Where you live matters: Area deprivation predicts poor survival and liver transplant waitlisting.

Social determinants of health (SDOH) are important predictors of poor clinical outcomes in chronic diseases, but their associations among the general cirrhosis population and liver transplantation (LT) are limited. We conducted a retrospective, multiinstitutional analysis of adult (≥18-years-old) patients with cirrhosis in metropolitan Chicago to determine the associations of poor neighborhood-level SDOH on decompensation complications, mortality, and LT waitlisting. Area deprivation index and covariates extracted from the American Census Survey were aspects of SDOH that were investigated. Among 15 101 patients with cirrhosis, the mean age was 57.2 years; 6414 (42.5%) were women, 6589 (43.6%) were non-Hispanic White, 3652 (24.2%) were non-Hispanic Black, and 2662 (17.6%) were Hispanic. Each quintile increase in area deprivation was associated with poor outcomes in decompensation (sHR [subdistribution hazard ratio] 1.07; 95% CI 1.05-1.10; P < .001), waitlisting (sHR 0.72; 95% CI 0.67-0.76; P < .001), and all-cause mortality (sHR 1.09; 95% CI 1.06-1.12; P < .001). Domains of SDOH associated with a lower likelihood of waitlisting and survival included low income, low education, poor household conditions, and social support (P < .001). Overall, patients with cirrhosis residing in poor neighborhood-level SDOH had higher decompensation, and mortality, and were less likely to be waitlisted for LT. Further exploration of structural barriers toward LT or optimizing health outcomes is warranted.

The V-BRCH Project: Strengthening HIV Research Capacity in Nigeria through Intensive Workshops in Implementation Science and Grant Writing.

As persons with HIV live longer as the result of antiretroviral therapy, morbidity from HIV-associated noncommunicable diseases (NCDs) is increasing. The Vanderbilt-Nigeria Building Research Capacity in HIV and Noncommunicable Diseases program is a training platform created with the goal of training a cohort of successful Nigerian investigators to become leaders in HIV-associated NCD research. We describe survey findings from two week-long workshops in Kano, Nigeria, where trainees received instruction in implementation science and grant writing. Surveys assessed participants' self-perceived knowledge and confidence in topics taught during these workshops. Thirty-seven participants (all assistant professors) attended the implementation science workshop; 30 attended the grant-writing workshop. Response rates for the implementation science workshop were 89.2% for the preworkshop survey and 91.9% for the postworkshop survey. For the grant-writing workshop, these values were 88.2% and 85.3%, respectively. Improvement in participant knowledge and confidence was observed in every domain measured for both workshops. On average, a 101.4% increase in knowledge and a 118.0% increase in confidence was observed across measured domains among participants in the implementation science workshop. For the grant-writing workshop, there was a 68.8% increase in knowledge and a 70.3% increase in confidence observed. Participants rated the workshops and instructors as effective for both workshops. These workshops improved participants' knowledge and competence in implementation science and grant writing, and provide a model for training programs that aim to provide physician scientists with the skills needed to compete for independent funding, conduct locally relevant research, and disseminate research findings.

Exposure to environmentally relevant concentrations of Bisphenol-A linked to loss of visual lateralization in adult zebrafish (Danio rerio).

Weak, but environmentally relevant concentrations of contaminants can have subtle, yet important, impacts on organisms, which are often overlooked due to the lack of acute impacts and the timing of exposure. Thus, recognizing simple, non-invasive markers of contamination events is essential for early detection and addressing the effects of exposure to weak environmental contaminants. Here, we tested whether exposure to an environmentally relevant concentration of Bisphenol-A (BPA), a common and persistent contaminant in aquatic systems, affects the lateralization of adult zebrafish (Danio rerio), a widely used model organism in ecotoxicology. We found that 73.5% of adult zebrafish displayed a left-side bias when they approached a visual cue, but that those exposed to weak BPA (0.02 mg/L) for 7 days did not exhibit laterality. Only 47.1% displayed a left-side bias. We found no differences in activity level and visual sensitivity, motor and sensory mechanisms, that regulate lateralized responses and that were unaffected by weak BPA exposure. These findings indicate the reliability of laterality as a simple measure of contaminant exposure and for future studies of the detailed mechanisms underlying subtle and complex behavioral effects to pollutants.

FDG PET/CT Imaging 1 Week after a Single Dose of Pembrolizumab Predicts Treatment Response in Patients with Advanced Melanoma.

PURPOSE: Immunologic response to anti-programmed cell death protein 1 (PD-1) therapy can occur rapidly with T-cell responses detectable in as little as one week. Given that activated immune cells are FDG avid, we hypothesized that an early FDG PET/CT obtained approximately 1 week after starting pembrolizumab could be used to visualize a metabolic flare (MF), with increased tumor FDG activity due to infiltration by activated immune cells, or a metabolic response (MR), due to tumor cell death, that would predict response. PATIENTS AND METHODS: Nineteen patients with advanced melanoma scheduled to receive pembrolizumab were prospectively enrolled. FDG PET/CT imaging was performed at baseline and approximately 1 week after starting treatment. FDG PET/CT scans were evaluated for changes in maximum standardized uptake value (SUVmax) and thresholds were identified by ROC analysis; MF was defined as >70% increase in tumor SUVmax, and MR as >30% decrease in tumor SUVmax. RESULTS: An MF or MR was identified in 6 of 11 (55%) responders and 0 of 8 (0%) nonresponders, with an objective response rate (ORR) of 100% in the MF-MR group and an ORR of 38% in the stable metabolism (SM) group. An MF or MR was associated with T-cell reinvigoration in the peripheral blood and immune infiltration in the tumor. Overall survival at 3 years was 83% in the MF-MR group and 62% in the SM group. Median progression-free survival (PFS) was >38 months (median not reached) in the MF-MR group and 2.8 months (95% confidence interval, 0.3-5.2) in the SM group (P = 0.017). CONCLUSIONS: Early FDG PET/CT can identify metabolic changes in melanoma metastases that are potentially predictive of response to pembrolizumab and significantly correlated with PFS.

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017-2020.

Importance: The prevalence of obesity among United States adults has increased from 34.9% in 2013-2014 to 42.8% in 2017-2018. Developing methods to model the increase of obesity over-time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency. Methods: A cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort. Results: Of the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30). Conclusion: United States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long-term weight status.

Stat5 opposes the transcription factor Tox and rewires exhausted CD8+ T cells toward durable effector-like states during chronic antigen exposure.

Rewiring exhausted CD8+ T (Tex) cells toward functional states remains a therapeutic challenge. Tex cells are epigenetically programmed by the transcription factor Tox. However, epigenetic remodeling occurs as Tex cells transition from progenitor (Texprog) to intermediate (Texint) and terminal (Texterm) subsets, suggesting development flexibility. We examined epigenetic transitions between Tex cell subsets and revealed a reciprocally antagonistic circuit between Stat5a and Tox. Stat5 directed Texint cell formation and re-instigated partial effector biology during this Texprog-to-Texint cell transition. Constitutive Stat5a activity antagonized Tox and rewired CD8+ T cells from exhaustion to a durable effector and/or natural killer (NK)-like state with superior anti-tumor potential. Temporal induction of Stat5 activity in Tex cells using an orthogonal IL-2:IL2Rß-pair fostered Texint cell accumulation, particularly upon PD-L1 blockade. Re-engaging Stat5 also partially reprogrammed the epigenetic landscape of exhaustion and restored polyfunctionality. These data highlight therapeutic opportunities of manipulating the IL-2-Stat5 axis to rewire Tex cells toward more durably protective states.

Impact of a Transitional Pain Service on postoperative opioid trajectories: a retrospective cohort study.

INTRODUCTION: It has been well described that a small but significant proportion of patients continue to use opioids months after surgical discharge. We sought to evaluate postdischarge opioid use of patients who were seen by a Transitional Pain Service compared with controls. METHODS: We conducted a retrospective cohort study using administrative data of individuals who underwent surgery in Ontario, Canada from 2014 to 2018. Matched cohort pairs were created by matching Transitional Pain Service patients to patients of other academic hospitals in Ontario who were not enrolled in a Transitional Pain Service. Segmented regression was performed to assess changes in monthly mean daily opioid dosage. RESULTS: A total of 209 Transitional Pain Service patients were matched to 209 patients who underwent surgery at other academic centers. Over the 12 months after surgery, the mean daily dose decreased by an estimated 3.53 morphine milligram equivalents (95% CI 2.67 to 4.39, p<0.001) per month for the Transitional Pain Service group, compared with a decline of only 1.05 morphine milligram equivalents (95% CI 0.43 to 1.66, p<0.001) for the controls. The difference-in-difference change in opioid use for the Transitional Pain Service group versus the control group was -2.48 morphine milligram equivalents per month (95% CI -3.54 to -1.43, p=0.003). DISCUSSION: Patients enrolled in the Transitional Pain Service were able to achieve opioid dose reduction faster than in the control cohorts. The difficulty in finding an appropriate control group for this retrospective study highlights the need for future randomized controlled trials to determine efficacy.

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.

Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.

IMPORTANCE: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. METHODS: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. RESULTS: Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. CONCLUSIONS: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population.

BACKGROUND: Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. OBJECTIVE: The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. METHODS: A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. RESULTS: 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). CONCLUSION: Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.

Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives.

Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.

Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations.

Background: Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern. Objective: The study aimed to identify feature importance in a machine learning model using solely nutrition covariates. Methods: A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). Depressive symptoms were evaluated using the validated 9-item Patient Health Questionnaire (PHQ-9), and all adult patients (total of 7929 individuals) who completed the PHQ-9 and total nutritional intake questionnaire were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. Results: 7929 patients met the inclusion criteria in this study. The machine learning model had 24 out of a total of 60 features that were found to be significant on univariate analysis (p < 0.01 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.603, Sensitivity = 0.943, Specificity = 0.163. The top four highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Potassium Intake (Gain = 6.8%), Vitamin E Intake (Gain = 5.7%), Number of Foods and Beverages Reported (Gain = 5.7%), and Vitamin K Intake (Gain 5.6%). Conclusion: Machine learning models with feature importance can be utilized to identify nutritional covariates for further study in patients with clinical symptoms of depression.

Prior vaccination promotes early activation of memory T cells and enhances immune responses during SARS-CoV-2 breakthrough infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of vaccinated individuals is increasingly common but rarely results in severe disease, likely due to the enhanced potency and accelerated kinetics of memory immune responses. However, there have been few opportunities to rigorously study early recall responses during human viral infection. To better understand human immune memory and identify potential mediators of lasting vaccine efficacy, we used high-dimensional flow cytometry and SARS-CoV-2 antigen probes to examine immune responses in longitudinal samples from vaccinated individuals infected during the Omicron wave. These studies revealed heightened spike-specific responses during infection of vaccinated compared to unvaccinated individuals. Spike-specific cluster of differentiation (CD)4 T cells and plasmablasts expanded and CD8 T cells were robustly activated during the first week. In contrast, memory B cell activation, neutralizing antibody production and primary responses to nonspike antigens occurred during the second week. Collectively, these data demonstrate the functionality of vaccine-primed immune memory and highlight memory T cells as rapid responders during SARS-CoV-2 infection.

Quantification of the Relationship of Pyridoxine and Spirometry Measurements in the United States Population.

Background: There has been evidence to suggest associations between vitamins and lung function. Objective: This study aimed to examine the association between vitamin B6 and spirometry values. Methods: A cross-sectional study was done using National Health and Nutritional Examination Surveys (NHANES) 2007-2012, which is a nationally representative, modern cohort. Spirometry, a clinical pulmonary function test, measured the amount and speed of air a person could exhale after taking the deepest possible breath after forceful expiratory volume at 1 s (FEV1) and forced vital capacity (FVC). After determination of the relationship of the linearity of variables, univariable and multivariable models were fitted to investigate the effect of vitamin B6 on FEV1 and FVC. The National Center for Health Statistics Ethics Review Board granted permission for the study's data collection and analysis. Results: Of 19,160 individuals who had complete information on vitamin B6 intake, FEV1, and FVC, it was found each mg of vitamin B6 intake was associated with increase in 166.41 mL of FEV1 (95% CI: 156.71, 176.12; P < 0.01) and 221.6 mL of FVC (95% CI: 209.62, 233.57; P < 0.01). After controlling for potential confounders (age, race, sex, body mass index, education, and income), multiple linear regression found that each mg of vitamin B6 was associated with increase in 25.98 mL of FEV1 (95% CI: 19.15, 32.80, P < 0.01) and 38.97 mL of FVC (95% CI: 30.65, 47.30, P < 0.01). Conclusion: Increased vitamin B6 intake is associated with improvement in lung function. Further prospective studies are required to ascertain whether increased vitamin B6 can lead to increased long-term spirometry measurements and the specific therapeutic dose-response relationship.

Increased vigorous exercise and decreased sedentary activities are associated with decreased depressive symptoms in United States adults: Analysis of The National Health and Nutrition Examination Survey (NHANES) 2017-2020.

Background and aims: Depression is a major public health concern that affects over 4% of the global population. Identification of new nonpharmacologic recommendations will help decrease the burden of disease. The overarching of this study was to examine the association between physical activity and depressive symptoms in a large sample of adults in the United States. Methods: Presently, researchers utilized data from the National Health and Nutrition Examination Surveys (NHANES 2017-2020), which is a retrospective, complex, multistage, representative, and modern cohort of the United States. Adult patients ( > 18 years; N = 8091) with complete 9-item Patient Health Questionnaire (PHQ-9) information were included in the study. The PHQ-9 is a well-validated survey, per literature, scores ≥10 are considered to have clinically relevant depression. Univariable and multivariable logistic regression was fit for active and sedentary activities on clinical depression (PHQ-9 ≥ 10). The acquisition and analysis of the data within this study were approved by the National Center for Health Statistics Ethics Review Board. Results: After adjusting for potential confounders like age, race, sex, and income, we found that increased vigorous exercise was associated with lower rates of depressive symptoms. Each extra day of vigorous exercise was associated with 11% decreased odds of depression (odd ratio [OR]: 0.89, confidence interval [CI]: 0.83-0.96, p < 0.01). Increased sedentary activity was associated with increased depression. Each extra hour per day of sedentary activity was associated with a 6% increase in odds of depression (OR: 1.06, (1.02-1.10, p < 0.01). Conclusion: To conclude, exercise appears to be protective against depressive symptoms; however, further prospective studies are required to ascertain whether exercise causes decreased depressive symptoms.

Diabetes is associated with increased risk of death in COVID-19 hospitalizations in Mexico 2020: A retrospective cohort study.

Background and Aim: The COVID-19 disease course can be thought of as a function of prior risk factors consisting of comorbidities and outcomes. Survival analysis data for diabetic patients with COVID-19 from an up to date and representative sample can increase efficiency in resource allocation. The study aimed to quantify mortality in Mexico for individuals with diabetes in the setting of COVID-19 hospitalization. Methods: This retrospective cohort study utilized publicly available data from the Mexican Federal Government, covering the period from April 14, 2020, to December 20, 2020 (last accessed). Survival analysis techniques were applied, including Kaplan-Meier curves to estimate survival probabilities, log-rank tests to compare survival between groups, Cox proportional hazard models to assess the association between diabetes and mortality risk, and restricted mean survival time (RMST) analyses to measure the average survival time. Results: A total of 402,388 adults age greater than 18 with COVID-19 were used in the analysis. Mean age = 16.16 (SD = 15.55), 214,161 males (53%). Twenty-day Kaplan-Meier estimates of mortality were 32% for COVID-19 patients with diabetes and 10.2% for those without diabetes with log-rank p < 0.01. Univariable analysis showed increased mortality in diabetic patients (hazard ratio [HR]: 3.61, 95% confidence interval [CI]: 3.54-3.67, p < 0.01) showing a 254% increase in death. After controlling for confounding variables, multivariate analysis continued to show increased mortality in diabetics (HR: 1.37, 95% CI: 1.29-1.44, p < 0.01) indicating a 37% increase in death. Multivariable RMST at Day 20 showed in Mexico, hospitalized COVID-19 patients were associated with less mean survival time by 2.01 days (p < 0.01) and a 10% increased mortality (p < 0.01). Conclusions: In the present analysis, COVID-19 patients with diabetes in Mexico had shorter survival times. Further interventions aimed at improving comorbidities in the population, particularly in individuals with diabetes, may contribute to better outcomes in COVID-19 patients.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database.

BACKGROUND: There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality. OBJECTIVE: The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency. METHODS: Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map. RESULTS: Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics. CONCLUSION: Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Phase I Trial of Autologous RNA-electroporated cMET-directed CAR T Cells Administered Intravenously in Patients with Melanoma and Breast Carcinoma.

Purpose: Treatments are limited for metastatic melanoma and metastatic triple-negative breast cancer (mTNBC). This pilot phase I trial (NCT03060356) examined the safety and feasibility of intravenous RNA-electroporated chimeric antigen receptor (CAR) T cells targeting the cell-surface antigen cMET. Experimental Design: Metastatic melanoma or mTNBC subjects had at least 30% tumor expression of cMET, measurable disease and progression on prior therapy. Patients received up to six infusions (1 × 10e8 T cells/dose) of CAR T cells without lymphodepleting chemotherapy. Forty-eight percent of prescreened subjects met the cMET expression threshold. Seven (3 metastatic melanoma, 4 mTNBC) were treated. Results: Mean age was 50 years (35-64); median Eastern Cooperative Oncology Group 0 (0-1); median prior lines of chemotherapy/immunotherapy were 4/0 for TNBC and 1/3 for melanoma subjects. Six patients experienced grade 1 or 2 toxicity. Toxicities in at least 1 patient included anemia, fatigue, and malaise. One subject had grade 1 cytokine release syndrome. No grade 3 or higher toxicity, neurotoxicity, or treatment discontinuation occurred. Best response was stable disease in 4 and disease progression in 3 subjects. mRNA signals corresponding to CAR T cells were detected by RT-PCR in all patients' blood including in 3 subjects on day +1 (no infusion administered on this day). Five subjects underwent postinfusion biopsy with no CAR T-cell signals seen in tumor. Three subjects had paired tumor tissue; IHC showed increases in CD8 and CD3 and decreases in pS6 and Ki67. Conclusions: Intravenous administration of RNA-electroporated cMET-directed CAR T cells is safe and feasible. Significance: Data evaluating CAR T therapy in patients with solid tumors are limited. This pilot clinical trial demonstrates that intravenous cMET-directed CAR T-cell therapy is safe and feasible in patients with metastatic melanoma and metastatic breast cancer, supporting the continued evaluation of cellular therapy for patients with these malignancies.

High-throughput interrogation of immune responses using the Human Immune Profiling Pipeline.

The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1.

Quantification of the Effect of Vitamin E Intake on Depressive Symptoms in United States Adults Using Restricted Cubic Splines.

Background: Depression is a rapidly increasing public health concern, affecting >4% of the global population. Identification of new nutritional recommendations is needed to help combat this increasing public health concern. Objectives: The study aimed to examine the association between vitamin E intake and depressive symptoms. Methods: A retrospective study was conducted by using a nationally representative, modern cohort (NHANES 2017-2020). Depressive symptoms were assessed through the validated 9-item Patient Health Questionnaire (PHQ-9). All adult patients ([≥18 y old], 8091 total adults) who answered the PHQ-9 and daily nutritional values questionnaires were selected for this study. Per literature, patients with PHQ-9 scores ≥10 were considered to have depressive symptoms. Univariable and multivariable logistic regressions were used to investigate the effect of vitamin E on depressive symptoms as ascertained by PHQ-9. The acquisition and analysis of the data within this study was approved by the NCHS ethics review board. Results: After controlling for potential confounders (age, race, sex, and income), we observed that increased vitamin E (up until 15 mg/d) was associated with decreased rates of depressive symptoms, with each 5 mg increase in vitamin E associated with 13% decreased odds of symptoms of depression (OR: 0.87; 95% CI: 0.77, 0.97; P < 0.01). Additional intake above 15 mg/d, the daily recommended amount by the Food and Nutrition Board, did not change the odds of depression (OR: 1.05; 95% CI: 0.92, 1.16; P = 0.44). Conclusions: Increased vitamin E intake (up to 15 mg/d) is associated with decreased depressive symptoms. Further prospective studies are required to ascertain whether increased vitamin E can protect against depressive symptoms and the specific therapeutic dose-response relationship.