The aim of this systematic review is to synthesize the current evidence regarding the efficacy, safety, and broader therapeutic potential of semaglutide across various clinical and preclinical contexts. Source: PubMed. Included original Abstracts/Papers: 1894. Total study participants (naïve ΣN): 6933647. Conclusion: Semaglutide treatment for obesity or overweight without type 2 diabetes is associated with a median body weight reduction of 15.3% (range: 13.4% to 19.5%) over approximately one year. This efficacy extends to significant improvements in glycemic control, cardiovascular, renal, and hepatic health, and shows promising neuroprotective effects across various patient populations. However, the heterogeneity of study designs and variability in follow-up durations remains the most significant limitation affecting the certainty of some conclusions. Clinicians should consider semaglutide as a powerful tool for comprehensive metabolic and cardiovascular risk reduction, while diligently monitoring for known and emerging adverse events.

This paper aims to systematically review the diverse impacts of economic inequality on individual well-being, societal dynamics, environmental sustainability, and public health, and to identify critical research gaps and policy implications based on recent empirical evidence. Systematic review with multilayer AI research agent: keyword normalization, retrieval & structuring, and paper synthesis. Included original Abstracts/Papers: 4258. Total study participants : 13397798. Conclusion: A synthesis of current research indicates that economic inequality is a pervasive determinant of adverse outcomes across diverse domains, including individual well-being, public health, environmental sustainability, and societal stability.

Errors in Retrieval-Augmented Generation (RAG) and agentic AI systems are commonly attributed to limitations of large language models. Based on two years of developing ☸️SAIMSARA, a large-scale agentic AI system for medical scientific article review, this editorial argues that most failures arise instead from architectural design and workload allocation. Key pitfalls include prompt overload, excessive batch size, oversized input items, and the use of speed-optimized models in multi-stage workflows. Practical mitigation strategies are outlined, emphasizing the importance of aligning prompt complexity, batch structure, and input size with model capacity. The findings highlight that robustness in RAG and agentic AI systems is primarily a systems engineering challenge rather than a model selection problem.

Systematic review with multilayer AI research agent: keyword normalization, retrieval & structuring, and paper synthesis. Source: Europe PMC. Scope: Titles/Abstracts (tiab). Keyword Gate: Fuzzy (≥60% of required terms, minimum 2 terms matched in title/abstract). Total Abstracts/Papers: 146. Downloaded Abstracts/Papers: 146. Included original Abstracts/Papers: 83. Total study participants (naïve ΣN): 52843. Conclusion: Whisky and wine exhibit distinct economic responses to price changes, with wine demonstrating a higher price elasticity (-0.955) compared to whisky (-0.587) in Taiwan, indicating greater sensitivity of wine consumption to price fluctuations [23]. These findings, drawn from diverse research settings including human physiological studies, chemical analyses, and socio-economic evaluations, highlight varied impacts across health, sensory, and economic domains. However, the heterogeneity in study designs and the scarcity of direct comparative data across identical metrics significantly limit the certainty of comprehensive conclusions. Future research should prioritize standardized comparative studies to elucidate the full spectrum of differential effects of whisky versus wine on human health and behavior.

The comparative safety and effectiveness of transcarotid artery revascularization (TCAR) versus carotid artery stenting (CAS), predominantly transfemoral CAS (TF-CAS), for carotid artery stenosis were evaluated in a systematic review of 65 studies including 33,220 patients. Evidence from mainly retrospective cohort and mixed-design studies was synthesized using an AI-assisted literature workflow. Lower median perioperative stroke or death rates were observed with TCAR (2.3%; range 0.7%–3.6%) than with TF-CAS (3.7%; range 2.0%–6.6%), and reduced long-term stroke risk with TCAR was reported in both asymptomatic and symptomatic patients. Additional advantages of TCAR were reported, including improved neuroprotection through flow reversal, shorter procedure times, and reduced radiation and contrast exposure, while similar benefits of optimized perioperative pharmacotherapy (P2Y12 inhibition, protamine) were described for both techniques. The overall certainty of evidence was limited by the predominance of observational designs, heterogeneity of CAS approaches, and variable follow-up durations. On the basis of current data, TCAR is suggested as the safer endovascular option compared with TF-CAS for many patients with carotid artery stenosis, although confirmation in adequately powered randomized controlled trials is still required.

This systematic review, generated by the autonomous multilayer AI research agent ☸️SAIMSARA, synthesizes evidence on the health effects of Daylight Saving Time (DST). From 1 777 retrieved publications, 95 original studies encompassing 1 268 808 participants were analyzed. Outcome-Sentiment Meta-Analysis (OSMA) revealed that DST was associated with beneficial outcomes in 25 % (ΣN = 312 472), harmful in 61 % (ΣN = 775 143), and neutral in 14 % (ΣN = 181 193) of participants. The most consistent effect was a median sleep loss of ≈ 50 minutes following the spring transition, alongside transient increases in cardiovascular events and accident rates. Evidence heterogeneity limits firm causal inference, but overall findings support Standard Time as physiologically more favorable for circadian and public health alignment.

A large-scale systematic review was conducted with ☸️SAIMSARA on Europe PMC titles/abstracts. Original studies assessing the association between COVID-19 vaccination and mortality were algorithmically identified and synthesized. From 12,371 records, 4,073 studies (ΣN Participants=19,780,831) were included. An LLM-only Outcome-Sentiment Meta-Analysis classified findings as beneficial for 55% of participants (ΣN Participants=10,896,598), neutral for 44% (ΣN Participants=8,663,236), and harmful for 1% (ΣN Participants=220,997). Across studies reporting vaccine effectiveness (VE) against COVID-19–related or all-cause mortality, an unweighted median VE of 84.24% (range 53–100%) was observed, with stronger protection after booster doses and across vulnerable groups. No excess non-COVID mortality after vaccination was detected. Heterogeneity of designs, variable outcome definitions, retrospective data, and potential “healthy vaccinee” bias limit causal inference. Overall, substantial mortality reduction associated with COVID-19 vaccination was found, and standardized, prospective, variant-aware evaluations were indicated to refine estimates and guide booster policy.

Autonomous systematic review generated by SAIMSARa analyzing 5,048 studies from Europe PMC on carotid shunting and perioperative stroke. Outcome-Sentiment Meta-Analysis (OSMA) found 55% harmful, 42% neutral, and 3% beneficial outcomes (ΣN=771,467). Results highlight variability in evidence and emphasize the need for standardized randomized trials.

Aim: This basic research study aimed to assess the ability of Web AI Vision to classify anatomical movement patterns in real-time B-mode ultrasound scans for controlling a virtual bionic limb. Methods: A MobileNetV2 model, implemented via the TensorFlow.js library, was used for transfer learning and feature extraction from 400 B-mode ultrasound images of the distal forearm of one individual participant, corresponding to four different hand positions: 100 images of a fist position, 100 images of thumb palmar abduction, 100 images of a fist with an extended forefinger, and 100 images of an open palm. Results: After 32 epochs of training with a learning rate of 0.001 and a batch size of 16, the model achieved 100% validation accuracy, 100% test accuracy, and a test loss (crossentropy) of 0.0067 in differentiating ultrasound patterns associated with specific hand positions. During manual testing with 40 ultrasound images excluded from training, validation, and testing, the AI was able to correctly predict the hand position in all 40 cases (100%), with a mean predicted probability of 98.9% (SD ± 0.6). When tested with B-mode cine loops and live ultrasound scanning, the model successfully performed real-time predictions with a 20 ms interval between predictions, achieving 50 predictions per second. Conclusion: This study demonstrated the ability of Web AI Vision to classify anatomical movement patterns in real-time B-mode ultrasound scans for controlling a virtual bionic limb. Such ultrasound- and Web AI-powered bionic limbs can be easily and automatically retrained and recalibrated in a privacy-safe manner on the client side, within a web environment, and without extensive computational costs. Using the same ultrasound scanner that controls the limb, patients can efficiently adjust the Web AI Vision model with new B-mode scans as needed, without relying on external services. The advantages of this combination warrant further research into AI-powered muscle movement analysis and the utilization of ultrasound-powered Web AI in rehabilitation medicine, neuromuscular disease management, and advanced prosthetic control for amputees.

With this editorial, we inaugurate the next issue of our journal, which introduces and explores the term Science 3.0, defined as human research driven by decentralized AI agents.