Search Results (73)

Background: The effectiveness of metformin in preventing Type-2 Diabetes Mellitus (T2DM) is examined. There are new available data. Currently, there are no available analyses classifying its effectiveness compared to placebo, standard care, or lifestyle interventions, and there is limited evidence on the combined action of metformin and lifestyle interventions in preventing T2DM. Objective: To calculate the updated overall effectiveness of metformin in preventing T2DM using all available and most recent data, and to explore the effectiveness of metformin and lifestyle interventions in preventing T2DM. Materials and Methods: A search was performed in PubMed and the Cochrane Library Central Register of Controlled Trials (CENTRAL) (from inception to 24 May 2025). A systematic review (SR) and meta-analysis (MA) of randomized controlled trials (RCTs) was carried out, including metformin-naïve adults with any identified diabetes risk factors. The overall effectiveness of metformin was estimated by combining studies that compare metformin against placebo, metformin and standard care against standard care, and metformin plus lifestyle interventions and the same lifestyle interventions. The combined action of metformin and lifestyle interventions was evaluated against standard care. We performed a GRADE assessment of the overall evidence. Results: Overall, metformin may reduce the incidence of T2DM by 23% in high-risk adults (OR 0.77, 95% CI 0.67, 0.88, p-value 0.0001) and 25% in patients with prediabetes (OR 0.75, 95%CI 0.66, 0.86, p-value < 0.0001). It is also effective in both obese and normal-weight patients, in Caucasians, in studies with female predominance, in studies with a mean age over 60 years, at 1700 mg daily, and after 18 months of administration. Effectiveness weakens after interruption of administration. Metformin is more effective compared to placebo and when combined with standard care than standard care alone, but not when combined with lifestyle interventions against lifestyle interventions alone. Metformin and lifestyle interventions reduce the incidence of diabetes in patients with prediabetes by 52% compared to standard care (OR 0.48, 95% CI 0.30, 0.77; p-value 0.002). There are effectiveness concerns in studies with more men than women, Asian Indians and Pakistanis, a mean age below 60 years, 500 mg of metformin daily, and after six months. The effect is reduced during post-intervention. Finally, metformin alone is more effective than standard care (OR 0.56, 95% CI 0.34, 0.90, p-value 0.02). The quality of evidence was moderate for the overall effectiveness of metformin and metformin combined with lifestyle interventions, and low for metformin against standard care. Conclusions: A 1700 mg dose of metformin daily is effective in preventing T2DM, especially in Caucasians, in women over 60 years, in prediabetes, and independent of obesity. Lifestyle interventions and 500 mg of metformin daily may prevent T2DM in patients with prediabetes, especially in men and Asian Indians or Pakistanis under 60 years. The effectiveness of complex interventions is more pronounced than that of metformin alone in patients with prediabetes. Further research is needed for post-intervention effectiveness, patients with any diabetes risk factors, patients from different regions, and women in complex interventions. Full article

Background/Objectives: Variations in the course of the maxillary artery (MA) relative to the lateral pterygoid muscle (LPM) pose critical challenges in surgical, anesthetic, and interventional procedures involving the infratemporal fossa (ITF). These variations can increase the risk of hemorrhage, nerve injury, or incomplete anesthesia. The present study aimed to elucidate the topographic relationship between the MA and LPM by combining high-resolution radiological imaging with a comprehensive analysis of anatomical literature. Materials and Methods: A retrospective review of 250 brain computed tomography angiographies (CTAs), totaling 500 sides, was conducted to classify the MA course as lateral (superficial), medial (deep), or intramuscular. Additionally, a systematic review and meta-analysis of 32 eligible studies—including 5938 arteries—was performed following PRISMA 2020 and Evidence-Based Anatomy (EBA) guidelines. Study quality and risk of bias were assessed using the Anatomical Quality Assurance (AQUA) tool. Results: In the imaging cohort, the MA coursed lateral to the LPM in 64.2% of sides, medial in 29.6%, and through the muscle fibers in 6.2%. A rare temporalis-traversing variant was identified in 3.0% of cases. Bilateral symmetry was observed in 77.6% of patients. Meta-analytic findings indicated a pooled prevalence of 79.6% for the lateral course, 19.9% for the medial course, and 0.01% for the intramuscular course. Cadaveric studies and Asian populations showed a higher incidence of lateral variants, while imaging-based studies more frequently detected medial and transmuscular paths. Conclusions: While the MA most often follows a lateral course relative to the LPM, clinically significant variation—including medial, intramuscular, and temporalis-traversing routes—exists. These variants complicate access during maxillofacial surgery, TMJ procedures, and regional anesthesia. Findings emphasize the importance of individualized preoperative vascular mapping to improve procedural safety and outcomes in the ITF. Full article

Introduction: Early-onset colorectal cancer (EOCRC) is rising rapidly, particularly among the Hispanic/Latino (H/L) populations, who face disproportionately poor outcomes. The transforming growth factor-beta (TGF-β) signaling pathway plays a critical role in colorectal cancer (CRC) progression by mediating epithelial-to-mesenchymal transition (EMT), immune evasion, and metastasis. However, integrative analyses linking TGF-β alterations to clinical features remain limited—particularly for diverse populations—hindering translational research and the development of precision therapies. To address this gap, we developed AI-HOPE-TGFbeta (Artificial Intelligence agent for High-Optimization and Precision Medicine focused on TGF-β), the first conversational artificial intelligence (AI) agent designed to explore TGF-β dysregulation in CRC by integrating harmonized clinical and genomic data via natural language queries. Methods: AI-HOPE-TGFbeta utilizes a large language model (LLM), Large Language Model Meta AI 3 (LLaMA 3), a natural language-to-code interpreter, and a bioinformatics backend to automate statistical workflows. Tailored for TGF-β pathway analysis, the platform enables real-time cohort stratification and hypothesis testing using harmonized datasets from the cBio Cancer Genomics Portal (cBioPortal). It supports mutation frequency comparisons, odds ratio testing, Kaplan–Meier survival analysis, and subgroup evaluations across race/ethnicity, microsatellite instability (MSI) status, tumor stage, treatment exposure, and age. The platform was validated by replicating findings on the SMAD4, TGFBR2, and BMPR1A mutations in EOCRC. Exploratory queries were conducted to examine novel associations with clinical outcomes in H/L populations. Results: AI-HOPE-TGFbeta successfully recapitulated established associations, including worse survival in SMAD4-mutant EOCRC patients treated with FOLFOX (fluorouracil, leucovorin and oxaliplatin) (p = 0.0001) and better outcomes in early-stage TGFBR2-mutated CRC patients (p = 0.00001). It revealed potential population-specific enrichment of BMPR1A mutations in H/L patients (OR = 2.63; p = 0.052) and uncovered MSI-specific survival benefits among SMAD4-mutated patients (p = 0.00001). Exploratory analysis showed better outcomes in SMAD2-mutant primary tumors vs. metastatic cases (p = 0.0010) and confirmed the feasibility of disaggregated ethnicity-based queries for TGFBR1 mutations, despite small sample sizes. These findings underscore the platform’s capacity to detect both known and emerging clinical–genomic patterns in CRC. Conclusions: AI-HOPE-TGFbeta introduces a new paradigm in cancer bioinformatics by enabling natural language-driven, real-time integration of genomic and clinical data specific to TGF-β pathway alterations in CRC. The platform democratizes complex analyses, supports disparity-focused investigation, and reveals clinically actionable insights in underserved populations, such as H/L EOCRC patients. As a first-of-its-kind system studying TGF-β, AI-HOPE-TGFbeta holds strong promise for advancing equitable precision oncology and accelerating translational discovery in the CRC TGF-β pathway. Full article

Background: With the inclusion of evidence-based dentistry, numerous systematic reviews (SRs) and meta-analyses (MAs) have been conducted in endodontics with the best available scientific evidence to improve diagnosis and treatment. Objective: To synthesize the scientific evidence on the effectiveness of irrigation protocols in endodontic therapy. Methods: Following the umbrella review methodology (UR), a comprehensive literature search was conducted using scientific and grey literature databases. A quality evaluation and a descriptive analysis of the included SRs and MAs were conducted. Quantitative comparability between MAs was carried out. Results: Four descriptive SRs and nine MAs were included. Eight articles evidenced high methodological quality. Studies showed the effectiveness and efficacy depending on the study design, the findings of primary clinical trials, and factors related to the type of irrigant, concentration, volume, and irrigation systems. Variability between irrigants and protocols was observed. Follow-up periods extend from hours to years, and there were different study samples. SRs and MAs evidenced limitations regarding methodological aspects. Low overlap of the primary studies was found. Quantitative analyses indicated greater efficacy in microbial reduction and apical healing in favor of passive ultrasonic irrigation (PUI; RD −0.15; 95% CI −0.28, −0.01; p = 0.03; I2 = 60%; RD −0.09; 95% CI −0.16, −0.02; p = 0.01; I2 = 0%, respectively). Conclusions: This UR highlights the importance of root canal disinfection, emphasizing sodium hypochlorite (NaOCl) as the primary irrigant. Enhanced activation methods, such as PUI and lasers, improve irrigant efficiency, while alternatives like chlorhexidine (CHX) offer better biocompatibility. Standardized protocols and evidence-based clinical guidelines are needed. PROSPERO register: CRD42023409044. Full article

Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces $MsMA$, a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, $MsMA$ divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques. Full article

Background: Ready-to-eat cereals (RTECs) are a large, heterogeneous category of cereals designed to fit into busy lifestyles with minimal preparation time. Methods: This systematic review evaluated nutrient intake data from seven national surveys. Using PubMed and Science Direct (1 January 2004 until 16 September 2024), we investigated RTECs in relation to their contributions to macro, micronutrient and food group intakes, breakfast/diet quality and effects on health with focus on non-communicable disease (NCD) prevention. The search was restricted to Systematic Reviews (SRs), meta-analyses (MAs), randomised controlled trials (RCTs) and observational studies. Fifty-one publications were obtained. Studies related to health outcomes and NCD risk were graded using an updated Scottish Intercollegiate Guidelines Network approach. Results: Grade A evidence: Based on high-quality MA, SRs, or RCTs, this showed that RTEC consumption was associated with improved nutrient intakes (particularly fibre and micronutrients), reduced cardiovascular disease and mortality. One good-quality Grade A meta-analysis showed that total whole grain intake which included cereals was associated with a reduced risk of total cancer. Grade B evidence: Based largely on observational evidence, this showed that RTEC consumption was associated with reduced risk of overweight and obesity, body mass index and composition improvements and type 2 diabetes risk. For food group intakes, breakfast/diet quality and lipid profiles, more well-designed studies were needed (Grade D evidence). Conclusions: There is consistent evidence that RTECs generally have positive or neutral effects on nutritional status and NCD prevention. Strongest evidence exists for RTEC and micronutrient intakes, reduced risk of cardiovascular diseases (CVDs), body weight regulation, and reduced type 2 diabetes risk. Public health messaging should recognise that RTECs, especially whole-grain, higher-fibre and lower-sugar varieties, may help to reinforce micronutrient intakes and a range of health outcomes. Full article

Background/Objectives: The majority of stroke survivors undergo physical therapy rehabilitation to regain functionality and improve their overall quality of life. Given the wide range of physical therapy modalities and approaches in post stroke cardiovascular fitness rehabilitation, this systematic review and meta-analysis (SR-MA) aims to assess their efficacy as measured by peak oxygen consumption (VO2peak). Methods: Adhering to PRISMA guidelines; a detailed search of the MEDLINE PubMed; Cochrane Library; and Scopus databases was conducted. Results: Thirty-seven studies with a total of 1310 post-stroke patients were included. The aggregated mean VO2 pre-intervention was 15.30 mL/kg/min ([14.09, 16.51], I² = 99.7%), increasing to 17.10 mL/kg/min post-intervention ([15.73, 18.46], I² = 99.8%). The standardized mean difference in VO2 was 1.76 ([1.20, 2.31], I² = 96.9%). Sensitivity analyses in a subset of RCTs revealed that cardiorespiratory rehabilitation demonstrates a statistically significant improvement in VO2peak levels compared to conventional physical therapy. There was a high degree of heterogeneity among included studies (potentially due to the lack of standardized protocols) while Egger’s test (β = 0.32, p = 0.72) and funnel plot inspection were indicative of moderate publication bias with small study effects. Conclusions: Based on the results of this meta-analysis, the increase in VO2peak levels post-interventions ranged from 0.28 to 3.36 mL/kg/min, depending on intervention type. The ideal time to commence aerobic training rehabilitation was found to be six months post-stroke. According to previous studies on cardiovascular diseases, VO2peak can potentially act as a predictor of (a) the efficacy of intervention and (b) the patient’s risk of stroke-recurrence and disability progression. Full article

Recently, there have been significant advances and wide-scale use of generative AI in natural language generation. Models such as OpenAI’s GPT3 and Meta’s LLaMA are widely used in chatbots, to summarize documents, and to generate creative content. These advances raise concerns about abuses of these models, especially in social media settings, such as large-scale generation of disinformation, manipulation campaigns that use AI-generated content, and personalized scams. We used stylometry (the analysis of style in natural language text) to analyze the style of AI-generated text. Specifically, we applied an existing authorship verification (AV) model that can predict if two documents are written by the same author on texts generated by GPT2, GPT3, ChatGPT and LLaMA. Our AV model was trained only on human-written text and was effectively used in social media settings to analyze cases of abuse. We generated texts by providing the language models with fanfiction snippets and prompting them to complete the rest of it in the same writing style as the original snippet. We then applied the AV model across the texts generated by the language models and the human written texts to analyze the similarity of the writing styles between these texts. We found that texts generated with GPT2 had the highest similarity to the human texts. Texts generated by GPT3 and ChatGPT were very different from the human snippet, and were similar to each other. LLaMA-generated texts had some similarity to the original snippet but also has similarities with other LLaMA-generated texts and texts from other models. We then conducted a feature analysis to identify the features that drive these similarity scores. This analysis helped us answer questions like which features distinguish the language style of language models and humans, which features are different across different models, and how these linguistic features change over different language model versions. The dataset and the source code used in this analysis have been made public to allow for further analysis of new language models. Full article

Background: The lack of definitive scientific evidence sustains uncertainty about the efficacy of glucosamine and its combination therapies for knee osteoarthritis (KOA), contributing to an ongoing debate among clinical practice guidelines and healthcare practitioners. This systematic review and network meta-analysis (NMA) aimed to identify the most effective glucosamine combination therapy for KOA patients. Methods: Frequentist random-effects models were employed for this NMA, with standardized mean differences (SMDs) and 95% confidence intervals (CIs) calculated for primary outcomes. We incorporated an SMD value of 0.40 as a minimum clinically important difference (MCID) to interpret the pain outcome. Confidence in evidence was evaluated using CINeMA. Results: Thirty randomized controlled trials (RCTs) covering 5265 patients were included. Glucosamine with omega-3 (G + omega-3, SMD –2.59 [95% CI –4.42 to –0.75], moderate quality) and glucosamine with ibuprofen (G + ibuprofen, SMD –2.27 [95% CI –3.73 to –0.82], moderate quality) significantly reduced overall pain compared to placebo. Similarly, glucosamine + chondroitin sulfate + methylsulfonylmethane showed effectiveness in pain reduction (SMD –2.25 [95% CI –3.84 to –0.67], low-quality). None of the other interventions met the MCID threshold for overall pain reduction. Moreover, clustered ranking results showed that glucosamine with omega-3 interventions was more effective than others in reducing overall pain and adverse events. Conclusions: For KOA, combining glucosamine with omega-3 and ibuprofen effectively reduces pain and may lower NSAID side effects, improving treatment guidelines and decision-making for better patient care. Full article

Objective: To quantify microvascular lesions in a large real-world data (RWD) set, based on single central retinal fundus images of diabetic eyes from different origins, with the aim of validating its use as a precision tool for classifying diabetic retinopathy (DR) severity. Design: Retrospective meta-analysis across multiple fundus image datasets. Sample size: The study analyzed 2445 retinal fundus images from diabetic patients across four diverse RWD international datasets, including populations from Spain, India, China and the US. Intervention: The quantification of specific microvascular lesions: microaneurysms (MAs), hemorrhages (Hmas) and hard exudates (HEs) using advanced automated image analysis techniques on central retinal images to validate reliable metrics for DR severity assessment. The images were pre-classified in the DR severity levels as defined by the International Clinical Diabetic Retinopathy (ICDR) scale. Main Outcome Measures: The primary variables measured were the number of MAs, Hmas, red lesions (RLs) and HEs. These counts were related with DR severity levels using statistical methods to validate the relationship between lesion counts and disease severity. Results: The analysis revealed a robust and statistically significant increase (p < 0.001) in the number of microvascular lesions and the DR severity across all datasets. Tight data distributions were reported for MAs, Hmas and RLs, supporting the reliability of lesion quantification for accurately assessing DR severity. HEs also followed a similar pattern, but with a broader dispersion of data. Data used in this study are consistent with the definition of the DR severity levels established by the ICDR guidelines. Conclusions: The statistically significant increase in the number of microvascular lesions across DR severity validate the use of lesion quantification in a single central retinal field as a key biomarker for disease classification and assessment. This quantification method demonstrates an improvement over traditional assessment scales, providing a quantitative microvascular metric that enhances the precision of disease classification and patient monitoring. The inclusion of a numerical component allows for the detection of subtle variations within the same severity level, offering a deeper understanding of disease progression. The consistency of results across diverse datasets not only confirms the method’s reliability but also its applicability in a global healthcare setting. Full article

This study investigates the effectiveness of a proposed version of Meta’s LLaMA 3 model in detecting fake claims across bilingual (English and Romanian) datasets, focusing on a multi-class approach beyond traditional binary classifications in order to better mimic real-world scenarios. The research employs a proposed version of the LLaMA 3 model, optimized for identifying nuanced categories such as “Mostly True” and “Mostly False”, and compares its performance against leading large language models (LLMs) including Open AI’s ChatGPT versions, Google’s Gemini, and similar LLaMA models. The analysis reveals that the proposed LLaMA 3 model consistently outperforms its base version and older LLaMA models, particularly in the Romanian dataset, achieving the highest accuracy of 39% and demonstrating superior capabilities in identifying nuanced claims, over all the compared large language models. However, the model’s performance across both languages highlights some challenges, with generally low accuracy and difficulties in handling ambiguous categories by all the LLMs. The study also underscores the impact of language and cultural context on model reliability, noting that even state-of-the-art models like ChatGPT 4.o and Gemini exhibit inconsistencies when applied to Romanian text and more than a binary true/false approach. Full article

(1) Background: Glioblastoma (GBM) is the most common malignant brain tumor in adults. Due to a lack of level 1 evidence, there is no clear consensus on the optimal extent of resection to improve overall survival. This umbrella review aggregates existing meta-analyses (MAs) to assess overall survival in patients undergoing subtotal resection (STR) versus gross total resection (GTR). (2) Methods: A systematic search of PubMed, Scopus, and Web of Science identified 441 studies, with four MAs meeting inclusion criteria. Data were analyzed using the metaumbrella R package, focusing on overall survival. Quality was assessed using AMSTAR2, with scores ranging from 0 to 11. The Ioannidis criteria were applied to evaluate the credibility of the evidence. (3) Results: The quality assessment rated all four studies highly, with a mean AMSTAR2 score of 10.25. The pooled analysis revealed a significant survival advantage for GTR over STR. However, the Ioannidis classification graded the evidence as Class III, indicating weak credibility. (4) Conclusions: GTR offers a slight survival benefit over STR in GBM patients, but the credibility of the evidence is weak, highlighting the need for further research. Full article

Meta-analysis is a statistical tool used to combine and synthesise the results of multiple independent studies on a particular topic. To this end, researchers isolate important moderators and mediators to investigate their influence on outcomes. This paper introduces a novel approach to meta-analysis, known as multidimensional meta-analysis (mi-MA), to study memory performance in those with obsessive–compulsive disorder (OCD). Unlike traditional meta-analyses, mi-MA allows researchers to extract multiple data points (e.g., using different measures) from single studies and groups of participants, facilitating the exploration of relationships between various moderators while avoiding multicollinearity issues. Therefore, in the first instance, we outline the use of the mi-MA approach to quantify the impact of complex models of memory performance in individuals with OCD. This approach provides novel insights into the complex relationship between various factors affecting memory in people with OCD. By showcasing the effectiveness of mi-MA in analysing intricate data and modelling complex phenomena, the paper establishes it as a valuable tool for researchers exploring multifaceted phenomena, both within OCD research and beyond. Full article

This meta-analysis aimed to summarise clinical evidence regarding the effect of supplementation with cornelian cherry (Cornus mas L.) on different cardiometabolic outcomes. An extensive literature survey was carried out until 10 April 2024. A total of 415 participants from six eligible studies were included. The overall results from the random-effects model indicated that cornelian cherry supplementation significantly reduced body weight (standardised mean difference [SMD] = −0.27, confidence interval [CI]: −0.52, −0.02, p = 0.03), body mass index (SMD = −0.42, CI: −0.73, −0.12, p = 0.007), fasting blood glucose (SMD = −0.46, CI: −0.74, −0.18, p = 0.001), glycated haemoglobin (SMD = −0.70, CI: −1.19, −0.22, p = 0.005), and HOMA-IR (SMD = −0.89, CI: −1.62, −0.16, p = 0.02), while high-density lipoprotein cholesterol significantly increased (SMD = 0.38, CI: 0.10, 0.65, p = 0.007). A sensitivity analysis showed that cornelian cherry supplementation significantly reduced total plasma triglycerides, total cholesterol, low-density lipoprotein cholesterol, and insulin levels. Cornelian cherry supplementation did not significantly affect waist circumference and liver parameters among the participants. Considering these findings, this meta-analysis indicates that supplementation with cornelian cherry may impact diverse cardiometabolic risk factors among individuals considered to be at a high risk. Full article