Sándor Nagy

Background/Objectives: Obesity in adolescent girls is associated with metabolic and reproductive changes that can persist into adult life. Methods: This review examines nutrition therapy and glucagon-like peptide-1 (GLP-1) receptor agonist pharmacotherapy in girls aged 8–18 years. PubMed/MEDLINE, Scopus and OpenAlex were searched; one author selected the studies and extracted the data. Each outcome is classified as demonstrated in adolescent trials, extrapolated from adults, mechanistic, unknown or an expert proposal. Results: In adolescents aged 12 to <18 years, semaglutide and liraglutide increased the proportion achieving a 5% reduction in body weight or body mass index compared with placebo, and semaglutide improved insulin resistance; a Cochrane review rates the anthropometric benefit of paediatric pharmacotherapy as low-certainty. Effects on androgen excess and menstrual function have been shown only in adults and cannot be separated from weight loss; pubertal timing, lean mass and bone accrual have not been studied as outcomes in randomised trials. Conclusions: Small trials in adolescents and young women with polycystic ovary syndrome suggest that dietary or lifestyle treatment with weight loss can improve menstrual regularity, whereas effects on androgens are inconsistent. Nutritional protection of lean mass, bone and micronutrient status during drug-induced weight loss is untested; we present it, with a stepped-care framework, as an expert proposal.

Endometriosis affects approximately 5%–10% of reproductive-aged women and imposes a substantial yet underexplored preventive research gap relative to treatment-focused literature. Diet is the central axis of this review: we organize the dietary evidence around an explicit mechanistic sequence—food-group-level epidemiological risk, hypothesised to be mediated by gut-microbiota dysbiosis and the estrogen-metabolizing “estrobolome”, and thence to candidate dietary and probiotic interventions—and then situate this dietary axis within a broader multidimensional prevention framework spanning additional primary prevention (other modifiable lifestyle factors, environmental endocrine-disruptor exposure), secondary prevention (non-invasive early screening, adolescent menstrual-health education, and, as a conceptual proposal, targeted early hormonal intervention following the neo-evolutionary model of Vercellini and colleagues), and tertiary-level management of established disease. While, to our knowledge, six recent reviews and meta-analyses (2024–2026) have quantitatively synthesized randomized-trial evidence on dietary and nutraceutical interventions for endometriosis-associated pain, none situates this evidence within an explicit gut microbiota-estrobolome mechanistic framework or a broader multidimensional prevention model—the distinguishing contribution of this review. We advance this framework as a hypothesis-generating prevention research agenda rather than a set of clinically established preventive measures. Cost-effectiveness is treated as a cross-cutting dimension: endometriosis-specific economic evaluations of prevention and screening remain scarce—with a digital symptom-checker Markov model as a notable exception—so analogous frameworks from chronic-disease lifestyle-intervention economics are proposed as methodological templates. We synthesize current evidence, controversies, and priority directions, with particular emphasis on the dietary axis and its mechanistic underpinnings.

Background/Objectives: Guidance on protein and physical activity rests on population-wide minima with little differentiation by age or disease. We examined how protein intake and resistance training relate to muscle, function and mortality across clinical populations. Methods: PRISMA-guided rapid review: Fifty-eight logged PubMed queries returned 7544 unique records for screening (the analytic corpus); Europe PMC, Semantic Scholar and ClinicalTrials.gov were tested for coverage only. Estimates were extracted by a deterministic script and adjudicated by the authors; random-effects models were fitted within strata sharing an effect measure and contrast, and randomized evidence was tabulated, not pooled. Screening was single-reviewer outside a duplicate-screened sample (889 records); risk of bias was rated in duplicate. Results: In randomized trials, added protein increased strength with resistance training (+2.01%) but not without it (+0.13%); no trial of protein with resistance training reported mortality. Meeting activity guidelines was associated with lower mortality (HR 0.76, 95% CI 0.72–0.79; k = 16; I² = 64%; low certainty). Plant protein was associated with lower mortality (HR 0.76, 0.63–0.93; k = 4; very low certainty). After excluding studies at high risk of bias, the protein strata fell to single studies. The direction of protein–outcome associations differed between populations. Conclusions: Protein with resistance training is a plausible strategy for preserving muscle and function; effects on survival are untested, and population-specific protein ranges are provisional and hypothesis-generating.

A TORCH-fertőzések (toxoplasmosis, egyéb [syphilis, bárányhimlő], rubeola, cytomegalovirus, herpes simplex vírus) a veleszületett infekciók klasszikus csoportját alkotják, amelyek súlyos magzati és újszülöttkori következményekkel járhatnak. A transplacentarisan terjedő fertőzések évtizedek óta a praenatalis gondozás kritikus részét képezik. Bár Magyarországon 1971 óta alkalmazzák a gyűjtőfogalomba tartozó szűréseket, a diagnosztikai szemlélet és a szűrési stratégiák jelentős átalakuláson mentek keresztül. A jelenlegi nemzetközi ajánlások és a hazai szakmai irányelvek nem specifikus ultrahangeltérések esetén már nem javasolják az indokolatlan, rutinszerű TORCH-panelek alkalmazását, mivel ezek diagnosztikai értéke rendkívül csekély. A közlemény célja a TORCH-fertőzések klinikai jelentőségének, a legfrissebb, bizonyítékokon alapuló diagnosztikai algoritmusoknak (szerológia, aviditásvizsgálat, polimeráz-láncreakció) és az új terápiás lehetőségeknek (például valaciklovir cytomegalovirus-fertőzésben) a bemutatása, hangsúlyozva a célzott kivizsgálás és a megelőzés jelentőségét. A kézirat összefoglalja a várandósság alatti TORCH-fertőzések diagnosztikájának és kezelésének hazai vonatkozásait, beleértve a magyar szűrési gyakorlat és a referenciaellátás sajátosságait is. A diagnosztika paradigmaváltáson ment keresztül, így a TORCH-panel vizsgálatát rutinszerűen nem javasolt végezni. A célzott vizsgálati stratégia alkalmazása javítja a diagnosztikai hatékonyságot, és csökkenti a felesleges beavatkozásokat. Orv Hetil. 2026; 167(39): 1535–1551.

Artificial intelligence is widely proposed against antimicrobial resistance, but how much of that literature could support clinical deployment is unclear. We searched PubMed (2016–2026) across four domains, retrieving all 2446 records. Of 725 eligible studies, 80 were appraised at full text against the artificial-intelligence appraisal extensions (PROBAST+AI, TRIPOD+AI). Selection favoured externally validated work, so the validation figures are upper bounds. Output grew from 10 studies in 2016 to 142 in 2025; within the appraised set there was no evidence that genuinely external evaluation became more likely (odds ratio 1.08/year, 95% CI 0.89–1.34). Of the 80, 45% were externally validated, 24% released a model a reader could apply, 23% assessed calibration, 16% justified a sample size, and five compared performances across patient characteristics against eight across sites; 8.8% called a random split external validation. Risk of bias was low in 16, unclear in 55 and high in nine, and none of the 18 deep-learning studies reported calibration (95% CI 0–17.6%, a loose bound on 18). We derive the design constraints these impose on a culture-independent diagnostic platform, a projection with no performance data. Validation, calibration and transparency limit this field more than accuracy does: discrimination was reported in every study appraised.

Artificial intelligence is widely proposed against antimicrobial resistance, but how much of that literature could support clinical deployment is unclear. We searched PubMed (2016–2026) across four domains — genomic prediction, clinical risk models, therapy, and rapid diagnostics — retrieving all 2,446 records. Of 725 eligible studies, 80 were appraised at full text against the artificial-intelligence extensions of the standard risk-of-bias and reporting tools (PROBAST+AI, TRIPOD+AI). Selection favoured externally validated work, so the proportions are upper bounds. Output grew from 10 studies in 2016 to 142 in 2025, with no evidence that genuinely external evaluation became more likely (odds ratio 1.08/year, 95% CI 0.89–1.34). Of the 80, 45% were externally validated, 24% released a model a reader could apply, 23% assessed calibration, 16% justified a sample size, and five compared performance across patient characteristics against eight across sites; 9% called a random split of their own data external validation. Risk of bias was low in 16, unclear in 55 and high in 9, and none of the 18 deep-learning studies reported calibration (95% CI 0–17.6%). Validation, calibration and transparency limit this field, not accuracy. We derive the design constraints these impose on a culture-independent diagnostic platform, a projection with no performance data.

Chronic non-communicable conditions—type 1 and type 2 diabetes mellitus (T1DM, T2DM), metabolic obesity syndrome (MOS), polycystic ovary syndrome (PCOS), colorectal and extra-intestinal cancers, and systemic autoimmune disease—share a prolonged asymptomatic phase during which conventional screening is invasive, insensitive, or resource-intensive. This review synthesizes the 2021–2026 literature on fecal microbiome-based artificial intelligence (AI) diagnostics across these conditions, extracting reported discrimination, validation strategy, microbial and short-chain fatty acid (SCFA) biomarkers, and cross-cohort reproducibility. Across the primary classifier studies tabulated here, reported areas under the curve (AUCs) span 0.76–0.99 under internal validation but 0.69–0.91 under external or cross-population validation; in the four studies reporting both, the median AUC falls from 0.875 to 0.810. Verified external-validation values include 0.82 for colorectal cancer, 0.79 for T2DM and 0.792 for discrimination of systemic lupus erythematosus from rheumatoid arthritis and controls. Clinical readiness turns on this internal-to-external gap more than on the headline AUC. We propose a multimodal deep learning architecture coupled with explainable AI; no component has been implemented or evaluated on data, and it is presented as a design proposal. Fecal-microbiome-based multimodal AI is technically feasible but clinically unvalidated, pending prospective, harmonized cross-cohort trials.

Smart manufacturing is undergoing a transformation due to the development of Generative Artificial Intelligence (GenAI), which introduces a new level of autonomy and data-driven decision-making in industrial settings. Unlike conventional prediction and classification type AI, GenAI can also be applied to develop optimized production plans, adjust production plans to changes, generate synthetic engineering data, intelligent design options, and real-time production recommendations. The review provides an in-depth overview of the use of GenAI in contemporary manufacturing systems such as process optimization, predictive maintenance, quality assurance, digital twins, intelligent robotics, supply chain resilience, and autonomous production control. It covers the most recent developments in generative models, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and diffusion models, as well as how these models are used in manufacturing process planning, defect detection, scheduling optimization and human-machine collaboration. This also encompasses the relationship between GenAI and Industrial Internet of Things (IIoT), cyber-physical systems, cloud-edge computing, and Industry 5.0, which will be used to create a self-adaptive manufacturing environment. Technical, ethical, and organizational issues, including data quality, model interpretable, cybersecurity, computational burden, workforce adaptation, and regulatory compliance issues, are explored. Lastly, the review spills out research gaps in this rapidly evolving technology field and provides directions for developing trustworthy, explainable and sustainable GenAI-enabled manufacturing systems.

Chronic non-communicable conditions – type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), metabolic obesity syndrome (MOS), polycystic ovary syndrome (PCOS), colorectal and extra-intestinal cancers, systemic autoimmune disease, and dermatologic and gynecologic disorders linked to gut dysbiosis – share a prolonged asymptomatic phase during which conventional screening is invasive, insensitive, or resource-intensive. This review synthesizes 2021-2025 literature on fecal microbiome-based artificial intelligence (AI) diagnostics across these conditions. We surveyed machine learning classifiers trained on 16S rRNA and shotgun metagenomic data, extracting reported discriminative performance, validated microbial and short-chain fatty acid (SCFA) biomarkers, and cross-cohort reproducibility. Reported classifiers achieve areas under the curve (AUC) typically between 0.79 and 0.93 across disease domains (e.g., 0.792 for autoimmune disease subtyping, 0.82-0.90 for colorectal cancer, 0.93 for PCOS subtyping), with dietary data integration and SCFA quantification further improving discrimination. We propose a multimodal deep learning architecture – combining a microbiome transformer encoder, dietary embedding module, host feature multilayer perceptron, phylogenetic graph neural network, and cross-attention fusion layer – coupled with explainable AI (SHAP, attention heatmaps, microbial risk scores) for clinical interpretability. We conclude that fecal microbiome-based multimodal AI is a technically mature but clinically unvalidated candidate for population-scale pre-symptomatic screening, pending prospective, harmonized cross-cohort trials.

Preterm birth is a major cause of neonatal morbidity and mortality, and many spontaneous cases remain idiopathic. Increasing evidence suggests that intrauterine inflammation may occur in the absence of detectable infection, leading to the recognition of sterile intrauterine inflammation as an important mechanism contributing to threatened preterm labor and spontaneous preterm birth. This review summarizes current knowledge regarding the role of damage-associated molecular patterns (DAMPs), alarmins, pattern recognition receptors, inflammasome activation, cellular senescence, and pyroptosis in the initiation of sterile inflammatory pathways associated with labor. Key mediators including HMGB1, IL-1α, fetal cell-free DNA, platelet-activating factor, and S100 proteins appear to promote inflammatory activation within fetal membranes and the amniotic cavity. The review also discusses the emerging contribution of fetal immune activation, maternal–fetal immune dysregulation, maternal microchimerism, and epigenetic mechanisms to idiopathic preterm birth. Current diagnostic and therapeutic options remain limited, and no targeted treatment for sterile intrauterine inflammation has yet been established. Future approaches may include precision biomarkers, multiomics-based risk stratification, targeted immunomodulatory therapies, and modulation of maternal–fetal immune interactions. Improved understanding of sterile inflammatory mechanisms may ultimately support development of personalized strategies to prevent preterm birth and improve perinatal outcomes.