Characteristics of COVID-19 and mortality-associated factors during the first year of the pandemic in southeast Mexico.

Aim: The present study was designed to evaluate the clinical characteristics of COVID-19 and mortality-associated factors during the first year of the pandemic in patients from southeastern Mexico. Patient & methods: A total of 953 records from patients with COVID-19 were cross-sectionally studied in a primary care hospital in southeast Mexico between 2020 and 2021. Results: The prevalent symptoms were fever (78.6%), cough (80.5%) and headache (82.8%) and dyspnea reached 13.5%. The mortality rate was 7.63% and the clinical variables associated with it were age >60 years, hypertension, severe disease, radiographic pneumonia, days to diagnosis and having two Mayan surnames. Conclusion: Future health strategies should consider age, comorbidities, disease severity, clinical manifestations and possessing ethnicity of risk (i.e., Mayan genetic background).

A domain-agnostic approach for characterization of lifelong learning systems.

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.

Neuroprotective effect of Mayan medicinal plant extracts against glutamate-induced toxicity.

Neurological disorders are a public health problem worldwide for which there is currently no direct treatment of the cause of the disorder. The goal of this study was to investigate the potential in vitro neuroprotective property of plants used in Mayan traditional medicine. Plant ethanolic extracts were prepared and tested on models in which neuronal damage was induced by glutamate, i.e., a human neuroblastoma cell line (SH-SY5Y) and rat cortical neurons. HPLC profiles from active extracts were also obtained. A total of 51 plant species were identified in the literature as plant species used in Mayan traditional medicine for the treatment of symptoms suggestive of neurological disorders, and we studied 34 of these in our analysis. Six extracts had a neuroprotective effect on SH-SY5Y cells, with the most active extract being that from Schwenckia americana roots (half maximal effective concentration [EC50] 11.3 ± 2.9 µg/mL), and three extracts exhibited a neuroprotective effect in the rat neuron cortical model, with the most active extract being that from Elytraria imbricata aerial parts (EC50 6.8 ± 3.1 µg/mL). These results suggest that the active extracts from such plants have the potential to be a great resource. Future studies should be performed that are more extensive and which isolate the active constituents.

Mercury levels assessment and its relationship with oxidative stress biomarkers in children from three localities in Yucatan, Mexico.

Mercury (Hg) is a global pollutant that is released into the environment from geologic and anthropogenic sources. Once it enters an organism, it generates several toxicity mechanisms and oxidative stress has been proposed as the main one. Metal susceptibility is greater in children, which is a result of their physiology and behavior. In Yucatan, Mexico, burning of unregulated garbage dumps and household trash, ingestion of top marine predators, and pottery manufacturing are among the conditions that could promote Hg exposure. However, for Yucatan, there are no published studies that report Hg levels and associated oxidative stress status in children. Therefore, this study aimed to assess Hg levels in blood and urine and oxidative stress biomarkers levels in a sample of 107 healthy children from three localities in Yucatan, Mexico, as well as investigate the relationship between these parameters. Hg was detected in 11 (10.28%) of blood samples and 38 (35.51%) of urine samples collected from the participating children. Fourteen subjects showed Hg above recommended levels. The oxidative stress biomarkers were slightly elevated in comparison with other studies and were statistically different between the sampling sites. No linear correlation between Hg levels and oxidative stress biomarkers was found. Nevertheless, exploratory univariate and multivariate analysis showed non-linear relations among the measured variables. Globally, the study provides, for the first time, information regarding Hg levels and their relationship with oxidative stress biomarkers in a juvenile population from Mexico's southeast (Yucatan) region. In agreement with worldwide concern about Hg, this study should stimulate studies on metal monitoring in humans (especially children) among scientists working in Mexico, the establishment of polices for its regulation, and the reduction of human health risks.