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Introduction: Patients with septic shock face an elevated risk of mortality compared to those with sepsis. Several biomarkers, including lactate dehydrogenase, albumin, and lactate/albumin (L/A), have been associated with increased mortality in COVID-19 patients. This study aims to assess the relationship between sepsis, septic shock, and mortality, as well as the need for mechanical ventilation in COVID-19 patients. Demographic, sepsis severity factors, and biomarkers are examined. Methods: A retrospective case series from June 2020 to March 2021 included 490 patients diagnosed with sepsis or septic shock secondary to SARS-CoV-2 pneumonia. Time-to-event analyses were conducted for mechanical ventilation and mortality. Statistical significance was set at p ≤ .0038. Serum lactate, albumin, lactate/albumin ratio, C-reactive protein, platelet levels, and three sepsis severity scales, (CCI, SOFA, APACHE IV) were assessed. Results: Sepsis was identified in 352 patients (71.8%), while 138 had septic shock. Patients with septic shock were more likely to require invasive ventilator support. Factors associated with a higher risk of intubation included higher APACHE IV scores, elevated serum albumin levels, and increased L/A ratio. L/A ratio and serum lactate levels demonstrated the best diagnostic accuracy for mechanical ventilation (AUC, 0.964 and 0.946, respectively), mortality (AUC, 0.926 and 0.887, respectively). Discussion: Increased C-reactive protein, combined with increased serum lactate and a high lactate/albumin ratio, may assist clinicians in identifying COVID-19 patients at risk of mechanical ventilation and mortality upon admission. Optimal cut-off values for lactate (1.45-1.65 mmol/L) and L/A ratio (0.413) can aid in prioritizing medical care for at risk COVID-19 patients.

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Persons with HIV (PWH) face an increased risk of cardiovascular events due to immune activation, comorbidities, and certain antiretrovirals (ARVs). However, the current cardiovascular risk (CVR) scores are not specifically directed toward PWH. This study aimed to assess the agreement between different predictive CVR scores and explore their relationship with clinical and demographic data in Mexican PWH. A descriptive cross-sectional analysis was conducted in 200 PWH with a mean age of 42 years who were treated at a Mexican urban center from 2017 to 2018. The majority (83%) was on ARV treatment and 79.5% had undetectable viral loads (VLs). Moderate- to high-risk scores were infrequent, with Framingham Risk Score for Hard Coronary Heart Disease scores showing higher values, with very low concordance among all scores. Logistic regression analysis revealed significant associations between the CVR scores and the initial recorded VL, CD4 cell count, and elevated triglyceride levels. However, no associations were found with measures such as body mass index or abdominal circumference. Treatment with integrase strand transfer inhibitors (INSTIs), particularly first-generation inhibitors, showed strong associations with all predictive scores, notably ASCVD (odds ratio = 7.03, 95% confidence interval 1.67-29.64). The poor concordance among the CVR scores in PWH highlights the need for a specific score that considers comorbidities and ARV drugs. Despite the relatively young age of the participants, significant correlations were observed between INSTI use, initial VL, CD4 cell count, and triglyceride levels, which are factors not considered in the existing risk scores. Regardless of the actual value of the scores, screening for CVR in PWH is recommended.

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Anthocyanins are a specific group of molecules found in nature that have recently received increasing attention due to their interesting biological and colorimetric properties that have been successfully applied in several fields such as food preservation and biomedicine. Consequently, reviews devoted to a general overview of these flavonoids have proliferated in recent years. Meanwhile, the incorporation of anthocyanins into polymeric systems has become an interesting strategy to widen the applicability of these molecules and develop new smart and functional polymers in the above cited areas. However, anthocyanin-based polymers have been scarcely reviewed in the literature. Accordingly, this review aims to be a systematic summary of the most recent approaches for the incorporation of anthocyanins into macro-, micro-, or nanostructured polymers. Moreover, this work describes the fundamentals of the applicability of smart anthocyanin-based polymers and offers an updated review of their most interesting applications as sensors, biological regulators, and active materials.

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Numerical simulations and finite-size scaling analysis have been carried out to study the problem of inverse percolation by removing semirigid rods from a L×L square lattice that contains two layers (and M=L×L×2 sites). The process starts with an initial configuration where all lattice sites are occupied by single monomers (each monomer occupies one lattice site) and, consequently, the opposite sides of the lattice are connected by nearest-neighbor occupied sites. Then the system is diluted by removing groups of k consecutive monomers according to a generalized random sequential adsorption mechanism. The study is conducted by following the behavior of two critical concentrations with size k: (1) jamming coverage θ_{j,k}, which represents the concentration of occupied sites at which the jamming state is reached, and (2) inverse percolation threshold θ_{c,k}, which corresponds to the maximum concentration of occupied sites for which connectivity disappears. The obtained results indicate that (1) the jamming coverage exhibits an increasing dependence on the size k-it rapidly increases for small values of k and asymptotically converges towards a definite value for infinitely large k sizes θ_{j,k→∞}≈0.2701-and (2) the inverse percolation threshold is a decreasing function of k in the range 1≤k≤17. For k≥18, all jammed configurations are percolating states (the lattice remains connected even when the highest allowed concentration of removed sites is reached) and, consequently, there is no nonpercolating phase. This finding contrasts with the results obtained in literature for a complementary problem, where straight rigid k-mers are randomly and irreversibly deposited on a square lattice forming two layers. In this case, percolating and nonpercolating phases extend to infinity in the space of the parameter k and the model presents percolation transition for the whole range of k. The results obtained in the present study were also compared with those reported for the case of inverse percolation by removal of rigid linear k-mers from a square monolayer. The differences observed between monolayer and bilayer problems were discussed in terms of vulnerability and network robustness. Finally, the accurate determination of the critical exponents ν, ß, and γ reveals that the percolation phase transition involved in the system has the same universality class as the standard percolation problem.

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Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.

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BACKGROUND: The course of chronic obstructive pulmonary disease (COPD) is different in men and women. There are limited data in Latin America regarding COPD exacerbations (ECOPD) in women. This study aims to determine the sociodemographic and clinical profile of ECOPD adjusted by gender.METHODS: Cross-sectional analytical study of all patients hospitalised due to an ECOPD in a tertiary university hospital in Colombia between 2015 and 2019. A group comparison analysis was performed between male and female groups.RESULTS: A total of 81 patients met the inclusion criteria (35.8% were women). The mean age was 71.49 years. Most of the patients were GOLD (Global Initiative for Obstructive Lung Disease) 3 and 4. A history of TB was present in 15% of our cohort. While the proportion of smokers was higher among men (OR 5.11; P = 0.013), exposure to wood smoke was significantly higher in women (OR 24; P < 0.001). Females were associated with a lower probability of having forced expiratory volume in 1 sec >0,87 L (OR 0.11; P = 0.013) and were associated with an increased probability of receiving inhaled corticosteroids during hospitalisation (OR 3.33; P = 0.023). No differences in terms of mortality or complications were found.CONCLUSION: Women with COPD are underrepresented in literature. This study was able to identify some factors related to female sex among patients hospitalised for severe ECOPD.

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Introduction: A high percentage of patients with non-severe (17.9%) and severe (2.9%) atypical pneumonia do not display pulmonary tomographic findings upon hospital admission; furthermore, lesion associated with COVI-19 are peripherally distributed in a multifocal ground-glass pattern, as well as displaying an irregular consolidation pattern, with a posterior or lower lobe predilection. The main objective of this study was to identify the pulmonary radiological patterns in patients diagnosed with SARS-CoV-2 pneumonia, the factors associated with the need for mechanical ventilation, as well as their survival rates at 30 days. Methods: We report the pulmonary tomographic findings of 490 consecutive patients with severe and critical pneumonia due to SARS-CoV-2. The patients were classified according to the tomography and demographic findings, sepsis severity prognostic scales, Charlson comorbidity index (CCI), the Sequential Organ Failure Assessment (SOFA), and the Acute Physiology and Chronic Health Evaluation (APACHE IV). The Kaplan-Meier method was used to calculate survival distributions. Results: 89.80% of patients had ground-glass opacities, 81.63% radiologic consolidation sign, 42.45% vascular thickening pattern, 37.55% lymphadenopathies, 14.90% pleural effusion, and 2.65% pulmonary thrombosis; meanwhile, 91.02% had bilateral lesions, 85.51% had peripheral lesions, and 75.92% had basal lobe lesions. APACHE IV (HR, 1.191, 95% CI [1.126, 1.260]), SOFA (HR, 5.178, 95%CI [3.103, 8.641]), and CCI (HR, 0.673, 95%CI [0.510, 0.889]), as well as the pulmonary damage severity index (HR, 1.282, 95%CI [1.151, 1.428]), predict the need for invasive mechanical ventilation. Only moderate ARDS patients with mild and severe lung disease showed different 30-day mortality distributions (χ2 = 7.00, p = 0.008). Discussion: Although the survival distributions did not vary significantly, an overwhelming majority of patients (i.e., 84.35%) with a higher pulmonary damage severity index (i.e., 23>) died within 30 days of hospital admission, while only 25.91% with moderate lung damage and 2.42% with mild lung damage.

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This dataset was compiled to estimate the levels of thermal comfort and mental health in a sample group of university students confined due to the COVID-19 pandemic. By the time research was carried out, these students of a hot and humid region of Mexico, had already spent 200 days on distance learning using online platforms. A total of 324 records were documented with a final sample of 316 valid participants. The total records were collected directly from the students through a web platform (Microsoft forms). This data set can be used to generate correlations between mental health, thermal comfort, and individual characteristics in the study population that will allow to identify the influence of the built environment and local climate on the levels of stress and anxiety that university students experienced under confinement. It can also be used to issue recommendations to improve the quality of built spaces and for the construction of adaptive models of thermal comfort considering mental health as a study variable.

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The Effective Reproduction Number Rt provides essential information for the management of an epidemic/pandemic. Projecting Rt into the future could further assist in the management process. This article proposes a methodology based on exposure scenarios to perform such a procedure. The method utilizes a compartmental model and its adequate parametrization; a way to determine suitable parameters for this model in México's case is detailed. In conjunction with the compartmental model, the projection of Rt permits estimating unobserved variables, such as the size of the asymptomatic population, and projecting into the future other relevant variables, like the active hospitalizations, using scenarios. The uses of the proposed methodologies are exemplified by analyzing the pandemic in a Mexican state; the main quantities derived from the compartmental model, such as the active and total cases, are included in the analysis. This article also presents a national summary based on the methodologies to illustrate how these procedures could be further exploited. The supporting information includes an application of the proposed methods to a metropolitan area to show that it also works well at other demographic disaggregation levels. The procedures developed in this article shed light on how to develop an effective surveillance system when information is incomplete and can be applied in cases other than México's.

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