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This dataset presents longitudinal changes in clinical and inflammatory outcomes following initiation of intravenous golimumab (IVgol) in patients with hidradenitis suppurativa (HS) who had previously failed infliximab due to infliximab-associated anaphylaxis or anti-infliximab antibody formation. We hypothesized that IVgol, a fully human TNF-α inhibitor with reduced immunogenicity, would produce meaningful and durable reductions in disease severity, pain, and systemic inflammation in this refractory population. Data were collected via retrospective chart review at a single academic HS center and include six outcome measures: HS-Physician Global Assessment (HS-PGA, 0–5), numerical rating scale pain score (NRS-pain, 0–10), interleukin-6 (IL-6, pg/mL), erythrocyte sedimentation rate (ESR, mm/h), C-reactive protein (CRP, mg/L), and white blood cell count (WBC, ×10⁹ cells/L). Each figure displays mean values at baseline and months 1, 6, and 12. All 30 patients had baseline and Month 1 data available; 17 had available Month 6 and Month 12 data and are represented at those timepoints. Across all six outcomes, IVgol was associated with reductions from baseline at Month 1, with sustained or progressive improvement through Month 12 among patients with available follow-up data. HS-PGA and ESR showed the most consistent trajectories, while CRP exhibited transient visit-to-visit variability at Month 6 consistent with the episodic, flare-driven nature of HS rather than treatment failure. WBC demonstrated a delayed pattern of significance, with statistically significant reductions emerging at months 6 and 12. Taken together, these figures provide objective clinical and biomarker evidence supporting IVgol as an effective alternative weight-based therapy for HS patients who cannot continue infliximab.

This dataset contains the raw and analyzed data for the fatigue characterization of the S460 steel. The characterization was achieved in four steps: (a) crack propagation tests were performed in 6 compact tension specimens and the crack tip position was registered by images of the specimens' surface taken periodically; (b) specimen geometry and crack length were used to generate numerical finite element models in Ansys software for each crack measurement instant and the displacements around the crack tip were extracted; (b) Williams' expansion series was used to calculate the KI stress intensity factor from the FE displacements, and (d) dK calculated from KI and crack propagation rate da/dN were used to generate the da/dN x dK curves for the crack propagation phase of Paris' law and to extract coefficients C and m.

This paper presents a cloud-integrated Artificial Intelligence of Things (AIoT) framework for real-time credit risk and supply chain analytics. The proposed system combines IoT-driven data streams with scalable cloud infrastructure to enable continuous monitoring and predictive modeling of financial and operational risks. Machine learning models, including Probability of Default (PD) and Loss Given Default (LGD), are deployed within a real-time analytics pipeline to assess dynamic risk exposure across interconnected systems. The framework supports decision intelligence through automated insights and anomaly detection. Experimental evaluation using synthetic datasets demonstrates improved predictive performance and reduced latency compared to traditional batch-processing approaches. SHAP-based explainability analysis provides transparent, regulator-compliant risk predictions achieving AUC of 0.86 and R-squared of 0.99 with gradient boosting. The results highlight the effectiveness of integrating AIoT and cloud computing for scalable, real-time business analytics in fintech and supply chain domains.

Supplementary materials for “Reversed inter-mineral Fe-Zn isotope fractionation in ophiolitic peridotites: Evidence for widespread hydration of oceanic lithospheric mantle”

Dataset of FRP-UHPSSC pullout test bond strength

Dataset used in the empirical analysis.

Este proyecto aborda la necesidad de atención de salud mental antes los eventos de desastres naturales, cuya consecuencia a incrementando el riesgo de desarrollar un trastorno de estrés postraumático. El objetivo general fue Implementar un plan psicoformativo sobre primeros auxilios psicológicos en caso de eventos socio naturales en el Centro de Diagnóstico Integral "Giandoménico Puliti", del municipio Tovar, Estado Mérida. La metodología fue de investigación cualitativa, haciendo uso de la investigación acción participante relacionándola con un diseño de campo del cual se efectuará el nivel de investigación interpretativo. Mediante un pre diagnóstico de una entrevista informal al CDI se obtuvo 3 informantes claves entre personal médico, de esta manera se identificó la ausencia de preparación en primeros auxilios psicológicos para eventos socionaturales, el plan de acción se ejecutó con diversas actividades como charlas informativas, grupos de discusión, intervenciones breves, estudios de casos y circuitos de escenario. La evaluación del proceso se realizó mediante observación directa y una escala de estimación. Los resultados demostraron la factibilidad del plan y una participación activa y receptiva del personal médico. Se observó un incremento significativo en la autoconfianza del personal para manejar situaciones de estrés colectivo y actuar como apoyo psicológico. El principal logro fue la formación del personal apto para abordar crisis en eventos socionaturales, fomentando la cohesión grupal y mejorando las relaciones interpersonales. En conclusión, el proyecto fortaleció la capacidad de repuesta individual de los médicos en atención comunitaria frente a futuras crisis psicológicas por eventos socionaturales. Palabras claves: primeros auxilios psicológico, eventos socionaturales, trastorno de estrés postraumático.

Supplementary Material for paper “Oral Dapsone in Refractory Rosacea: A Prospective Exploratory Study Defining a ‘High Inflammatory Burden’ Subtype and a Testable Precision Treatment Hypothesis”

This dataset contains real-world measurements of residential energy consumption collected from 39 houses in Erbil. The dataset spans from 14 May 2020 to 17 June 2025 and integrates daily energy usage, occupancy information, building characteristics, and detailed weather variables. Energy consumption (kWh) was recorded using smart meters installed by the Ministry of Electricity. Weather data were collected from sensors located in close proximity to the houses. Building and household characteristics were obtained through a structured survey conducted in December 2025 by a professionally trained field team. This dataset is designed to support research in smart home energy modeling, occupancy behavior analysis, and the impact of environmental factors on energy consumption.

Using Sequencing-by-Synthesis (SBS) technology, the Illumina high-throughput sequencing platform sequenced the cDNA libraries, generating a large amount of raw data. After quality control of this raw data, the blank control group, the 2 mg/L ketomusk exposure group, and the 20 mg/L ketomusk exposure group yielded an average of 46,599,849, 45,667,817, and 55,288,099 clean reads, respectively. Across the nine samples, Q30 ≥ 94.88%, Q20 ≥ 98.31%, and GC content ranged from 46.3% to 46.83%. Transcriptomic sequencing was used to investigate the mechanism of ketomusk’s toxicity to Caenorhabditis elegans. The GO terms enriched among differentially expressed genes between the ketomusk-exposed group and the control group were primarily associated with the regulation of certain protein metabolic processes, cellular structure, phosphoprotein and phosphatase activity, protein serine/ threonine kinase activity, regulation of epithelial cell differentiation, centriole satellites, superoxide production, and NADPH oxidase activity. Enrichment analysis of KEGG pathways revealed that differentially expressed genes were primarily associated with sulfur metabolism, the biosynthesis of certain amino acids, fatty acid metabolism, nitrogen metabolism, and arginine biosynthesis.