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Total agenesis from the corpus callosum within paranoid schizophrenia-a situation report.
Given the uncertainty that accompanies the SARS-CoV-2 pandemic and the need to respond to multiple chronic and acute health problems affecting the general population, including those requiring surgical intervention, the recommendations implemented in clinics and hospitals in Colombia are presented as a guide to achieve a reopening of elective surgery services in a safe, staggered and monitored manner in accordance with the dynamics imposed by the pandemic, national and international guidelines and the speed of production of scientific evidence related to COVID-19.
To identify and systematize available empirical evidence on factors and interventions that affect working conditions and environment in order to increase the attraction, recruitment and retention of human resources for health at the primary care level in rural, remote or underserved areas.

Rapid review of reviews selected according to relevance, eligibility and inclusion criteria. The search was conducted on electronic and manual databases, including grey literature. AMSTAR I was used to assess the quality of systematic reviews and a thematic analysis for synthesis of the results.

Sixteen reviews were included, one of which contained 14 reviews. Of the total, 20 reviews analyzed factors and 9 evaluated the effectiveness of interventions. The evidence on factors is abundant, but of limited quality. Individual, family and "previous exposure to a rural setting" factors were associated with higher recruitment; organizational and external context factors were important for human resource retention. Networking and professional support influenced recruitment and retention. selleck products Evidence on the effectiveness of interventions was limited, both in quantity and quality. The most frequently used intervention was incentives.

Evidence on factors that are positively related to recruitment and retention of workers at the first level of care in rural, remote or underserved areas is sufficient and should be taken into account when designing interventions. Quality evidence on the effectiveness of interventions is scarce. More controlled studies with methodological rigor are needed, particularly in the Americas.
Evidence on factors that are positively related to recruitment and retention of workers at the first level of care in rural, remote or underserved areas is sufficient and should be taken into account when designing interventions. Quality evidence on the effectiveness of interventions is scarce. More controlled studies with methodological rigor are needed, particularly in the Americas.
To analyze the evolution of the COVID-19 pandemic in Latin American and Caribbean countries in its first 90 days and its association with variables related to public health measures, and demographic, health and social characteristics.

he trend in new daily cases and the crude mortality rate (CMR) from COVID-19 were analyzed through the Joinpoint regression analysis methodology, using the Joinpoint Regression Program 4.8.0.1. Data was obtained from the Our World in Data registry. A multiple correspondence analysis was performed between the public health measures adopted in each country to face the COVID-19 pandemic (measured through the stringency index, Oxford University) and sanitary, demographic and social conditions, and the results of the evolution of the pandemic. SPSS software was used.

The Joinpoint regression analysis showed that the highest increase in the number of cases was observed in Brazil (11.3%) and the highest increase in CMR in Mexico (16.2%). The multiple correspondence analysis showed that CMR was associated with the total population, the stringency index, the level of urbanization, the proportion of the population living on less than one dollar a day, the prevalence of diabetes and the number of hospital beds.

The countries of the region show a heterogeneous evolution in the incidence of COVID-19. This heterogeneity is associated with both the public health measures adopted, as well as with the population size, poverty levels and pre-existing health systems.
The countries of the region show a heterogeneous evolution in the incidence of COVID-19. This heterogeneity is associated with both the public health measures adopted, as well as with the population size, poverty levels and pre-existing health systems.
Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions.

In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI.

The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912).

The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.
The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.
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