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.

1-Naphthaleneacetic acid (NAA) is a synthetic auxin used repeatedly in apple production and routinely in plant propagation, yet its direct effects on microorganisms have not been reviewed. We compiled every observation we could locate and verify of a measured NAA effect on a named fungus, bacterium or microalga, drawing on a systematically screened literature corpus, a taxon-directed sweep and targeted database searches, and recorded each with its concentration, replication and evidence tier. The effects were organism-specific. Plant-pathogenic fungi were inhibited at concentrations of 100–400 mg L⁻¹, at which the biocontrol fungus Trichoderma harzianum lost at most 13% of its radial growth. Fungal secondary metabolism was strongly modulated, and inhibition of Saccharomyces cerevisiae occurred in minimal but not in rich medium. Direct bacterial growth inhibition was weak, but the bacterial auxin-catabolism repressor IadR bound NAA with slightly higher affinity than indole-3-acetic acid. Microalgae showed biphasic responses spanning 0.037–18.6 mg L⁻¹. No study measured a microbial community before and after exposure, and no field study measured a beneficial microorganism. We outline implications for biocontrol compatibility, inoculant use in propagation and phyllosphere exposure, and the experiments that would close these gaps.

1-Naphthaleneacetic acid (NAA) is a synthetic auxin used repeatedly in apple production and routinely in plant propagation, yet its direct effects on microorganisms have not been reviewed. We compiled the observations we could locate and verify of a measured NAA effect on a named fungus, bacterium or microalga, from a screened literature corpus, a taxon-directed sweep, targeted searches and, at revision, Scopus and AGRICOLA, and two authors independently assigned each an evidence tier. The result is a structured evidence map of a defined corpus. Effects were organism-specific. Plant-pathogenic fungi were inhibited by about 40% or more only at 100 mg L⁻¹ and above, at which Trichoderma harzianum lost at most 13% of its radial growth, whereas several cultivated fungi were stimulated at 1–8 mg L⁻¹. NAA altered fungal and bacterial secondary metabolism and bacterial virulence gene expression, a rhizobial inoculant degraded it, and microalgae responded biphasically between 0.037 and 18.6 mg L⁻¹. Among the studies retrieved, one field trial, in tobacco, characterised a soil microbial community after NAA application; none did so in an orchard, and no field study measured a named beneficial microorganism. We outline implications for biocontrol compatibility, inoculant use and phyllosphere exposure.

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.