The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence.

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Maternal GBS vaccination for preventing group B streptococcus disease in newborns: A mini review of current evidence.

Group B streptococcus (GBS) poses a significant threat to neonates, leading to morbidity and mortality. Intrapartum antibiotics, although effective, have limitations, prompting the exploration of maternal vaccination. This study reviews the current evidence for maternal GBS vaccination in the prevention of early-onset GBS disease in newborns. A search on Google Scholar, PubMed, and Scopus identified studies assessing the impact of maternal GBS vaccination on early-onset GBS disease. Inclusion criteria comprised English-language clinical trials or observational studies. Data extraction included study details, immunogenicity profiles, effectiveness, safety outcomes, and relevant findings. Qualitative synthesis was employed for data analysis. Five studies meeting the inclusion criteria were reviewed. Maternal GBS vaccines demonstrated efficacy with sustained immunogenicity. Adverse events, although documented, were predominantly non-severe. Variability in immune responses and maternal-to-infant antibody ratios show the need for tailored vaccination approaches. Long-term follow up and surveillance are essential to assess persistence and identify unintended effects. Positive outcomes in vaccine efficacy support GBS vaccination integration into maternal health programs. Implementation challenges in diverse healthcare infrastructures require tailored approaches, especially in resource-limited settings. Overcoming cultural barriers and ensuring healthcare provider awareness are crucial for successful vaccination.

Closing the diagnostic gap: Liquid biopsy potential to transform ovarian cancer outcomes in sub-Saharan Africa.

Ovarian cancer presents a significant health challenge in sub-Saharan Africa (SSA), where late-stage diagnosis contributes to high mortality rates. This diagnostic gap arises from limited resources, poor healthcare infrastructure, and a lack of awareness about the disease. However, a potential game-changer is emerging in the form of liquid biopsy (LB), a minimally invasive diagnostic method. This paper analyses the current diagnostic gap in ovarian cancer in SSA, highlighting the socio-economic, cultural, and infrastructural factors that hinder early diagnosis and treatment. It discusses the challenges and potential of LB in the context of SSA, emphasizing its cost-effectiveness and adaptability to resource-limited settings. The transformative potential of LB in SSA is promising, offering a safer, more accessible, and cost-effective approach to ovarian cancer diagnosis. This paper provides recommendations for future directions, emphasizing the need for research, infrastructure development, stakeholder engagement, and international collaboration. By recognizing the transformative potential of LB and addressing the diagnostic gap, we can pave the way for early detection, improved treatment, and better outcomes for ovarian cancer patients in SSA. This paper sheds light on a path toward better healthcare access and equity in the region.