Deepak Kaji

Experimental and Computational Insight into Human Mesenchymal Stem Cell Paracrine Signaling and Heterocellular Coupling Effects on Cardiac Contractility and Arrhythmogenicity

Rationale: Myocardial delivery of human mesenchymal stem cells (hMSCs) is an emerging therapy for treating the failing heart. However, the relative effects of hMSC-mediated heterocellular coupling (HC) and paracrine signaling (PS) on human cardiac contractility and arrhythmogenicity remain unresolved. Objective: The objective is to better understand hMSC PS and HC effects on human cardiac contractility and arrhythmogenicity by integrating experimental and computational approaches. Methods and Results: Extending our previous hMSC–cardiomyocyte HC computational model, we incorporated experimentally calibrated hMSC PS effects on cardiomyocyte L-type calcium channel/sarcoendoplasmic reticulum calcium-ATPase activity and cardiac tissue fibrosis. Excitation–contraction simulations of hMSC PS-only and combined HC+PS effects on human cardiomyocytes were representative of human engineered cardiac tissue (hECT) contractile function measurements under matched experimental treatments. Model simulations and hECTs both demonstrated that hMSC-mediated effects were most pronounced under PS-only conditions, where developed force increased ≈4-fold compared with non–hMSC-supplemented controls during physiological 1-Hz pacing. Simulations predicted contractility of isolated healthy and ischemic adult human cardiomyocytes would be minimally sensitive to hMSC HC, driven primarily by PS. Dominance of hMSC PS was also revealed in simulations of fibrotic cardiac tissue, where hMSC PS protected from potential proarrhythmic effects of HC at various levels of engraftment. Finally, to study the nature of the hMSC paracrine effects on contractility, proteomic analysis of hECT/hMSC conditioned media predicted activation of PI3K/Akt signaling, a recognized target of both soluble and exosomal fractions of the hMSC secretome. Treating hECTs with exosome-enriched, but not exosome-depleted, fractions of the hMSC secretome recapitulated the effects observed with hMSC conditioned media on hECT-developed force and expression of calcium-handling genes (eg, SERCA2a, L-type calcium channel). Conclusions: Collectively, this integrated experimental and computational study helps unravel relative hMSC PS and HC effects on human cardiac contractility and arrhythmogenicity, and provides novel insight into the role of exosomes in hMSC paracrine-mediated effects on contractility.

Association between probiotic and yogurt consumption and kidney disease: insights from NHANES

BackgroundData from experimental animals suggest that probiotic supplements may retardxa0CKD progression. However, the relationship between probiotic use, frequent yogurt consumption (as a natural probiotic source), and kidney parameters have not been evaluated in humans.FindingsWe utilized NHANES data, and analyzed the association of probiotic alone (1999–2012) and yogurt/probiotic (2003–2006) use with albuminuria and eGFR after adjustment for demographic and clinical parameters. Frequent yogurt consumption was defined as thrice or more weekly over the year prior to the interview. Frequent yogurt/probiotic consumers had lower adjusted odds of developing combined outcome (albuminuria and/or eGFRu2009<u200960xa0ml/min/1.73xa0m2) compared to infrequent consumers (ORu2009=u20090.76; 95xa0% CIu2009=u20090.61-0.94). When evaluated separately, frequent consumers had lower odds of albuminuria and nonsignificant trend towards decreased odds of low eGFR compared to infrequent consumers. In the probiotic cohort, probiotic consumers were found to have a lower adjusted odds of albuminuria compared to nonusers (ORu2009=u20090.59; 95 % CIu2009=u20090.37–0.94).ConclusionFrequent yogurt and/or probiotics use is associated with decreased odds of proteinuric kidney disease. These hypothesis-generating results warrant further translational studies to further delineate the relationship between yogurt/probiotics with kidney dysfunction, as well as microbiome and dysbiosis as potential mediators.

Association of traffic air pollution and rhinitis quality of life in Peruvian children with asthma

Background Air pollution exposure may contribute to rhinoconjunctivitis morbidity in children with underlying airways disease. Prior studies have not assessed rhinoconjunctivitis-related quality of life (QOL) in children with asthma chronically exposed to air pollution. Methods Children ages 9–19 years with asthma from peri-urban Peru, self-reporting rhinoconjunctivitis symptoms (n = 484), were administered the Rhinoconjunctivitis QOL Questionnaire (RQLQ) at repeated intervals over one year, with scores dichotomized into bothered (>0) and not bothered (= 0). Individual weekly exposures to particulate matter<2.5μm (PM2.5) and its black carbon (BC) component were estimated by inverse distance weighted methods. Generalized estimating equations, adjusting for covariates, estimated associations of PM2.5 and BC with QOL. Results Participants were on average 13 years old, 55% female, and majority were atopic (77%). Mean (SD) PM2.5 and BC concentrations were 21(3.2) μg/m3 and 4.4(1.5) μg/m3, respectively. In adjusted multi-pollutant models, each 10μg/m3 increase in PM2.5 was associated with increased odds of worse rhinoconjunctivitis QOL (OR;[95% CI]: 1.83;[1.33,2.52]). A 10% increase in the BC proportion was associated with higher rhinitis burden (OR;[95% CI]: 1.80;[1.22,2.66]), while increases in the non-BC component of PM did not significantly impact rhinoconjunctivitis QOL. Associations were similar regardless of atopy. Conclusion Higher PM2.5 and BC exposure is associated with worse rhinitis QOL among asthmatic children.

Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning

STUDY DESIGNnCross-sectional database study.nnnOBJECTIVEnTo train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD).nnnSUMMARY OF BACKGROUND DATAnMachine learning models such as logistic regression (LR) and artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex data sets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.nnnMETHODSnThe American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent surgery for ASD. This query returned 4,073 patients, which data were used to train and evaluate our models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society of Anesthesiologists (ASA) class >3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating characteristic curves (AUC) was used to determine the accuracy of our machine learning models.nnnRESULTSnThe mean age of patients was 59.5 years. Forty-one percent of patients were male whereas 59.0% of patients were female. ANN and LR outperformed ASA scoring in predicting every complication (p<.05). The ANN outperformed LR in predicting cardiac complication, wound complication, and mortality (p<.05).nnnCONCLUSIONSnMachine learning algorithms outperform ASA scoring for predicting individual risk prognosis. These algorithms also outperform LR in predicting individual risk for all complications except VTE. With the growing size of medical data, the training of machine learning on these large data sets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.nnnLEVEL OF EVIDENCEnLevel III.STUDY DESIGNnCross-sectional database study.nnnOBJECTIVEnTo train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD). Machine learning models such as logistic regression (LR) and artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex data sets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.nnnMETHODSnThe American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent surgery for ASD. This query returned 4,073 patients, which data were used to train and evaluate our models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society of Anesthesiologists (ASA) class >3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating characteristic curves (AUC) was used to determine the accuracy of our machine learning models.nnnRESULTSnThe mean age of patients was 59.5 years. Forty-one percent of patients were male whereas 59.0% of patients were female. ANN and LR outperformed ASA scoring in predicting every complication (p<.05). The ANN outperformed LR in predicting cardiac complication, wound complication, and mortality (p<.05).nnnCONCLUSIONSnMachine learning algorithms outperform ASA scoring for predicting individual risk prognosis. These algorithms also outperform LR in predicting individual risk for all complications except VTE. With the growing size of medical data, the training of machine learning on these large data sets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.nnnLEVEL OF EVIDENCEnLevel III.

Hypoalbuminemia as an Independent Risk Factor for Perioperative Complications Following Surgical Decompression of Spinal Metastases

Study Design: Retrospective cohort study. Objective: Malnutrition has been shown to be a risk factor for poor perioperative outcomes in multiple surgical subspecialties, but few studies have specifically investigated the effect of hypoalbuminemia in patients undergoing operative treatment of metastatic spinal tumors. The aim of this study was to assess the role of hypoalbuminemia as an independent risk factor for 30-day perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database from 2011 to 2014. Methods: We identified 1498 adult patients in the ACS-NSQIP database who underwent laminectomy and excision of metastatic extradural spinal tumors. Patients were categorized into normoalbuminemic and hypoalbuminemic (ie, albumin level <3.5 g/dL) groups. Univariate and multivariate regression analyses were performed to examine the association between preoperative hypoalbuminemia and 30-day perioperative mortality and morbidity. Subgroup analysis was performed in the hypoalbuminemic group to assess the dose-dependent effect of albumin depletion. Results: Hypoalbuminemia was associated with increased risk of perioperative mortality, any complication, sepsis, intra- or postoperative transfusion, prolonged hospitalization, and non-home discharge. However, albumin depletion was also associated with decreased risk of readmission. There was an albumin level–dependent effect of increasing mortality and complication rates with worsening albumin depletion. Conclusions: Hypoalbuminemia is an independent risk factor for perioperative mortality and morbidity following surgical decompression of metastatic spinal tumors with a dose-dependent effect on mortality and complication rates. Therefore, it is important to address malnutrition and optimize nutritional status prior to surgery.