A reproducible ensemble machine learning approach to forecast dengue outbreaks.

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.

Effects of resistance exercise training on acyl-ghrelin and obestatin levels in hemodialysis patients.

BACKGROUND: Patients undergoing hemodialysis (HD) present altered levels of appetite hormones such as acyl-ghrelin (orexigenic) and obestatin (anorexigenic), which may contribute to anorexia. Physical exercise may affect these hormones and improve appetite in these patients. OBJECTIVES: The objective of this study is to evaluate the effects of a resistance exercise program in appetite hormones, body composition, and nutritional status in HD patients. DESIGN: Intervention study with the control group. SUBJECTS: Fifty-two patients on regular HD program were enrolled into two groups: 37 patients performed exercises (56.7% male, 45 ± 12.8 years, 57 (9-192) months on HD) and 15 patients comprised the control group (66.7% men, 50 ± 10.6 years, 57 (11-153) months on HD). MEASUREMENTS: Exercise program (performed with elastic bands and ankle cuffs in both lower limbs) was supervised three times a week during 6 months (72 sessions). Patients had their blood drawn in a regular HD day after overnight fasting, before and after 6 months of exercise program. Obestatin, acyl-ghrelin, routine biochemical parameters, quality of life, and anthropometric data were collected and analyzed before and after 6 months. RESULTS: After 6 months of exercise, obestatin levels reduced [from 3.0 ng/mL (2.3-3.4) to 1.9 ng/mL (0.6-3.4)] and acyl-ghrelin levels increased [from 21.5 pg/mL (1.3-77.7) to 37.2 pg/mL (16.7-94.1)] and the control group presented no significant differences in both plasma levels of hormones. Body composition and physical functional assessed by SF-36 and albumin levels (3.7 ± 0.3 to 3.9 ± 0.2, p < 0.05) improved after exercises. CONCLUSION: Six months of resistance exercises contributed to changes in plasma appetite hormones, body composition, and nutritional status in hemodialysis patients.

Resistance exercise: a strategy to attenuate inflammation and protein-energy wasting in hemodialysis patients?

PURPOSE: Patients undergoing hemodialysis (HD) present persistent inflammation and protein-energy wasting (PEW), which contributes to high rates of mortality. This study aimed to assess the effects of a resistance exercise training program (RETP) on inflammation and PEW in HD patients. METHODS: Thirty-seven patients (56.7 % men, 45.9 ± 14.1 years, 23.5 ± 3.9 kg/m(2)) performed 6 months of intradialytic RETP. Plasma adhesion molecules levels (ICAM-1 and VCAM-1) were measured using the enzyme immunometric assay, and interleukin-6 (IL-6), C-reactive protein, and tumor necrosis factor-alpha by ELISA. Anthropometric, physical capacity, and PEW (simultaneously presence of: BMI <23 kg/m(2), serum albumin <3.8 g/dL, and reduced arm muscle area) were analyzed. RESULTS: There was a reduction of ICAM-1 [(1,934.1 pg/mL (1,031.8-2,875.0) vs. 1,571.1 pg/mL (447.1-2,985.5), p < 0.05], VCAM-1 [5,259.51 pg/mL (3,967.4-6,682.4) vs. 3,062.11 pg/mL (2,034.0-5,034.4), p < 0.05], and CRP levels (2.3 ± 0.9 to 1.6 ± 0.6 pg/mL, p < 0.001) after 6 months of RETP. Body composition improved, albumin increased (3.7 ± 0.3 to 3.9 ± 0.2, p < 0.05), and the number of patients presenting PEW was decreased (p = 0.005). CONCLUSIONS: Resistance exercise program for 6 months seems to be effective in reducing inflammation and PEW of HD patients. The universal trial number of this study is U1111-1139-1326.