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Τετάρτη 18 Μαΐου 2022

Sparstolonin B inhibits pancreatic adenocarcinoma through the NF-κB signaling pathway

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Publication date: Available online 18 May 2022

Source: Experimental Cell Research

Author(s): Yang Lyu, Bowen Duan, Ziming Liu, Fan Yang, Chen Chen, Xuejiao Jiang, Xiang Liu

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Efficacy and safety of Lenzumestrocel (Neuronata-R® inj.) in patients with amyotrophic lateral sclerosis (ALSUMMIT study): study protocol for a multicentre, randomized, double-blind, parallel-group, sham procedure-controlled, phase III trial

alexandrossfakianakis shared this article with you from Inoreader

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A single cycle (two repeated treatments) with intrathecal autologous bone marrow-derived mesenchymal stem cells (BM-MSCs, 26-day interval) showed safety and provided therapeutic benefit lasting 6 months in pat...
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High flow nasal cannula in the management of obstructive sleep Apnoea postoperatively. Is flow a new alternative to positive pressure?

alexandrossfakianakis shared this article with you from Inoreader

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Publication date: Available online 17 May 2022

Source: American Journal of Otolaryngology

Author(s): Abhijit S. Nair, Antonio M. Esquinas

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Magnetic resonance imaging assessment of juvenile idiopathic arthritis using OMERACT and EuroTMjoint classifications

alexandrossfakianakis shared this article with you from Inoreader
This retrospective case –control study compared inflammatory and structural damage in the temporomandibular joint of patients with juvenile idiopathic arthritis (JIA) and its subtypes and healthy patients using the Outcome Measures in Rheumatology Clinical Trials (OMERACT) and EuroTMjoint classifications. Correlations be tween the scores of the two classifications and time of diagnosis were evaluated. Twenty-nine JIA patients and 48 age-matched healthy participants were examined. TMJ images on each side were considered individually. (Source: International Journal of Oral and Maxillofacial Surgery)
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Machine Learning Based Forecast of Dengue Fever in Brazilian Cities using Epidemiological and Meteorological Variables

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Abstract
Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we develop a model to predict monthly dengue cases in Brazilian cities one month ahead from 2007-2019. We compare different machine learning algorithms and feature selection methods using epidemiological and meteorological variables. We find that different models work best in different cities, and a random forests model trained on monthly dengue cases performs best overall. It produces lower errors than a seasonal naïve baseline model, gradient boosting regression, feed-forward neural network, and support vector regression. For each city, we compute the mean absolute error between predictions and true monthly dengue cases on the test set. For the median city, the error is 1 2.2 cases. This error is reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.
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