Aging significantly impacts several age-related neurological problems, such as stroke, brain tumors, oxidative stress, neurodegenerative diseases (Alzheimer’s, Parkinson’s, and dementia), neuroinflammation, and neurotoxicity. Current treatments for these conditions often come with side effects like hallucinations, dyskinesia, nausea, diarrhea, and gastrointestinal distress. Given the widespread availability and cultural acceptance of natural remedies, research is exploring the potential effectiveness of plants in common medicines. The ancient medical system used many botanical drugs and medicinal plants to treat a wide range of diseases, including age-related neurological problems. According to current clinical investigations, berries improve motor and cognitive functions and protect against age-related neurodegenerative diseases. Additionally, berries may influence signaling pathways critical to neurotransmission, cell survival, inflammation regulation, and neuroplasticity. The abundance of phytochemicals in berries is believed to contribute to these potentially neuroprotective effects. This review aimed to explore the potential benefits of berries as a source of natural neuroprotective agents for age-related neurological disorders.

Background and aim: Intraoperative hypotension is one of the most common surgical adverse effects, often associated with postoperative organ damage and complications. The use of non-invasive methods to predict hypotension during surgery could significantly reduce these complications. Recently developed machine learning algorithms show promise for improving the prediction of hypotension and enhancing detection during surgical procedures. Our meta-analysis aimed to evaluate the clinical performance of machine learning algorithms for predicting intraoperative hypotension. Method: A systematic search of PubMed, Scopus, Web of Science, and Google Scholar was conducted to retrieve articles published up to August 2024. Two independent researchers screened observational accuracy studies and also identified randomized controlled trials (RCTs) of AI-guided intraoperative management. Observational studies reporting diagnostic/prognostic accuracy metrics were included in the quantitative synthesis. After evaluating the quality of the studies, data extraction and analysis were accomplished. Results: Five observational studies, including a total of 42,509 participants, were included in this study. Four studies were conducted in South Korea, and one in Taiwan. The machine learning algorithms applied in these studies for electrocardiography, basal systolic, and diastolic blood pressure. These studies assessed model performance based on sensitivity, specificity, and the area under the ROC curve (AUROC). The overall effect size, represented in a forest plot, showed specificity of 0.73 (95% CI: 0.67–0.79), sensitivity of 0.75 (95% CI: 0.71–0.79), and an AUROC of 0.83 (95% CI: 0.77–0.89). In addition, we identified RCTs evaluating AI/HPI-guided intraoperative management, due to heterogeneous endpoints. Conclusion: Machine learning algorithms demonstrate acceptable performance and advantages in predicting intraoperative hypotension during surgical procedures. The application of these algorithms could be beneficial in future surgeries to reduce complications.

Background: Type 2 diabetes mellitus (T2DM) is increasingly seen as a chronic inflammatory state, with the neutrophil‐to‐lymphocyte ratio (NLR) studied as a new biomarker of systemic inflammation. Evidence links NLR to glycemic control, but its consistency and strength vary. This review summarizes data on NLR's relationship with glycemic indicators, such as hemoglobin A1c (HbA1c), across different T2DM glycemic states. Methods: A systematic literature search was carried out in PubMed/MEDLINE, Scopus, Web of Science, and Embase through November 2025 to find studies that reported both NLR and glycemic control indicators in adults with T2DM. Data were systematically extracted based on predefined criteria. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were combined using random‐effects models. Heterogeneity was measured using the I² statistic, and robustness was assessed using sensitivity analyses. Publication bias was investigated using Begg's and Egger's tests, trim‐and‐fill analysis, and funnel plot examination. All statistical analyses were conducted using STATA 18 (StataCorp, College Station, TX, USA). Results: The meta‐analysis consisted of 27 studies (37 comparisons) with a total of 48,714 individuals from 11 nations. NLR demonstrated a stepwise gradient with deteriorating glycemic control: poorly controlled T2DM (HbA1c > 7%) had higher NLR than well‐controlled T2DM (HbA1c ≤ 7%) ( p < 0.001), prediabetes (HbA1c 5.7–6.4%) ( p < 0.001), and normoglycemia (HbA1c ≤ 5.7%) ( p = 0.037). Conclusions: NLR rises progressively with deteriorating glycemic control and is markedly raised in poorly controlled T2DM. These observations endorse NLR as an inexpensive, widely available inflammatory biomarker that can augment conventional glycemic measures in the clinical setting.

Herbal medicine has gained massive popularity among researchers worldwide. The leading causes of this rapid development are lower side effects, lower prices, and higher availability of plant extracts compared to synthetic drugs. Ginger (Zingiber officinale), from the Zingiberaceae family, is one of the most commonly used and popular plants utilized as a dietary spice, herbal medicine, and food preservative. Its biologically active components, such as shogaol, paradol, zingerone, and especially gingerol, can be highly effective for the treatment of several illnesses through various anti-inflammatory, anti-neoplastic, anti-emetic, anti-oxidant, antihyperlipidemic and anti-hyperglycaemic activities. Ginger can also be used as an antiviral agent. Numerous studies have been conducted to investigate the antiviral efficacy of ginger on several viruses; for instance, severe acute respiratory syndrome 2 (SARS-COV-2), influenza, dengue, hepatitis, herpes, Human Papilloma Virus (HPV), Human Immunodeficiency Virus (HIV), Epstein- Barr Virus (EBV), Chikungunya virus and so on. This review summarizes the efficacy of ginger in preventing or treating several viral diseases and its mechanism of action, emphasizing coronavirus disease 2019 (COVID-19) due to the current high burden of disease worldwide.

The possible involvement of numerous chemical elements in the pathogenesis of neurodegenerative diseases has long been studied by researchers, yet no clear consensus regarding the concentration of Zinc (Zn) and the onset of Parkinson's disease (PD) has emerged. The objective of this study was to conduct a robust meta-analysis to clarify the association between Zn levels across different biological matrices and Parkinson’s disease. A comprehensive literature search was conducted across six databases up to April 2024. We included 29 case-control studies reporting Zn concentrations in serum, plasma, and cerebrospinal fluid (CSF) and performed subgroup analyses by biological matrix, continent, and detection method to extend and update previous meta-analyses on this topic. Statistical meta-analysis was performed using STATA v18 software, calculating the weighted mean difference (WMD) and 95 % CIs using a random-effects REML model. Heterogeneity was assessed using the I2 statistic, and publication bias was tested using Begg's and Egger's tests. This meta-analysis pooled data from 29 unique studies. The analysis of Zn in serum (N=22 data points) revealed a statistically significant reduction in PD patients (WMD=−108.23 μg/L, 95% CI: [−205.27, −11.18], p=0.03), with extreme heterogeneity (I²=99.43%). A similar significant deficit was found in plasma (WMD=−258.15 μg/L, 95% CI: [−481.20, −35.11], p=0.02). In contrast, Zn levels in CSF (N=8 studies) showed no statistically significant overall difference (WMD=−15.88 μg/L, 95% CI: [−36.21, 4.46], p=0.13), exhibiting the highest heterogeneity (I2=99.91%). All pooled estimates were characterized by extremely high heterogeneity (I²>99 %), primarily driven by the Asian subgroup and methodological differences, indicating substantial between-study variability and the need for cautious interpretation. Begg’s and Egger’s tests did not suggest substantial publication bias in serum or plasma, but CSF findings should be interpreted with caution. Our research demonstrates a significant association between lower Zn levels in the peripheral circulation (serum and plasma) and susceptibility to PD, suggesting that Zn deficiency may contribute to oxidative stress in PD but without establishing a causal relationship. Our findings reiterate the necessity of large-scale longitudinal cohort studies to validate this association, address the issue of reverse causation, and rigorously evaluate whether correcting Zn deficiency has any therapeutic value in the prevention or progression of PD.

Background and Aim: Numerous studies on the impact of weight changes on nonalcoholic fatty liver disease are being conducted; therefore, this systematic review aims to critically discuss the impact of change, fluctuation, or variability in weight on the risk of nonalcoholic fatty liver disease in the general population. Methods: Data from three databases, PubMed/Medline, Google Scholar, and Scopus, which were available until June 2024, were used to compile the materials for our research. Only English‐language publications were submitted for this study, and they were evaluated based on their titles, abstracts, and complete texts and. Result: Eight studies (three cross‐sectional articles and five cohort articles) involving 147,601 participants from Japan, China, Korea, and the United States were included. The results of this study showed that weight loss had significantly increased odds of developing NAFLD independently and per 1 kg compared to the stable weight control group. (OR = 1.16, 95% CI: 1.118–1.208, p < 0.0001, I ² = 86%, OR = 1.186, 95% CI: 1.142–1.230, p < 0.0001, I ² = 89.2%). The findings of seven articles indicated that the odds of experiencing NAFLD among patients with increased weight was 0.697 (OR = 0.697, 95% CI: 0.391–1.002, p < 0.0001, I ² = 99.9%), suggesting a slightly reduced likelihood compared to the control group. The sensitivity analysis supported the robustness of the findings. Conclusion: Nonalcoholic fatty liver disease is increasing in industrialized nations and has a significant financial impact on people′s health and healthcare systems. It can be controlled by emphasis on reducing weight change and maintaining a healthy weight.