Publications by authors named "Jessica De Freitas"

This mini-review explores the potential of precision medicine and personalized nutrition in addressing health challenges faced by perimenopausal women, focusing on the role of genetic polymorphisms in key metabolic pathways. Specifically focus on the single nucleotide polymorphisms (SNPs) in the COMT, FUT2, and MTHFR genes, which influence neurotransmitter metabolism, gut microbiota composition, and folate homeostasis, respectively. These polymorphisms are critical in modulating hormonal fluctuations, metabolic imbalances, and nutrient absorption during perimenopause.

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Interleukin 27 (IL-27) is a cytokine that regulates susceptibility to Leishmania infantum infection in humans and experimental models. This cytokine has not yet been described in canine leishmaniasis (CanL). Therefore, we investigated whether IL-27 has a regulatory role in CanL.

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Nutraceutical interventions supporting microbiota and eliciting clinical improvements in metabolic diseases have grown significantly. Chronic stress, gut dysbiosis, and metainflammation have emerged as key factors intertwined with sleep disorders, consequently exacerbating the decline in quality of life. This study aimed to assess the effects of two nutraceutical formulations containing prebiotics (fructooligosaccharides (FOS), galactooligosaccharides (GOS), yeast β-glucans), minerals (Mg, Se, Zn), and the herbal medicine Silybum marianum L.

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Overweight and obesity are closely linked to gut dysbiosis/dysmetabolism and disrupted De-Ritis ratio [aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio], which may contribute to chronic noncommunicable diseases onset. Concurrently, extensive research explores nutraceuticals, and health-enhancing supplements, for disease prevention or treatment. Thus, sedentary overweight volunteers were double-blind randomized into two groups: Novel Nutraceutical_(S) (without silymarin) and Novel Nutraceutical (with silymarin).

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Domestic dogs are the primary urban reservoirs of Leishmania infantum, the causative agent of visceral leishmaniasis. In Canine Leishmaniasis (CanL), modulation of the host's immune response may be associated with the expression of small non-coding RNAs called microRNA (miR). miR-194 expression increases in peripheral blood mononuclear cells (PBMCs) of dogs with leishmaniasis with a positive correlation with the parasite load and in silico analysis demonstrated that the TRAF6 gene is the target of miR-194 in PBMCs from diseased dogs.

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Leishmania infantum causes visceral leishmaniosis, a neglected tropical disease that can modulate the host immune response by altering the expression of small non-coding RNAs called microRNAs (miRNAs). Some miRNAs are differentially expressed in peripheral blood mononuclear cells (PBMCs) of dogs with canine visceral leishmaniosis (CanL), like the down-regulated miR-150. Even though miR-150 is negatively correlated with L.

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Canine Visceral leishmaniasis (CanL) poses a severe public health threat in several countries. Disease progression depends on the degree of immune response suppression. MicroRNAs (miRs) modulate mRNA translation into proteins and regulate various cellular functions and pathways associated with immune responses.

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Purpose: It is known that obesity has a multifactorial etiology that involves genetic and environmental factors. The WHO estimates the worldwide prevalence of 1.9 billion overweight adults and more than 650 million people with obesity.

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Canine leishmaniasis (CanL) is a severe public health threat. Infected animals mediate transmission of the Leishmania protozoan to humans via the sandfly's bite during a blood meal. CanL progression depends on the degree of suppression of the immune response, possibly associated with microRNAs (miR), which can modulate mRNA translation into proteins and (consequently) regulate cell function.

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The evident genetic, pathological, and clinical heterogeneity of Alzheimer's disease (AD) poses challenges for traditional drug development. We conducted a computational drug repurposing screen for drugs to treat apolipoprotein (apo) E4-related AD. We first established apoE-genotype-dependent transcriptomic signatures of AD by analyzing publicly-available human brain database.

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Article Synopsis
  • - The study explores the effectiveness of deep learning models utilizing electrocardiogram (ECG) data to improve the specificity of screening for pulmonary embolism (PE), addressing the issue of overusing computed tomography pulmonary angiograms (CTPAs).
  • - Researchers built a cohort of over 21,000 patients and developed three predictive models: one based on ECG data, one on electronic health records (EHR), and a Fusion model combining both, finding the Fusion model significantly outperformed the others in PE detection accuracy.
  • - The findings suggest that integrating ECG waveforms with clinical data can enhance the specificity and overall performance in detecting PE, offering a potential improvement over traditional clinical risk scoring methods.
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The use of natural products and derivatives for the prevention and control of non-communicable chronic diseases, such as type-2 diabetes (T2D), obesity, and hepatic steatosis is a way to achieve homeostasis through different metabolic pathways. Thus, male C57BL/6 mice were divided into the following groups: high-fat diet (HFD) vehicle, HFD + Supplemented, HFD + Supplemented_S, and isolated compounds. The vehicle and experimental formulations were administered orally by gavage once a day over the four weeks of the diet (28 consecutive days).

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Purpose: This study aimed at assessing the effect of chemotherapy on dietary intake and nutritional status of patients with colorectal cancer undergoing chemotherapy.

Methods: Observational, cross-sectional study conducted with 35 patients of both sexes, aged 50 years or older. Dietary intake was assessed four times: before (T), twice during (T and T), and after (T) chemotherapy.

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Article Synopsis
  • Deep learning models in healthcare need large, balanced datasets to work effectively, but COVID-19 data is often imbalanced, presenting a challenge for model training.! -
  • Traditional cross-entropy loss (CEL) can struggle with imbalanced data, but the study shows that using contrastive loss (CL) enhances the performance of CEL, particularly with COVID-19 electronic health records.! -
  • The research demonstrates that CL models consistently perform better than CEL models in predicting patient outcomes like mortality and ICU transfers, achieving notable improvements in precision and recall metrics.!
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Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF-PRS. Methods and Results Using summary statistics from a recent genome-wide association study for HF, we developed candidate PRSs in the Mount Sinai Bio CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred.

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Canine leishmaniasis (CanL) is a chronic disease caused by , and the limitations of the current treatments have encouraged new alternatives, such as the use of immunomodulatory nutrients. The objective of this study was to determine the serum levels of vitamin A (retinol), vitamin D (25(OH)VD), and zinc (Zn) in dogs with CanL and the effect of in vitro supplementation with the respective active forms ATRA, 1,25(OH)VD, and SZn on spleen leukocyte cultures. Serum retinol, 25(OH)VD, and Zn were determined by HPLC, ELISA, and ICP-MS, respectively.

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Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history.

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Objective: To evaluate the synergic effects of a novel oral supplement formulation, containing prebiotics, yeast β-glucans, minerals and silymarin (Silybum marianum), on lipid and glycidic metabolism, inflammatory and mitochondrial proteins of the liver, in control and high-fat diet-induced obese mice.

Methods: After an acclimation period, 32 male C57BL/6 mice were divided into the following groups: nonfat diet (NFD) vehicle, NFD supplemented, high-fat diet (HFD) vehicle and HFD supplemented. The vehicle and experimental formulation were administered orally by gavage once a day during the last four weeks of the diet (28 consecutive days).

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Article Synopsis
  • * The proposed solution introduces a heterogeneous graph model (HGM) that incorporates relational learning to better predict mortality in COVID-19 ICU patients by utilizing large EHR datasets from multiple hospitals.
  • * Experimental results indicate that the HGM model, using a unique Skip-Gram relational learning strategy, significantly outperforms traditional models in accuracy and recall, achieving higher area under the receiver operating characteristic curve (auROC) across different prediction time frames.
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Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI.

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Introduction: The aim of this study was to evaluate the postoperative pain in patients after endodontic treatment using 8.25% sodium hypochlorite (NaOCl) compared with other concentrations and 2% chlorhexidine (CHX).

Methods: In this double-blind randomized trial, 180 patients were evaluated who underwent a single session of endodontic treatment under irrigation with 2.

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Article Synopsis
  • Deep learning has become crucial in analyzing large healthcare datasets for disease classification, predictions, and decision-making in the past decade.
  • Public ECG datasets have been around since the 1980s, mainly for specific cardiology issues, while private institutions now offer significantly larger databases for deep learning applications.
  • This review aims to educate clinicians on deep learning basics, its current uses in ECG analysis, as well as its limitations and potential future developments.
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Article Synopsis
  • Machine learning models require large datasets, often limited by data silos across healthcare institutions, particularly in COVID-19 research focused on single hospitals.
  • The study utilized federated learning to predict 7-day mortality in hospitalized COVID-19 patients, using data from five hospitals within the Mount Sinai Health System without aggregating sensitive patient data.
  • Results showed that the LASSO model performed better at three hospitals and the multilayer perceptron (MLP) model outperformed at all five, indicating that federated learning can create effective predictive models while protecting patient privacy.
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  • The study aimed to analyze clinical characteristics and outcomes of hospitalized COVID-19 patients, comparing those who died in the hospital to those who were discharged alive.
  • Data was collected from five hospitals in the Mount Sinai Health System for patients confirmed with COVID-19 between February and April 2020, focusing on demographics, clinical features, and mortality rates.
  • Results showed that nearly half of the 2199 hospitalized patients were discharged, with a 29% overall mortality rate, higher rates of pre-existing conditions and lower lymphocyte percentages observed in patients who died compared to those who recovered.
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