Medicine and Pharmacology

Abstract: Claudin-1, encoded by CLDN1, is the principal epidermal tight-junction protein, and four tagging variants have been linked to atopic dermatitis in population- and exposure-specific studies. Because transfer of these associations is uncertain, we tested whether they mark atopic dermatitis in Vietnamese adults and quantified the precision of the null. We genotyped rs17501010, rs893051, rs9290927, and rs9290929 by Sanger sequencing in 82 adults with atopic dermatitis, 82 with other chronic dermatoses, and 83 without chronic skin disease, and estimated odds ratios by age- and sex-adjusted Firth-penalized logistic regression. The null was evaluated by equivalence testing, replication Bayes factors, and exclusion bounds, with robustness checked by propensity weighting, onset restriction, and SKAT-O. No variant was associated under any inheritance model; against pooled controls, adjusted per-allele odds ratios were 1.65, 1.11, 0.88, and 0.63 (all p ≥ 0.18). Joint four-marker and SKAT-O tests were likewise null, and results held in the 71 genuinely childhood-onset cases. Equivalence testing excluded odds ratios ≥ 2.0 at the two common variants, and at rs9290927 a Bayes factor of 183 favored no effect over the published odds ratio of 1.8. Linkage disequilibrium differed markedly across ancestries, this panel capturing only 15% of common CLDN1 variation in Vietnamese. These four variants therefore do not mark atopic dermatitis susceptibility in Vietnamese adults: the null is tightly bounded at the two common variants, underpowered at the two rare ones, and cross-ancestry transfer of this tag-SNP panel cannot be assumed.

Abstract: Multimodal artificial intelligence can integrate medical imaging with clinical information to support diagnosis and prognosis. However, inputs are frequently missing, acquired at different times, or inconsistent, reflecting clinical judgement, measurement limitations, or barriers to care. This Review critically examines approaches to using available inputs, reconstructing missing information, integrating sources according to reliability, and incorporating clinical knowledge. Particular attention is given to foundation and vision-language models, informative missingness, and the distinction between predictive confidence and demonstrated reliability. A proposed evidence-to-action framework connects information limitations and uncertainty to supporting a decision, obtaining further information, or deferring to expert assessment, illustrated through cardiovascular, ophthalmic, dental, and oncology applications. Evaluation must extend beyond predictive accuracy to calibration, equitable performance, deferral outcomes, workload, diagnostic delays, and patient benefit. The framework requires prospective validation across the complete care pathway. This includes assessing whether missing information disproportionately affects particular patient groups and their access to appropriate further assessment.

Abstract: Ruthenium-based complexes have emerged as promising alternatives to platinum-containing anticancer drugs because of their favorable pharmacological properties and potential immunomodulatory activity. However, their effects on mesenchymal stem cells (MSCs), an important component of the tumor microenvironment, remain poorly understood. The biological effects of two ruthenium(II) terpyridine complexes (Ru-1 and Ru-2), compared with oxaliplatin as a reference compound, were investigated in mouse mesenchymal stem cells (mMSCs) using MTT, LDH, Annexin V/PI, cell-cycle and calreticulin exposure assays. Extracellular ATP and HMGB1 release were additionally assessed as immunogenic cell death-associated markers. The immunomodulatory effects of Ru-1 and Ru-2 were evaluated in healthy and CT26 tumor-bearing mice by flow cytometric analysis of lymphoid and myeloid cell populations. Both ruthenium complexes induced concentration-dependent cytotoxicity in mMSCs, with Ru-1 consistently exhibiting greater cytotoxic activity than Ru-2 and comparable potency to that of oxaliplatin after 24 h of exposure. In mMSC, cell death occurred predominantly through apoptosis and was accompanied by G2/M cell-cycle arrest. Both complexes also induced the emission of ICD-associated DAMPs, including calreticulin exposure and extracellular ATP and HMGB1 release, in mMSCs and CT26 cells. In vivo, Ru-1 increased the proportions of CD8⁺ T lymphocytes, NK cells and NKT cells in CT26 tumor-bearing mice whereas in healthy mice it increased NK and NKT cells without affecting T lymphocytes. Selected myeloid-cell and monocyte populations were also modulated, predominantly in healthy animals. This is the first study to characterize the biological effects of ruthenium(II) terpyridine complexes in mesenchymal stem cells alongside their in vivo immunomodulatory activity. Ru-1 combines direct cytotoxic effects with immunomodulatory activity and induces multiple features associated with immunogenic cell death in both tumor and stromal cell populations. These findings support further preclinical investigation of ruthenium(II) terpyridine complexes as potential immunomodulatory anticancer agents.

Abstract: Background: Antigenic cartography is a computational approach that quantitatively visualizes antigenic relationships among viral strains using serological data, including hemagglutination inhibition and virus-neutralization assays. Since its introduction in the early 2000s, it has substantially advanced the study of viral antigenic evolution by transforming complex serological measurements into interpretable antigenic maps, thereby facilitating assessment of antigenic diversity, immune escape, cross-reactivity, and vaccine–virus relationships. Objective: This review examines the historical development, methodological principles, applications, limitations, and emerging directions of antigenic cartography across clinically relevant viruses, with particular emphasis on its relevance to viral surveillance, antigenic drift monitoring and vaccine-strain selection. Methods: The literature was synthesized comparatively across major applications of antigenic cartography, encompassing human seasonal influenza and other viruses, including dengue, lyssaviruses, foot-and-mouth disease virus, Enterovirus A71, norovirus, rotavirus, SARS-CoV-2, and HIV. Particular attention was given to real-world applications, methodological advances, sources of uncertainty, and integration with genomic, structural, evolutionary, and computational approaches. Findings: Influenza remains the most extensively studied viral pathogen, where antigenic cartography has supported recognition of epidemiologically relevant antigenic transitions and informed vaccine decisions. Examples include the emergence of A/Fujian/411/2002-like H3N2 viruses during 2004–2005 and assessment of A/California/07/2009 during 2010–2011. Applications to other viruses demonstrate broader utility for characterizing antigenic landscapes and evaluating cross-protective immunity and vaccine performance. However, map reliability is influenced by serological assay variability, serum-panel composition, measurement noise, incomplete and geographically biased datasets. Limited inter-operability among serological, genomic, and structural datasets further restricts reproducibility and cross-study comparability. Future Directions and Conclusions: The field is transitioning from retrospective visualization toward predictive and mechanistically informed antigenic surveillance. Integration of artificial intelligence, protein language models, evolutionary modelling, structural vaccinology, and real-time genomic–serological surveillance could enable earlier prediction of antigenic drift, identification of emerging vaccine mismatches, and prioritization of candidate vaccine strains. Future platforms should incorporate standardized and automated serological measurements, inter-laboratory calibration, harmonized data standards, geographically representative datasets, experimental validation, and interpretable continuously updated models. Such integration could establish antigenic cartography as a scalable predictive framework for vaccine design and for strengthening preparedness against emerging viral threats.

Abstract: Artificial intelligence (AI) has expanded the scale, resolution, and modalities through which biological aging can be estimated, but evidence that these estimates are valid intervention targets or surrogate endpoints remains limited. We conducted a narrative review of PubMed-indexed literature identified through documented PubMed and Consensus searches in October 2026, plus a separate search of primary US Food and Drug Administration (FDA) and European Medicines Agency (EMA) documents. No protocol was registered, Embase, Scopus, and Web of Science were not searched, and screening was by a single reviewer. We distinguish four biomarker properties: chronological-age estimation, prognostic association, responsiveness to intervention, and validated surrogacy. Deep-learning clocks across epigenomic, proteomic, retinal, electrocardiographic, and magnetic resonance imaging (MRI) modalities report low chronological-age error, and several show prognostic associations with mortality, but calibration was rarely reported in the records reviewed and head-to-head comparisons with mortality-trained clocks in the same cohort were uncommon. Randomized trials show that responsiveness is clock-specific: in the CALERIE trial, caloric restriction slowed DunedinPACE but did not significantly change PhenoAge or GrimAge, and effect sizes were small. Machine learning has accelerated senolytic and mTOR-pathway candidate nomination, and a generative-AI-designed TNIK inhibitor reached phase 2a in idiopathic pulmonary fibrosis, an age-related disease indication rather than an aging indication. Coverage of the twelve hallmarks of aging by AI studies is uneven: epigenetic alteration and cellular senescence are best represented, whereas autophagy, proteostasis, genomic instability, and telomere attrition are thinly represented. Constraints include reliability, tissue specificity, confounding by ancestry and social factors, the gap between prediction and causation, and the absence, in the primary documents consulted, of a pathway that accepts a biological-age measure as a surrogate endpoint. We propose an evidence ladder and a translational chain for appraising AI-derived aging biomarkers and set out priorities for 2026-2030.

Abstract: Background/Objectives: Lung cancer is the leading cause of cancer death worldwide, and growing slide volumes add to pathologists’ cognitive load. Deep learning can assist histopathological diagnosis, but two obstacles limit its clinical use: privacy regulations keep patient images in institutional “data silos”, and the “black-box” behavior of neural networks undermines clinical trust. Inspired by Topol’s Deep Medicine, we aimed to build a diagnostic platform that preserves privacy and explains its decisions. Methods: We developed the Lung Cancer Analysis System (LCAS), which combines federated learning (FL), explainable artificial intelligence (XAI), and a locally deployed medical large language model (LLM). Three public H&E datasets (LC25000, HistoSet-5×14, and CLID; 18,000 images; adenocarcinoma, squamous cell carcinoma, benign tissue) acted as three hospital nodes with source-induced non-independent and identically distributed (non-IID) data. DenseNet121, EfficientNet-B3, and MobileNetV3-Small were trained centrally and with FedAvg/FedProx for 20 rounds. Grad-CAM produced visual evidence, and MedGemma 1.5, served offline through LM Studio, converted predictions and heatmap descriptors into structured reports. Results: Centralized training reached 99.50% ± 0.21% accuracy with DenseNet121. Under non-IID federated training with FedProx, DenseNet121 and EfficientNet-B3 achieved 96.97% and 97.49% accuracy on an independent external blind test set of 958 images, whereas MobileNetV3-Small reached 91.23%. Accuracy remained 99.78% under Gaussian blur. t-SNE showed that global features clustered by tissue class rather than by hospital, and Grad-CAM highlighted glandular and tumor-nest regions consistent with histopathological criteria. Conclusions: FL, XAI, and on-premise LLM reporting can be integrated into a single workflow that keeps raw images within each institution while giving pathologists numerical, visual, and textual explanations, providing a practical path toward trustworthy cross-institutional computational pathology.

Abstract: Background: Axial spondyloarthritis (axSpA) is characterized by coexisting bone loss and pathological new bone formation. Although alterations in circulating markers and osteoclastogenesis from peripheral blood monocytes (PBM) are documented in radiographic axSpA (r-axSpA), their status in non-radiographic axSpA (nr-axSpA) and association with bone loss remain unresolved. In this study, we evaluated serum markers of bone metabolism and inflammation, PBM osteoclastogenic capacity, and their association with bone mineral density across both axSpA phenotypes. Methods: In 81 axSpA patients, clinical characteristics and DEXA-based BMD were recorded. Serum TSH, PTH, 25(OH)D3, bCTX, PINP, osteocalcin, bALP, RANKL, OPG, CTSK, CRP, IL-6 and IL-17 were measured. PBM from a patient subset (N=26) were used for in vitro osteoclastogenesis assays. Results: In a multivariable model, femoral neck Z-scores were strongly negatively associated with serum OPG/RANKL ratio in males (48% OPG/RANKL increase per one-unit Z-score decrease, p< 0.01). Additionally, each one-unit decrease in femoral neck Z-score was associated with increased odds of r-axSpA (OR 1.96, p< 0.05), independently of OPG/RANKL. Though patients with r-axSpA had lower femoral neck Z-scores than nr-axSpA (mean −0.50 vs. 0.03, p< 0.05), their in vitro osteoclastogenesis did not differ significantly (median 21 vs. 40, p=NS). Instead, osteoclastogenesis associated with BMD, being lower in patients with reduced femoral neck BMD compared to normal BMD (mean 22 vs. 51, p< 0.05). Conclusions: This study highlights the femoral neck as a region of interest for studying bone loss in nr-axSpA and r-axSpA and supports a link between systemic bone loss, circulating OPG/RANKL levels, and precursor osteoclastogenic potential in axSpA.

Abstract: Psychological well-being is associated with favorable cardiovascular health, but its physiological mechanisms remain incompletely understood. We examined associations between psychological well-being and inflammatory, hemodynamic, and neuroendocrine measures in 846 participants from the community-based Health, Environment, and Action in Louisville study. Well-being was assessed with the Warwick-Edinburgh Mental Well-Being Scale; perceived stress, depressive symptoms, and neighborhood social cohesion were evaluated as complementary psychosocial measures. Primary outcomes were high-sensitivity C-reactive protein (hsCRP), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Multivariable models adjusted for demographic, socioeconomic, and smoking-related factors, with false discovery rate correction. Greater well-being was associated with lower hsCRP (β = −0.12, 95% CI: −0.20 to −0.04; p = 0.002), SBP (β = −2.03 mmHg, 95% CI: −3.16 to −0.90; p < 0.001), and DBP (β = −1.56 mmHg, 95% CI: −2.36 to −0.76; p < 0.001). Perceived stress showed reciprocal associations, whereas depressive symptoms and neighborhood social cohesion were less consistent. Selected urinary catecholamines, but not hair cortisol, were associated with well-being. These findings identify inflammatory and hemodynamic correlates of psychological well-being and support further investigation of positive psychological health in cardiovascular disease prevention.

Abstract: Background: Staffing methods in healthcare assume that the time required for a service depends only on its clinical content. Under the Italian special detention regime (Article 41-bis of the Penitentiary Act), security barriers, escorts, one-person-at-a-time access and separation between socialization groups add a logistical, partly unpredictable time to every clinical act. Methods: We propose the Logistic–Temporal Correction Factor (LTCF), defined as the ratio between the actual and the standard time of a service, and embed it in a time-driven activity-based staffing model. A time-and-motion protocol for its measurement is described, and the model is applied to an illustrative scenario of a dedicated 41-bis institution. Results: For 210 persons under 41-bis and 45 medium-security inmates, the nursing requirement of the detention units rises from 10.9 full-time equivalents with LTCF = 1 to 17.9 with LTCF = 2 and 24.8 with LTCF = 3 (FTE ≈ 3.9 + 7.0 × LTCF). Conclusions: The LTCF makes measurable an organizational cost that is widely recognized but has never been quantified, and provides an objective basis for staffing and funding healthcare in high-security settings, where the compatibility between health and detention depends on the organizational model of care more than on the disease itself.

Abstract: Background/Objectives: Children with neurodevelopmental and neuromotor disabili-ties may be vulnerable to both excess adiposity and undernutrition, while the inter-pretation of simple anthropometric measures can be challenging when functional im-pairment, feeding difficulties, medication exposure, and body-composition differences are present. This exploratory cross-sectional study reports a single clinical assessment performed by the authors between February and April 2023 in a rehabilitation setting. Its objectives were to describe weight-status categories in the assessed children and to examine what practical lessons the assessment provides for disability-sensitive nutri-tional screening. Methods: Twenty children aged 9–15 years with spastic quadriplegia (n = 10), autism spectrum disorder (n = 5), or attention-deficit/hyperactivity disorder (n = 5) underwent a one-time assessment. Weight, height, diagnosis, ongoing rehabilita-tion, medication classes, and maternal anthropometry were recorded. Pediatric BMI was interpreted using age- and sex-specific categories. Given the small heterogeneous sample, analyses were descriptive. Results: Ten children (50%) were classified as hav-ing obesity, three (15%) as overweight, and seven (35%) as healthy weight. Six of the ten children with obesity had mothers with overweight or obesity. The assessment al-so highlighted clinically relevant domains that had not been incorporated systemati-cally, including structured dietary intake, feeding and swallowing difficulties, stand-ardized functional classification, physical activity, body composition, and detailed medication exposure. Conclusions: The findings should be interpreted as lessons from a small real-world clinical assessment rather than as a comprehensive nutrition-al-status evaluation or prevalence estimate. They support the use of a standardized, disability-sensitive screening framework that combines appropriate anthropometry with feeding, dietary, functional, medication, activity, and family-context information.

Abstract: Three-dimensional Gaussian splatting is the latest computer-graphics technique to be adopted for medical visualisation, following volume rendering and cinematic rendering. Unlike its predecessors, which render images directly from the acquired volumetric data, Gaussian splatting is a reconstruction: a cloud of anisotropic Gaussian primitives is optimised against a finite set of rendered two-dimensional views, after which the source volume plays no further part. This distinction has practical consequences that are not evident from the exterior images typically used to demonstrate the technique. In this narrative review we describe what we term the view-space limitation: because training cameras orbit an anatomical structure from outside, the interior is never observed, and no clipping-plane position recovers intracardiac anatomy from a cardiac splat. We argue that this is a structural consequence of camera distribution rather than an implementation defect, that a splat is an inferred representation whose geometric and densitometric fidelity must be measured rather than assumed, and that until such validation is reported, splat reconstructions should be reserved for communication, education, and qualitative review rather than primary quantitative interpretation. We also argue that the case in favour is stronger than usually made: a splat of a patient’s heart renders at interactive frame rates in a consumer smartphone browser and can be shared by a link, extending photorealistic anatomy to referring clinicians, multidisciplinary meetings, trainees, patients, and health systems without workstation-class hardware. We propose a framework for matching visualisation technique to clinical setting and outline validation priorities, including accuracy against ground truth, projection-domain sparse-view formulations, clippable variants, and dynamic extensions for gated cardiac data, that should precede further application papers.

Abstract: Cell fate decisions are often studied as static transcriptomic snapshots even though differentiation is intrinsically a complex dynamical, multistable, and plastic process. To better elucidate cell fate plasticity dynamics, we developed a complexity-theoretic and machine learning framework to model neural stem cell state-transitions using a two-time point single-cell RNA-sequencing subset of GSE138121 (iPSC-derived motor neurons). The primary cohort comprised 1,960 quality-controlled cells from the Batch A control iPSC line at days 12 and 18, represented by 815 genes and 62 metacells. Ten neural/progenitor states were annotated and contrasted with NNMF and Leiden communities. We integrated forward-in-time metacell transport, quasi-potential landscapes, basin and dwell-time analysis, Spearman and Kauffman Random Boolean Networks, BDM perturbation analysis, and 5-fold cross-validation of classical machine learning and deep learning models. Transition kernels showed broad funneling toward the neuroblast/immature-neuron state. The continuous attractor landscape resolved four basins, while the 180-node, 1,080-edge Boolean Kauffman abstraction produced 18 attractors, basin entropy of 2.75 bits, and Derrida one-bit sensitivity of 1.69, indicating damage amplification rather than directly demonstrated critical point. Consensus steering prioritized ONECUT2, ZFP36L1, DCX, HOXB2, TMEM123, TUBA1A, ENO1, ELAVL4, MAP1B, and MLLT11. ESN/reservoir computing yielded the lowest latent endpoint MAE (0.138), while CTM and Transformer achieved the lowest transition-kernel RMSE after validation-selected blending with a training-only state/time prior (0.0402 and 0.0404). These results support a complex systems view of iPSC-derived neural differentiation as trajectories through a multi-basin state-space, linking attractor geometry, network topology, and explainable AI to generate testable candidates for cell fate control and reprogramming.

Abstract: Yellow fever (YF) remains a major public health threat in South America, where transmission is closely linked to climatic conditions, forest ecosystems, sylvatic mosquito vectors, and non-human primates. Climate change, together with deforestation, biodiversity loss, and expanding human activities, is reshaping the ecological interactions that sustain yellow fever virus (YFV) circulation. This narrative review examines how climatic and environmental changes influence YF epidemiology, emerging transmission risks, and potential future scenarios in South America. Increasing temperatures, altered precipitation patterns, humidity changes, and extreme weather events may affect mosquito abundance, survival, vector competence, and geographic distribution. At the same time, forest fragmentation, agricultural expansion, mining, and urbanization modify interactions among vectors, non-human primates, and human populations, potentially increasing spillover opportunities. Recent outbreaks in Brazil, Colombia, and other South American countries underscore the continued vulnerability of populations living near forest ecosystems, particularly where vaccination coverage remains insufficient. Future climate scenarios suggest possible shifts in transmission seasonality, expansion into previously less receptive geographical areas, and increased exposure at forest–urban interfaces. Although sustained urban transmission has not been documented for decades, the widespread presence of Aedes aegypti warrants continued vigilance. Addressing these challenges requires integrated One Health strategies combining climate-informed surveillance, monitoring of non-human primate epizootics, ecological modeling, targeted vaccination, ecosystem conservation, and regional cooperation. Strengthening these approaches is essential to anticipate emerging risks and prevent future YF outbreaks in South America.

Abstract: This communication proposes a new monomer that will self-assemble into a cancer-targeting glycosylated dendritic nanoparticle (DNP). This combines previously developed DNP technology used for genetic payload delivery (Ionizable Amphiphilic Janus Dendrimers or IAJDs) with experimentation on glycosylated PAMAM dendrimers’ interactions with cancer cells. The combined result is a DNP that can target cancer cells for delivery of genetic payload. To demonstrate feasibility, a rough approximation of synthetic pathway, adjustment to the self-assembly mechanism, and possible improvements to payload retention are described. This work represents a hypothesis-driven synthetic proposal rather than a validated result, intended to motivate experimental testing of a combined targeting-and-delivery dendrimer platform for cancer-directed gene therapies.

Abstract: Background: The Middle East North Africa Committee for Treatment and Research in Multiple Sclerosis (MENACTRIMS) has developed congresses, clinical guidance, registry research, training, certification, and advocacy partnerships. Evaluating this infrastructure requires distinctions between organizational activity, implementation, and attributable benefit.Objectives: To characterize documented achievements, compare the geographic distribution of major functions, identify public reporting gaps, and propose a measurable regional learning-health-system roadmap.Methods: A structured public-document audit and targeted literature synthesis covered activities from 2014 through 23 August 2026, with a documented update on 16 October 2026. Sources were classified by provenance and evidentiary purpose, without performance scoring. Geographic coverage used a fixed 19-jurisdiction Middle East and North Africa frame. Reported attendance terms and registry denominators were preserved.Results: Ten completed congresses were identified; seven with exact headline attendance had a median of 934 participants (interquartile range 761.5–1006.5). Seven congresses were hosted in the United Arab Emirates. Five major guideline outputs or revisions, multicountry registry studies, policy endorsements, and traceable grant- and fellowship-linked publications demonstrated sustained scientific activity. The public registry headline was 15,387, compared with 14,438 enumerable center records; variable displays represented 78.8%–93.9% of the headline, without establishing true field completeness. Iran, Egypt, and Kuwait contributed 75.4% of enumerable records. Council listings covered 15/19 jurisdictions, registry centers 8/19, and certified centers 2/19. Certification standards already specify three-year renewal and prefer registry participation; fellowship terms require reporting and career follow-up. Aggregated results of these processes were less readily ascertainable.Conclusions: MENACTRIMS has established substantial regional infrastructure. Differing geographic distributions identify questions about coordination and opportunity but do not establish organizational failure. A phased roadmap for 2027–2035 should build on existing systems through denominator reconciliation, publicly reported program outcomes, guideline-linked quality improvement, and meaningful patient and professional participation. Public-source limitations and the non-systematic synthesis preclude causal attribution of regional patient outcomes to MENACTRIMS.

Abstract: AI-assisted nomination of phytochemical derivatives requires a verifiable molecular identity and a search that distinguishes exact structures from neighbouring analogues. This computational communication specifies AIIP-FIS-7O-DMAE-001, fisetin 7-O-[2-(dimethylamino)ethyl] ether, proposed in an author-held immunopharmacology book manuscript. The graph retains the flavonol double bond and free 3, 3′ and 4′ hydroxyl groups. The reported RDKit analysis gives C19H19NO6, molecular weight 357.36 g/mol, monoisotopic mass 357.1212373 Da and InChIKey SVQICVJPUYZYSA-UHFFFAOYSA-N, correcting the source formula and mass. Relative to fisetin, calculated polar surface area decreases from 111.13 to 103.37 Ų and neutral Crippen cLogP increases from 2.2824 to 2.5172. Searches completed on 12 October 2026 returned no matching candidate record in PubChem identity searches or ChEBI and ChEMBL InChIKey queries, with successful parent controls. PubChem returned 47 substructure records; their retrieved formulae and keys differed from the candidate. Additional aminoethyl-flavonoid literature and a registered fisetin aminoethyl conjugate establish substantial prior modification precedent. Incomplete literature drawing coverage and specialist patent analysis keep exact-structure novelty provisional. The project code identifies the computational structure and is not evidence of AI generation, synthesis, antibody binding or efficacy.

Abstract: Ring chromosomes (RCs) are rare structural chromosomal abnormalities caused by terminal breaks and fusion of chromosome ends, occurring in approximately 1 in 50,000 live births. They are often associated with “ring chromosome syndrome,” characterized by growth restriction, mild dysmorphism, and variable intellectual disability, even without large deletions. Ring chromosome 2 is exceptionally rare, with fewer than 20 reported cases and limited molecular characterization. We report a 4-year-old male with proportional growth restriction, mild facial dysmorphism, and delayed expressive speech. Cytogenetic analysis identified a mosaic ring chromosome 2, and fluorescence in situ hybridization demonstrated preserved subtelomeric regions. However, array comparative genomic hybridization revealed a 46 kb deletion at 2p25.3 involving the protein-coding gene FAM110C. This case broadens the phenotypic and molecular spectrum of ring chromosome 2 and underscores the value of combining cytogenetic and high-resolution genomic techniques to detect cryptic imbalances contributing to clinical presentation.

Abstract: AI-assisted phytochemical nomination requires independent checks of chemical identity and supporting evidence. This computational case study examines AIIP-BAI-6C-DMAE-002, the proposed 2-(N,N-dimethylamino)ethyl ester of baicalin's glucuronide C-6″ carboxyl group, originating in an author-held AI immunopharmacology book manuscript. The nominated connectivity was reconciled with inconsistent formula and mass entries in that source. RDKit 2025.09.6 generated identifiers, physicochemical descriptors and comparisons with eight related structures. The encoded neutral candidate has formula C25H27NO11, molecular weight 517.49 g/mol, monoisotopic mass 517.1584 Da and InChIKey BIUNXNZQUMPJLF-LYVDORBWSA-N. Relative to baicalin, calculated polar surface area decreases from 187.12 to 179.36 Ų, while neutral-form Crippen cLogP changes from 0.1422 to 0.1624. These results do not establish improved absorption. Public literature and patent searching found established esterification and amino-substitution precedents but no individualised exact disclosure in the retrieved evidence; candidate PubChem InChIKey queries returned no matching record. Novelty remains provisional pending comprehensive structure and Markush analysis. The historical AI-generation trace is incomplete, and no synthesis, binding or efficacy is reported. The study provides a reproducible chemical assessment and a validation framework for a phytochemical-derived immunopharmacology hypothesis.

Abstract: Rosmarinic acid is a chemically attractive immunopharmacology scaffold, but its high polarity, ionization, metabolic lability, and incomplete target definition limit translation. This original communication defines AIIP RA DMAE 001, the internal code assigned to rosmarinic acid 2-(N,N-dimethylamino)ethyl ester, an artificial-intelligence-guided phytochemical-derived analogue generated within an AI-based immunopharmacology program. The design replaces the parent carboxylic acid with an ionizable 2-(dimethylamino)ethyl ester while retaining the natural rosmarinic-acid stereocenter and E-caffeoyl geometry. The free-base structure has the formula C22H25NO8, an average molecular weight of 431.44 g mol−1, and the isomeric SMILES CN(C)CCOC(=O)[C@@H](Cc1ccc(O)c(O)c1)OC(=O)/C=C/c1ccc(O)c(O)c1. Public exact-identity searches performed on 10 October 2026 found no individualized record for this structure, although closely related alkyl rosmarinates, methyl-rosmarinate derivatives, and a permanently charged rosmarinylcholine analogue are known and broad patent Markush language may encompass aminoalkyl esters. The compound is therefore described as putatively structurally novel, not as pharmacologically or therapeutically novel. A prior design record reported hypothesis-generating AutoDock Vina scores of −7.8, −8.1, and −8.3 kcal mol−1 against generic IgG, IgA, and IgM structures, respectively; these values do not establish binding and require prospective reproduction. The tertiary amine is expected to permit salt formation and substantial protonation near physiological pH, whereas the catechols and ester linkages create oxidation, assay-interference, hydrolysis, and distribution liabilities. We propose an evidence-gated program spanning synthesis and spectroscopic confirmation, pKa and stability measurement, orthogonal target-engagement studies, innate and adaptive immune phenotyping, and exposure-linked disease models. The most credible initial translational hypotheses concern type I interferon signaling and macrophage inflammatory state; direct immunoglobulin modulation should remain a higher-bar secondary hypothesis. AIIP RA DMAE 001 is thus a testable molecular proposal and a framework for transparent progression from AI design to immunological evidence.

Abstract: Polygonatum odoratum is widely consumed as a functional food and is traditionally used for respiratory complaints, yet the material basis of its anti-pneumonia activity and the effect of extraction solvent on that activity remain unclear. Here, untargeted LC-QTOF-MS profiling of the aqueous extract was combined with TCMSP database mining to screen potential active components, and their core targets and binding modes were predicted by network pharmacology and molecular docking. Of 38 potential active components screened by oral bioavailability and drug-likeness, 28 were mapped to 123 pneumonia-related targets. IL6, AKT1, IL1B, TNF, TGFB1, TP53, MMP9, PIK3CG, HIF1A and CASP3 occupied hub positions in the PPI network and were enriched in the IL-17, TNF and PI3K-Akt pathways. Molecular docking of 7 representative components against 9 core targets gave 24 systems with binding energies all below −4.8 kcal/mol, and different components frequently occupied the same binding pocket, indicating a multi-component, multi-target mode of action. Critically, all nine docking targets were functionally interrogated in LPS/IFN-γ-stimulated MLE-12 cells: the 50% methanol extract significantly improved 9 of 10 indicators, outperforming the aqueous extract on all 10, with significant differences in p-AKT and p-CREB (P < 0.05). Medium-lipophilic homoisoflavonoids are therefore the principal material basis of the anti-pneumonia activity, and hydroalcoholic extraction better retains them. These findings support the use of P. odoratum as a functional food ingredient for respiratory health.