Search Results (1,740)

Background: Alzheimer’s disease (AD) features progressive cognitive decline and amyloid-beta (Aβ) accumulation. Insulin resistance in type 2 diabetes mellitus (T2DM) is increasingly recognised as a mechanistic link between metabolic dysfunction and neurodegeneration. Although sodium–glucose cotransporter-2 inhibitors (SGLT2is) have established glycaemic and cardioprotective benefits, their neuroprotective role remains less well defined. Objectives: This systematic review examines animal studies on the neuroprotective effects of SGLT2i in T2DM and AD models. Methods: A literature search was conducted across the Web of Science, Scopus, and PubMed databases, covering January 2014 to November 2024. Heterogeneity was assessed with I², and data were pooled using fixed-effects models, reported as standardised mean differences with 95% confidence intervals. We focus on spatial memory performance as measured by the Morris Water Maze (MWM) test, including escape latency and time spent in the target quadrant, as the primary endpoints. The secondary endpoints of Aβ accumulation, oxidative stress, and inflammatory markers were also analysed and summarised. Results: Twelve studies met the inclusion criteria for this review. A meta-analysis showed that SGLT2i treatment significantly improved spatial memory by reducing the escape latency in both T2DM and AD models. In addition, SGLT2i yielded a significant improvement in spatial memory, as indicated by an increased target quadrant time for both T2DM and AD. Furthermore, SGLT2i reduced Aβ accumulation in the hippocampus and cortex, which met the secondary endpoint; the treatment also lessened oxidative stress and inflammatory markers in animal brains. Conclusions: Our findings indicate that SGLT2is confer consistent neuroprotective benefits in experimental T2DM and AD models. Full article

Background: Vitamin E has been studied for its role in reducing the growth of colorectal cancer (CRC). CRC is a worldwide health concern. A meta-analysis reported that CRC patients have a lower concentration of serum vitamin E, suggesting it to be a risk factor. Although rodent models are widely used in disease research, their application in studying vitamin E as a preventive or therapeutic agent in CRC is not well characterized. To address this gap, we conducted a scoping review to examine the available evidence, adhering to the PRISMA-ScR checklist. Methods: We searched PubMed, Google Scholar, Scopus, and Web of Science (WoS) for full-text English original articles published before May 2024, using Medical Subject Headings (MeSH) terms and free text. The following search string strategy was applied: (Vitamin E OR tocopherol$ OR tocotrienol$) AND (Colo$ cancer OR colo$ carcinoma) AND (Rodentia OR mouse OR Rodent$ OR mice OR murine OR rats OR guinea OR rabbit OR hamsters OR Animal model OR Animal testing OR animals) AND (neoplasm$ OR “tumor mass” OR tumor volume OR tumor weight OR tumor burden). Data were charted into five categories using a standardized, pretested form. The charted data were synthesized using descriptive and narrative methods. Conclusions: This study highlights that γ- and δ-tocopherols, as well as δ-tocotrienol and its metabolites, were reported to reduce tumor volume and formation in various rodent models. While these results are promising, this scoping review identifies a need for further research to address translational barriers such as dosing, bioavailability, and long-term safety before clinical application. Full article

Background: Recently, based on HOMA-IR, we estimated empirical optimal cut-off values for SHBG levels of ≤41.5 nmol/L in women with PCOS. Other proposed markers of insulin resistance include triglyceride and glucose levels, and anthropometric measurements. Therefore, our current study aimed to analyze its consistency with the cut-off values that discriminate insulin resistance based on the TyG, TyG-BMI, and TyG-WC indices in women with PCOS. Methods: Age, body weight, height, waist circumference, glucose, insulin, triglyceride, and SHBG levels were retrieved from the medical records of 264 Caucasian women diagnosed with PCOS. The TyG, TyG-BMI, and TyG-WC indices were calculated. The mean meta-cut-off SHBG level was calculated using receiver-operating characteristic (ROC) analysis combined with diagnostic test accuracy meta-analysis. Results: The mean meta-cut-off value for SHBG levels for the assessment of insulin resistance was less than 43.1 (95% CI: 37.0–49.2) nmol/L. The pooled sensitivity and specificity of SHBG levels for the assessment of insulin resistance were 74.7% and 66.9%, respectively. The pooled mean prevalence of insulin resistance based on all indices was 36.1% (95% CI: 33.5–38.7%) with a standard deviation of 18.7% and positive predictive value (PPV) of 52.8% (95% CI: 12.2–87.5%) and the negative predictive value (NPV) of 80.2% (95% CI: 45.1–97.7%). Conclusions: Our study confirms the usefulness of SHBG level as a marker of insulin resistance in Caucasian women with PCOS. A value below 43 nmol/L, with high sensitivity and specificity, enables the detection of insulin resistance and a high risk of prediabetes, prompting close monitoring of liver function. Full article

Background and Objectives: Although several meta-analyses have evaluated the effects of motor imagery (MI) on upper-limb recovery using the Fugl-Meyer Assessment for the Upper Extremity (FM-UE), evidence based on more specific (Action Research Arm Test, ARAT) and functional (Barthel Index, BI) outcomes remains scarce. This study examined the effect of MI combined with conventional rehabilitation therapy (CRT), which translates into meaningful improvements in upper-limb performance and functional independence after stroke, accounting for methodological quality and publication bias. Materials and Methods: A systematic review and meta-analysis were carried out in accordance with PRISMA recommendations, with prior registration in PROSPERO (CRD420251120044). Comprehensive searches were conducted across six electronic databases up to July 2025. The methodological rigor of the included studies was evaluated using the PEDro scale, and risk of bias was appraised with the Cochrane RoB 2 instrument. Random-effects models estimated pooled effect sizes (ESs) for the ARAT and BI, alongside analyses of heterogeneity, publication bias, and moderators. Results: Eleven RCTs (n = 425) were included. A small pooled improvement in ARAT was observed (ES = 0.25; 95% CI: 0.13–0.37; p < 0.001); however, this effect was rendered non-significant after correction for publication bias (ES = 0.08; 95% CI: −0.14–0.31). No significant differences were found for the BI (ES = 0.41; 95% CI: −0.35–1.18; p = 0.268), with substantial heterogeneity (I² = 96.6%). The mean PEDro score was 6.6, indicating moderate methodological quality. Conclusions: MI combined with CRT yields small and inconsistent effects on upper-limb recovery and no improvement in functional independence. Current evidence does not support its routine use in stroke rehabilitation. Well-designed, adequately powered randomized controlled trials employing standardized MI protocols are required to determine its true clinical relevance. Full article

Metallic engineered nanomaterials (ENMs) have enormous technological potential and are increasingly applied across different fields and products. However, substances (including ENMs) can be detrimental to the environment and human health, thus requiring systematic testing to uncover potential hazardous effects (in compliance with REACH). Although hazard testing traditionally involves the use of animal experiments, recent years have seen a shift towards in silico modeling. High-quality data is required for in silico modeling, which is frequently not readily available for ENMs. Vast amounts of data have been published in literature but they are unstructured and scattered across numerous sources. To mitigate the limitations in data availability, we have compiled and created a nanotoxicity dataset based on published literature. The compiled dataset focuses mainly on acute in vivo endpoints conducted in a laboratory setting using metallic nanomaterials. The data extracted from literature include material information, physico-chemical properties, experimental conditions, endpoint information, and literary meta-data. The dataset presented here is useful for meta-analysis or in silico modeling purposes. Full article

Autism spectrum disorder (ASD) and intellectual disability (ID) frequently coexist and share heterogeneous cognitive manifestations, yet their specific performance patterns on Wechsler scales remain poorly systematized. This meta-analysis synthesized data from 31 studies using the WISC-IV, WISC-V, WAIS-III, and WAIS-IV to compare cognitive index profiles in individuals with ASD, ID and ASD+ID. Standardized mean differences (Hedges’ g) were calculated using random-effects models, adopting a normative reference of mean 100 and SD 15. Results showed a distinct profile for ASD, with greater impairments in the Processing Speed Index (PSI) and Working Memory Index (WMI), while the Vocabulary Comprehension Index (VCI), Perceptual/Fluid Reasoning Index (PRI/FRI), and Visual Processing Index (VPI) remained close to normative scores. In contrast, ID and ASD+ID exhibited generalized deficits across all indices, with the lowest scores in Full-Scale IQ (FSIQ) and broad effects above g = −2.5. No significant differences emerged between Wechsler versions or age-based test types. Heterogeneity was high in ASD and ID across outcomes, but negligible in ASD+ID due to reduced k. These findings reinforce that ASD presents a specific cognitive pattern, whereas ID and ASD+ID display diffuse impairment, and that Wechsler scales are consistent across versions for identifying these profiles. Full article

Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R² = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments. Full article

Background: Plantar fasciitis is a prevalent musculoskeletal disease characterized by heel pain and functional impairment. Both high-intensity laser therapy (HILT) and extracorporeal shockwave therapy (ESWT) have demonstrated efficacy in managing plantar fasciitis; however, their relative effectiveness remains unclear. Purpose: This systematic review and meta-analysis aimed to compare the effects of HILT and ESWT for treating plantar fasciitis. Methods: A comprehensive literature search of PubMed, the Cochrane Library, EMBASE, and Scopus was conducted from inception to 13 July 2025 to identify randomized controlled trials (RCTs) investigating both interventions. Two reviewers independently extracted data and assessed the methodological quality of the trials using the Physiotherapy Evidence Database (PEDro) scale. The certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. The primary outcomes of this study were pain intensity and foot function. The visual analog scale (VAS) was used for pain assessment. Foot function was evaluated by the total scores of the Foot Function Index (FFI) and American Orthopedic Foot & Ankle Society Scale (AOFAS) and the activities of daily living (ADL) subscale scores of the Foot and Ankle Ability Measure (FAAM). Outcomes were assessed at the end of treatment and during short-, medium-, and long-term follow-ups. The meta-analysis utilized standardized mean differences (SMDs), assessed heterogeneity using the I² test, applied the inverse variance method for pooling continuous variables, and employed a random-effects model because of the variable study methods used across the included articles. Results with p < 0.05 were considered statistically significant. The I² test was used to objectively measure statistical heterogeneity, with I² ≥ 50% indicating significant heterogeneity. Results: Five RCTs met the inclusion criteria, with methodological quality scores ranging from 6 to 7 on the 10-point PEDro scale. In total, 120 participants received HILT and 116 received ESWT. Regarding pain intensity (VAS), no statistically significant differences were detected between HILT and ESWT at any time point, including short-term morning pain (SMD = −0.11, 95% CI −0.42 to 0.19, p = 0.40), resting pain (SMD = 0.01, 95% CI −0.48 to 0.49, p = 0.05), and activity pain (SMD = −0.08, 95% CI −0.41 to 0.26, p = 0.89), as well as medium-term morning, resting, and activity pain (all p > 0.05). For foot function (FFI), the pooled analysis of all studies showed no significant short-term difference (SMD = 0.37, 95% CI −0.22 to 0.95, p = 0.01; I² = 73%); however, a subsequent sensitivity analysis, which excluded one studyreduced heterogeneity to 0% and revealed a significant short-term advantage of ESWT (SMD = 0.64, 95% CI 0.32 to 0.95, p < 0.01). Medium-term FFI also favored ESWT (SMD = 0.53, 95% CI 0.14 to 0.92, p < 0.01). Overall, the certainty of evidence ranged from moderate to low, mainly due to risk of bias and heterogeneity, as assessed by the GRADE approach. Conclusions: While the pooled results suggested a trend toward greater functional improvement with ESWT than with HILT in the short- and medium-term, the effect sizes were small. No significant between-group differences were observed in pain-related outcomes. Given the limited number of available trials and variability in treatment protocols, current evidence remains insufficient to draw definitive conclusions about the comparative efficacy of ESWT and HILT. Further high-quality, large-scale randomized controlled trials with standardized methodologies are needed to better inform clinical decision-making. Full article

Conventional and organic agriculture can both cause soil microbial community structure (SMCS) destruction, infertility, and abandonment. The application of soil productivity-improving biofertilizers is a sustainable practice that requires holistic knowledge, including complex biointeractions, diverse microbial metabolism, and culture requirements, the last of which rely on methodology and technology. In this study, a holistic culture-based and meta-analysis approach was employed to explore pristine and domesticated soils for presumptive plant growth-promoting (PGP) bacteria. Various soil samples were logistically acquired and processed using enrichment and heat alternatives. Morphologically diverse isolates were streak-purified and analyzed for 16S rRNA bacterial identification. Meta-analysis of PGP bacteria in domesticated environments was conducted using Google Search and NCBI PubMed. Soil fertility was analyzed for the pH and nitrogen/phosphorus/potassium (NPK) contents using biochemical tests. Notably, 7 genera and 15 species were differentially recovered, with Bacillus being the most prevalent and diverse in species. Conversely, Aeromonas, Lactobacillus, Lelliottia, Pseudomonas, and Staphylococcus were found only in pristine soil. While soil pH was consistent in all pristine soil samples, NPK contents ranged widely across the pristine (i.e., P/K) and domesticated samples (i.e., N/P/K). These findings could enhance biofertilizer SMCS, function, and effectiveness in the agricultural productivity needed to feed the expanding population. Full article

Industrial wastewater treatment processes often exhibit highly nonlinear, dynamic behavior, making accurate prediction and control difficult when using conventional modeling approaches. This study presents an enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for modeling the ion-exchange purification process based on 200 experimentally collected data samples obtained from a laboratory-scale treatment system. The initial ANFIS structure was generated using subtractive clustering to automatically derive the rule base, while hybrid learning combining backpropagation and least-squares estimation was applied to train the model. The training results demonstrated stable convergence across 100, 200, and 300 epochs, with progressive improvements in model accuracy. To further enhance performance, several meta-heuristic optimization methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the Adam optimizer, were integrated within a Python 3.13-based environment to refine model parameters. Ensemble learning and an extended Boosting++ strategy was subsequently employed to reduce variance, correct residual errors, and strengthen generalization capability. The optimized ANFIS model achieved strong predictive accuracy across both training and unseen test datasets. The performance metrics for the full dataset yielded RMSE (Root Mean Square Error) = 1.3369, MAE (Mean Absolute Error) = 0.9989, and R² = 0.9313, while correlation analysis showed consistently high R-values for training (0.96745), validation (0.95206), test (0.95754), and overall data (0.96507). The results demonstrate that the combination of subtractive clustering, hybrid learning, meta-heuristic optimization, and ensemble boosting produces a highly reliable soft-computing model capable of effectively capturing the nonlinear dynamics of ion-exchange wastewater treatment. The proposed approach provides a robust foundation for intelligent monitoring and control strategies in industrial purification systems. Full article

Plasma cell-free RNA (cfRNA) metagenomics is increasingly explored for blood-based pathogen detection, but the structure of the shared background “blood microbiome”, the reproducibility of reported signals, and the practical limits of this approach remain unclear. We performed a critical re-analysis and benchmarking (“stress test”) of host-filtered blood RNA sequencing data from two cohorts: a bacteriologically confirmed tuberculosis (TB) cohort (n = 51) previously used only to derive host cfRNA signatures, and a coronary artery disease (CAD) cohort (n = 16) previously reported to show a CAD-shifted “blood microbiome” enriched for periodontal taxa. Both datasets were processed with a unified pipeline combining stringent human read removal and taxonomic profiling using the latest versions of specialized tools Kraken2 and MetaPhlAn4. Across both cohorts, only a minority of non-host reads were classifiable; under strict host filtering, classified non-host reads comprised 7.3% (5.0–12.0%) in CAD and 21.8% (5.4–31.5%) in TB, still representing only a small fraction of total cfRNA. Classified non-host communities were dominated by recurrent, low-abundance taxa from skin, oral, and environmental lineages, forming a largely shared, low-complexity background in both TB and CAD. Background-derived bacterial signatures showed only modest separation between disease and control groups, with wide intra-group variability. Mycobacterium tuberculosis-assigned reads were detectable in many TB-positive samples but accounted for ≤0.001% of total cfRNA and occurred at similar orders of magnitude in a subset of TB-negative samples, precluding robust discrimination. Phylogeny-aware visualization confirmed that visually “enriched” taxa in TB-positive plasma arose mainly from background-associated clades rather than a distinct pathogen-specific cluster. Collectively, these findings provide a quantitative benchmark of the background-dominated regime and practical limits of plasma cfRNA metagenomics for pathogen detection, highlighting that practical performance is constrained more by a shared, low-complexity background and sparse pathogen-derived fragments than by large disease-specific shifts, underscoring the need for transparent host filtering, explicit background modeling, and integration with targeted or orthogonal assays. Full article

Background: SGLT2 inhibitors are key therapies in heart failure (HF), but their combined multidomain effects have not been analyzed together. Methods: We conducted a PROSPERO-registered (CRD420251235850) systematic review and meta-analysis of all randomized controlled trials (RCTs) comparing SGLT2i (dapagliflozin, empagliflozin, canagliflozin, sotagliflozin) with placebo in adults with HF, regardless of ejection fraction or diabetes status. We searched PubMed/MEDLINE, Embase, Cochrane CENTRAL, and Web of Science through 1 February 2025. Outcomes were grouped into four domains: (1) clinical events, (2) symptoms/health status (Kansas City Cardiomyopathy Questionnaire, KCCQ), (3) functional capacity (6 min walk distance, peak VO₂), and (4) cardiac remodeling/energetics (cardiac MRI, 31P-MRS). We used random-effects models with Hartung–Knapp adjustment and assessed heterogeneity by I² and prediction intervals. Results: Eleven RCTs with 23,812 patients (HFrEF, HFmrEF, HFpEF, and acute or recently decompensated HF) were included. SGLT2i lowered the risk of cardiovascular death or first HF hospitalization by 23% (HR 0.77, 95% CI 0.72–0.82; p < 0.0001; I² = 28%; prediction interval 0.68–0.87), with similar effects across ejection fraction, diabetes status, and presentation type. All-cause and cardiovascular mortality dropped by 12% (HR 0.88, 95% CI 0.81–0.96) and 14% (HR 0.86, 95% CI 0.78–0.95), respectively. SGLT2i improved KCCQ—Clinical Summary Score by 4.6 points (95% CI 3.4–5.8; p < 0.0001) and increased the odds of a ≥5-point improvement (OR 1.49, 95% CI 1.32–1.68; NNT = 12). Six-minute walk distance increased by 21.8 m (95% CI 9.4–34.2; p = 0.001), and mechanistic trials showed significant reverse remodeling (ΔLVEDV −19.8 mL, ΔLVEF +6.1%; both p < 0.001). No improvement was observed in myocardial PCr/ATP ratio. Safety was favorable, with no excess ketoacidosis or severe hypoglycemia. Conclusions: This multidomain synthesis demonstrates that SGLT2 inhibitors provide consistent, robust reductions in mortality and hospitalizations, while also delivering early, clinically meaningful improvements across multiple patient-centered domains. These results establish SGLT2i as a foundational component of contemporary HF management. Full article

Background: Pleural effusion (PE) is a common condition where accurate detection is essential for management. Thoracic ultrasound (TUS) is the first-line modality owing to safety, portability, and high sensitivity, but accuracy is operator-dependent. Artificial intelligence (AI)-based automated analysis has been explored as an adjunct, with early evidence suggesting potential to reduce variability and standardise interpretation. This review evaluates the diagnostic accuracy of AI-assisted TUS for PE detection. Methods: This review was registered with PROSPERO (CRD420251128416) and followed PRISMA guidelines. MEDLINE, Scopus, Google Scholar, IEEE Xplore, Cochrane CENTRAL, and ClinicalTrials.gov were searched through 20 August 2025 for studies assessing AI-based TUS analysis for PE. Eligible studies required recognised reference standards (expert interpretation or chest CT). Risk of bias was assessed with QUADAS-2, and certainty with GRADE. Owing to heterogeneity, structured narrative synthesis was performed instead of meta-analysis. Results: Five studies (7565 patients) published between 2021–2025 were included. All used convolutional neural networks with varied architectures (ResNet, EfficientNet, U-net). Sensitivity ranged 70.6–100%, specificity 67–100%, and AUC 0.77–0.99. Performance was reduced for small, trace, or complex effusions and in critically ill patients. External validation showed attenuation compared with internal testing. All studies had high risk of bias in patient selection and index test conduct, reflecting retrospective designs and inadequate dataset separation. Conclusions: AI-assisted TUS shows promising diagnostic performance for PE detection in curated datasets; however, evidence is inconsistent and limited by key methodological weaknesses. Overall certainty is low-to-moderate, constrained by retrospective designs, limited dataset separation, and scarce external validation. Current evidence is insufficient to support routine clinical use. Robust prospective multicentre studies with rigorous independent validation and evaluation of clinically meaningful outcomes are essential before clinical implementation can be considered. Full article

Background: The role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in detecting soft-tissue sarcoma (STS) local recurrence (LR) following therapeutic intervention was evaluated. Method: PubMed, Embase, and Scopus were systematically searched from January 1990 to 1 February 2024 for studies evaluating DCE-MRI for LR detection in histologically confirmed STS following surgery. Two independent reviewers screened studies and extracted data, and a bivariate diagnostic test accuracy meta-analysis was performed to estimate pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve. Results: Six studies, including 309 patients (110 with LR and 199 without LR), met the inclusion criteria. Across studies, DCE-MRI qualitative features (such as early rapid arterial enhancement and malignant time–intensity curves) and quantitative or semiquantitative parameters (such as volume transfer constants [Ktrans and Kep], initial area under the curve [iAUC], and relative plasma flow [rPF]) consistently differentiated LR from post-treatment change. When DCE-MRI parameters were added to conventional MRI, the pooled sensitivity and specificity for LR detection were 98% and 83%, respectively, with an SROC area under the curve of 0.94, indicating high overall diagnostic accuracy. Conclusions: DCE-MRI increases the accuracy of LR detection when combined with conventional MRI and offers a higher specificity and sensitivity in distinguishing LR from post-surgical changes, which support consideration of adding DCE-MRI when LR is suspected; prospective standardized studies are warranted. Full article

Background. Extensive research confirms that hormonal fluctuations during the menstrual cycle significantly influence female athletic performance, with profound implications for public health, including promoting equitable access to sports and enhancing women’s overall physical and mental well-being. Numerous scientifically validated methods are available to monitor hormonal status and menstrual cycle phases. However, our prior investigations revealed that these insights are rarely applied in practice due to the complexity and invasiveness of existing methods. This study examines the effects of hormonal fluctuations on elite female basketball players. It assesses practical, non-invasive, cost-effective, and field-applicable methods for hormonal monitoring, with a focus on cervical mucus analysis for estrogen crystallization. The goal is to optimize training, promote equity in women’s sports, and support public health strategies for female empowerment through sustained physical activity, addressing the limitations of male-centric training models. Materials and Methods. This exploratory field study employed a multifaceted approach, beginning with a comprehensive meta-analysis via literature searches on PubMed, SCOPUS, and Google Scholar to evaluate hormonal impacts on physical performance, supplemented by an expert survey of 20 sports scientists and coaches using Kendall’s concordance coefficient for reliability and an experimental phase involving 25 elite female Ukrainian basketball players assessed over three months through daily performance tests (e.g., sprints, jumps, agility drills, and shooting) integrated into six weekly training sessions, with cycle phases tracked via questionnaires, basal body temperature, and the fern leaf method for estrogen levels. Results. Performance peaked during the postmenstrual and post-ovulatory phases (e.g., a 7.5% increase in sprint time and a 5.1% improvement in running jump). It declined in the premenstrual phase (e.g., a 2.3% decrease in acceleration). The estrogen crystallization test using cervical mucus provided preliminary insights into hormonal status but was less precise than laboratory-based methods, such as LC-MS/MS, which remain impractical for routine use due to cost and complexity. The fern test and basal body temperature showed limited precision due to external factors. Conclusions. There is a critical need to develop simple, non-invasive, field-applicable devices for accurate, real-time hormonal monitoring. This will bridge the gap between research and practice, enhancing training personalization, equity in women’s fitness and sports, and public health outcomes by increasing female participation in physical activities, reducing gender-based health disparities, and fostering inclusive wellness programs. Full article