Successful Treatment for BK Virus Nephropathy by Leflunomide in a Kidney Transplant Patient: A Case Report

IntroductionThe immunosuppressant agents in kidney transplantation (KT) may lead to various complications such as opportunistic infections and malignancies. BK virus associated nephropathy is a significant complication following KT, and it can result in graft failure. BK virus causes tubulointerstitial nephritis, ureter stenosis, and even graft failure in KT recipients with impaired immune system. We described a 63-year-old woman, who was a hepatitis C carrier and on dialysis for 22 years before KT, who received cadaveric-donor KT 2 years previously. She reported decreasing urine output and general weakness. The serum creatinine level was slightly increased from 2.94 to 4.38 mg/dL.MethodsImmunosuppressant medications including prednisolone, everolimus, cyclosporin, and mycophenolate sodium were continued as maintenance therapy post KT. Kidney biopsy was performed due to deterioration of graft function.ResultsThe kidney biopsy showed consistent results with early-stage polyomavirus nephropathy, characterized by focal viral cytopathic changes with positive immunohistochemical signals and mesangial proliferative glomerulonephritis, immune-complex-mediated (Fig 1 and Fig 2). Negative C4d staining at peritubular capillary was reported. The dosage of mycophenolate sodium was tapered from 720 to 360 mg daily and that of everolimus increased from 0.5 to 1.0 mg daily due to BK viral infection with BK nephropathy. The serum creatinine level was 2.75 mg/dL after treatment.ConclusionEarly detection of BK nephropathy and decreasing immunosuppressant agents are the mainstay of treatment. Substituting leflunomide for mycophenolate sodium and increasing dosage of everolimus has been proposed to solve BK nephropathy. We presented that the use of leflunomide in such situation is in a timely manner.     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

The effect of different statistical approaches on image quality data obtained from radiological examinations

IntroductionSelection of optimal image acquisition protocols in medical imaging remains a grey area, the superimposed use of the Likert scale in radiological image quality evaluations creates an additional challenge for the statistical analysis of image quality data.Using a simulation study, we have trialled a novel approach to analysing radiological image quality Likert scale data.MethodsA simulation study was undertaken where simulated datasets were generated based on the distribution of Likert scale values according to varying image acquisition protocols from a real dataset. Simulated Likert scale values were pooled in four different ways; the mean, median, mode and the summation of patient Likert scale values of which the total was assigned a categorical Likert scale value. Estimates of bias, MAPE and RMSPE were then calculated for all four pooling approaches to determine which method most accurately represented an expert's opinion.ResultsWhen compared to an expert's opinion, the method of summation and categorisation of Likert scale values was most accurate 49 times out of the 114 (43.0%) tests. The mean 28 times out of 114 (24.6%), the median 23 times out of 114 (20.2%) and the mode 17 times out of 114 (14.9%).ConclusionWe conclude that our method of summation and categorisation of Likert scale values is most often the best representation of the simulated data compared to the expert's opinion.Implications for practiceThere is scope to reproduce this simulation study with multiple observers to reflect clinical reality more accurately with the dynamic nature of multiple observers. This also prompts future investigation into other anatomical areas, to see if the same methods produce similar results.     

Combining CAPRA-S With Tumor IDC/C Features Improves the Prognostication of Biochemical Recurrence in Prostate Cancer Patients

Background: Intraductal carcinoma and cribriform (IDC/C) tumor features are well-established prognosticators of biochemical recurrence (BCR), metastasis, and prostate cancer (PCa)-specific mortality. However, approximately 70% of PCa patients undergoing a radical prostatectomy are IDC/C negative, yet up-to 20% of these patients progress and experience BCR. Thus, tumor histopathologic characteristics such as IDC/C alone are limited in their ability to predict disease progression. Conversely, several nomograms such as Cancer of the Prostate Risk Assessment-Surgery (CAPRA-S) have been developed to aid in the prognostication of BCR, but not yet widely applied in clinical settings. Materials and methods: In this study, we assessed the combined prognostic utility of IDC/C, and CAPRA-S for BCR in 3 PCa patient cohorts. Results: CAPRA-S+IDC/C improved the predictive accuracy of BCR in all 3 cohorts (P < .001). Specifically, among IDC/C negative cases, CAPRA-S improved the prognostication of BCR in low-risk (Cohort 1; P < .001, Cohort 2; P < .001, Cohort 3; P = .003), intermediate (Cohort 1; P < .001, Cohort 2; P = .006, Cohort 3; P = .03) and high-risk (Cohort 1-3; P < .001) patients. Conversely, IDC/C improved the prognostication of BCR among CAPRA-S low-risk (Cohorts 1; P < .001 and Cohort 3; P = .003) patients. Conclusion: Our results suggest the investigation of histopathological IDC/C features in CAPRA-S low-risk patients and conversely, nomogram CAPRA-S among IDC/C negative patients improves the identification of patients likely to experience BCR, which would otherwise be missed through current assessment regimens. These patients can be offered more intensive monitoring and adjuvant therapies upfront to circumvent the development of recurrent cancer or overtreatment at the time of surgery.     

Clinical Outcomes at 1 Year Following Transcatheter Left Atrial Appendage Occlusion in the United States

ObjectivesThe aim of this study was to report 1-year clinical outcomes following commercial transcatheter left atrial appendage occlusion (LAAO) in the United States.BackgroundThe National Cardiovascular Data Registry LAAO Registry was initiated to meet a condition of Medicare coverage and allow the assessment of clinical outcomes. The 1-year rates of thromboembolic events after transcatheter LAAO in such a large cohort of “real-world” patients have not been previously reported.MethodsPatients entered into the National Cardiovascular Data Registry LAAO Registry for a Watchman procedure between January 1, 2016, and December 31, 2018, were included. The primary endpoint was ischemic stroke. Key secondary endpoints included the rate of ischemic stroke or systemic embolism, mortality, and major bleeding. Major bleeding was defined as any bleeding requiring hospitalization, and/or causing a decrease in hemoglobin level > 2g/dL, and/or requiring blood transfusion that was not hemorrhagic stroke. The Kaplan-Meier method was used for 1-year estimates of cumulative event rates.ResultsThe study population consisted of 36,681 patients. The mean age was 76.0 ± 8.1 years, the mean CHA₂DS₂-VASc score was 4.8 ± 1.5, and the mean HAS-BLED score was 3.0 ± 1.1. Prior stroke was present in 25.5%, clinically relevant bleeding in 69.5%, and intracranial bleeding in 11.9%. Median follow-up was 374 days (IQR: 212-425 days). The Kaplan-Meier–estimated 1-year rate of ischemic stroke was 1.53% (95% CI: 1.39%-1.69%), the rate of ischemic stroke or systemic embolism was 2.19% (95% CI: 2.01%-2.38%), and the rate of mortality was 8.52% (95% CI: 8.19%-8.87%). The 1-year estimated rate of major bleeding was 6.93% (95% CI: 6.65%-7.21%). Most bleeding events occurred between discharge and 45 days following the procedure.ConclusionsThis study characterizes important outcomes in a national cohort of patients undergoing transcatheter LAAO in the United States. Clinicians and patients can integrate these data in shared decision making when considering this therapy.