the safer utilization of marginal grafts and holds significant potential to improve transplant success rates. This review discusses the principles of MP technology, its current and potential applications in liver transplantation, and explores innovative approaches for its future implementation.
DOI: 10.62351/gmhs.2025.0026
Mechanisms and clinical implications of long non-coding RNAs
Authors: Seçil Aksoy, Beyza Nur Yağcı, Beril Solak, Çağla Sevinç
Abstract
Long non-coding RNAs (lncRNAs) are RNA transcripts longer than 200 nucleotides that do not encode proteins and have attracted increasing attention in recent years due to their roles in the multilayered regulation of gene expression. Initially considered “transcriptional noise” of the genome, they have since been shown to function across a wide spectrum of biological processes, ranging from epigenetic modifications to post-transcriptional regulation. Classified according to their structural diversity and genomic location, lncRNAs are distinguished by their functional flexibility within molecular regulatory mechanisms. Recent studies have demonstrated that these molecules act as key regulators in fundamental biological processes, including cell proliferation, differentiation, apoptosis, and immune regulation. This functional diversity underscores their potential as biomarkers and therapeutic targets in a variety of diseases. However, limited evolutionary conservation, low expression levels, and complex secondary structures continue to pose significant challenges for their functional characterization. With advances in biotechnological tools and multi-omics approaches, these limitations are expected to be progressively overcome, positioning lncRNAs at the center of both basic research and clinical investigations in the coming years.
DOI: 10.62351/gmhs.2025.0025