Nutraceutical blends predict enhanced health via microbiota reshaping improving cytokines and life quality: a Brazilian double-blind randomized trial.

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 ß-glucans), minerals (Mg, Se, Zn), and the herbal medicine Silybum marianum L. Gaertn., Asteraceae (Milk thistle or Silymarin). These formulations, namely NSupple (without silymarin) and NSupple_Silybum (with silymarin) were tested over 180 days in overweight/obese volunteers from Brazil's southeastern region. We accessed fecal gut microbiota by partial 16S rRNA sequences; cytokines expression by CBA; anthropometrics, quality of life and sleep, as well as metabolic and hormonal parameters, at baseline (T0) and 180 days (T180) post-supplementation. Results demonstrated gut microbiota reshaping at phyla, genera, and species level post-supplementation. The Bacteroidetes phylum, Bacteroides, and Prevotella genera were positively modulated especially in the NSupple_Silybum group. Gut microbiota modulation was associated with improved sleep patterns, quality-of-life perception, cytokines expression, and anthropometric parameters post-supplementation. Our findings suggest that the nutraceutical blends positively enhance cardiometabolic and inflammatory markers. Particularly, NSupple_Silybum modulated microbiota composition, underscoring its potential significance in ameliorating metabolic dysregulation. Clinical trial registry number: NCT04810572. 23/03/2021.

Modulation of the gut microbiome and Firmicutes phylum reduction by a nutraceutical blend in the obesity mouse model and overweight humans: A double-blind clinical trial.

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). Experimental formulations were orally administered twice daily over 180 consecutive days. We evaluated fecal gut microbiota, based on partial 16S rRNA sequences, biochemistry and endocrine markers, steatosis biomarker (AST/ALT ratio), and anthropometric parameters. Post-supplementation, only the Novel Nutraceutical group reduced Clostridium clostridioforme (Firmicutes), Firmicutes/Bacteroidetes ratio (F/B ratio), and De-Ritis ratio, while elevating Bacteroides caccae and Bacteroides uniformis (Bacteroidetes) in Brazilian sedentary overweight volunteers after 180 days. In summary, the results presented here allow us to suggest the gut microbiota as the action mechanism of the Novel Nutraceutical promoting metabolic hepatic recovery in obesity/overweight non-drug interventions.

MicroRNA-194 regulates parasitic load and IL-1ß-dependent nitric oxide production in the peripheral blood mononuclear cells of dogs with leishmaniasis.

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. Here, we isolated PBMCs from 5 healthy dogs and 28 dogs with leishmaniasis, naturally infected with L. infantum. To confirm changes in miR-194 and TRAF6 expression, basal expression of miR-194 and gene expression of TRAF6 was measured using qPCR. PBMCs from healthy dogs and dogs with leishmaniasis were transfected with miR-194 scramble, mimic, and inhibitor and cultured at 37° C, 5% CO2 for 48 hours. The expression of possible targets was measured: iNOS, NO, T-bet, GATA3, and FoxP3 were measured using flow cytometry; the production of cytokines IL-1ß, IL-4, IL-6, IL-10, TNF-α, IFN-γ, and TGF-ß in cell culture supernatants was measured using capture enzyme-linked immunosorbent assays (ELISA). Parasite load was measured using cytometry and qPCR. Functional assays followed by miR-194 inhibitor and IL-1ß blockade and assessment of NO production were also performed. Basal miR-194 expression was increased in PBMC from dogs with Leishmaniasis and was negatively correlated with TRAF6 expression. The mimic of miR-194 promoted an increase in parasite load. There were no significant changes in T-bet, GATA3, or FoxP3 expression with miR-194 enhancement or inhibition. Inhibition of miR-194 increased IL-1ß and NO in PBMCs from diseased dogs, and blockade of IL-1ß following miR-194 inhibition decreased NO levels. These findings suggest that miR-194 is upregulated in PBMCs from dogs with leishmaniasis and increases parasite load, possibly decreasing NO production via IL-1ß. These results increase our understanding of the mechanisms of evasion of the immune response by the parasite and the identification of possible therapeutic targets.

MiR-150 regulates the Leishmania infantum parasitic load and granzyme B levels in peripheral blood mononuclear cells of dogs with canine visceral leishmaniosis.

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. infantum parasitic load, it is unclear if miR-150 directly affects L. infantum parasitic load and (if so) how this miRNA would contribute to infection. Here, we isolated PBMCs from 14 naturally infected dogs (CanL group) and six healthy dogs (Control group) and treated them in vitro with miR-150 mimic or inhibitor. We measured L. infantum parasitic load using qPCR and compared treatments. We also measured miR-150 in silico predicted target protein levels (STAT1, TNF-α, HDAC8, and GZMB) using flow cytometry or enzyme-linked immunosorbent assays. Increasing miR-150 activity diminished L. infantum parasitic load in CanL PBMCs. We also found that inhibition of miR-150 reduced GZMB (granzyme B) levels. These findings demonstrate that miR-150 plays an important role in L. infantum infection in canine PBMCs, and they merit further studies aiming at drug development.

MicroRNA-21 and microRNA-148a affects PTEN, NO and ROS in canine leishmaniasis.

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. MiR-21 and miR-148a can alter the parasite load and M1 macrophages are the principal cells in dogs' leishmanicidal activity. A previous study found increased miR-21 and miR-148a in splenic leukocytes (SL) of dogs with CanL using microarray analysis and in silico analysis identified PTEN pathway targets. PTEN is involved in the immune regulation of macrophages. We measured PTEN and the production of reactive oxygen species (ROS) and nitric oxide (NO) before and after transfection SLs of dogs with CanL with mimic and inhibition of miR-21 and miR-148a. PTEN levels increased, NO and ROS decreased in SLs from dogs with CanL. Inhibition of miRNA-21 resulted in PTEN increase; in contrast, PTEN decreased after miR-148a inhibition. Nitrite (NO2) levels increased after transfection with miR-21 inhibitor but were decreased with miR-148a inhibitor. The increase in miR-21 promoted a reduction in ROS and NO levels, but miR-148a inhibition increased NO and reduced ROS. These findings suggest that miR-21 and miR-148a can participate in immune response in CanL, affecting PTEN, NO, and ROS levels.

miR-148a regulation interferes in inflammatory cytokine and parasitic load in canine leishmaniasis.

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. Increased miR-148a in splenic leukocytes (SL) of dogs with CanL was observed in previous studies, and in silico analysis, identified possible pathways involved in immune response regulation that are affected by this miR. Therefore, we evaluated the involvement of miR-148a in the regulation of TNF-α, IL-6, IL-12, IL-1ß, iNOS, MHCII, CD80, CD3, T-bet, and GATA-3 transcription factors and their relationship with parasite load in SL of dogs with CanL. Splenic leukocytes obtained from healthy and diseased dogs were transfected with miR-148a mimic and inhibitor oligonucleotides. After 48 hours, expression levels of MHCII, CD80, iNOS, CD3, T-bet, and GATA-3 were evaluated by flow cytometry, and concentrations of TNF-α, IL-12, IL-6, and IL-1ß were measured in culture supernatants by capture enzyme-linked immunosorbent assays. Transfection of SL with miR-148a mimics decreased iNOS levels in cells and TNF-α, IL-6, and IL-12 in the supernatants of cultured SL from CanL dogs. Interestingly, transfection with miR-148a inhibitor decreased parasite load in SL cells. These results suggest a direct or not regulatory role of this miR in the immune response to Leishmania infantum infection. We conclude that miR-148a can modulate immune responses by regulating inflammatory cytokines during CanL. Our results contribute to understanding the complex host/parasite interaction in CanL and could assist the development of treatments.

Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening.

Aims: Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods and results: We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells' Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50-0.58, specificity 0.00-0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66-0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77-0.84) subgroups. Conclusion: Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.

The Symbiotic Effect of a New Nutraceutical with Yeast ß-Glucan, Prebiotics, Minerals, and Silybum marianum (Silymarin) for Recovering Metabolic Homeostasis via Pgc-1α, Il-6, and Il-10 Gene Expression in a Type-2 Diabetes Obesity Model.

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). We evaluated the energy homeostasis, cytokines, and mitochondrial gene expression in these groups of mice. After four weeks of supplementation, only the new nutraceutical group (HFD + Supplemented) experienced reduced fasting glycemia, insulin, HOMA index, HOMA-ß, dyslipidemia, ectopic fat deposition, and hepatic fibrosis levels. Additionally, the PPARγ coactivator 1 α (Pgc-1α), interleukin-6 (Il-6), and interleukin-10 (Il-10) gene expression were augmented, while hepatic steatosis decreased and liver parenchyma was recovered. The glutathione-S-transferase activity status was found to be modulated by the supplement. We discovered that the new nutraceutical was able to improve insulin resistance and hepatic steatosis mainly by regulating IL-6, IL-10, and Pgc-1α gene expression.

The effect of chemotherapy on dietary intake and nutritional status in patients with colorectal neoplasms and the importance of nutritional counseling.

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 (T0), twice during (T1 and T2), and after (TF) chemotherapy. Data on energy, macronutrients, and micronutrients were used for assessing dietary intake. Nutritional status was assessed on the first day and at the end of the chemotherapy cycle. The data were treated statistically using a 5% significance level. RESULTS: The intake of energy, carbohydrates, lipids, selenium, and iron was lower after chemotherapy infusion (p < 0.05). Energy consumption decreased when comparing T0 (1419.8 ± 527.1 kcal) with T1 (1181.9 ± 423.2 kcal). Regarding macronutrients, carbohydrates and lipids showed a drop (p < 0.05), but there were no differences in protein intake, and it was observed that the consumption of food sources such as dairy was prioritized. In the analysis of vitamins and minerals, vitamins dropped but without a significant difference. There were significant differences between T1 and TF for iron (9.7 mg ± 4.5 mg and 12.0 ± 4.0 mg) and selenium (77.4 ± 29.7 µg and 93.6 ± 37.8 µg). There was no change in body composition and nutritional status between chemotherapy cycles. CONCLUSION: The treatment reduced patients' food consumption after the chemotherapy session and impacted dietary intake, which demonstrates the importance of nutritional counseling and intervention, especially on energy consumption.

Novel nutraceutical supplements with yeast ß-glucan, prebiotics, minerals, and Silybum marianum (silymarin) ameliorate obesity-related metabolic and clinical parameters: A double-blind randomized trial.

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. These alarming data highlight the high and growing prevalence of obesity and represent a risk factor for the development and aggravation of other chronic diseases, such as nonalcoholic fatty liver disease (NAFLD) that is frequently considered the hepatic outcome of type 2 diabetes. The use of non-pharmacological therapies such as food supplements, nutraceuticals, and natural integrative therapies has grown as an alternative tool for obesity-related diseases compared to conventional medications. However, it is a still little explored research field and lacks scientific evidence of therapeutic effectiveness. Considering this, the aim is to evaluate whether a new nutraceutical supplement composition can improve and supply essential mineral nutrients, providing an improvement of obesity-related metabolic and endocrine parameters. Methods: Sedentary volunteers (women and men) with body mass index (BMI) ≤34.9 kg/m2 were divided into two groups: Novel Nutraceutical Supplement_(S) (n = 30) and Novel Nutraceutical Supplement (n = 29), differing in the absence (S) or presence of silymarin, respectively. Volunteers were instructed to take two capsules in the morning and two capsules in the evening. No nutritional intervention was performed during the study period. The data (anthropometrics and anamneses) and harvest blood (biochemistry and hormonal exams) were collected at three different time points: baseline time [day 0 (T0)], day 90 (T90), and day 180 (T180) post-supplementation. Results: In the anthropometric analysis, the waist circumference in middle abdomen (WC-mid) and waist circumference in iliac crest (WC-IC) were reduced. Also, the waist-to-height ratio (WHt R) and waist-to-hip ratio (WHR) seem to slightly decrease alongside the supplementation period with both nutraceutical supplements tested as well as transaminase enzyme ratio [aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (AAR)], a known as a biomarker of NAFLD, and endocrine hormones cortisol and thyroid-stimulating hormone (TSH) at 90 and 180 days post-supplementation. Conclusions: In a condition associated with sedentary and no nutritional intervention, the new nutraceutical supplement composition demonstrated the ability to be a strong and newfangled tool to improve important biomarkers associated with obesity and its comorbidities.

Contrastive learning improves critical event prediction in COVID-19 patients.

Deep learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus disease 2019 (COVID-19) pandemic, where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL), which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL, especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR dataset to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full dataset and a restricted dataset. CL models consistently outperform CEL models, with differences ranging from 0.04 to 0.15 for area under the precision and recall curve (AUPRC) and 0.05 to 0.1 for area under the receiver-operating characteristic curve (AUROC).

Derivation and Validation of Genome-Wide Polygenic Score for Ischemic Heart Failure.

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 BioMe CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high-risk cohort who had undergone coronary catheterization. We observed a significant association between HF-PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both BioMe (odds ratio [OR], 1.14 per SD; 95% CI, 1.05-1.24; P=0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01-1.13; P=0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2-fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1-3.7; P=0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.

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. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.

Vitamins A and D and Zinc Affect the Leshmanicidal Activity of Canine Spleen Leukocytes.

Canine leishmaniasis (CanL) is a chronic disease caused by Leishmania infantum, 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)VD3), and zinc (Zn) in dogs with CanL and the effect of in vitro supplementation with the respective active forms ATRA, 1,25(OH)2VD3, and SZn on spleen leukocyte cultures. Serum retinol, 25(OH)VD3, and Zn were determined by HPLC, ELISA, and ICP-MS, respectively. Spleen leukocyte cultures were used for the detection of NO and ROS by flow cytometry; the IFN-γ, TNF-α, and IL-10 levels were determined by ELISA; and the parasite load was determined by microscopy. We detected low serum levels of retinol and Zn and high levels of 25(OH)VD3 in the CanL group. The in vitro supplementation of CanL spleen leukocytes with ATRA, 1,25(OH)2VD3, and SZn, in addition to a soluble leishmania antigen (SLA) treatment, increased the NO and ROS levels, while the treatments with only ATRA and SZn increased the TNF-a levels. Increased IL-10 and IFN-g levels were observed with the addition of SLA to the medium, although the addition of the three nutrients led to a reduction of the IL-10 levels, and the addition of 1,25(OH)2VD3 and SZn led to a reduction of IFN-g. A supplementation with 1,25(OH)2VD3 and SZn reduced the parasite load but only in the absence of SLA. We suggest that the nutrients we tested are involved in the leishmanicidal mechanism, showing a potential for investigation in future studies.

A novel supplement with yeast ß-glucan, prebiotic, minerals and Silybum marianum synergistically modulates metabolic and inflammatory pathways and improves steatosis in obese mice.

OBJECTIVE: To evaluate the synergic effects of a novel oral supplement formulation, containing prebiotics, yeast ß-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). We then evaluated energy homeostasis, inflammation, and mitochondrial protein expression in these groups of mice. RESULTS: After four weeks of supplementation, study groups experienced reduced glycemia, dyslipidemia, fat, and hepatic fibrosis levels. Additionally, proliferator-activated receptor-α, AMP-activated protein kinase-1α, peroxisome proliferator-activated receptor γ co-activator-1α, and mitochondrial transcription factor A expression levels were augmented; however, levels of inhibitor of nuclear factor-κB kinase subunit α and p65 nuclear factor-κB expression, and oxidative markers were reduced. Notably, the cortisol/C-reactive protein ratio, a well-characterized marker of the hypothalamic-pituitary-adrenal axis immune interface status, was found to be modulated by the supplement. CONCLUSION: We discovered that the novel supplement was able to modify different antioxidant, metabolic and inflammatory pathways, improving the energy homeostasis and inflammatory status, and consequently alleviated hepatic steatosis.

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.

Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.

Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study.

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. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19. METHODS AND FINDINGS: We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01-1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02-1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile. CONCLUSIONS: VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI.

Deep learning and the electrocardiogram: review of the current state-of-the-art.

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

Postoperative Pain after Endodontic Treatment under Irrigation with 8.25% Sodium Hypochlorite and Other Solutions: A Randomized Clinical Trial.

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 2.5%, 5.25%, or 8.25% NaOCl or 2% CHX solutions. The presence of postoperative pain was assessed 24, 48, and 72 hours after treatment and recorded using the visual analog scale (VAS). A descriptive analysis, logistic regression, and Wald test were performed. RESULTS: Altogether 169 patients participated, of whom 107 were women, and the mean age was 38.1 ± 14.4 years. No significant differences occurred between the irrigants and the pain outcomes, not even for the use of postoperative pain medication and responses to VAS (P > .05). In the multivariate model including irrigants, after 24 hours or at any time, a significant difference (P < .05) remained in preparation time ≥10 minutes and the presence of overfilling. Also, in relation to the VAS, the overfilling presented significantly different results (P < .05). CONCLUSIONS: There was no significant difference in the presence of postoperative pain between the 8.25% NaOCl and the other irrigation solutions. However, the extended preparation time and the overfilling material were responsible for the increase of postoperative pain.

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.