Does renal denervation require cardiovascular outcome-driven data?

Hypertension is a major driver of cardiovascular disease with a prevalence of 32-34% in adults worldwide. This poses a formidable unmet challenge for healthcare systems, highlighting the need for enhanced treatment strategies. Since 2017, eight major sham-controlled randomised controlled trials have examined the effectiveness and safety of renal denervation (RDN) as therapy for BP control. Although most trials demonstrated a reduction in systolic 24-hour/daytime ambulatory BP compared to control groups, open to discussion is whether major adverse cardiovascular events (MACE)-driven RDN trials are necessary or whether the proof of BP reduction as a surrogate for better cardiovascular outcomes is sufficient. We conducted an analysis of the statistical methods used in various trials to assess endpoint definitions and determine the necessity for MACE-driven outcome data. Such comprehensive analysis provides further evidence to confidently conclude that RDN significantly reduces blood pressure compared to sham controls. Importantly, this enables the interpolation of RDN trial endpoints with other studies that report on outcome data, such as pharmacological trials which demonstrate a significant reduction in MACE risk with a decrease in BP. Moreover, limitations associated with directly evaluating outcome data further support the use of BP as a surrogate endpoint. For example, conducting lengthier trials with larger numbers of participants to ensure robust statistical power presents a substantial challenge to evaluating outcome data. Thus, in light of the crucial need to tackle hypertension, there are notable advantages of considering BP as a surrogate for outcome data.

Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging.

In certain healthcare settings, such as emergency or critical care units, where quick and accurate real-time analysis and decision-making are required, the healthcare system can leverage the power of artificial intelligence (AI) models to support decision-making and prevent complications. This paper investigates the optimization of healthcare AI models based on time complexity, hyper-parameter tuning, and XAI for a classification task. The paper highlights the significance of a lightweight convolutional neural network (CNN) for analysing and classifying Magnetic Resonance Imaging (MRI) in real-time and is compared with CNN-RandomForest (CNN-RF). The role of hyper-parameter is also examined in finding optimal configurations that enhance the model's performance while efficiently utilizing the limited computational resources. Finally, the benefits of incorporating the XAI technique (e.g. GradCAM and Layer-wise Relevance Propagation) in providing transparency and interpretable explanations of AI model predictions, fostering trust, and error/bias detection are explored. Our inference time on a MacBook laptop for 323 test images of size 100x100 is only 2.6 sec, which is merely 8 milliseconds per image while providing comparable classification accuracy with the ensemble model of CNN-RF classifiers. Using the proposed model, clinicians/cardiologists can achieve accurate and reliable results while ensuring patients' safety and answering questions imposed by the General Data Protection Regulation (GDPR). The proposed investigative study will advance the understanding and acceptance of AI systems in connected healthcare settings.

Development and assessment of the performance of a shared ventilatory system that uses clinically available components to individualize tidal volumes.

OBJECTIVES: To develop and assess a system for shared ventilation using clinically available components to individualize tidal volumes. DESIGN: Evaluation and in vitro validation study SETTING: Ventilator shortage during the SARS-CoV-2 pandemic. PARTICIPANTS: The team consisted of physicians, bioengineers, computer programmers, and medical technology professionals. METHODS: Using clinically available components, a system of ventilation consisting of two ventilatory limbs was assembled and connected to a ventilator. Monitors for each limb were developed using open-source software. Firstly, the effect of altering ventilator settings on tidal volumes delivered to each limb was determined. Secondly, the impact of altering the compliance and resistance of one limb on the tidal volumes delivered to both limbs was analysed. Experiments were repeated three times to determine system variability. RESULTS: The system permitted accurate and reproducible titration of tidal volumes to each limb over a range of ventilator settings and simulated lung conditions. Alteration of ventilator inspiratory pressures, of respiratory rates, and I:E ratio resulted in very similar tidal volumes delivered to each limb. Alteration of compliance and resistance in one limb resulted in reproducible alterations in tidal volume to that test lung, with little change to tidal volumes in the other lung. All tidal volumes delivered were reproducible. CONCLUSIONS: We demonstrate the reliability of a shared ventilation system assembled using commonly available clinical components that allows titration of individual tidal volumes. This system may be useful as a strategy of last resort for Covid-19, or other mass casualty situations, where the need for ventilators exceeds supply.

Analyzing PKC Gamma (+ 19,506 A/G) polymorphism as a promising genetic marker for HCV-induced hepatocellular carcinoma.

BACKGROUND: HCC is a major health concern worldwide. PKC gamma, a member of the conventional PKC subclass, is involved in many cancer types, but the protein has received little attention in the context of single nucleotide polymorphisms and HCC. Therefore, the study aims to investigate the association of PKC gamma missense SNP with HCV-induced hepatocellular carcinoma. METHODS: The PKC gamma nsSNPs were retrieved from the ENSEMBL genome browser and the deleterious nsSNPs were filtered out through involvingPredictSNP2, CADD, DANN, FATHMM, FunSeq2 and GWAVA. Among the filtered nsSNPs, nsSNP rs1331262028 was identified to be the most pathogenic one. Through involving I-TASSER, ProjectHOPE, I-Mutant, MUpro, mCSM, SDM, DynaMut and MutPred, the influence of SNP rs1331262028 on protein structure, function and stability was estimated. A molecular Dynamic simulation was run to determine the conformational changes in mutant protein structure compared to wild. The blood samples were collected for genotyping analysis and for assessing ALT levels in the blood. RESULTS: The study identified for the first time an SNP (rs1331262028) of PRKCG to strongly decrease protein stability and induce HCC. The RMSD, RMSF, and Rg values of mutant and wild types found were significantly different. Based on OR and RR values of 5.194 and 2.287, respectively, genotype analysis revealed a higher correlation between the SNP homozygous wild Typeform, AA, and the disease while patients with genotype AG have higher viral load. CONCLUSION: Outcomes of the current study delineated PKC gamma SNP rs1331262028 as a genetic marker for HCV-induced HCC that could facilitate disease management after further validation.

Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset.

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Insilico prediction and functional analysis of nonsynonymous SNPs in human CTLA4 gene.

The CTLA4 receptor is an immune checkpoint involved in the downregulation of T cells. Polymorphisms in this gene have been found to be associated with different diseases like rheumatoid arthritis, autosomal dominant immune dysregulation syndrome, juvenile idiopathic arthritis and autoimmune Addison's disease. Therefore, the identification of polymorphisms that have an effect on the structure and function of CTLA4 gene is important. Here we identified the most damaging missense or non-synonymous SNPs (nsSNPs) that might be crucial for the structure and function of CTLA4 using different bioinformatics tools. These in silico tools included SIFT, PROVEAN, PhD-SNP, PolyPhen-2 followed by MutPred2, I-Mutant 2.0 and ConSurf. The protein structures were predicted using Phyre2 and I-TASSER, while the gene-gene interactions were predicted by GeneMANIA and STRING. Our study identified three damaging missense SNPs rs1553657429, rs1559591863 and rs778534474 in coding region of CTLA4 gene. Among these SNPs the rs1553657429 showed a loss of potential phosphorylation site and was found to be highly conserved. The prediction of gene-gene interaction showed the interaction of CTlA4 with other genes and its importance in different pathways. This investigation of damaging nsSNPs can be considered in future while studying CTLA4 related diseases and can be of great importance in precision medicine.

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications.

Purpose: Respiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method. Methods: This paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise. Results: The Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes. Conclusion: The results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting. Supplementary Information: The online version contains supplementary material available at 10.1007/s40846-022-00700-z.

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection.

Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress.

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare.

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.

Development of a New Detection Algorithm to Identify Acute Coronary Syndrome Using Electrochemical Biosensors for Real-World Long-Term Monitoring.

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between -1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions.