6 research outputs found

    Estrategía de control eficiente aplicada a la fase 1, edificio administrativo. Construcción a ejecutarse en el lote H1, ubicado en el parque industrial el Viso Palermo-Huila

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    Este informe contiene una estrategia de control complementaria aplicada a la FASE 1 del Proyecto de CADEFIHUILA “Edificio administrativo”, mediante la información obtenida por parte del director de obra del proyecto, el Ing. Yoalveth Lozano Losada en representación de SCI “Servicio Colombiano de Ingeniería”, quien se encargará de suministrar la mano de obra del proyecto. Esta estrategia de control eficiente está basada en 3 pilares fundamentales “Control preventivo, Control concurrente y Control correctivo”.This report contains a complementary control strategy applied to PHASE 1 of the CADEFIHUILA Project "Administrative Building", through the information obtained by the project's work manager, Eng. Yoalveth Lozano Losada on behalf of SCI "Colombian Engineering Service ”, Who will be in charge of supplying the labor for the project. This efficient control strategy is based on 3 fundamental pillars "Preventive Control, Concurrent Control and Corrective Control".Introducción. -- 1. Alcance. -- 2. Justificación. -- 3. Objetivos. -- 3.1. Objetivo general. -- 3.2. Objetivos específicos . -- 4. Marco teórico. -- 4.1. Obra civil. -- 4.2. Construcción. -- 4.3. Estrategia. -- 4.4. Proceso. -- 4.5. Control. -- 4.6. Características de control . -- 4.7. Etapas de control. -- 4.7.1. Control Previo. -- 4.7.2 Control Concurrente. -- 4.7.3 Control Correctivo. -- 5. Diseño metodológico. -- 5.1 control previo. -- 5.2 control concurrente . -- 5.2.1 Documentos necesarios en las etapas del proyecto. -- 5.3. Control correctivo. -- 5.3.1 Datos Complementarios. -- 5.3.2 Control Jerárquico de mano de obra. -- 6. Conclusiones. -- Bibliografía. -- Anexos

    Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach

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    Abstract Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains challenging due to the inherent limitations of visible light otoscopy and user interpretation. Here we describe a powerful diagnostic approach to otitis media utilizing advancements in otoscopy and machine learning. We developed an otoscope that visualizes middle ear structures and fluid in the shortwave infrared region, holding several advantages over traditional approaches. Images were captured in vivo and then processed by a novel machine learning based algorithm. The model predicts the presence of effusions with greater accuracy than current techniques, offering specificity and sensitivity over 90%. This platform has the potential to reduce costs and resources associated with otitis media, especially as improvements are made in shortwave imaging and machine learning

    Barosinusitis: Comprehensive Review and Proposed New Classification System

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    Background Barosinusitis, or sinus barotrauma, may arise from changes in ambient pressure that are not compensated by force equalization mechanisms within the paranasal sinuses. Barosinusitis is most commonly seen with barometric changes during flight or diving. Understanding and better classifying the pathophysiology, clinical presentation, and management of barosinusitis are essential to improve patient care. Objectives To perform a comprehensive review of the available literature regarding sinus barotrauma. Methods A comprehensive literature search that used the terms “barosinusitis,” “sinus barotrauma,” and “aerosinusitis” was conducted, and all identified titles were reviewed for relevance to the upper airway and paranasal sinuses. All case reports, series, and review articles that were identified from this search were included. Selected cases of sinus barotrauma from our institution were included to illustrate classic signs and symptoms. Results Fifty-one articles were identified as specifically relevant to, or referencing, barosinusitis and were incorporated into this review. The majority of articles focused on barosinusitis in the context of a single specific etiology rather than independent of etiology. From analysis of all the publications combined with clinical experience, we proposed that barosinusitis seemed to fall within three distinct subtypes: (1) acute, isolated barosinusitis; (2) recurrent acute barosinusitis; and (3) chronic barosinusitis. We introduced this terminology and suggested independent treatment recommendations for each subtype. Conclusion Barosinusitis is a common but potentially overlooked condition that is primed by shifts in the ambient pressure within the paranasal sinuses. The pathophysiology of barosinusitis has disparate causes, which likely contribute to its misdiagnosis and under diagnosis. Available literature compelled our proposed modifications to existing classification schemes, which may allow for improved awareness and management strategies for barosinusitis

    Determinants of SARS-CoV-2 entry and replication in airway mucosal tissue and susceptibility in smokers.

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    Understanding viral tropism is an essential step towards reducing SARS-CoV-2 transmission, decreasing mortality from COVID-19, and limiting opportunities for mutant strains to arise. Currently, little is known about the extent to which distinct tissue sites in the human head & neck region and proximal respiratory tract selectively permit SARS-CoV-2 infection and replication. In this translational study, we discover key variabilities in the expression of ACE2 and TMPRSS2, essential SARS-CoV-2 entry factors, among the mucosal tissues of the human proximal airways. We show that SARS-CoV-2 infection is present in all examined head & neck tissues, with a notable tropism for the nasal cavity and tracheal mucosa. Finally, we uncover an association between smoking and higher SARS-CoV-2 viral infection in the human proximal airway, which may explain the increased susceptibility of smokers to developing severe COVID-19. This is at least partially explained by differences in IFN-β1 levels between smokers and non-smokers
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