RESUMEN

Background: In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%. Methods: In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis. Results: The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases. Conclusions: Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.

RESUMEN

Protective masks are critical to impeding microorganism transmission but can propagate infection via pathogen buildup and face touching. To reduce this liability, we integrated electrospun photocatalytic graphitic carbon nitride (g-C3N4) nanoflakes into standard surgical masks to confer a self-sanitization capacity. By optimizing the purine/melamine precursor ratio during synthesis, we reduced the g-C3N4 band gap from 2.92 to 2.05 eV, eliciting a 4× increase in sterilizing hydrogen peroxide production under visible light. This narrower band gap enables robust photocatalytic generation of reactive oxygen species from environmental and breath humidity to swiftly eliminate accumulated microbes. Under ambient sunlight, the g-C3N4 nanocomposite mask layer achieved a 97% reduction in the bacterial viability during typical use. Because the optimized band gap also allows photocatalytic activity under shadowless lamp illumination, the self-cleaning functionality could mitigate infection risk from residual pathogens in routine hospital settings. Both g-C3N4 and polycaprolactone demonstrate favorable biocompatibility and biodegradability, making this approach preferable over current commercially available metal-based options. Given the abundance and low cost of these components, this scalable approach could expand global access to reusable self-sanitizing protective masks, serving as a sustainable public health preparedness measure against future pandemics, especially in resource-limited settings.

Asunto(s)

Antibacterianos , Grafito , Ensayo de Materiales , Compuestos de Nitrógeno , Antibacterianos/farmacología , Antibacterianos/química , Grafito/química , Grafito/farmacología , Compuestos de Nitrógeno/química , Compuestos de Nitrógeno/farmacología , Purinas/química , Purinas/farmacología , Tamaño de la Partícula , Escherichia coli/efectos de los fármacos , Textiles/microbiología , Máscaras , Pruebas de Sensibilidad Microbiana , Materiales Biocompatibles/química , Materiales Biocompatibles/farmacología , Staphylococcus aureus/efectos de los fármacos , Humanos

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As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Niño Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure.

RESUMEN

In the present paper, a vinyl ester (VE) resin, potentially used as a resin matrix for fiber-reinforced polymer (FRP) composite sucker rods in oil drilling, FRP bridge cables, or FRP marine structures, was investigated on its resistance to water and alkaline solution immersion in terms of water uptake, hydrothermal expansion, and mechanical properties. A two-stage diffusion model was applied to simulate the water uptake processes. Alkaline solution immersion led to a slightly higher mass loss (approx. 0.4%) compared to water immersion (approx. 0.23%) due to the hydrolysis and leaching of uncured small molecules (e.g., styrene). Water immersion caused the expansion of VE plates monitored with Fiber Bragg Grating (FBG). With the same water uptake, the expansion increased with immersion temperatures, which is attributed to the increased relaxation extent of the resin molecular networks. Although an obvious decrease of the glass transition temperatures (Tg) of VE due to water immersion (5.4 to 6.1 °C/1% water uptake), Tg can be recovered almost completely after drying. Tensile test results indicate that a short-term immersion (less than 6 months) enhances both the strength and elongation at break, while the extension of the immersion time degrades both the strength and elongation. The modulus of VE shows insensitive to the immersion even at elevated temperatures.