Telomeres shorten with age, partly due to oxidative stress and inflammation, and this process is linked to higher risk of chronic disease. Creatine supports cellular energy production and may reduce metabolic stress, while vitamin D has anti-inflammatory and antioxidant properties. Together, they may help protect telomeres and support healthy aging. This exploratory, open-label pilot study examined the effects of daily creatine monohydrate (2.5 g) plus vitamin D3 (2000 IU) over 12 months in 30 healthy men aged 65 years and older. Telomere length in blood cells was assessed at the end of the study and compared with an age-matched reference group. Adherence to supplementation was high (81%), and no side effects were reported. After 12 months, participants receiving creatine and vitamin D had significantly longer telomeres than controls (0.90 ± 0.21 vs. 0.81 ± 0.17), corresponding to an approximately 11% difference (P = 0.004). These preliminary findings suggest that combined creatine and vitamin D supplementation may help preserve telomere length in older adults. This simple and well-tolerated strategy warrants further investigation in larger randomized controlled trials to confirm its potential role in promoting healthy aging.
Royal Jelly (RJ) is a secretion of the hypopharyngeal and mandibular glands of worker bees, and is the exclusive food of the queen bee during her larval stage, so that she reaches almost twice the size of the worker bee. Over the years, the use of RJ has been associated with longevity and improved health quality during aging, post-menopausal hormonal modulation, as an adjuvant in the treatment of Chronic Non-Communicable Diseases, different types of cancer, in dermatology, as an antimicrobial treatment, to enhance fertility, as a nephroprotector, neuroprotector, hepatoprotector, immunometabolism modulator, and cognitive improvement. Only at the beginning of the 21st century did research with RJ and exercise emerge, identifying anti-fatigue effects, decreased serum lactate and ammonia, reduced muscle glycogen depletion, improved athletic performance, attenuating oxidative stress induced by High Intensity Interval Exercise (HIIE), to interrupt neurodegeneration in multiple sclerosis, in the positive modulation of cannabinoid-1 receptors as therapeutic targets for the treatment of autoimmune diseases such as multiple sclerosis, for reducing sarcopenia, skeletal muscle loss and weakness, and inducing mitochondrial adaptation with resistance training by activating AMPK in muscles.
Background: Breast cancer has the highest mortality rate among women worldwide, and early detection through mammography is critical for reducing breast cancer mortality. Artificial intelligence (Al) has become a promising tool to support radiologists in mammography interpretation. However, the diagnostic accuracy of AI-assisted interpretation needs to be evaluated and compared with current methods. Objectives: This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of AI-assisted mammography interpretation versus radiologist-alone interpretation in detecting breast cancer. Methods: A comprehensive literature search identified studies that compared AI-assisted mammography interpretation with radiologist-alone assessment. Twenty-five eligible studies were included, encompassing retrospective cohorts, prospective trials, and multi-reader studies across diverse healthcare settings and AI algorithms. Data were extracted for sensitivity, specificity, likelihood ratios, and diagnostic odds ratios. Pooled estimates were calculated using a bivariate I² statistic, and heterogeneity was assessed with variance measures, including median odds ratios. The risk of bias was evaluated using ROBINS-I and QUADAS-2 tools. Results: The pooled sensitivity of AI-assisted interpretation was 0.82 (95% CI: 0.77–0.85), and specificity was 0.90 (95% CI: 0.85–0.93). The area under the SROC curve (AUC) was 0.91 (95% CI: 0.88–0.93), demonstrating high overall diagnostic performance. The pooled Positive likelihood ratio (PLR) was 7.9 (95% CI: 5.2–11.8), the negative likelihood ratio (NLR) was 0.21 (95% CI: 0.16–0.26), and the diagnostic odds ratios (DOR) were 38 (95% CI: 22–66). These findings suggest that AI significantly enhances the sensitivity of breast cancer detection while maintaining high specificity. However, substantial heterogeneity was observed across studies, reflecting differences in populations, algorithms, and thresholds. Conclusion: AI-assisted mammography interpretation comparable to radiologists alone demonstrates high diagnostic accuracy, which may improve early detection rates and reduce false positives. It has the potential to augment radiologist performance in breast cancer screening and integrate into larger-scale breast cancer screening programs. While these results support integration of AI into clinical workflows, further prospective, real-world validation with standardized thresholds is required before widespread adoption.
Metabolic syndrome is a cluster of risk factors that increases the likelihood of cardiovascular disease and type 2 diabetes. Hydrogen-rich water (HRW) has been proposed as a nutritional strategy to improve metabolic health, although individual differences in endogenous hydrogen production may influence its effectiveness. In this randomized, double-blind, placebo-controlled trial, 40 adults with metabolic syndrome (mean age 51.2 ± 7.9 years; 27 women) were assigned to consume 1,000 mL/day of HRW or placebo for 12 weeks. Baseline breath hydrogen levels stratified participants into low (< 10 ppm) or high (≥ 10 ppm) hydrogen groups. Anthropometric, body composition, and metabolic biomarkers were assessed at baseline and post-intervention, with change in waist circumference as the primary outcome. HRW supplementation led to significant improvements in body weight, BMI, waist circumference, fat mass, fat-free mass, muscle mass, and total body water compared with placebo (P ≤ 0.05), with large effect sizes observed for reductions in waist circumference. Significant improvements were also noted in fasting glucose, lipid profile, apolipoproteins A and B, and high-sensitivity C-reactive protein (P ≤ 0.05). Treatment effects were generally more pronounced in participants with lower baseline breath hydrogen levels. No major adverse events were reported. These findings indicate that HRW supplementation safely improves multiple components of metabolic syndrome, with greater benefits observed in individuals with lower endogenous hydrogen production, supporting its potential role as a personalized adjunctive intervention.
This research article studies building a Water Cascade Analysis (WCA) method that can rationalize multiple industrial environments that involve multiple pollutants. The method uses an integrated numerical targeting step and network redistribution to minimize freshwater usage as well as wastewater discharge. The method was, thus, applied using the case study by Hashemi et al. (2024) [1], where the analysis first identified a total of three units that accounted for most water consumption: these were the Desalter unit, the cooling tower, and the firewater system. Both Chemical Oxygen Demand (COD) and Total Dissolved Solids (TDS) were adopted as the two main contaminants. The results of the single-contaminant analysis showed that the minimum freshwater requirement was 44.62 m³/h when considering COD, while it decreased to 34.68 m³/h when considering TDS. This reduction amounts to between 19% and 25% of the refinery's total consumption (175 m³/h). The volume of wastewater produced was, therefore, identical to these figures, reflecting the accomplishment of the Pinch Point as evidenced by the cascade tables for each pollutant individually. Next, the sequential contaminant analysis (A→B) and (B→A) was performed. The results of the first scenario showed consumption rising to 44.87 m³/h owing to the need to dilute the concentrations of the second contaminant. On the other hand, the best performance was achieved in the second scenario (B→A) with freshwater requirements as low as 44.83 m³/h. As a result, TDS was identified as the Limiting Pollutant. A comparison between the methodology developed and the Water Pollutant Analysis (WPA) method employed in the reference study revealed that the use of freshwater was lowered from 99 m³/h to less than 45 m³/h in the proposed method, whereas wastewater discharge was reduced from 52 m³/h to about 44.8 m³/h. The findings verify that the proposed methodology serves as a simplified, efficient, and numerical means of water reuse network design in multi-contaminant industrial systems. Besides achieving considerable water savings, it also lessens the environmental impact, thus presenting itself as a suitable instrument for use in oil refineries and industrial facilities with complex water systems.
