Francisca Villanueva-Flores

Introduction: Public toxicology databases are increasingly used for evidence synthesis, but their value depends not only on the availability of studies but also on whether endpoint-level data can be recovered for reuse. Using the Chemical Effects in Biological Systems (CEBS) database as the primary source, we reviewed oral heavy-metal immunotoxicity in rodents. Methods: We assessed whether splenic sheep red blood cell (SRBC) T-dependent antibody response data, measured as antibody-forming/plaque-forming cells (TDAR AFC/PFC), were sufficiently recoverable for structured synthesis and quantitative reanalysis. Recoverability was defined as the availability of endpoint information needed for reuse, including group size, mean, and variance when quantitative reanalysis was attempted. Results: Fifteen records were screened, and 12 records were retained across vanadium, chromium, tungsten, cadmium, and lead; one of these 12 records contained separate lead and cadmium exposure arms. Three retained records were CEBS package-level studies, yielding 30 module-level entries spanning primary AFC, serum anti-SRBC IgM, natural killer (NK) cell function, splenic phenotype, hematology, body weight, and organ weights. Only one retained study, which examined sodium metavanadate in female B6C3F1/N mice exposed via drinking water for 28 days, provided a fully recoverable primary dataset with extractable group sizes, means, and variances, enabling quantitative reanalysis of five AFC contrasts from 31.3 to 500 ppm. AFC/10 ⁶ spleen cells shifted from +5.2% at 31.3 ppm to –29.6% at 500 ppm (lnRR –0.351, 95% CI –0.634 to –0.067), and benchmark-like thresholds for 10%, 20%, and 25% AFC decline were estimated at approximately 84, 184, and 283 ppm, respectively. The same package revealed marked assay- and normalization-dependent divergence, with plaque-based AFC declining, ELISpot-based IgM AFC increasing, and serum IgM and NK responses showing limited concordance. Discussion: Overall, CEBS module-level data supported a stronger endpoint-centered synthesis than narrative review alone. However, incomplete endpoint recoverability, rather than study scarcity, remained the main barrier to broader quantitative integration across oral metal studies.

Exercise induces rapid systemic metabolic perturbations, but the chemical organization of post-exercise recovery remains incompletely resolved across public LC-MS datasets. Here, we performed a public-data reanalysis of human exercise metabolomics to determine whether named metabolites can be organized into chemically interpretable recovery landscapes. The primary dataset was the Metabolomics Workbench race-walking study ST003348/PR002083, which includes serum LC-MS profiles from race-walking athletes sampled at rest, immediately after exercise, and after 3 h and 22 h of recovery. Two independent public exercise datasets, ST003662/PR002271 and ST001789/PR001133, were used to externally validate the direction of change. Named features were deduplicated by RefMet identity, log2 fold-changes were estimated with paired subject-level contrasts, and false-discovery-rate correction was applied within each time point. We assigned rule-based chemical classes and summarized residual 22 h displacement as the median absolute paired log2 fold-change within each chemical class. This quantity was used only as a transparent descriptive aggregation of the observed 22 h contrasts, not as an independent, validated, or outcome-linked recovery index. In the primary dataset, 561 deduplicated features were retained from 19 subjects with a complete time point structure. Immediate post-exercise perturbation was detected in 188 features, whereas only seven features remained significantly displaced at 22 h. Lipids and lipid mediators showed the largest immediate chemical-class perturbation, while vitamins/steroids/signaling molecules and lipids had the highest class-level residual 22 h displacement—kinetic classification identified 180 acute-and-recovered features, 33 delayed/3 h-dominant features, and seven persistent features. External validation was strongest in the independent plasma running dataset ST001789, where primary acute responders showed 90.7% sign concordance at Time 0 and 75.9% at 60 min. By contrast, the DBS/VAMS dataset ST003662 showed weak and non-significant direction-of-change concordance, indicating limited transportability to that matrix/protocol. Overall, this study does not claim discovery of new exercise metabolites or pathways; it provides a reproducible applied chemistry framework for describing exercise recovery as a structured metabolochemical process rather than a purely physiological endpoint.

Massive pelagic Sargassum influxes along Caribbean coasts have created an urgent need for valorization routes for this biomass. Here, sodium alginate was extracted from Sargassum fluitans collected at Chuburná Beach, Yucatán, Mexico, using a multistep extraction involving 0.2% formaldehyde pretreatment at 4 °C and brief heating at 65–70 °C, and subsequently used to prepare calcium-crosslinked alginate nanoparticles by ionotropic gelation. To our knowledge, this is the first direct synthesis of alginate nanoparticles from non-commercial alginate extracted from pelagic S. fluitans. An extraction yield of 18.7 ± 0.05% (mean ± SD, n = 3) was obtained, and UV–Vis, FTIR, and NMR analyses confirmed the characteristic structural features of alginate. ¹H NMR revealed an M-rich composition (F_M = 0.61, F_G = 0.39; M/G = 1.54) with short guluronate blocks (N_G>1 = 2.42), whereas ¹³C NMR corroborated the presence of both β-D-mannuronic and α-L-guluronic acid residues. SEM images showed predominantly spherical-to-subspherical nanoparticles with representative dry diameters of 233–269 nm, whereas DLS measurements at 0, 24, and 72 h revealed a dominant volume-based nanoscale population with main peaks at 12.75–15.31 nm and PDI values of 0.229–0.291, indicating reasonable short-term colloidal stability at room temperature. These results demonstrate that pelagic S. fluitans can serve as a viable feedstock for the production of structurally preserved alginate and calcium-crosslinked alginate nanoparticles. The study supports converting recurrent Sargassum biomass into higher-value polysaccharide-based materials and provides a basis for future application-specific evaluation of these nanomaterials.

Open, well-documented datasets are essential for the reproducible development of vision systems for urban utility management. This Data Descriptor presents a curated RGB object-detection benchmark of four classes associated with electrical distribution and street-level utility assets: Inspection Chamber, Overhead-to-Underground Transition, General Protection Box, and Transformer Substation. The public release contains 997 valid image-label pairs partitioned into 698 training, 150 validation, and 149 test images. Images were acquired during 2019 in multiple localities across Spain, predominantly with a mobile phone and, in occasional cases, using Google Maps as a complementary visual source, and were manually annotated with LabelImg before export to YOLO format. During curation, four invalid image-label pairs were removed because at least one YOLO bounding box exceeded the normalized image domain. The benchmark contains 1,939 object instances, with marked class imbalance: General Protection Box accounts for 50.2% of objects whereas Transformer Substation represents 4.7%. Images are heterogeneous in size and viewpoint, ranging from 90 × 170 to 4160 × 4032 pixels, with a median resolution of 619 × 544 pixels and a median of two annotated objects per image. The public GitHub release is organized into images/, labels/, and metadata/ directories; metadata stores split definitions, classes.txt, data.yaml, inventory information, annotation schema documentation, and diagnostic summary figures. Beyond detector benchmarking, the dataset can support scalable mapping of visible distribution-grid assets, with potential value for smart-city digital twins and data-informed EV charging deployment.

Background: Automated analysis of color fundus photographs can support scalable screening for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma, but single-run reporting and accuracy-only summaries can mask clinically relevant instabilities and failure modes. Methods: Using the public FIVES dataset, we benchmarked six deep learning configurations for four-class fundus screening (AMD, DR, glaucoma, normal): three DeepLabv3–backbone hybrids (ResNet50, DenseNet121, EfficientNet-B0) and three backbone-only classifiers. All experiments were evaluated using five independent stratified splits generated with different random seeds ( n = 5 runs), each defining a distinct 20% held-out test set. Models were trained on the remaining 80% (training/validation), and all reported metrics are computed on the 20% test set of each run and summarized as mean ± SD across runs. Performance was summarized with accuracy, sensitivity, specificity, and one-vs.-rest AUC; we further characterized clinical behavior via row-normalized confusion matrices, per-class precision/recall/F1, and a screening-style binary triage setting (referable = AMD ∪ DR ∪ glaucoma vs. normal). Results: Hybrid models consistently achieved higher discrimination than simple classifiers (AUC 0.969–0.979 vs. 0.908–0.920), despite similar accuracies (0.924–0.941). The selected model, DeepLabv3–DenseNet121, reached the highest AUC (0.979 ± 0.009). Class-wise analysis revealed strong performance for Normal (F1 0.970 ± 0.014) and Glaucoma (F1 0.896 ± 0.048), while DR was the main bottleneck (sensitivity 0.738 ± 0.117), with most DR errors redistributed to AMD (13.6%) and Glaucoma (12.0%) and minimal confusion with Normal (0.5%). In binary triage, the model achieved sensitivity 0.993 ± 0.011 and specificity 0.963 ± 0.034, with PPV 0.987 ± 0.013 and NPV 0.980 ± 0.032, and a stable referral rate (∼0.73–0.77) across runs. Conclusion: DeepLabv3-based hybrids provide a robust advantage in AUC for multiclass fundus screening on FIVES. The residual risk concentrates in the DR–AMD–Glaucoma decision boundary, suggesting that deployment-oriented policies should prioritize conservative handling of DR-adjacent cases while leveraging the stability of Normal predictions for screening workflows.

Many therapeutics fail due to suboptimal pharmacokinetics/pharmacodynamics (PK/PD), and the development of nanomedicine remains partly empirical. We present a protocol-guided, mechanistic review that links nanoparticle (NP) physicochemical traits (such as size, surface charge/hydrophobicity, elasticity, and composition) to protein-corona remodeling, immune recognition, biodistribution, and clearance across the ADME, and examines how these shifts translate into pharmacodynamics and therapeutic index. We systematically searched PubMed, Web of Science, Scopus, and ClinicalTrials.gov (primarily 2000–2025) using predefined Boolean strings. We included in vivo animal or human studies directly comparing the same drug in NP versus non-NP formulations with matched route and comparable dose, reporting at least one PK and/or PD endpoint, while excluding in vitro-only reports, reviews/opinion articles, and studies with undisclosed or non-comparable vehicles. We curated in vivo , route-matched comparisons of the same drug in NP versus non-NP forms and compiled an eight-drug cross-compound dataset. Where variance permitted, we quantified effects using standardized ln-ratios for AUC, half-life, and clearance, and flagged vehicle-matched comparisons to avoid overattributing gains to nanoscale architecture. Across oral and intravenous delivery, NPs consistently increased systemic exposure and prolonged half-life, with the most significant benefits in poorly soluble, lipophilic molecules; effect sizes were context-dependent and, in some formulations, reached multi-fold improvements. Mechanistically, we delineate when lymphatic uptake (long-chain lipid carriers), receptor-mediated transcytosis (e.g., BBB-oriented designs), or context-limited EPR are most contributory, and we highlight stiffness-dependent corona effects (e.g., ApoA-I enrichment) as a lever to extend circulation. To enable translation, we contribute: (i) a mechanism-to-metric map that connects trait tuning to PK readouts and PD/TI implications; (ii) a standardized, route-stratified table of ln-ratio effects with explicit vehicle/excipient flags; and (iii) a pragmatic decision framework to triage candidates (by logP/solubility and barrier goals) and design reproducible studies with safety checks (CARPA, anti-PEG/ABC, inorganic retention). Collectively, these elements advance anticipatory, model-informed nanoformulation design and provide quantitative anchors for future vehicle-matched, mechanism-driven comparisons.