Evaluation of a novel digital environment for learning medical parasitology.

open access articleEukaryotic parasites represent a serious human health threat requiring health professionals with parasitology skills to counteract this threat. However, recent surveys highlight an erosion of teaching of parasitology in medical and veterinary schools, despite reports of increasing instances of food and water borne parasitic infections. To address this we developed a web-based resource, DMU e-Parasitology®, to facilitate the teaching and learning of parasitology, comprising four sections: theoretical; virtual laboratory; virtual microscopy; virtual clinical case studies. Testing the package was performed using a questionnaire given to ninety-five Pharmacy students in 2017/18 to assess effectiveness of the package as a teaching and learning tool. 89.5% of students reported appropriate acquisition of knowledge of the pathology, prevention and treatment of some parasitic diseases. 82.1% also welcomed the clinical specialism of the package as it helped them to acquire basic diagnostic skills, through learning infective features/morphology of the parasites

Isolation and Characterization of Plant Growth-Promotion Diazotrophic Endophytic Bacteria Associated to Sugarcane (Saccharum officinarum L.) Grown in Paraíba, Brazil

Sugarcane is an important Brazilian commodity, being usually cultivated in soils with low natural fertility. This study aimed to isolate diazotrophic endophytes from sugarcane tissues and evaluate the morphological and physiological characteristics of their colonies as well as their plant growth-promoting (PGP) traits in select diazotrophic endophytic bacteria. Fifty-six bacterial isolates were identified in the sugarcane tissues, and these isolates presented distinct morphological and physiological traits. A total of thirty-five bacterial isolates were biochemically evaluated. Overall, Bacillus was the dominant genus. Isolates of Methylobacterium spp. and Brevibacillus agri were present only in leaves, while Herbaspirillum seropedicae occurred only in stems. Except to IPA-CF45A, all isolates were nitrogenase positive. All endophytes exhibit production of indol 3-acetic acid. Over 50% of endophytes solubilize phosphate, release N-acyl homoserine lactones, and present the activity of 1-aminocyclopropane-1-carboxylic acid deaminase, catalase, lipase and protease. The network analysis showed that isolates belonged to Burkholderia, Herbaspirillum, and Methylobacterium interact with Bacillus. Bacterial endophytes exhibited distinct morphological, physiological, and PGP traits that are useful for sustainable agriculture, highlighting the isolates IPA-CC33, IPA-CF65, IPA-CC9 and IPA-CF27. Further studies on the effects of these diazotrophic endophytes and their potential for providing microbial inoculants for improving sugarcane fields will provide valuable information to maintain the sustainability and environment quality.National Council for Scientific and Technological Development 426655/2018-

Machine Learning–Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study

BackgroundFunctional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. ObjectiveThis study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. MethodsA 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. ResultsOverall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. ConclusionsUsing commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters

Prevalence of and risk factors for erectile dysfunction in young nondiabetic obese men: results from a regional study.

Erectile dysfunction (ED), a condition closely related to cardiovascular morbidity and mortality, is frequently associated with obesity. In this study, we aimed to determine the prevalence of ED and evaluate the associated risk factors in a cohort of 254 young (18-49 years) nondiabetic obese (body mass index [BMI] ≥ 30 kg m-2) men from primary care. Erectile function (International Index of Erectile Function [IIEF-5] questionnaire), quality of life (Aging Males' Symptoms [AMS scale]), and body composition analysis (Tanita MC-180MA) were determined. Total testosterone was determined using high-performance liquid chromatography-mass spectrometry. Multivariate logistic regression analysis was used to study the factors associated with ED. ED prevalence was 42.1%. Subjects with ED presented higher BMI, waist circumference, number of components of the metabolic syndrome, AMS score, insulin resistance, and a more unfavorable body composition than those without ED. Multivariate logistic regression analysis showed that a pathological AMS score (odds ratio [OR]: 4.238, P 40% of subjects. A pathological AMS score, the degree of obesity, and age were positively associated with ED, while elevated HDL-cholesterol levels were inversely associated with the odds of presenting ED. Further prospective studies are needed to evaluate the long-term consequences of ED in this population