Search Results (75)

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

Dormancy occurs when Mycobacterium tuberculosis (Mtb) enters a non-replicating and metabolically inactive state in response to hostile environment. During this state, it is highly resistant to conventional antibiotics, which increase the urgency to develop new potential drugs against dormant bacilli. In view of this, the dormancy survival regulator (DosR) protein is thought to be an essential component that plays a key role in bacterial adaptation to dormancy during hypoxic conditions. Herein, the NP-lib database containing natural product compounds was screened virtually against the binding site of the DosR protein using the MTiopen screen web server. A series of computational analyses were performed, including redocking, intermolecular interaction analysis, and MDS, followed by binding free energy analysis. Through screening, 1000 natural product compounds were obtained with docking energy ranging from −8.5 to −4.1 kcal/mol. The top four lead compounds were then selected for further investigation. On comparative analysis of intermolecular interaction, dynamics simulation and MM/GBSA calculation revealed that M3 docked with the DosR protein (docking score = −8.1 kcal/mol, RMSD = ~7 Å and ΔG Bind = −53.51 kcal/mol) exhibited stronger stability than reference compound Ursolic acid (docking score = −6.2 kcal/mol, RMSD = ~13.5 Å and ΔG Bind = −44.51 kcal/mol). Hence, M3 is recommended for further validation through in vitro and in vivo studies against latent tuberculosis infection. Full article

This paper presents a novel, low-cost, portable impedance analyzer system designed for biopotential electrode evaluation and skin/electrode impedance measurement, critical for enhancing bioelectrical signal quality in healthcare applications. In contrast with conventional systems that depend on external PCs or host devices for data acquisition, visualization, and analysis, this design integrates all functionalities into a single, compact platform powered by the Analog Devices AD5933 impedance converter and a Raspberry Pi 4. The design incorporates custom analog circuitry to extend the measurement range from 10 Hz to 100 kHz and supports a wide impedance spectrum through switchable feedback resistors. Validated against a benchtop impedance analyzer, the system demonstrates high accuracy with normalized root-mean-square errors (NRMSEs) of 1.41% and 3.77% for the impedance magnitude and phase of passive components, respectively, and 1.43% and 1.29% for the biopotential electrode evaluation and skin/electrode impedance measurement. This cost-effective solution, with a total cost of USD 159, addresses the accessibility challenges faced by smaller research labs and healthcare facilities, offering a compact, low-power platform for reliable impedance analysis in biomedical applications. Full article

Guided mode resonance (GMR)-based sensors have emerged as a promising technology for the early screening of cancer, offering advantages such as sensitivity, specificity, low cost, non-invasiveness, and portability. This review article provides a comprehensive overview of the latest advancements in GMR technology and its applications in biosensing, with a specific focus on cancer. The current state of cancer diagnosis and the critical need for point-of-care (POC) devices to address these challenges are discussed in detail. Furthermore, the review systematically explores various strategies employed in GMR-based cancer detection including design principles and the integration of advanced technologies. Additionally, it aims to provide researchers valuable insights for developing GMR sensors capable of detecting cancer biomarkers outside the laboratory environment. Full article

In the field of drug development, the quest for novel compounds that bind to DNA with high affinity and specificity never ends. In the present work, we report the newest development in this field, namely, triplex DNA-specific binding ligands based on the 5-substituted flavone scaffold in our lab. Biophysical studies showed that the newly synthesized flavone derivatives (depending on the side chains) bind to triplex DNA with binding affinities better than or similar to 5-substituted 3,3′,4′,7-tetramethoxyflavonoids. These compounds selectively stabilize triplex DNA while having little effect on duplex DNA, as verified by various biophysical methods. A detailed structural analysis suggested that the binding of these compounds to triplex DNA depends on the type of amino groups in the side chains and the length of the side chains. Viscosity studies suggested that these ligands bind to triplex DNA via intercalation. A representative ligand, compound 4b, showed a positive inhibitory effect on the activity of a restriction endonuclease (DraI) via ligand-mediated triplex formation. Several of these compounds exhibited excellent cytotoxicity toward various cancer cell lines (HT-29, HCT116, and HL-60), as indicated by the MTT assay. The work presented here is part of a continued effort from our laboratory to explore the novel structural motifs of natural product flavonoids for the development of triplex-specific ligands as antigene enhancers. Full article

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with a particularly high burden in India. Non-invasive methods like Phonocardiogram (PCG) analysis capture the acoustic activity of the heart. This holds significant potential for the early detection and diagnosis of heart conditions. However, the complexity and variability of PCG signals pose considerable challenges for accurate classification. Traditional methods of PCG signal analysis, including time-domain, frequency-domain, and time-frequency domain techniques, often fall short in capturing the intricate details necessary for reliable diagnosis. This study introduces an innovative approach that leverages harmonic–percussive source separation (HPSS) to extract distinct harmonic and percussive spectral features from PCG signals. These features are then utilized to train a deep feed-forward artificial neural network (ANN), classifying heart conditions as normal or abnormal. The methodology involves advanced digital signal processing techniques applied to PCG recordings from the PhysioNet 2016 dataset. The feature set comprises 164 attributes, including the Chroma STFT, Chroma CENS, Mel-frequency cepstral coefficients (MFCCs), and statistical features. These are refined using the ROC-AUC feature selection method to ensure optimal performance. The deep feed-forward ANN model was rigorously trained and validated on a balanced dataset. Techniques such as noise reduction and outlier detection were used to improve model training. The proposed model achieved a validation accuracy of 93.40% with sensitivity and specificity rates of 82.40% and 80.60%, respectively. These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. This research highlights the potential for deploying such models in non-invasive cardiac diagnostics, particularly in resource-constrained settings. It also lays the groundwork for future advancements in cardiac signal analysis. Full article

Background: The PediCenter and Niles Children’s Clinic provide pediatric primary and urgent care services in central California. We remained open throughout the COVID-19 pandemic, providing scheduled well child-care and sick visits. Methods: Beginning in September 2020, we implemented a COVID-19 screening program. Screening was performed on all patients presenting for care and was made available to patients requiring testing for any purpose. Herein, we provide results from that program, including a description of clinical characteristics of COVID-19 in our patients. Results: Key findings: A total of 11,649 COVID-19 antigen screening tests were performed (age range 0.1 to 17.0, mean 8.7, SD 4.5). In total, 1560 pts. (13.4%) tested positive. Among these, 665 (43%) were asymptomatic, 560 (36%) had mild disease, 318 (20%) had moderate disease, and 17 (1%) had severe disease. No critical cases or transfers to the emergency room were reported. Younger patient age was associated with an increased severity of illness, as was time from the onset of the pandemic. A total of 4446 patients reported no symptoms at the time of screening, 15% of whom tested positive. In total, 7203 patients reported symptoms at the time of testing. Among these, 87.6% tested negative and 12.4% tested positive. Disease severity was similar between these two groups. COVID-19 is generally a mild respiratory tract infection in healthy children. Conclusions: Screening is effective in identifying cases, including asymptomatic cases. Statistical models further revealed associations between patient age, time from the onset of the pandemic, and disease severity. Full article

We are proposing a novel self-assembled monolayer (SAM) functionalized ZnO nanowires (NWs)-based conductometric sensor for the selective detection of hydrogen (H₂). The modulation of the surface electron density of ZnO NWs due to the presence of negatively charged terminal amine groups (−NH₂) of monolayers leads to an enhanced electron donation from H₂ to ZnO NWs. This, in turn, increases the relative change in the conductance (response) of functionalized ZnO NWs as compared to bare ones. In contrast, the sensing mechanism of bare ZnO NWs is determined by the chemisorbed oxygen ions. The functionalized ZnO NWs exhibit an eight times higher response compared to bare ZnO NWs at an optimal working temperature of 200 °C. Finally, in comparison to studies in the literature involving strategies to enhance the sensing performance of metal oxides toward H₂, like decoration with metal nanoparticles, heterostructures, and functionalization with a metal–organic framework, etc., SAM functionalization showed superior sensing results. Full article

The purpose of this research paper is to present a framework for selecting a biogas plant for the Indian rural community, considering performability factors such as reliability, quality, maintainability, safety, and sustainability. This will ensure that the plant operates reliably, efficiently, and safely over its entire life cycle and can play a significant role as a decision-support tool for decision-makers (e.g., managers, engineers, stakeholders). The proposed framework integrates the Analytic Hierarchy Process (AHP), and the Weighted Aggregated Sum Product Assessment (WASPAS) to optimally evaluate and prioritize the best alternative based on performability factors. The findings show that the suitable biogas plant in the context of the Indian rural population is a fixed-dome-type plant. The decision-making process in selecting the best biogas plant can be effectively aided by using this suggested tool. Currently, there are no proper tools or methods for selecting biogas plants for rural areas due to a lack of data or relevant literature on operational issues. The proposed method uses performability factors for the selection, which has not been researched so far. Moreover, the AHP–WASPAS approach offers a robust method for selecting biogas plants, ensuring efficient and sustainable energy production. The proposed method will help policymakers and stakeholders to choose the best biogas plant in the context of Indian rural application. Full article

Chronic obstructive pulmonary disease (COPD) is a common condition with an estimated prevalence of 12% in adults over the age of 30 years worldwide. COPD is a leading cause of morbidity and mortality globally, with a substantial economic and social burden. There are an estimated 3 million deaths annually due to COPD. However, most of the patients with COPD respond to routine interventions like bronchodilator therapy, assessing supplemental oxygen needs, smoking cessation, vaccinations, and pulmonary rehabilitation. There is a significant number of patients who unfortunately progress to have persistent symptoms despite these interventions. Refractory COPD is not yet formally defined. Patients with severe persistent symptoms or exacerbations despite appropriate care can be considered to have refractory COPD. Managing refractory COPD needs a multidimensional approach. In this review article, we will discuss essential interventions like ensuring adequate inhaler techniques, exploring the need for non-invasive ventilatory support, use of chronic antibiotics and phosphodiesterase inhibitors to advanced therapies like bronchoscopic lung volume reduction surgery, and the upcoming role of anti-IL5 agents in managing patients with refractory COPD. We will also discuss non-pharmacologic interventions like psycho-social support and nutritional support. We will conclude by discussing the palliative care aspect of managing patients with refractory COPD. Through this review article, we aim to better the approach to managing patients with refractory COPD and discuss new upcoming therapies. Full article

This study was carried out in Amritsar, Punjab, to find out how efficiently nutrients were used and how much energy was employed in direct-seeded rice (DSR) production. In this study, four levels of nitrogen (0, 40, 50, and 60 kg N ha⁻¹) and three levels of phosphorus (0, 37.5, and 45 kg P₂O₅ ha⁻¹) were tested. In a rice production system, the energy indices of various inputs and outputs were evaluated through the application of energy equivalency. The nutrient-use efficiencies in rice were assessed using different efficiency indices. The maximum grain yields of 38.9 q ha⁻¹ and 36.9 q ha ⁻¹ were recorded at 50 kg N ha⁻¹ and 45 kg P₂O₅ ha⁻¹, respectively. On the other hand, application of nitrogen at 60 kg N ha⁻¹ and phosphorus at 45 kg P₂O₅ ha⁻¹ resulted in maximum straw yield of 57.1 q ha⁻¹ and 51.1 q ha⁻¹, respectively. In comparison with the control, application of 60 and 50 kg N ha⁻¹ resulted in 161.9% and 151.0% higher grain yield, respectively. On the other hand, with applications of 45 kg P₂O₅ ha⁻¹ and 37.5 kg P₂O₅ ha⁻¹, an increase in the grain yield of 17.3 and 28.6%, respectively, over the control was recorded. Moving further towards nutrient-use efficiencies (NUEs), the highest values of partial factor productivity of nitrogen (PFP_N), agronomic efficiency of nitrogen (AE_N), partial nutrient balance of nitrogen (PNB_N), and recovery efficiency of nitrogen (RE_N) were 89.1, 50.4, 1.78 and 0.72, respectively, which were obtained at 40 kg N ha⁻¹, after which the values started decreasing steadily. In the case of phosphorus, the partial factor productivity (PFP_P) of 88.6 was the maximum at 37.5 kg P₂O₅ ha⁻¹, but partial nutrient balance (PNB_P) of 0.36 and recovery efficiency (RE_P) of 0.08 were highest at 45 kg P₂O₅ ha⁻¹. The main results revealed that the farmer field had an excessive amount of non-renewable energy inputs. The experimental field depicted greater energy-usage efficiency (EUE) of 4.5, energy productivity (EP) of 0.14, and energy profitability (EP₁) of 3.5. These results were primarily ascribed to a significant drop in energy inputs under direct-seeded rice (DSR). In the case of non-renewable energy inputs, fertilizer made the maximum contribution to energy input (47.9%) in the farmer’s field. We conclude that nutrient-use efficiencies and energy-use efficiency were highest at 50 kg N and 45 kg P₂O₅ ha⁻¹. This recommendation is beneficial for farmers because lower inputs and higher outputs are the main objective of every farmer. Full article

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory condition characterized by obstruction of airways and emphysematous lung tissue damage, with associated hypoxic vasoconstriction in the affected lung parenchyma. In our study, we evaluate the role of oxygen-enhanced (OE) MRI and dynamic contrast enhanced (DCE)-MRI in COPD patients for assessment of ventilation and perfusion defects and compared their severity with clinical severity. A total of 60 patients with COPD (diagnosed based on clinical and spirometry findings) and 2 controls with normal spirometry and no history of COPD were enrolled. All patients underwent MRI within 1 month of spirometry. OE-MRI was performed by administering oxygen at 12 L/min for 4 min to look for ventilation defects. DCE-MRI was performed by injecting intravenous gadolinium contrast, and perfusion abnormalities were detected by subtracting the non-enhanced areas from the first pass perfusion contrast images. A total of 87% of the subjects demonstrated ventilation and perfusion abnormalities on MRI independently. The lobe-wise distribution of ventilation and perfusion abnormalities correlated well with each other and was statistically significant in all lobes (p < 0.05). The severity of ventilation-perfusion defects also correlated well with clinical severity, as their median value (calculated using a Likert rating scale) was significantly lower in patients in the Global initiative for chronic Obstructive Lung Disease (GOLD) I/II group (3.25) compared to the GOLD III/IV group (7.25). OE- and DCE-MRI provide functional information about ventilation-perfusion defects and their regional distribution, which correlates well with clinical severity in patients with COPD. Full article

Numerous scholars have thoroughly studied the topic of choosing machines considering the progress and technological growth seen in machinery options. This scholarly investigation explores decision-making methods specifically designed to aid the selection of machines in manufacturing businesses. Additionally, this research emphasizes the need for decision-making frameworks in manufacturing facilities, highlighting the importance of smart machine selection strategies in those contexts. In this research, we show a dual-MCDM approach that includes DEX—decision experts—and the EDAS method that are popularly employed to solve decision-making problems in both academic and practical industries. Throughout the previous decade, business leaders and managers increasingly use MCDM solutions to overcome machine selection challenges. At this time, while various decision-support technologies and procedures have been developed and used, it is essential that we discuss the sequence of our study objectives and drive the proposed method for widening use in practical firms. In short, this research may be helpful as a literature review for MDCM studies and related topics. It will also help executives, engineers, and specialists determine which equipment or machines to create and increase product quality in manufacturing and industry. Full article

Site diversity is the most effective way to recover a signal lost during heavy downpours, especially in tropical regions since other mitigation techniques such as adaptive power control and code modulation may be unreliable during such. Duplicated links at diverse sites are deployed, and the least-attenuated signal of either site will be routed to the prime site for further operation. Since the deployment is costly, a diversity-gain model is used to estimate the appropriateness of selected sites. Diversity gain is known to depend on site-separation distance and elevation angle and, optionally, baseline angle and signal frequency, based on the region of research. In addition to these factors, the horizontal rain-cell span and the wind’s impact on the gain are ongoing investigations, especially in tropical regions. This article presented the rain analysis from the year 2014 to mid-July 2017 at eight sites in the Gombak and Sepang districts of Malaysia to investigate the dependency relevancies. The rain rates were then used to predict the attenuation using the ITU-R P.618-13 rain-attenuation model, and the inter- and cross-district gain characteristics were evaluated. The observation of diurnal rain during the northeast seasons yielded that the northeast wind stimulates intense rain at locations along its direction, thus, extending the horizontal rain-cell span to 15 km distant from a host. Meanwhile, sites located at 5 km distant, slightly perpendicular to the wind direction, and from 90° to 180° from due north of the host, experience less rain. The baseline angle variation establishes nonimpact to the gain and lengthening the site-separation distance presented equal chances to the shorter span towards diversity-gain increment. The research outcome is necessary to formulate a more reliable diversity-gain model to be used in the industry. Full article

Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques. Full article