Human exposure to uranium through drinking water and its detrimental impact on the human body organs.

Human exposure to high concentrations of uranium is a major concern due to the risk of developing numerous internal organ malignancies over time. In addition to the numerous attributes of uranium in the nuclear power industry, the radiological characteristics and chemical toxicity of uranium present a substantial risk to human health. This study aims to evaluate potential negative health impacts associated with the ingestion of uranium through drinking water in the Noida and Greater Noida region within the Gautam Buddha districts of Uttar Pradesh (India), due to extreme industrial revolution in this geological location. The mean concentration of uranium in drinking water of the examined area was estimated to range from 0.23 to 78.21 µg l-1. The hair compartment biokinetic model is used to estimate the retention and radiological doses of uranium in distinct organs and tissues. Studies on time-dependent factors revealed variations in uranium retention, with lower levels observed in the Gastrointestinal Tract (GIT) region and higher levels on cortical bone surfaces causes the skeletal deformities. The kidney, liver, and other soft tissues (OST) exhibited a non-saturation pattern in the retention of uranium via exposure of drinking water. The age-wise non-carcinogenic and carcinogenic doses were estimated for the health hazards studies. The outcome of this study will be useful for water resource management authorities to supply safe potable water to the local residents.

SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation.

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel and global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.

Investigation of heavy metal contamination and associated health risks in groundwater sources of southwestern Punjab, India.

Human body exposure to various toxic and non-toxic heavy metals in groundwater is a significant health concern, especially in developing countries. The present study was planned and carried out to appraise the potential health risks of eight heavy metals (Mn, Co, Cu, As, Se, Cd, Hg, and Pb) in different water sources of the Mansa and Muktsar districts of Punjab. The measurements of heavy metals were performed using the inductively coupled plasma mass spectrometry (ICPMS) technique. The health (carcinogenic and non-carcinogenic) risks and doses (ingestion and dermal) associated with exposure to heavy metals in water were estimated from the measured concentrations using USEPA guidelines. The average concentrations of heavy metals were observed in the order of Mn (13.93) > Cu (13.12) > Se (4.14) > As (3.28) > Hg (3.27) > Pb (1.29) > Co (0.20) > Cd (0.10) µg L-1. The results show that the Hg, Pb, As, and Se concentrations are above the guideline values of the World Health Organization (WHO) in 10.34%, 3.45%, 6.90%, and 6.90% locations, respectively. The high values of these heavy metals may be due to geogenic anthropogenic activities. The hazard quotients (non-carcinogenic risk) for ingestion and dermal exposures were observed in the range of 0.32-3.79 and 8.05 × 10-6-1.34 × 10-4, respectively. On the other hand, the carcinogenic health risks due to ingestion and dermal exposure were observed to be 0.02-0.38 and 6.67 × 10-8-1.15 × 10-6, respectively. The results of this study will be helpful to the drinking water supplying agencies, water resource development authorities, etc.

A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions.

This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users' feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

Subgroup Preference Neural Network.

Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups' multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction's probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.

Salient Object Detection Techniques in Computer Vision-A Survey.

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis.

Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.

Machine learning models help differentiate between causes of recurrent spontaneous vertigo.

BACKGROUND: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmography and audiovestibular tests, we developed machine learning models to separate these two disorders. METHODS: We recruited patients with VM or MD from a neurology outpatient facility. One hundred features from six "feature subsets": history, acute video-nystagmography and four laboratory tests (video head impulse test, vestibular-evoked myogenic potentials, caloric testing and audiogram) were used. We applied ten machine learning algorithms to develop classification models. Modelling was performed using three "tiers" of data availability to simulate three clinical settings. "Tier 1" used all available data to simulate the neuro-otology clinic, "Tier 2" used only history, audiogram and caloric test data, representing the general neurology clinic, and "Tier 3" used history alone as occurs in primary care. Model performance was evaluated using tenfold cross-validation. RESULTS: Data from 160 patients with VM and 114 with MD were used for model development. All models effectively separated the two disorders for all three tiers, with accuracies of 85.77-97.81%. The best performing algorithms (AdaBoost and Random Forest) yielded accuracies of 97.81% (95% CI 95.24-99.60), 94.53% (91.09-99.52%) and 92.34% (92.28-96.76%) for tiers 1, 2 and 3. The best feature subset combination was history, acute video-nystagmography, video head impulse test and caloric testing, and the best single feature subset was history. CONCLUSIONS: Machine learning models can accurately differentiate between VM and MD and are promising tools to assist diagnosis by medical practitioners with diverse levels of expertise and resources.

Characteristics of 222Rn and 220Rn equilibrium factors in the indoor environments.

Humans receive a significant portion (˃50%) of the total dose attributed to all the natural radiation sources from indoor radon (222Rn), thoron (220Rn), and their progeny. While progeny contributes an overwhelming part to the dose, in most surveys, only radon gas is measured because of the simplicity of measurement. Progeny concentration is usually estimated by multiplying gas concentration with an assumed factor, called the equilibrium factor, and taken from literature. Recently, results of the measurements of equilibrium factors for 222Rn and 220Rn were reported from various parts of the globe. In India, many such studies have been conducted in the current decade. The studies show a wide variation of equilibrium factors which suggests that they depend on environmental factors and measurement conditions. Therefore, they should be determined site specifically if accurate site-specific dose estimation is targeted. This paper summarizes concepts, definitions, and methods to determine equilibrium factors and reviews literature about reported equilibrium factors worldwide, focusing on data reported from India.

Estimation of radiation dose due to thoron and progeny inhalation in high background natural radiation area of Odisha, India.

Results of the preliminary measurements of indoor radon, thoron and progeny concentrations showed very high values of thoron concentrations in the eastern coastal region of Odisha, India. Therefore, measurements of thoron and its progeny concentrations were extended to a larger number of houses in this area for the assessment of the radiation dose received by the public. The measured values of thoron concentrations were used for the calculation of annual effective doses. The estimated values of the annual effective dose due to thoron exposure were observed in the range of 0.2-14.7 mSv. The estimated radiation doses responsible for thoron exposure were observed considerably high in the region. The results obtained are compared with those obtained in other studies performed so far in the study area and a review of different studies involving different measurement techniques is presented in the paper. The results of this study support the preliminary studies showing high values of thoron levels in the study area.

Recent advances in perioperative blood management.

Transfusion of blood and blood products has many adverse effects and should be done only if patient benefits outweigh the associated risks. Current understanding of blood transfusion has improved dramatically, revolutionising the care of surgical, trauma, obstetric and critically ill patients. Most guidelines advise a restrictive approach for stable patients with non-haemorrhagic anaemia for red blood cell transfusion. The rationale for red blood cell transfusion has historically been to improve oxygen transport capacity and consumption-related parameters in anaemic patients. Current understanding casts serious doubts about the true potential of red blood cell transfusions to improve these factors. There may not be any benefit from blood transfusion beyond a haemoglobin threshold of 7 g/dL. In fact, liberal transfusion may be associated with higher complications. Guideline-based transfusion policy should be adopted for the administration of all blood products including fresh frozen plasma, platelet concentrates and cryoprecipitate etc. This should be integrated with clinical judgement.

Comparison between peripheral nerve stimulator-guided serratus anterior plane block versus transversus abdominis plane block for postoperative analgesia in laparoscopic cholecystectomy under general anaesthesia: A double-blind randomised controlled trial.

Background and Aims: Laparoscopic cholecystectomy (LC) is a widely accepted surgical procedure associated with postoperative pain. This study was done to compare peripheral nerve stimulator (PNS)-guided serratus anterior plane block (SAP) and transversus abdominis plane (TAP) block for postoperative analgesia for patients undergoing LC. Methods: Following approval from the ethical committee, 70 patients for LC were randomly assigned to Group S: SAP block and Group T: TAP block. The blocks were performed under PNS guidance, and 20 ml of 0.375% ropivacaine was administered. The severity of pain was measured using a visual analogue scale (VAS). The study's primary objective was the evaluation of the postoperative VAS score. The time of the first dose of rescue analgesia and total tramadol consumption for 24 h postoperatively were secondary objectives. All the statistical calculation was done using statistical analyses for Social Sciences for Windows version 23.0 (IBM Corp, NY, USA). Results: Lower VAS score was seen in patients of TAP block at rest as well as movement at 6 h (P = 0.001), 12 h (P = 0.001) and 18 h (P = 0.001) postoperatively compared with SAP. The TAP group showed a significantly increased time of first rescue analgesic compared to the SAP group (7.97 ± 0.51 vs. 5.89 ± 1.45, P = 0.001). Tramadol usage was significantly higher in the SAP group than in the TAP group (128.9 ± 36.22 vs. 72.43 ± 44.80, P = 0.001). Conclusion: TAP block guided by the PNS improves postoperative pain with less tramadol consumption and during the postoperative period without significant complications.

Explainable hybrid word representations for sentiment analysis of financial news.

Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are their long-term benefits. However, it is challenging to analyze the sentiments of texts related to the financial domain, given the enormous amount of information available. The existing approaches are unable to capture complex attributes of language such as word usage, including semantics and syntax throughout the context, and polysemy in the context. Further, these approaches failed to interpret the models' predictability, which is obscure to humans. Models' interpretability to justify the predictions has remained largely unexplored and has become important to engender users' trust in the predictions by providing insight into the model prediction. Accordingly, in this paper, we present an explainable hybrid word representation that first augments the data to address the class imbalance issue and then integrates three embeddings to involve polysemy in context, semantics, and syntax in a context. We then fed our proposed word representation to a convolutional neural network (CNN) with attention to capture the sentiment. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of financial news. The experimental results also show that the proposed model outperforms several baselines of word embeddings and contextual embeddings when they are separately fed to a neural network model. Further, we show the explainability of the proposed method by presenting the visualization results to explain the reason for a prediction in the sentiment analysis of financial news.

Occurrences, sources and health hazard estimation of potentially toxic elements in the groundwater of Garhwal Himalaya, India.

High concentrations of potentially toxic elements (PTEs) in potable water can cause severe human health disorders. Present study examined the fitness of groundwater for drinking purpose based on the occurrence of nine PTEs in a heavy pilgrim and tourist influx region of the Garhwal Himalaya, India. The concentrations of analyzed PTEs in groundwater were observed in the order of Zn > Mn > As > Al > Cu > Cr > Se > Pb > Cd. Apart from Mn and As, other PTEs were within the corresponding guideline values. Spatial maps were produced to visualize the distribution of the PTEs in the area. Estimated water pollution indices and non-carcinogenic risk indicated that the investigated groundwater is safe for drinking purpose, as the hazard index was < 1 for all the water samples. Assessment of the cancer risk of Cr, As, Cd, and Pb also indicated low health risks associated with groundwater use, as the values were within the acceptable range of ≤ 1 × 10-6 to 1 × 10-4. Multivariate statistical analyses were used to describe the various possible geogenic and anthropogenic sources of the PTEs in the groundwater resources although the contamination levels of the PTEs were found to pose no serious health risk. However, the present study recommends to stop the discharge of untreated wastewater and also to establish cost-effective as well as efficient water treatment facility nearby the study area. Present work's findings are vital as they may protect the health of the massive population from contaminated water consumption. Moreover, it can help the researchers, governing authorities and water supplying agencies to take prompt and appropriate decisions for water security.

Severe post-burn neck contracture release and skin graft harvest using tumescent local anaesthesia as the sole anesthetic technique.

Severe post-burn contractures in the neck often cause anatomical distortion and restriction of neck movements, resulting in varying degrees of difficulty in airway management. Any mode of anesthesia that may obviate the need for imperative airway control may be desirable in such situations in which a difficult airway may be anticipated. Here we present one such situation where tumescent local anesthesia was employed to manage a case of severe post-burn neck contractures posted for contracture release and split-skin grafting. The other benefits of this method were minimal blood loss and excellent postoperative analgesia. In conclusion, it can be emphasized that the application of tumescent anesthesia is an important anesthetic tool in patients with predicted difficult airway management.

Effects of IV dexmedetomidine as a pre-medication on clinical profile of bupivacaine spinal anaesthesia in lower abdominal surgeries: a randomized clinical study.

Introduction: to evaluate the effects of intravenous (IV) dexmedetomidine as a pre-medication on clinical profile of bupivacaine spinal anaesthesia in lower abdominal surgeries. Methods: this prospective randomized double blind study was done on 60 patients with ASA grade I/II undergoing lower abdominal surgeries under bupivacaine spinal anaesthesia. They were allocated to group-1 and group-2. Group-1 (control group) received normal saline and group-2 (study group) received IV dexmedetomidine 1 µg/kg over 10 min as premedication. Five minutes after premedication, subarachnoid block (SAB) was given with 3 ml of 0.5% hyperbaric bupivacaine following which sensory and motor blockade, hemodynamic changes, sedation, and complications of the surgery were recorded and this data was analyzed statistically using χ2 test, corrected χ2 test, Fisher´s exact test, and test of proportion (Z-test). Results: the results of the present study showed that in group-2 there was significant decrease in the onset of sensory block, higher level of sensory blockade achieved, less time required to attain highest level of anaesthesia, prolonged time required for 2 dermatomal regression, prolonged duration of sensory blockade, prolonged duration of analgesia, decrease in onset of motor blockade, no significant increase in duration of motor blockade, there was overall hemodynamic stability except for few cases of bradycardia responding to atropine and hypotension responding to mephentramine, adequate and acceptable intraoperative sedation, and significantly less incidence of shivering in perioperative period. Conclusion: IV infusion of dexmedetomidine 1 µg/kg body weight prior to SAB can be recommended to achieve better sensory blockade and adequate hemodynamic stability and sedation.

A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients.

Coronavirus disease-19 (COVID-19) is a very dangerous infectious disease for the entire world in the current scenario. Coronavirus spreads from one person to another person very rapidly. It spreads exponentially throughout the globe. Everyone should be cautious to avoid the spreading of this novel disease. In this paper, a fuzzy rule-based approach using priority-based method is proposed for the management of hospital beds for COVID-19 infected patients in the worst-case scenario where the number of hospital beds is very less as compared to the number of COVID-19 infected patients. This approach mainly attempts to minimize the number of hospital beds as well as emergency beds requirement for the treatment of COVID-19 infected patients to handle such a critical situation. In this work, higher priority has given to severe COVID-19 infected patients as compared to mild COVID-19 infected patients to handle this critical situation so that the survival probability of the COVID-19 infected patients can be increased. The proposed method is compared with first-come first-serve (FCFS)-based method to analyze the practical problems that arise during the assignment of hospital beds and emergency beds for the treatment of COVID-19 patients. The simulation of this work is carried out using MATLAB R2015b.

Comparison of Hemodynamic Changes in Patients Undergoing Laparoscopic Cholecystectomy using rocuronium and vecuronium for Intubation and Maintenance under General Anesthesia.

Context: The context of the study is to compare and find better muscle relaxant between rocuronium and vecuronium for intubation and maintenance under general anesthesia in patients undergoing laparoscopic cholecystectomy. Aims: The aim of the study is to measure intubating condition, hemodynamic changes during intubation and also during maintenance of general anesthesia and to record complications, if any. Settings and Design: A prospective clinical study conducted in the Department of Anesthesiology in tertiary care center. Materials and Methods: A total of 100 patients of the American Society of Anesthesiologists Classes Grade I and II were planned for laparoscopic cholecystectomy were divided into two groups of 50 each. The subjects in the control and study group were put under anesthesia using injection propofol 2.0 mg.kg-1 along with injection vecuronium 0.10 mg.kg-1 and injection propofol 2.0 mg.kg-1 along with injection rocuronium 0.60 mg.kg-1, respectively. Hemodynamic monitoring and oxygen saturation (SPO2) were recorded at various intervals. Statistical Analysis Used: All the collected data were imported into Microsoft Excel, and the statistical analysis was done by using SPSS 25.0 version. Results: The mean heart rate before and after carboperitoneum at different time intervals and before and after extubation was significantly lower in vecuronium group. The mean systolic blood pressure and mean arterial pressure at 1 and 5 minutes after extubation were significantly more among vecuronium group. Conclusions: Rocuronium is reasonably cardiostable, produces excellent intubation conditions, has a shorter duration of action, and shows minimal cumulative effect.