Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments.

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

Awareness About Ebola Virus Disease Among the Health Care Professionals in Karachi, Pakistan.

Background: Ebola virus disease is an acute and life-threatening illness, which, if untreated, is fatal. It was first discovered in 1976, which aware the world with sporadic outbreaks of Ebola in some parts of Africa. According to Centers for Disease Control and Prevention (CDC), the natural reservoir for Ebola virus remains unknown; however, it is yet to be affirmed that the natural habitat is animal-borne where bats are most likely to carry over this virus. Therefore, the aim of this study was to estimate awareness of health care professionals as they serve as the integral part of our health care system. Methods: A cross-sectional survey was conducted among 149 health care workers (HCWs) in Civil Hospital of Karachi from June 2015 to August 2015. The study participants were randomly selected individuals who were students of and/or were working in the university's affiliated tertiary care hospital in Karachi, Pakistan. Results: Baseline characteristics of the study participants are shown in Table 1. Median age of the participants is 21 (range: 17-24) years. Female preponderance was found to be higher (104; 69.8%) as compared with the males (45; 30.2%). Discipline of majority of the study participants was medical technology (80; 53.7%), followed by nurses (38; 25.5%) and doctors (31; 20.8%). Majority of the study respondents were undergraduate medical students (60; 75%) as compared with medical technologist and the nurses (17; 24.6%) with a P value < .001 as shown in Figure 1. The mean for correct responses was 8.43 ± 4.08 (range: 3-17). Appropriate knowledge was observed in 84 (56.4%) responders and inappropriate knowledge was observed in 65 (43.6%) of the study respondents. Conclusion: Our study concludes that there is an unsatisfactory knowledge about Ebola virus disease among health care professionals. Moreover, public health authorities should signify the importance of prevention against Ebola virus disease not only among the health care professionals but also into the communities through mass media and awareness campaigns which can thus halt the panic and incidence of Ebola virus outbreaks in coming decades.

Synergistic fungicidal activity with low doses of eugenol and amphotericin B against Candida albicans.

Candida albicans frequently causes variety of superficial and invasive disseminated infections in HIV infected patients. Further, the emergence of non albicans species causing candidiasis predominantly in patients with advanced immune-suppression and drug resistance brings great apprehension. Hence, in this study we evaluate the capability of eugenol (EUG), a natural compound in combination with less toxic concentrations of amphotericin B (AmpB) for enhanced antifungal effects and reduced toxicity. Antifungal activity and time-kill assay were employed according to Clinical Laboratory Standard Institute (CLSI) guidelines with minor modifications on clinical isolates of Candida albicans. To confirm the synergistic interaction of EUG and AmpB, checkerboard experiments were employed. Interestingly, EUG-Amp B combination shows many fold higher anti-candida activity compared to single component treatment. Furthermore, our results depicts reactive oxygen species (ROS) driven killing and mitochondrial hyperpolarisation on treatment. Our data also suggests inhibition of calcium channel by EUG and predicts longer retainment of AmpB. Pronounced cellular damage was observed with combination treatment than to EUG and AmpB alone. Our finding is helpful for the removal of toxic concentrations of antifungal agents.

Effects of two functionally important SLCO1B1 gene polymorphisms on pharmacokinetics of atorvastatin.

Organic anion transporter polypeptide 1B1 (OATP1B1) encoded by (SLCO1B1) gene, an uptake transporter involved in the transport of drugs and endogenous compounds and located in hepatocyte sinusoidal membrane. Objective of study was to investigate the effects of two functionally significant SNPs (388A>G and 521T>C) and their respective genotypes of SLCO1B1 gene encoding OATP1B1 on the pharmacokinetics of atorvastatin. A total of 100 subjects divided into 6 groups as per their genotype profile were recruited. A single dose of 80mg atorvastatin was orally administered and plasma concentration measured up to 48 hours. The 388A>G and 521T>C genotypes were significantly associated with each other when compared for AUC and Cmax but exhibited no significant variations in Tmax and t1/2. 521 SNP is rather more strongly associated with altered pharmacokinetics of atorvastatin when compared with the 388 SNP, though the homozygous bi-allelic variant of 388 SNP also exhibited a fairly significant variation along with homozygous bi-allelic variant of 521 SNP. The inter-individual variation in pharmacokinetics can be explained by SLCO1B1 polymorphism.

GeneXpert assay for rapid detection of Mycobacterium tuberculosis complex in respiratory specimens from a high TB endemic area of Pakistan.

Tuberculosis is a global health problem, and its early diagnosis is the ultimate strategy for prevention and control. The current study was undertaken to evaluate conventional and molecular diagnostic assays for the detection of mycobacteria in pulmonary tuberculosis (TB) patients from Khyber Pakhtunkhwa region of Pakistan. A total of 259 clinically suspected patients of TB were processed for Zeihl Neelsen (ZN) microscopy, BACTEC MGIT liquid culture and GeneXpert assay. Among 259 samples, 28 (10.81%) were positive for acid fast bacilli (AFB) on ZN microscopy. In liquid culture, the growth of mycobacterium species was obtained in 36 (13.89%) samples while the GeneXpert assay detected Mycobacterium tuberculosis (MTB) in 49 (18.91%) samples. Detection rate of MTB was significantly high (n = 49, p < 0.0095) on GeneXpert as compared to microscopy (n = 28); however no significant difference (p = 0.1230) was observed on GeneXpert (n = 49) and culture (n = 36) based detection of MTB. The strength of agreement between GeneXpert and microscopy was also poor (Kappa value < 0.114, 95% CI: -0.72 - 0.301) which support our results. MTB detection rate among female was high as compared to male TB patients while in age wise, the age group 55-64 years has almost high detection rate on microscopy, culture and GeneXpert assay. Findings of the present study highlighted that GeneXpert is more efficient tool for timely diagnosis and proper TB control in high TB endemic area.

Spectroscopic, Viscositic, DNA Binding and Cytotoxic Studies of Newly Synthesized Steroidal Imidazolidines.

A series of new steroidal imidazolidine derivatives (4-6) were synthesized after reacting steroidal thiosemicarbazones with chloro ethylacetate in absolute ethanol. After characterization by spectral and analytical data, the interaction studies of compounds (4-6) with DNA were carried out by UV-vis, fluorescence spectroscopy, hydrodynamic measurements, molecular docking and gel electrophoresis. The compounds bind to DNA preferentially through electrostatic and hydrophobic interactions with Kb; 2.63 × 10(3) M(-1), 1.81 × 10(3) M(-1) and 2.06 × 10(3) M(-1), respectively indicating the higher binding affinity of compound 4 towards DNA. Gel electrophoresis demonstrated that compound 4 showed strong interaction during the concentration dependent cleavage activity with pBR322 DNA. The molecular docking study suggested the intercalation of imidazolidine moiety of steroid derivative in minor groove of DNA. During in vitro cytotoxicity, compounds (4-6) revealed potential toxicity against the different human cancer cells (MTT assay). The uptake of compound 4 by MCF-7 and HeLa cells was studied by confocal microscopy which determined cell shrinkage and hence leading to the apoptosis. The results revealed that compound 4 has better prospectus to act as cancer chemotherapeutic candidate which warrants further in vivo anticancer investigations.

Calcium fluoride nanoparticles induced suppression of Streptococcus mutans biofilm: an in vitro and in vivo approach.

Biofilm formation on the tooth surface is the root cause of dental caries and periodontal diseases. Streptococcus mutans is known to produce biofilm which is one of the primary causes of dental caries. Acid production and acid tolerance along with exopolysaccharide (EPS) formation are major virulence factors of S. mutans biofilm. In the current study, calcium fluoride nanoparticles (CaF2-NPs) were evaluated for their effect on the biofilm forming ability of S. mutans in vivo and in vitro. The in vitro studies revealed 89 % and 90 % reduction in biofilm formation and EPS production, respectively. Moreover, acid production and acid tolerance abilities of S. mutans were also reduced considerably in the presence of CaF2-NPs. Confocal laser scanning microscopy and transmission electron microscopy images were in accordance with the other results indicating inhibition of biofilm without affecting bacterial viability. The qRT-PCR gene expression analysis showed significant downregulation of various virulence genes (vicR, gtfC, ftf, spaP, comDE) associated with biofilm formation. Furthermore, CaF2-NPs were found to substantially decrease the caries in treated rat groups as compared to the untreated groups in in vivo studies. Scanning electron micrographs of rat's teeth further validated our results. These findings suggest that the CaF2-NPs may be used as a potential antibiofilm applicant against S. mutans and may be applied as a topical agent to reduce dental caries.

A graphene/zinc oxide nanocomposite film protects dental implant surfaces against cariogenic Streptococcus mutans.

Oral biofilms play a crucial role in the development of dental caries and other periodontal diseases. Streptococcus mutans is one of the primary etiological agents in dental caries. Implant systems are regularly employed to replace missing teeth. Oral biofilms accumulate on these implants and are the chief cause of dental implant failure. In the present study, the potential of graphene/zinc oxide nanocomposite (GZNC) against the cariogenic properties of Streptococcus mutans was explored and the anti-biofilm behaviour of artificial acrylic teeth surfaces coated with GZNC was examined. Acrylic teeth are a good choice for implants as they are low cost, have low density and can resist fracture. Microscopic studies and anti-biofilm assays showed a significant reduction in biofilm in the presence GZNC. GZNC was also found to be nontoxic against HEK-293 (human embryonic kidney cell line). The results indicate the potential of GZNC as an effective coating agent for dental implants by efficiently inhibiting S. mutans biofilms.

DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.

This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model's performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model's decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.

Relationship Between Cardiometabolic Index in Early Pregnancy and Hypertensive Disorder Complicating Pregnancy.

Background This study aimed to examine the cardiometabolic index during early pregnancy in individuals with hypertension-complicating pregnancy, especially preeclampsia. Additionally, this study sought to determine the relationship between cardiometabolic index and the incidence of varying degrees of preeclampsia. Methodology This study included 289 pregnant women diagnosed with preeclampsia who were registered and delivered at our hospital. These women were assigned to the preeclampsia group. Additionally, a group of 289 healthy pregnant women of identical gestational ages within the same time frame was included for comparison. Clinical data on pregnancy, including body mass index (BMI), blood pressure, waistline, triglyceride levels, and cardiometabolic index, were compared between the two groups. An analysis was conducted to examine the association between early pregnancy cardiometabolic index and the occurrence of preeclampsia. Results There was a significant association between the quartile of cardiometabolic index and the proportion of preeclampsia patients (p < 0.001). Furthermore, after controlling for age and BMI, the risk of preeclampsia remained significantly elevated and was associated with the cardiometabolic index. Conclusions A positive correlation was observed between cardiometabolic index during early pregnancy and the occurrence of preeclampsia.

Feature elimination and stacking framework for accurate heart disease detection in IoT healthcare systems using clinical data.

Introduction: Heart disease remains a complex and critical health issue, necessitating accurate and timely detection methods. Methods: In this research, we present an advanced machine learning system designed for efficient and precise diagnosis of cardiac disease. Our approach integrates the power of Random Forest and Ada Boost classifiers, along with incorporating data pre-processing techniques such as standard scaling and Recursive Feature Elimination (RFE) for feature selection. By leveraging the ensemble learning technique of stacking, we enhance the model's predictive performance by combining the strengths of multiple classifiers. Results: The evaluation metrics results demonstrate the superior accuracy and obtained the higher performance in terms of accuracy, 99.25%. The effectiveness of our proposed system compared to baseline models. Discussion: Furthermore, the utilization of this system within IoT-enabled healthcare systems shows promising potential for improving heart disease diagnosis and ultimately enhancing patient outcomes.

Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation.

Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset.

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture.

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

Hybrid Majority Voting: Prediction and Classification Model for Obesity.

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). However, challenges arise when dealing with sensitive data due to the dependence on large datasets. To address this issue, we propose an approach that combines different magnification factors of histopathological images using a residual network and information fusion in Federated Learning (FL). FL is employed to preserve the privacy of patient data, while enabling the creation of a global model. Using the BreakHis dataset, we compare the performance of FL with centralized learning (CL). We also performed visualizations for explainable AI. The final models obtained become available for deployment on internal IoMT systems in healthcare institutions for timely diagnosis and treatment. Our results demonstrate that the proposed approach outperforms existing works in the literature on multiple metrics.

U-Net-Based Models towards Optimal MR Brain Image Segmentation.

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture's performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms.

The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world's top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one's life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.

Clinical assessment of a low-cost, hand-held, smartphone-attached intraoral imaging probe for 5-aminolevulinic acid photodynamic therapy monitoring and guidance.

Significance: India has one of the highest rates of oral squamous cell carcinoma (OSCC) in the world, with an incidence of 15 per 100,000 and more than 70,000 deaths per year. The problem is exacerbated by a lack of medical infrastructure and routine screening, especially in rural areas. New technologies for oral cancer detection and timely treatment at the point of care are urgently needed. Aim: Our study aimed to use a hand-held smartphone-coupled intraoral imaging device, previously investigated for autofluorescence (auto-FL) diagnostics adapted here for treatment guidance and monitoring photodynamic therapy (PDT) using 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence (FL). Approach: A total of 12 patients with 14 buccal mucosal lesions having moderately/well-differentiated micro-invasive OSCC lesions (<2 cm diameter and <5 mm depth) were systemically (in oral solution) administered three doses of 20 mg/kg ALA (total 60 mg/kg). Lesion site PpIX and auto-FL were imaged using the multichannel FL and polarized white-light oral cancer imaging probe before/after ALA administration and after light delivery (fractionated, total 100 J/cm2 of 635 nm red LED light). Results: The handheld device was conducive for access to lesion site images in the oral cavity. Segmentation of ratiometric images in which PpIX FL is mapped relative to auto-FL enabled improved demarcation of lesion boundaries relative to PpIX alone. A relative FL (R-value) threshold of 1.4 was found to segment lesion site PpIX production among the patients with mild to severe dysplasia malignancy. The segmented lesion size is well correlated with ultrasound findings. Lesions for which R-value was >1.65 at the time of treatment were associated with successful outcomes. Conclusion: These results indicate the utility of a low-cost, handheld intraoral imaging probe for image-guided PDT and treatment monitoring while also laying the groundwork for an integrated approach, combining cancer screening and treatment with the same hardware.