Development of a User-Friendly Self-Screening Tool for Assessing Metabolic Syndrome Risk in Youths from Economically Challenged Regions.

BACKGROUND: Metabolic syndrome increases the risk of heart disease and diabetes. Early identification and management are crucial, especially in economically challenged regions with limited healthcare access. AIMS: To develop nomograms for individualized risk estimation for metabolic syndrome in young people from low-income regions. METHODS: We assessed 496 college students from two Brazilian cities with Gini indices ≤0.56. Of these, 69.9% were female, 65.1% were younger than 20 years, 71.8% were non-white, and 64.3% were enrolled in health-related courses. For external validity, we assessed metabolic syndrome in a subset of 375 students. RESULTS: We found 10 variables associated with abdominal obesity by logistic regression: age, biological sex, physical education facilities, enrollment in sports competitions during elementary school, grade retention, physical education as the preferred subject, physical education classes per week, and enrollment in sports training in secondary school (score A); adherence to 24 h movement behaviors (B score); and body weight (score C). We designed three nomograms (for scores A, B, and C), all of which showed acceptable performance according to the area under the receiver operating characteristic curve (≥0.70) and calibration (Hosmer-Lemeshow test, p > 0.05). In the external validation, we observed higher predictive capability for the A and B scores, while the C score had lower but still acceptable predictive ability. CONCLUSIONS: User-friendly self-reported data accurately predict metabolic syndrome among youths from economically challenging areas.

ß-Glucosidase activity and antimicrobial properties of potentially probiotic autochthonous lactic cultures.

Background: The demand for lactic acid bacteria products, especially probiotics, has increased. Bacteria that increase polyphenol bioavailability and act as bio preservatives are sought after. This study aims to identify autochthonous lactic acid cultures from EMBRAPA that demonstrate ß-glucosidase activity and inhibitory effect on microbial sanitary indicators. Methods: Cell-free extracts were obtained by sonicating every 5 s for 40 min. The extracts were mixed with cellobiose and incubated at 50 °C. The reaction was stopped by immersing the tubes in boiling water. The GOD-POD reagent was added for spectrophotometer readings. Antimicrobial activity was tested against reference strains using the agar well diffusion method. Lactic cultures in MRS broth were added to 0.9 cm wells and incubated. The diameter of the inhibition zones was measured to determine the extension of inhibition. Results: Only L. rhamnosus EM1107 displayed extracellular ß-glucosidase activity, while all autochthonous strains except L. plantarum CNPC020 demonstrated intracellular activity for this enzyme. L. plantarum CNPC003 had the highest values. On the other hand, L. plantarum CNPC020, similarly to L. mucosae CNPC007, exhibited notable inhibition against sanitary indicators. These two strains significantly differed from the other five autochthonous cultures regarding S. enterica serovar Typhimurium ATCC 14028 inhibition (P < 0.05). However, they did not differ from at least one positive control in terms of inhibition against S. aureus ATCC 25923 and E. coli ATCC 25922 (P > 0.05). Therefore, it is advisable to consider these cultures separately for different technological purposes, such as phenolics metabolism or bio preservative activity. This will facilitate appropriate selection based on each specific property required for the intended product development.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

Prediction models for carbapenem-resistant Enterobacterales carriage at liver transplantation: A multicenter retrospective study.

BACKGROUND: Carbapenem-resistant Enterobacterales (CRE) colonisation at liver transplantation (LT) increases the risk of CRE infection after LT, which impacts on recipients' survival. Colonization status usually becomes evident only near LT. Thus, predictive models can be useful to guide antibiotic prophylaxis in endemic centres. AIMS: This study aimed to identify risk factors for CRE colonisation at LT in order to build a predictive model. METHODS: Retrospective multicentre study including consecutive adult patients who underwent LT, from 2010 to 2019, at two large teaching hospitals. We excluded patients who had CRE infections within 90 days before LT. CRE screening was performed in all patients on the day of LT. Exposure variables were considered within 90 days before LT and included cirrhosis complications, underlying disease, time on the waiting list, MELD and CLIF-SOFA scores, antibiotic use, intensive care unit and hospital stay, and infections. A machine learning model was trained to detect the probability of a patient being colonized with CRE at LT. RESULTS: A total of 1544 patients were analyzed, 116 (7.5%) patients were colonized by CRE at LT. The median time from CRE isolation to LT was 5 days. Use of antibiotics, hepato-renal syndrome, worst CLIF sofa score, and use of beta-lactam/beta-lactamase inhibitor increased the probability of a patient having pre-LT CRE. The proposed algorithm had a sensitivity of 66% and a specificity of 83% with a negative predictive value of 97%. CONCLUSIONS: We created a model able to predict CRE colonization at LT based on easy-to-obtain features that could guide antibiotic prophylaxis.

Cox model and decision trees: an application to breast cancer data.

Objective: To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer. Methods: A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the Kaplan-Meier method, Cox regression, and conditional decision tree. Results: Women with triple-negative molecular subtypes had a shorter survival time compared to women with positive hormone receptors. The addition of hormone therapy reduced the risk of a patient dying by 5.5%, and the risk of a HER2-positive patient dying was 34.5% lower compared to those who were negative for this gene. Patients undergoing hormone therapy had a median survival time of 4 753 days. Conclusions: This paper shows a favorable scenario for the use of immunotherapy for patients with HER2 overexpression. Further studies could assess the effectiveness of immunotherapy in patients with other conditions, to favor the prognosis and better quality of life for the patient.

Evaluation of dental enamel microproperties after bleaching with 35% hydrogen peroxide and different light sources: An in vitro study.

BACKGROUND: To evaluate the tooth enamel surface morphology after the action of 35% hydrogen peroxide with and without LED activation. MATERIAL AND METHODS: 70 bovine incisors with an enamel surface of 4x4x3 mm were used, prepared for reading superficial microhardness and roughness. Specimens were randomly distributed and divided into 7 experimental groups (n = 10); G1 = artificial saliva; G2 = 35% HP - 2 sessions (3x15´); G3 = Phosphoric Acid + 35% HP - 3 sessions (3x15´); G4 = 35% HP - 2 sessions (3x15´) + blue LED; G5 = 35% HP - 2 sessions (3x15´) + green LED; G6 = 35% HP - 2 sessions (3x20´) + violet LED; G7 = Violet LED - 2 sessions (3x20´). The results were analyzed by the Anova, Wilcoxon, Dunnett and Tukey tests (α = 0.05). RESULTS: The G4 group showed a greater change in microhardness. Regarding roughness, the biggest mean difference between groups occurred in G2, G5 and G7. Optical microscopy showed a smooth enamel surface in groups G2, G5 and G7. CONCLUSIONS: Changes in the enamel surface were observed in relation to microhardness, but without significant changes in roughness, where the LED (green and violet) resulted in a smooth surface. Key words:Tooth whitening, superficial morphology, light, photoradiatio.

The role of renin angiotensin system in the pathophysiology of rheumatoid arthritis.

BACKGROUND: In rheumatoid arthritis (RA) and osteoarthritis (OA), chronic inflammatory processes lead to progresive joint destruction. The renin-angiotensin system (RAS) is involved in the pathogenesis of RA and OA. The aim of this mini-review article is to summarize evidence on the role of RAS in RA and OA. METHODS: A non-systematic search in Pubmed included terms as "rheumatoid arthritis", "renin angiotensin system", "osteopenia", "RANKL", "DKK-1", "MMP", "inflammation", "angiogenesis", "local renin-angiotensin system", "angiotensin converting enzyme", "AT2 receptor", "Ang-(1-7)", "VEGF", "angiotensine receptor blocker", "angiotensin converting enzyme inhibitors", "renin inhibitors". RESULTS: Both RAS axes, the classical one, formed by angiotensin converting enzyme (ACE), angiotensin (Ang) II and AT1 receptor (AT1R) and the counter-regulatory one, composed by ACE2, Ang-(1-7) and the Mas receptor, modulate inflammation and tissue damage. Ang II activates pro-inflammatory mediators and oxidative stress. Conversely, Ang-(1-7) exerts anti-inflammatory actions, decreasing cytokine release, leukocyte attraction, density of vessels, tissue damage and fibrosis. Angiogenesis facilitates inflammatory cells invasion, while osteopenia causes joint dysfunction. Up-regulated osteoclastogenisis and down-regulated osteoblastogeneses were associaed with the activation of the classical RAS axis. Three different pathways, RANKL, DKK-1 and MMPs are enhanced by classical RAS activation. The treatment of RA included methotrexate and corticosteroids, which can cause side effects. Studies with angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACEi) and renin inhibitors have been conducted in experimental and clinical RA with promising results. CONCLUSION: The classical RAS activation is an important mechanism in RA pathogenesis and the benefit of ARB and ACEi administration should be further investigated.

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.

Comparison of dairy desserts produced with a potentially probiotic mixed culture and dispersions obtained from Gracilaria birdiae and Gracilaria domingensis seaweeds used as thickening agents.

Dairy desserts have emerged as interesting options for the incorporation of probiotics, bioactive ingredients and alternative sources of thickeners. This shows an opportunity to investigate the use of Gracilaria seaweeds in the formulation of potentially probiotic dairy desserts. This study aimed to compare the effects of dispersions obtained from Gracilaria domingensis and Gracilaria birdiae used as thickening agents on texture properties of dairy desserts fermented with SAB 440-A, composed of the starter Streptococcus thermophilus and the potential probiotics Bifidobacterium animalis and Lactobacillus acidophilus, and also to study their physicochemical characteristics, microbial viability and sensory acceptability. No significant differences between desserts with G. birdiae or G. domingensis dispersions regarding total solids, ash and fat content, as well as pH, titratable acidity, the viability of the microorganisms of the mixed culture and sensory acceptability were verified (P > 0.05). Nonetheless, the dessert with G. domingensis dispersion showed higher dietary fibre content and significantly increased firmness than the one produced with G. birdiae (P < 0.05). Moreover, B. animalis was able to maintain higher populations, above 7 log cfu g-1 during 21 days of storage of desserts, in the presence of either G. birdiae or G. domingensis dispersions, despite the fact that L. acidophilus has shown low viability in the final products. Therefore, the G. domingensis dispersion is suitable to be used as a thickening agent to produce dairy desserts with enhanced firmness and good sensory acceptability, it being also advisable to use only B. animalis as a probiotic for this product.

Effect of Aqueous Extract of the Seaweed Gracilaria domingensis on the Physicochemical, Microbiological, and Textural Features of Fermented Milks.

The effects of the Gracilaria domingensis seaweed aqueous extract in comparison with gelatin on the physicochemical, microbial, and textural characteristics of fermented milks processed with the mixed culture SAB 440 A, composed of Streptococcus thermophilus, Lactobacillus acidophilus, and Bifidobacterium animalis ssp. lactis, were investigated. The addition of G. domingensis aqueous extract did not affect pH, titratable acidity, and microbial viability of fermented milks when compared with the control (with no texture modifier) and the products with added gelatin. Fermented milk with added the seaweed aqueous extract showed firmness, consistency, cohesiveness, and viscosity index at least 10% higher than those observed for the control product (P < 0.05). At 4 h of fermentation, the fermented milks with only G. domingensis extract showed a texture comparable to that observed for products containing only gelatin. At 5 h of fermentation, firmness and consistency increased significantly (P < 0.05) in products with only seaweed extract added, a behavior not observed in products with the full amount of gelatin, probably due to the differences between the interactions of these ingredients with casein during the development of the gel network throughout the acidification of milk. The G. domingensis aqueous extract appears as a promising gelatin alternative to be used as texture modifier in fermented milks and related dairy products.