Learning to Address Health Inequality in the United States with a Bayesian Decision Network

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stable-families within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.Comment: 8 pages, 4 figures, 1 table (excluding the supplementary material), accepted for publication in AAAI 201

May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension

Aims Raised blood pressure (BP) is the biggest contributor to mortality and disease burden worldwide and fewer than half of those with hypertension are aware of it. May Measurement Month (MMM) is a global campaign set up in 2017, to raise awareness of high BP and as a pragmatic solution to a lack of formal screening worldwide. The 2018 campaign was expanded, aiming to include more participants and countries. Methods and results Eighty-nine countries participated in MMM 2018. Volunteers (≥18 years) were recruited through opportunistic sampling at a variety of screening sites. Each participant had three BP measurements and completed a questionnaire on demographic, lifestyle, and environmental factors. Hypertension was defined as a systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg, or taking antihypertensive medication. In total, 74.9% of screenees provided three BP readings. Multiple imputation using chained equations was used to impute missing readings. 1 504 963 individuals (mean age 45.3 years; 52.4% female) were screened. After multiple imputation, 502 079 (33.4%) individuals had hypertension, of whom 59.5% were aware of their diagnosis and 55.3% were taking antihypertensive medication. Of those on medication, 60.0% were controlled and of all hypertensives, 33.2% were controlled. We detected 224 285 individuals with untreated hypertension and 111 214 individuals with inadequately treated (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg) hypertension. Conclusion May Measurement Month expanded significantly compared with 2017, including more participants in more countries. The campaign identified over 335 000 adults with untreated or inadequately treated hypertension. In the absence of systematic screening programmes, MMM was effective at raising awareness at least among these individuals at risk

Symmetrical peripheral gangrene of all four limbs: An unusual complication of ureteroscopy

Ureteroscopy (URS) is a commonly performed and a safe urological intervention. However, potentially serious infective complications are possible after URS. A young nondiabetic woman developed severe Gram-negative septicemia after ureteroscopy for a lower ureteric calculus. The sepsis progressed to symmetrical peripheral gangrene of all the four limbs. She required left below-elbow amputation, right below-knee amputation, and loss of all toes and digits of the other two limbs

Kidney transplant in patients with abnormal bladder: Experience of tertiary care center in developing country-Is the outcome same?

Background: Fifteen percent of adults and 20%–30% of pediatric patients develop renal failure, results from structural urological abnormalities. Successful renal transplantation depends partly on a bladder which has adequate capacity, good compliance, and efficient voluntary emptying. Urinary bladder rehabilitation with augmentation or diversion is necessary before transplant in these patients to achieve good graft outcome. We, hereby report our last 10 years' experience of such patients undergoing kidney transplant in abnormal bladder. Materials and Methods: A total of 14 patients underwent renal transplantation in rehabilitated bladder from 2006 to 2016. Demographic details, prereconstruction bladder and urodynamic findings, and type of pretransplant reconstruction were recorded. Posttransplant creatinine levels, graft survival at 7 days, 3 months, 1 year, and 3 years were recorded. Results: Mean (± standard deviation) serum creatinine posttransplant at 7 days, 3 months, 1 year, and 3 years was 0.9 (±0.20), 1.58 (±0.65), 1.92 (±1.02), and 2.47 (1.17) mg/dl, respectively. Four patients developed rejection within 6 months of transplant. Kidney biopsy was suggestive of acute cellular rejection in all cases, which was treated successfully. At three years follow-up, four patients who had rejection-have rising creatinine levels and diminishing renal functions. No patient needed dialysis support till last follow-up. All these four patients had rejection, urinary tract infection (UTI) episodes and pyelonephritis in the past. Conclusion: Native bladder is the best reservoir for urinary storage and drainage. The main cause of graft dysfunction in rehabilitated bladder is UTI as a result of poor hygiene, contamination during clean intermittent self-catheterization (CISC) and noncompliance for CISC leading to high residual urine. Controlling frequent attacks of UTI posttransplant is essential, otherwise long-term graft survival and function will deteriorate faster and might trigger rejection

Design and evaluation of celecoxib porous particles using melt sonocrystallization.

NoPurpose The purpose of the article was to study melt sonocrystallization (MSC) for a drug forming a viscous melt when processed below its glass transition temperature. Methods A molten mass of drug was poured in a vessel containing deionized water, maintained at 40°C using cryostatic bath, and sonicated for 1 min using probe ultrasonicator at an amplitude of 80% and a cycle of 0.8 per second. The product obtained after solidification of dispersed droplets was separated by filtration and dried at room temperature. MSC celecoxib was characterized by solubility determination, scanning electron microscopy, differential scanning calorimetry, X-ray powder diffraction, and stability study. Results The MSC technique was designed for celecoxib, which undergoes fast solidification. The particles obtained by MSC were porous, irregular in shape, and amorphous in nature. An increase in the apparent solubility was observed for the MSC particles. These amorphous particles also exhibited a higher stability in the amorphous state as compared with particles obtained by melt quenching. Conclusions The reported MSC technique for celecoxib demonstrates advantages over other approaches and can be exploited in area of particle design for the amorphization of drugs

Pressure induced elastic softening in framework aluminosilicate-albite (NaAlSi3O8)

Albite (NaAlSi3O8) is an aluminosilicate mineral. Its crystal structure consists of 3-D framework of Al and Si tetrahedral units. We have used Density Functional Theory to investigate the high-pressure behavior of the crystal structure and how it affects the elasticity of albite. Our results indicate elastic softening between 6–8 GPa. This is observed in all the individual elastic stiffness components. Our analysis indicates that the softening is due to the response of the three-dimensional tetrahedral framework, in particular by the pressure dependent changes in the tetrahedral tilts. At pressure <6 GPa, the PAW-GGA can be described by a Birch-Murnaghan equation of state with  = 687.4 Å3,  = 51.7 GPa, and  = 4.7. The shear modulus and its pressure derivative are  = 33.7 GPa, and  = 2.9. At 1 bar, the azimuthal compressional and shear wave anisotropy  = 42.8%, and  = 50.1%. We also investigate the densification of albite to a mixture of jadeite and quartz. The transformation is likely to cause a discontinuity in density, compressional, and shear wave velocity across the crust and mantle. This could partially account for the Mohorovicic discontinuity in thickened continental crustal regions

Energy-efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

Wireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy-efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long-range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross-sections. It improves alive nodes\u27 performance for around 50% more rounds than the optimizing-LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross-section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively

Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features

Abstract Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)‐based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold‐out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri‐nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion‐based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI‐level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium‐rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors

Comprehensive Spinal Tuberculosis Score: A Clinical Guide for the Management of Thoracolumbar Spinal Tuberculosis

Study Design A newly proposed scoring tool was designed to assist in the clinical management of adult thoracolumbar spinal tuberculosis (TB). Purpose To formulate a comprehensive yet simple scoring tool to guide decision-making in the management of adult thoracolumbar spinal TB. Overview of Literature Spine surgeons have differing consensus in defining the threshold grade for clinico-radiological parameters when deciding between operative or conservative treatment for adult thoracolumbar spinal TB. Currently, the void in decision-making from the lack of well-defined guidelines is compensated by the surgeon’s experience in treating these patients. To the best of our knowledge, no scoring system holistically integrates multiple facets of spinal TB to guide clinical decision-making. Methods The RAND/University of California, Los Angeles appropriateness method was employed among an expert panel of 10 spine surgeons from four apex tertiary care centers. Vital characteristics that independently influenced treatment decisions in spinal TB were identified, and a scoring tool was formulated. Points were assigned for each component based on their severity. The cutoff scores to guide clinical management were determined from the receiver operating characteristic curve based on the retrospective records of 151 patients treated operatively or non-operatively with improved functional outcomes at the 1-year follow-up. Results The components of the comprehensive spinal TB score (CSTS) are pain, kyphosis angle, vertebral destruction, and neurological status. A score classification of 6.5 was established to guide the patient toward conservative, conservative/operative, and operative management, respectively. Conclusions The CSTS was designed to reflect the essential indicators of mechanical stability, neurological stability, and disease process stabilization in spinal TB. The scoring tool is devised to be practical and serve as a common language in the spine community to facilitate discussions and decision-making in thoracolumbar spinal TB. The validity, reliability, and reproducibility of this tool must be assessed through multicenter long-term studies