Intrusive Procedures Research Articles

Islamic and Scientific Views on The Law of Using Urine Samples as Candidates for Early Detection of Cancer

Abstract
 The growing prevalence of cancer patients is a significant obstacle to global human growth. Prostate carcinoma is one of the types of cancer that kills the most men. The second most frequently diagnosed kind of carcinoma is prostate cancer (Pca), which is also the sixth biggest cause of death in males globally. Urine can be a candidate for early diagnosis of non-invasive cancer, however the rule on the use of urine as a sample in Islam has not yet been thoroughly addressed. Prostate cancer testing is still an intrusive procedure. The goal of this study was to ascertain the legality of using urine samples as potential candidates for the early identification of cancer from both an Islamic and scientific standpoint. Meta-analysis is combined with a qualitative methodology. According to the study's findings, urine may be a candidate for the early detection of cancer. Legally speaking, urine can be used as long as it is not applied directly to patients either topically or orally in order to prevent it from becoming haram. This is because the authors were unable to find any evidence to support the prohibition of using urine on patients.
 

Performing the classification of pulsation cardiac beats automatically by using CNN with various dimensions of kernels

The clinical indication of arrhythmia identifies specific aberrant circumstances in heart pumping that may be detected using electrical impulses during conduction or by allowing a little amount of current to travel through the electrodes, disrupting the cardiac muscle's resistance. The electrocardiogram (ECG) is one of the most important instruments for detecting cardiac arrhythmia since it is the most least intrusive and effective procedure. Physically or visually inspecting the heart is time-consuming and difficult, hence the development of computer aided diagnosis (CAD) is being developed to aid clinical decision-making. In this suggested research, a convolutional neural network (CNN)-based approach is used to automate the heartbeat classification process in order to identify cardiac arrhythmia. The improved enhancement of CNN structure has been implemented in this suggested research. The feature maps are then subjected to the max pooling process. Finally, feature maps are generated by concatenating kernels of different sizes and delivering them as an input to the fully linked layers. The MIT BIH arrhythmia database is used to implement this approach, and the total average accuracy is 99.21%. The proof of the suggested study's efficiency and efficacy in identifying cardiac arrhythmia has also been done via an experimental comparison

Intelligent Healthcare Provided by Nano-Enhanced Biosensing Systems: Progress in COVID-19 Management via the Artificial Neural Network Approach

Biosensors using opto electronics mechanisms are evolving as efficient (sensitive and selective) and low-cost analytical diagnostic devices for early-stage disease diagnosis, which is crucial for person-centered health and wellness management. Due to advancements in nanotechnology in the areas of sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at the point-of-care (POC), personalized diagnostics are now possible, allowing doctors to tailor tests to each patient’s unique disease profile and management requirements. Innovative biosensing technology is being pushed as the diagnostic tool of the future because of its potential to provide accurate results without requiring intrusive procedures. Because of this, this visionary piece of writing explores analytical methods for managing personalised health care that can enhance the health of the general population. The end goal is to take control of a healthier tomorrow as soon as possible. Right now, the most crucial part of controlling the COVID-19 pandemic, a potentially fatal respiratory viral disease, is the rapid, specific, and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-CoV-2) protein.

Fungal Infections in Neonatal Intensive Care, An Overview

fungal infections in immature newborns have become more common in the previous two decades, especially when they are admitted to a Neonatal Intensive Care Unit (NICU). Thus, it is projected that the mortality rate of children under the age of five is estimated to be about 6 million children a year, with even about 40% of these mortalities during the newborn period. Considering the increased prevalence of death rates related to invasive fungal infections, proper preventative medication is still critical in their treatment. The proper utilization antifungals medicines are critical in the primary prevention and management of invasive fungal infection in newborns; however, there are no specific guidelines to determine the proper medication selection. The most appropriate cure of fungal infection in this population necessitates extensive research into the pharmacokinetic, tolerability, and effectiveness of antifungal medicines. This paper aims to overview epidemiology, diagnosis and management of neonatal fungal infections.Children's invasive fungal infections appear to have become more common during the previous few decades. Children with primary and secondary immunodeficiencies are at danger, as well as newborns. The most often isolated microbes are Candida and Aspergillus species. Improved outcomes depend on prompt diagnosis and administration of the proper antifungal medication. Traditional methods take a lot of time, and obtaining relevant sample material in a paediatric setting may require intrusive procedures. The improvements in detection and quick species identification are summarised in this paper. In light of the antifungal spectrum of the available drugs and the distinct pharmacokinetic features in various age groups, the current antifungal therapy options for newborns and kids are next examined.

Implementation of Non-Invasive Blood Glucose Monitoring System

In the case of diabetes, measuring blood sugar levels is made challenging by the need to pick one's finger. Laboratory testing and one-touch glucometers are intrusive procedures that increase the risk of blood-related illnesses. In the current work, we propose a novel edge device with IoMT (Internet-of-Medical-Things) capabilities for precise, non-invasive blood glucose monitoring to solve this crucial issue. In this study, a NIR (Near-Infrared) spectroscopy method is used to identify the glucose molecule in human blood at two wavelengths (940 nm, and 1300 nm). The cutting-edge gadget known as iGLU is based on high-accuracy ML (Machine Learning) models and NIR spectroscopy. For accurate measurement, a DNN (Deep Neural Network) model and an ideal multiple polynomial regression model have been provided. An open IoT platform is used to evaluate the proposed gadget, and blood glucose levels are then saved there for endocrinologist remote monitoring. For device validation, the blood glucose measurements obtained from the invasive device and the projected blood glucose levels have been compared. The AvgE (Average Error) & MARD (Mean Absolute Relative Difference) of the predicted blood glucose concentration levels were determined to be 4.66 percent and 4.61 percent, respectively. There is a 0.81 regression coefficient. An accurate and economical solution for smart healthcare is offered by the suggested spectroscopic non-invasive gadget.

Evaluating the Effect of Tideglusib-Loaded Bioactive Glass Nanoparticles as a Potential Dentine Regenerative Material.

Dental pulp treatment is the least intrusive procedure currently available for preserving the vitality of the pulp. Several studies are underway to improve the bioactivity of pulp capping materials. Tideglusib isa potent anti-inflammatory, antioxidant, and a regenerative drug developed against Alzheimer’s disease and has been shown to be effective in the treatment of dental cavities. However, its bioactive properties encapsulated within the nanoparticles as a component of pulp capping material are largely unknown. In this study, tideglusib-loaded bioactive glass nanoparticles were synthesized (tideglusib-BgNPs) and mixed at various concentrations into the calcium silicate cement to testits physiomechanical and bioactivitiescompared with biodentine (control). The calcium silicate cement with 10wgt% tideglusib-BgNPs showed comparable physiomechanical properties to that of biodentine. Additionally, the assessment of cytotoxicity and bioactivity (cell proliferation, wound healing, and cell migration assays) showed increased bioactivity in terms of better wound healing, increased proliferation, and better migration of human dental pulp stem cells than biodentine. These findings suggest new opportunities to use tideglusib-BgNPs in pulp therapy.

COVID-19 vaccine hesitancy, chemical retraumatization, and madness

Protecting the Section 7 autonomy rights of Canadian consumers/survivors/ex-patients and the mad (c/s/x/m), as enshrined in the Canadian Charter of Rights and Freedoms, with past experiences of enduring intrusive procedures forced upon them, to decline immunization against COVID-19 is a mad liberation issue, to which Mad Studies must not shut its eyes. To some c/s/x/m, vaccine mandates mirror traumatic moments from their psychiatric past, when they had their autonomy stripped, and their right to legal consent infringed upon by physicians of the mind claiming diminished capacity. In this article, I propose expanding categories of medical exemptions to consider mental health contexts, and accommodations be provided to those with diverging minds who are vaccine skeptic. I conclude with a clarion call to madvocates to stand against the violence of coercing vaccinations.

Construction of porous calcite structure using microbially induced calcite precipitation

Sporosarcina pasteurii is a well-known ureolytic bacteria that promotes the microbially induced calcite precipitation (MICP) process for several environmental and engineering purposes. In our work, for the first time, MICP has been implemented to form pure, porous calcite structures. The maximum urease activity of S. pasteurii was 1.91 mM urea hydrolyzed min−1 at the late-exponentially phase. A reactor has been designed to achieve semi-continuous treatments, and reagents were introduced to it by a peristaltic pump. A new alternating injection pattern was adopted to obtain well-distributed precipitation. SEM images of treated structures indicated the shapes of CaCO3 crystals at a microscale level and revealed the successful formation of calcite-calcite bonds, which has contributed to achieving a consolidated porous structure. Such synthetic pure, porous calcite structure has found growing industrial interest where chemically intrusive procedures (e.g., acidizing, fracturing) should be performed on expensively excavated core samples.

Early Detection of Cardiovascular Diseases using Deep Learning Feature Fusion and MRI Image Analysis

Deaths from cardiovascular disease (CVD) are more common than any other kind of mortality in the world. Electrocardiograms, two-dimensional echocardiograms, and stress tests are only a few of the diagnostic tools available to combat the rising incidence of cardiovascular disease. Since the electrocardiogram (ECG) is a clinical therapeutic agent that does not need any intrusive procedures, it may be used to diagnose cardiovascular disease (CVD) early and prescribe the appropriate treatment to prevent its fatal consequences. However, it may be time-consuming and demanding for a physical examination to interpret all these signals from various pieces of equipment, especially if they are non-stationary and repeating. It is necessary to use a computer-assisted model for rapid and precise prediction of CVDs since the Heart Signal from an ECG machine is not a stationary sign, the differences may not be repeated and may manifest at different intervals. In this paper, we offer a fully deep convolutional neural network-based automated diagnosis technique for cardiovascular illness. In order to extract those form characteristics from the Kaggle cardio-vascular disease dataset, CVD-MRI is employed in this detection method. In this case, the risk of cardiovascular disease is estimated using a completely deep convolution neural network and deep learning convolution filters (CVD). The suggested operation's main goal is to improve the accuracy of completely deep convolution neural network while simultaneously reducing the complexity of the computation and the cost function. Accuracy of 88 percent is achieved by the proposed fully deep convolutional neural network.

Raman spectroscopic insight into osteoarthritic cartilage regeneration by mRNA therapeutics encoding cartilage-anabolic transcription factor Runx1.

While joint arthroplasty remains nowadays the most popular option available to repair chronically degenerated osteoarthritic joints, possibilities are recently emerging for regeneration of damaged cartilage rather than its replacement with artificial biomaterials. This latter strategy could allow avoiding the quite intrusive surgical procedures associated with total joint replacement. Building upon this notion, we first apply Raman spectroscopy to characterize diseased cartilage in a mice model of instability-induced knee osteoarthritis (OA) upon medial collateral ligament (MCL) and medial meniscus (MM) transections. Then, we examine the same OA model after cartilage regeneration by means of messenger RNA (mRNA) delivery of a cartilage-anabolic runt-related transcription factor 1 (RUNX1). Raman spectroscopy is shown to substantiate at the molecular scale the therapeutic effect of the Runx1 mRNA cartilage regeneration approach. This study demonstrates how the Raman spectroscopic method could support and accelerate the development of new therapies for cartilage diseases.

On proposing a non-intrusive device and methodology to monitor motor degradation

Monitoring the degradation of induction motors is an essential concern for industries, especially when there are high costs related to maintenance and their stops. Nowadays, there are many non-invasive ways to monitor the health status of induction motors, including sensors to measure temperature, magnetic field, vibration, current, and power. However, installing these sensors may not be a simple task since intrusive procedures such as engine disassembling, wire separation, and engine interruption are commonly observed. Thus, we propose MUCLA (Multiphase Clamp and Tracking - patent pending) as a new device and methodology for monitoring motor degradation by analyzing its stator current. MUCLA introduces a non-invasive current sensors array on a proposed clamp in the hardware perspective, while a spectral tracking methodology refers to our novelty in the software viewpoint. For end-users, the main MUCLA contribution relies on the combination of the simplicity of installation and the possibility of using the proposed device even when considering Variable-Frequency Drive powered engines. The results obtained with both simulation and practical tests show encouraging results over different input workloads and emulated degradation. Compared to the current initiatives, we can classify MUCLA as a better solution for industrial plants in terms of installation time, installation complexity, and cost.

Two-Step Predict and Correct Non-Intrusive Parametric Model Order Reduction for Changing Well Locations Using a Machine Learning Framework

The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a two-step PMOR procedure, where, in the first step, a Proper Orthogonal Decomposition (POD)-based strategy that is non-intrusive to the simulator source code is introduced, as opposed to the convention of using POD as a simulator intrusive procedure. The non-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML)-based framework used with POD. The features of the ML model (Random Forest was used here) are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoid simulator access for the time dependency of the solutions. The proposed PMOR method is global, since a single reduced-order model can be used for all the well locations of interest in the reservoir. We address the major challenge of the explicit representation of the well location change as a parameter by introducing geometry-based features and flow diagnostics-inspired physics-based features. In the second step, an error correction model based on reduced model solutions is formulated to correct for discrepancies in the state solutions at well grid blocks expected from POD basis for new well locations. The error correction model proposed uses Artificial Neural Networks (ANNs) that consider the physics-based reduced model solutions as features, and is proved to reduce the error in QoI (Quantities of Interest), such as oil production rates and water cut, significantly. This workflow is applied to a simple homogeneous reservoir and a heterogeneous channelized reservoir using a section of SPE10 model that showed promising results in terms of model accuracy. Speed-ups of about 50×–100× were observed for different cases considered when running the test scenarios. The proposed workflow for Reduced-Order Modeling is “non-intrusive” and hence can increase its applicability to any simulator used. Additionally, the method is formulated such that all the simulation time steps are independent and hence can make use of parallel resources very efficiently and also avoid stability issues that can result from error accumulation over time steps.

The dawn of radiofrequency catheter ablation for cardiac arrhythmias

More than 50 years ago, surgeons had attempted to find less intrusive surgical procedures to interrupt the atrioventricular (AV) conduction (Online Supplement). Later, in the late 70s and the early 80s when antiarrhythmic drug therapy for supraventricular or ventricular tachyarrhythmias failed, the last resort was map-guided antitachycardia surgery.

The role of emotion covariation and psychological flexibility in coping with chronic physical pain: an integrative model

Objective This study focussed on the mutual role of emotion covariation and psychological flexibility in understanding the reciprocal effects of chronic pain and psychological distress. Design A longitudinal design was applied with a sample of 177 adults (mean age = 58.3, 57.1% women) suffering from chronic back/neck pain. Main outcome measures Assessments were conducted at three timepoints: pain and psychological distress upon referral to a pain clinic (T1); emotion covariation (the correlation between daily reports of positive and negative emotions) and psychological flexibility (self-reported ability to withstand unpleasant experiences while acting upon personal values) during the weeks before undergoing an intrusive procedure (T2); pain and psychological distress one day prior to the procedure (T3). Results T2 psychological flexibility and emotion covariation mediated the effect of T1 pain on T3 psychological distress. However, T2 psychological flexibility and emotion covariation did not mediate the effect of T1 psychological distress on T3 pain. Conclusion The findings highlight two complimentary mechanisms – emotion covariation and psychological flexibility – that mediate the relationship between pain and psychological distress for people with chronic back/neck pain. The findings inform future research on the effects of intervening on these mechanisms, which may lead to clinical interventions aimed at improving coping with pain.

Use of Urinary Reagent Strips in Testing Cerebrospinal Fluid For Meningitis - A Review

Analysis of cerebrospinal fluid (CSF) is needed to make a meningitis diagnosis to assist in the propermanagement of patients. There is a good correlation between the results of reagent strip test and laboratorymethods of CSF protein, sugar, and leukocyte estimation. CSF supplies nutrients to tissue in the nervoussystem. CSF eliminates waste products from cerebral metabolism. Cerebrospinal fluid has three mainfunctions: CSF protects against trauma on the brain and spinal cord. Meningitis is a rare infection thataffects the delicate membranes called meninges, which cover the brain and spinal cord. Index test wasdone for testing meningitis using urinary reagent strips. An intrusive outpatient procedure used to removea sample of cerebrospinal fluid (CSF) from the spine’s subarachnoid area is a lumbar puncture (LP), alsocalled a spinal tap. A urinary test strip or dipstick is a simple medical tool used in normal urinalysis to assessabnormal differences in a patient’s urine. There is a good correlation between the results of reagent strip testand laboratory methods of CSF protein, sugar, and leukocyte estimation. The review gives a clear view andknowledge about the usage of urinary reagent strips in measuring CSF for meningitis.

Meta-modeling of a simulation chain for urban air quality

Urban air quality simulation is an important tool to understand the impacts of air pollution. However, the simulations are often computationally expensive, and require extensive data on pollutant sources. Data on road traffic pollution, often the predominant source, can be obtained through sparse measurements, or through simulation of traffic and emissions. Modeling chains combine the simulations of multiple models to provide the most accurate representation possible, however the need to solve multiple models for each simulation increases computational costs even more. In this paper we construct a meta-modeling chain for urban atmospheric pollution, from dynamic traffic modeling to air pollution modeling. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a non-intrusive reduced order scheme, and study its application to a full chain of operational models. Reduced basis are constructed using principal component analysis (PCA), and the concentration fields are approximated as projections onto this reduced space. We use statistical emulation to approximate projection coefficients in a non-intrusive manner. We apply a multi-level meta-modeling technique to a chain using the dynamic traffic assignment model LADTA, the emissions database COPERT IV, and the urban dispersion-reaction air quality model SIRANE to a case study on the city of Clermont-Ferrand with over 45, 000 daily traffic observations, a 47, 000-link road network, a simulation domain covering 180,text {km}^2. We assess the results using hourly NO_2 concentration observations measured at stations in the agglomeration. Computational times are reduced from nearly 3 h per simulation to under 0.1 s, while maintaining accuracy comparable to the original models. The low cost of the meta-model chain and its non-intrusive character demonstrate the versatility of the method, and the utility for long-term or many-query air quality studies such as epidemiological inquiry or uncertainty quantification.

Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

Treating malignant growth in the beginning times gives greater treatment choices, less intrusive medical procedure and expands the endurance rate. Finding lung disease just as liver malignancy at a beginning period is a difficult assignment since there are hardly any manifestations right now larger part of the cases are analyzed in later stages. In this paper, an augmented method of segmentation is proposed using the Fuzzy K-means algorithm to observe the initial stage of lung cancer from the human chest X-Ray images. Initially, the investigator used strategies like Histogram Equalization for image improvement, Watershed technique for segmentation and Edge detection for Extraction, etc. Existing Approaches decline to provide certainty in a problem-solving time operation. The datasets are collected from LIDC-IDRI and Kaggle. In this, 100 training dataset input images are taken for training the model: 80 Cancerous images and 20 non-cancerous images. To evaluate the data 20 images are taken. The detection means classifying tumour into three classes (i) non-cancerous tumour, (ii) Less affected tumour (iii) Highly affected tumour. Hence, a new technique to detect lung carcinoma nodules using median filters, Fuzzy K-means clustering for segmentation and SVM (Support Vector Machine) as the classifier is planned on this work. Hence, this way is profitable to the medical instruments producing industries and additionally guide the radiologist for the early analysis of cancer.

Ensuring the Epidemiological Safety of Bladder Catheterization

Urinary tract infections (UTI) represent actual form of healthcare-associated infections. The main risk factor of UTI is bladder catheterization. Currently, there are not valid and systematic data on the frequency of atheter-associated urinary tract infections (CAUTI) in Russia, because standard case determination for the official registration is not being used and, consequently, no full-scale records and registration of this nosological form is being maintained. Research objective: to estimate incident rate and potential risk factors of UTI in patients when making bladder catheterization in the urology department at the multidisciplinary clinic for adults. In the course of diagnostic and treatment process it has been determined that bladder catheterizations ratio among all indoor patients amounted to 49,6%; in 2,3% of cases intrusive procedure was carried out urgently in the form of a single urine drainage; the overwhelming majority of catheterized patients were men – 61,5%; catheterization of 9,7% of patients has been lasted for more than 7 days; 48,5% of catheterized patients were individuals of 60 years old and over. According to retrospective analysis data with the use of standard case determination it was found out that CAUTI case rate amounted to 10,68 (95% CI 7,08–14,28) per 100 patients; stratified index of CAUTI incidence amounted to 32,86 per 1000 patient-days of catheterization (95% CI: 21,66–44,06). Epidemical process of CAUTI is characterized by high intensity, predominance of S. epidermidis. The result of the work was the development of a standard operating procedure for setting up a urethral catheter, which includes 8 stages of epidemiologically safe catheterization of the bladder. The proposed SOP on catheterization focuses on individual actions that require mandatory implementation and control.

Fertiloscopy in women with unexplained infertility: Long-term cumulative pregnancy rate

ObjectiveFertiloscopy is as safe as laparoscopy in literature, but we don’t know its relevance in women with unexplained infertility (UI). Our objective was to assess the effects of fertiloscopy procedures on the outcomes of subsequent pregnancy occurrences in patients with UI. MethodsRetrospective, single-center study of all patients followed up after fertiloscopy procedures between 2002 and 2007. The occurrence and outcome of pregnancies were studied in the five years following the procedure. Results124 fertiloscopies were performed. Pelvic exploration was considered as sufficient in 83.8% of cases, of which no abnormalities were found in 78.2%. Laparoconversions occurred for 19 patients (16.5%). The pregnancy rate at five years was 76.9%. The mean delay for pregnancy occurrence was 10.7 months (±17). ConclusionThe pregnancy rate in our study is similar to that after laparoscopy. Fertiloscopy, a less intrusive procedure, should be considered as a reliable option for the management of patients with UI.

Survival, Growth, and Tag Retention in Estuarine Fishes Implanted with Passive Integrated Transponder (PIT) Tags

We examined the effects of passive integrated transponder (PIT) tags on the survival, growth, and tag retention for two common estuarine fishes, mummichog (Fundulus heteroclitus) and pinfish (Lagodon rhomboides). A separate laboratory experiment was conducted for each species where individuals, ranging in initial size from 45 to 82 mm standard length (SL), were implanted with 8-mm or 12-mm PIT tags. Across both experiments, the probability of survival for tagged individuals ranged from 82 to 92%, probability of tag retention ranged from 82 to 91%, and probability of survival and tag retention ranged from 73 to 83%. For both species, most mortality and tag loss occurred for small (< 60 mm) individuals tagged with 12-mm tags, and generally early in the experiments. There was little effect of tagging on growth for individuals of either species compared to individuals in untagged control treatments. We recommend a minimum length of 55 mm SL for mummichog and 65 mm SL for pinfish when tagging with 8-mm or 12-mm PIT tags, as almost all fishes above these sizes survived and retained their tags. Direct comparison of our results with other studies was difficult because of variation in the tagging, husbandry, and experimental protocols used in experiments. A comparison among these different protocols would be useful to determine the least intrusive, most effective, and optimal procedure for a given species or group of estuarine fishes.