Pelvic prehabilitation: pelvic exercises assist in minimizing inter-fraction sacral slope variability during radiation therapy

Introduction: Prehabilitation for radiation therapy is not well studied. Retrospective data shows variability in set-up positioning of patients during daily pelvic RT. We hypothesize that a brief structured daily exercise regimen is feasible for subjects to perform before RT and may minimize variability in positioning as measured by sacral slope angles (SSA) on lateral views. Determining feasibility and effectiveness of these exercises in decreasing set-up variability has clinical implications, both for targeting treatment sites and preventing adverse effects. Methods: Subjects in the exercise intervention condition (n=8, 8 F) performed a structured daily hip exercise regimen throughout the duration of RT, and subjects in the historical control condition (n=20, 17 F, 3 M) had usual care. For each patient, SSA measurements were compared to SSA measurements from the simulation CT for 5 weeks during RT. The extent of variability of measurements between two conditions was studied using a linear mixed model. For all patients in both conditions, the same two readers independently measured SSA to compare angles on day of simulation against the angles measured from each day of RT. Results: The average variation in SSA for intervention condition was 0.913° (±0.582°), with range among patients 0.57°-1.3°. The average variation for control condition was 2.27° (±1.43°), with range among patients 1.22° - 5.09°. The difference between two conditions was statistically significant (p=0.0019). Comparison of SSA variation between conditions demonstrated a statistically significant difference at each week (wk 1: p = 0.0071, wk 2: p = 0.0077, wk 3: p = 0.011, wk 4: p = 0.005, wk 5: p = 0.0079). The exercise intervention condition had no significant variation between week 1 and later weeks (wk 2: p = 0.876, wk 3: p = 0.741, wk 4: p = 0.971, wk 5: p = 0.397). The control condition showed greater SSA variation between week 1 and later weeks (wk 2: p = 0.868, wk 3: p = 0.915, wk 4: p = 0.015, wk 5: p = 0.224), with significant variation between weeks 1 and 4. No subject reported any adverse effects. Conclusion: We observed a significant decrease in sacral slope variability in our exercise cohort as compared to historical controls. SSA variation for control condition increased over the course of treatment with significant difference noted between week 1 and 4. A larger clinical trial is required to evaluate the potential clinical benefits of a structured daily exercise regimen during pelvic RT. References: Silver JK, Baima J. Cancer prehabilitation: an opportunity to decrease treatment-related morbidity, increase cancer treatment options, and improve physical and psychological health outcomes. American journal of physical medicine & rehabilitation. 2013 Aug 1;92(8):715-27. Lukez A, O’Loughlin L, Bodla M, Baima J, Moni J. Positioning of port films for radiation: variability is present. Medical Oncology. 2018 May 1;35(5):77. Kwon JW, Huh SJ, Yoon YC, Choi SH, Jung JY, Oh D, Choe BK. Pelvic bone complications after radiation therapy of uterine cervical cancer: evaluation with MRI. American Journal of Roentgenology. 2008 Oct;191(4):987-94. Stubblefield MD. Radiation fibrosis syndrome: neuromuscular and musculoskeletal complications in cancer survivors. PM&R. 2011 Nov 1;3(11):1041-54

A Novel Blended State Estimated Adaptive Controller for Voltage and Current Control of Microgrid against Unknown Noise

© 2013 IEEE. In this study, a novel blended state estimated adaptive controller is designed for voltage and current control of microgrid against unknown noise. The core feature of the microgrid (MG) is its ability to integrate more than one distributed energy resource into the main grid. The state of a microgrid may deteriorate due to many reasons, for example malicious cyber-attacks, disturbances, packet losses, etc. Therefore, it is necessary to achieve the true state of the system to enhance the control requirement and automation of the microgrid. To achieve the true state of a microgrid, this study proposes the use of an algorithm based on the unscented kalman filter (UKF). The proposed state estimator technique is developed using an unscented-transformation and sigma-points measurement technique capable of minimizing the mean and covariance of a nonlinear cost function to estimate the true state of a single-phase, three-phase single-source and three-phase multi-source microgrid system. The advantage of the proposed estimator over using extended kalman filter (EKF) is investigated in simulations. The results demonstrate that the use of the UKF estimator produces a superior estimation of the system compared with the use of the EKF. An adaptive PID controller is also developed and used in system conjunction with the estimator to regulate its voltage and current against the number of loads. Deviation in load parameters hamper the function of the MG system. The performance of the developed controller is also evaluated against number of loads. Results indicate the controller provides a more stable and high-tracking performance with the inclusion of the UKF in the system

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. METHODS: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. RESULTS: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. CONCLUSIONS: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans

A comparative analysis of active learning for biomedical text mining

Data Availability Statement: The code and data are available from https://github.com/usmaann (accessed on 14 March 2021).Copyright © 2021 by the authors. An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling.This research received no external funding

Lung Cancer Prediction Using Curriculum Learning Based Deep Neural Networks

The authors would like to thank the National Cancer Institute for access to the data collected over the PLCO cancer screening trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by the National Cancer Institute

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resource

Advanced treatment technologies efficacies and mechanism of per- and poly-fluoroalkyl substances removal from water

© 2020 Institution of Chemical Engineers The increasing occurrence of chemically resistant per- and poly-fluoroalkyl substances (PFASs) in the natural environment, animal tissues and even the human body poses a significant health risk. Temporal trend studies on water, sediments, bird, fish, marine mammal and the human show that the exposure of PFAS has significantly increased over the last 20–30 years. Different physical, biological and chemical treatment processes have been investigated for PFAS removal from water. However, there is a lack of detailed understating of the mechanism of removal by different methods, especially by different advanced chemical treatment processes. This article reviews PFASs removal efficacy and mechanism by the advanced chemical treatment methods from aqueous solution. Review shows that several advanced oxidation processes (e.g., electrochemical oxidation, activated persulfate oxidation, photocatalysis, UV-induced oxidation) are successful in degrading PFASs. Moreover, defluorination treatment, some thermal and non-thermal degradation processes are also found to be prominent for the degradation of PFASs with some limitations including process costs over physical treatment (e.g., sorption), production of toxic by-products and greenhouse gases. Finally, knowledge gaps concerning the advanced chemical treatment of PFASs are discussed

Zeolite synthesis from low-cost materials and environmental applications: A review

Zeolites with the three-dimensional structures occur naturally or can be synthesized in the laboratory. Zeolites have versatile applications such as environmental remediation, catalytic activity, biotechnological application, gas sensing and medicinal applications. Although, naturally occurring zeolites are readily available, nowadays, more emphasis is given on the synthesis of the zeolites due to their easy synthesis in the pure form, better ion exchange capabilities and uniform in size. Recently, much attention has also been paid on how zeolite is being synthesized from low-cost material (e.g., rice husk), particularly, by resolving the major environmental issues. Hence, the main purpose of this review is to make an effective resolution of zeolite synthesis methods together with potential applications in environmental engineering. Among different synthesis methods, hydrothermal method is commonly found to be used widely in the synthesis of various zeolites from inexpensive raw materials such as fly ash, rice husk ash, blast furnace slag, municipal solid waste, paper sludge, lithium slag and kaolin. Besides, future expectation in the field of synthetic zeolites research is also included

Hypothermia for encephalopathy in low and middle-income countries (HELIX): Study protocol for a randomised controlled trial

BACKGROUND: Therapeutic hypothermia reduces death and disability after moderate or severe neonatal encephalopathy in high-income countries and is used as standard therapy in these settings. However, the safety and efficacy of cooling therapy in low- and middle-income countries (LMICs), where 99% of the disease burden occurs, remains unclear. We will examine whether whole body cooling reduces death or neurodisability at 18-22 months after neonatal encephalopathy, in LMICs. METHODS: We will randomly allocate 408 term or near-term babies (aged ≤ 6 h) with moderate or severe neonatal encephalopathy admitted to public sector neonatal units in LMIC countries (India, Bangladesh or Sri Lanka), to either usual care alone or whole-body cooling with usual care. Babies allocated to the cooling arm will have core body temperature maintained at 33.5 °C using a servo-controlled cooling device for 72 h, followed by re-warming at 0.5 °C per hour. All babies will have detailed infection screening at the time of recruitment and 3 Telsa cerebral magnetic resonance imaging and spectroscopy at 1-2 weeks after birth. Our primary endpoint is death or moderate or severe disability at the age of 18 months. DISCUSSION: Upon completion, HELIX will be the largest cooling trial in neonatal encephalopathy and will provide a definitive answer regarding the safety and efficacy of cooling therapy for neonatal encephalopathy in LMICs. The trial will also provide important data about the influence of co-existent perinatal infection on the efficacy of hypothermic neuroprotection. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02387385. Registered on 27 February 2015