SANE Mental Health Charity

Industry 6.0 is an emerging concept that extends earlier industrial paradigms toward hyper-connected, AI-driven, adaptive, and sustainability-oriented systems. Recent literature describes it as a cognitive and autonomous industrial environment built around technologies such as generative AI, advanced digital twins, pervasive connectivity, and distributed intelligence. In the education sector, these shifts are pushing institutions to redesign teaching, curriculum, assessment, and governance. The present paper examines the impact of Industry 6.0 on education with special reference to management education. The study is conceptual and descriptive in nature and is based on secondary sources. The paper finds that Industry 6.0 is likely to reshape management education through personalized learning, AI-enabled decision support, simulation-based pedagogy, stronger industry-academia integration, and greater emphasis on sustainability, ethics, innovation, and interdisciplinary skills. At the same time, concerns relating to academic integrity, data privacy, faculty readiness, and digital inequality remain significant. The paper concludes that management institutions must move from conventional content delivery to future-ready, technology-enabled, human-centered learning systems.

En Perú, el algodón, maíz amarillo duro y papa, son cultivados por más de setecientas mil unidades agrícolas en casi novecientas mil hectáreas que contribuyen a la alimentación y a la industria. Empero, los rendimientos por hectárea de algodón, maíz y papa, son menores que las de muchos países y el Perú no es competitivo en el mercado mundial, estando forzado a importar cantidades masivas de algodón y maíz amarillo duro y no logra exportar la papa por su alto costo unitario. Entonces, el objetivo es evaluar el beneficio de liberar el uso de semillas editadas genéticamente, disponibles en el mercado mundial, de estos cultivos. Se aplicó la metodología de presupuesto parcial en entorno probabilístico con el software @Risk, con información secundaria de países con condiciones productivas similares. Se encontró que los coeficientes beneficio/costo, de las semillas mejoradas genéticamente son superiores a uno para los tres cultivos y que la pérdida anual por no implementar estas semillas de alto rendimiento bordea los 300 millones de dólares. Se recomienda estudiar el impacto de semillas mejoradas en otros cultivos

Studies in human medicine have demonstrated that rotavirus infection can also affect extraintestinal sites due to its systemic effects. However, in veterinary medicine, the injury caused by rotavirus diarrhea is limited to the intestines, and its effects on various systemic structures remain poorly understood. In this observational case–control study, we aimed to determine the effects of HSP-27, Caspase-3, IL-2, γ-H2AX, HMGB-1, SP-D, and GDH (or GLDH) on the pathogenesis of rotavirus infection by using biomarkers for diagnostic purposes in lung and liver injury in neonate diarrheic calves naturally infected with rotavirus, both alive and post-mortem. Fifty-two Simmental calves (1–28 days old) of both sexes, 40 infected with rotavirus and 12 healthy, were studied. Twenty-eight out of 40 survived, while the remainder underwent necropsy for histopathological and immunopathological (HSP-27, Caspase-3, IL-2, γ-H2AX) examination of the lungs and livers. Lung and liver-specific serum E-selectin, glutamate dehydrogenase, surfactant protein-D, and high mobility group box-1 were analyzed by a bovine-specific ELISA kit (Shanghai Coon Koon Biotech Co., Ltd., China). Histopathological and immunohistochemical analyses confirmed lung and liver injury in naturally infected calves. HMGB-1, SP-D, and GDH concentrations were significantly higher in naturally infected calves than in the control group (p < 0.001, p < 0.001, and p < 0.05, respectively), showing an excellent diagnostic predictive capacity for lung and liver injury. Also, IL-2, HSP-27, CASP-3, and γ-H2AX were significantly expressed in the lungs (p < 0.001, p < 0.001, p < 0.001, and p < 0.05, respectively) and liver (p < 0.001, p < 0.001, p < 0.01, and p < 0.01, respectively). All these observations led us to hypothesize that oxidative stress, apoptosis, and DNA damage may underlie the pathogenesis of this condition. Nevertheless, further studies on large populations of rotavirus-infected calves are needed to confirm the data reported in the current study.

In Costa Rica, human-tapir conflicts are increasing, including disease transmission, as Baird's tapirs (Tapirus bairdii) move into human-altered landscapes. We investigated the death of a wild male tapir, identifying a systemic Actinomyces israelii infection. The potential of this pathogen being an emerging health threat for tapir conservation warrants further study.

Abstract Introduction Pulmonary arterial hypertension (PAH) is a progressive disease leading to right ventricular (RV) dysfunction and right heart failure. RV adaptation to increased afterload is a key prognostic factor. Traditional echocardiographic parameters offer insight but may not fully capture RV remodeling complexity. Phenotypic classifications combining RV size, contractility, and ventricular-arterial coupling may improve risk stratification. Methods A multicenter prospective cohort study was conducted in patients with de novo diagnosis of PAH as part of the TREPostinil Subcutáneo a LARgo Plazo en Hipertensión Pulmonar registry (TREPAR-HP). Six right ventricular (RV) phenotypes (A–F) were evaluated based on the RV basal diameter, fractional area change (FAC), and the ratio of tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP). This classification ranges from a normal and adapted RV (phenotypes A–C) to severe dysfunction and dilation (phenotypes D–F). Results Figure 1 shows the distribution of phenotypes. In a cohort of 93 patients (mean age 46.7 years, 86% women), the most common etiology was idiopathic (57%). Most patients presented with functional class III (62.4%) and received dual therapy (86%). Height (p=0.002), right atrial area (p=0.001), RV basal diameter (p&amp;lt;0.001), and TAPSE (p&amp;lt;0.001) showed significant differences between groups. The median BNP was 290.27 pg/mL, and the average cardiac output was 4.38 L/min (p=0.012). These findings highlight the functional and hemodynamic burden of the cohort. Figure 2 shows the distribution of phenotypes with respect to risk classification according to REVEAL. Conclusion RV phenotypic classification reveals distinct adaptation patterns in PAH. Advanced phenotypes (D–F) showed worse functional class, larger right atrial areas, and greater RV dysfunction. This approach may enhance risk stratification and guide management, underscoring the importance of early detection of RV maladaptation Figure 1. Distribution of phenotypes. Figure 2. distribution of phenotypes with respect to risk classification according to REVEAL.

Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients’ risk of recurrence by analyzing the pathology images of their cancer histology. We analyzed 125 hematoxylin and eosin-stained whole slide images (WSIs) from 125 patients across two institutions (National Cancer Center and Korea University Medical Center Guro Hospital) to predict breast cancer recurrence risk using deep learning. Sensitivity reached 0.857, 0.746, and 0.529 for low, intermediate, and high-risk categories, respectively, with specificity of 0.816, 0.803, and 0.972, and a Pearson correlation of 0.61 with histological grade. Class activation maps highlighted features like tubule formation and mitotic rate, suggesting a cost-effective approach to risk stratification, pending broader validation. These findings suggest that deep learning models trained exclusively on hematoxylin and eosin stained whole slide images can approximate genomic assay results, offering a cost-effective and scalable tool for breast cancer recurrence risk assessment. However, further validation using larger and more balanced datasets is needed to confirm the clinical applicability of our approach.

Abstract Rapid and reliable DNA extraction from urine is a critical bottleneck in advancing molecular diagnostics for urinary tract infections (UTIs) in both centralized and decentralized settings. Here, we present an ionic liquid-based DNA extraction method (IL-DEx) that enables recovery of bacterial DNA from urine samples in under 30 minutes using minimal equipment and no hazardous chemicals. IL-DEx was benchmarked against a widely used commercial kit (QIAamp DNA Mini Kit, QIAGEN) using reference strains, clinical isolates, and spiked urine samples. For gram-negative bacteria, IL-DEx achieved comparable DNA yields (47–102% relative efficiency), while recoveries from gram-positive bacteria were lower (0.7–8%) but sufficient for downstream detection. Quantitative PCR (qPCR) revealed linear DNA recovery across five to six orders of magnitude (10 8 –10 2 CFU/ml, R 2 &gt;0.99), with detection limits of ∼10 2 –10 3 CFU/ml for gram-negatives and ∼10 3 –10 4 CFU/ml for gram-positives using 1 ml urine. Clinical evaluation with 13 patient urine samples (ten culture-positive, three culture-negative) demonstrated that IL-DEx reliably enabled pathogen detection by qPCR and full-length 16S rRNA gene sequencing (Oxford Nanopore). Performance was comparable to three other extraction methods tested head-to-head, including the QIAamp DNA Mini Kit (QIAGEN), the MagaZorb DNA Mini-Prep Kit (Promega), and a phenol-chloroform extraction method. These findings establish IL-DEx as the first ionic liquid-based approach evaluated for DNA recovery from clinical urine samples, providing a fast, simple, and low-cost method suitable for integration into molecular workflows for UTI diagnostics across diverse laboratory and clinical settings. Importance Urinary tract infections (UTIs) are among the most common infections worldwide and a major driver of antibiotic use. Rapid and accurate diagnosis is critical to guide therapy, reduce inappropriate antibiotic prescriptions, and improve patient outcomes. While molecular diagnostics can drastically reduce time to identify uropathogens, their implementation remains constrained by upstream DNA extraction – a step that is often laborious, cost-intensive, or incompatible with rapid diagnostic workflows. We developed a fast, simple, and low-cost DNA extraction method (IL-DEx) that uses an ionic liquid and magnetic beads to recover bacterial DNA directly from urine. IL-DEx eliminates hazardous reagents and complex equipment while delivering performance comparable to established extraction kits. By streamlining this critical pre-analytical step, IL-DEx enables faster molecular diagnostics and broadens access to modern UTI testing. Its simplicity and robustness position it as a valuable tool for improving diagnostic speed, antimicrobial stewardship, and patient care across healthcare settings.

This study analyzes the impact of agricultural emissions on food security in Mexico using a quantitative, correlational, and explanatory approach. Historical data from 1961–2020, projected to 2025, were used, extracted from international sources such as the FAO and the World Bank. The dependent variable was the percentage of agricultural land (AG.LND.AGRI.ZS), and three independent variables related to CH₄, CO₂, and N₂O emissions were evaluated. Pearson's correlation analysis and a Random Forest Regressor model were applied to identify structural relationships and the relative importance of each variable. The results show that methane and carbon dioxide emissions have a greater impact on agricultural land use, suggesting a direct relationship between emissions and agricultural expansion. It is concluded that mitigating these emissions is key to ensuring the sustainability of the agri-food system and improving food security in the country.