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Heart diseases is the world’s principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals.

Accurate spatiotemporal demand forecasting in distributed environments is challenging because of data heterogeneity, non-stationarity across clients, and privacy constraints. Traditional federated learning approaches often suffer from poor performance when global model updates do not align with local data distribution. This study proposes a novel VMD-structured LSTM–DSTGCRN with GAT framework with a Client-Side Validation (CSV) mechanism to address these challenges. Variational Mode Decomposition (VMD) is applied locally to decompose raw broadband demand signals into Intrinsic Mode Functions (IMFs), isolating characteristic frequency components and reducing cross-frequency interference. The decomposed signals are processed using an LSTM—MultiHead Attention—AGCRN backbone to jointly capture temporal dependencies and adaptive spatial correlations with Graph Attention Networks (GATs). In the federated setting, the CSV enables the selective integration of aggregated global parameters at the module level, allowing clients to retain locally optimal parameters while adopting beneficial global updates. Experimental results on multimodal transport demand datasets demonstrate that implemented approach achieves higher prediction accuracy, faster convergence, and improved robustness compared with baseline federated graph learning models. The proposed framework provides an effective, privacy-preserving solution for nonstationary heterogeneous spatiotemporal forecasting tasks. The simulation results demonstrate that the proposed model significantly improves the accuracy compared to the baseline model by reducing the MAE by 28% in centralized models. Furthermore, in federated learning setup, the MAE decreases by 40.6% and RMSE by 20.1%.

Preclinical studies on pathological pain rely on the von Frey test to examine changes in mechanical thresholds and the acetone spray test to determine alterations in cold sensitivity in rodents. These tests are typically conducted on rodent hindpaws, where animals with pathological pain show reliable nocifensive responses to von Frey filaments and acetone drops applied to the hindpaws. Pathological pain in orofacial regions is also an important clinical problem and has been investigated with rodents. However, performing the von Frey and acetone spray tests in the orofacial region has been challenging, largely due to the high mobility of the head of testing animals. To solve this problem, we implemented a sheltering tube method to assess orofacial nociception in mice. In experiments, mice were sheltered in elevated tubes, where they were well accommodated because the tubes provided safe shelters for mice. Examiners could reliably apply mechanical stimuli with von Frey filament, cold stimuli with acetone spray, and light stimuli with a laser beam to the orofacial regions. We validated this method in Nav1.8-ChR2 mice treated with oxaliplatin that induced peripheral neuropathy. Using the von Frey test, orofacial response frequencies and nociceptive response scores were significantly increased in Nav1.8-ChR2 mice treated with oxaliplatin. In the acetone spray test, the duration of orofacial responses was significantly prolonged in oxaliplatin-treated mice. The response frequencies to laser light stimulation were significantly increased in Nav1.8-ChR2 mice treated with oxaliplatin. Our sheltering tube method allows us to reliably perform the von Frey, acetone spray, and optogenetic tests in orofacial regions to investigate orofacial pain.

Noxious chemicals like chlorine induce extreme distress, pain, and irritation in exposed individuals, yet methods to evaluate pain-related behavioral responses are absent. It is also unknown whether analgesics would alleviate pain and physical discomfort induced by such noxious chemicals. The grimace scale (GS), which evaluates facial expression features such as orbital tightening (OT), is a valuable indicator of pain and distress in animals. However, conventional GS approaches are labor-intensive, prone to subjectivity, and lack quantitative precision. In this study, we employed machine learning with DeepLabCut to annotate key facial landmarks in video recordings of chlorine-exposed rats. Focusing on the superior and inferior eyelid margins and the medial and lateral canthi, we quantified eyelid distance and palpebral fissure width as measures of OT. Rigorous inclusion and exclusion criteria for annotated images were established to ensure accuracy and reproducibility. The quantitative GS in rats subjected to chlorine exposure was validated. Significant reductions in eyelid distance and palpebral fissure width were observed upon chlorine exposure as compared to unexposed control animals. Administration of the opioid analgesic buprenorphine significantly reduced the OT caused by chlorine. This study establishes a robust, quantitative method for assessing OT in chlorine-exposed rats using DeepLabCut, providing a scalable, objective tool for assessing pain induced by noxious chemicals in preclinical research. This study also suggests that opioids can alleviate pain and physical discomfort induced by inhalation of noxious chemicals, providing a new therapeutic strategy for managing the respiratory hazard of noxious chemicals.

A rapid survey was carried out in Shaheed Bhagat Singh Nagar District of Punjab state in India to ascertain health seeking behavior and out-of-pocket health expenditures. Using multistage cluster sampling design, 1,008 households (28 clusters x 36 households in each cluster) were selected proportionately from urban and rural areas. Households were selected through a house-to-house survey during April and May 2014 whose members had (a) experienced illness in the past 30 days, (b) had illness lasting longer than 30 days, (c) were hospitalized in the past 365 days, or (d) had women who were currently pregnant or experienced childbirth in the past two years. In these selected households, trained investigators, using a tablet computer-based structured questionnaire, enquired about the socio-demographics, nature of illness, source of healthcare, and healthcare and household expenditure. The data was transmitted daily to a central server using wireless communication network. Mean healthcare expenditures were computed for various health conditions. Catastrophic healthcare expenditure was defined as more than 10% of the total annual household expenditure on healthcare. Chi square test for trend was used to compare catastrophic expenditures on hospitalization between households classified into expenditure quartiles. The mean monthly household expenditure was 15,029 Indian Rupees (USD 188.2). Nearly 14.2% of the household expenditure was on healthcare. Fever, respiratory tract diseases, gastrointestinal diseases were the common acute illnesses, while heart disease, diabetes mellitus, and respiratory diseases were the more common chronic diseases. Hospitalizations were mainly due to cardiovascular diseases, gastrointestinal problems, and accidents. Only 17%, 18%, 20% and 31% of the healthcare for acute illnesses, chronic illnesses, hospitalizations and childbirth was sought in the government health facilities. Average expenditure in government health facilities was 16.6% less for acute care, 15% less for hospitalization and 50% less for childbirth than in the private healthcare facilities. Out-of-pocket expenditure was mostly on medicines followed by diagnostic and laboratory tests. Among households experiencing hospitalization, 56.5% had incurred catastrophic expenditures, which was significantly higher in the poorest compared to richest household expenditure quartile (p <0.002). Expenditure on healthcare remains high in Punjab state of India. Efforts to increase utilization of the public sector could decrease out-of-pocket healthcare expenditure.

Hepatitis C Virus (HCV) co-infection and its genotypic distribution in people living with Human Immunodeficiency Virus (HIV) show global inconsistency. Therefore, the present study aimed to investigate the prevalence and genotypic distribution patterns of HCV, along with viral load, in people living with HIV. This cross-sectional study was conducted at SRL Diagnostics Nepal, Pvt. Ltd. in 203 HIV-seropositive patients attending the Tribhuvan University Teaching Hospital (TUTH), Maharajgunj, Kathmandu, Nepal from October 2021 to May 2022. The viral load and HCV genotypes were estimated from RNA extracted from the blood sample (plasma) of PLHIV by using a standard Q-PCR protocol. HCV infection was considered as a core variable, whereas covariates used for this study were duration of HIV infection, age, sex, and ART regimen. Out of total 203 PLHIV, the estimated prevalence of HCV co-infection was 115 (56.6%). Male gender was a unique characteristic associated with a high prevalence of HCV co-infection compared to females. The HCV viral load among PLHIV ranged from 34 to 3,000,000 IU/mL. Among HCV co-infected PLHIV, 56 (48.69%) had a low level of HCV viral load. Interestingly, only 3 (2.6%) patients had an HCV viral load higher than 3,000,000 IU/mL. Diverse HCV genotypes were found in the population, including genotypes 1, 1a, 3a, 5a, and 6. However, genotype 3 was the most prevalent HCV variant among HCV-co-infected PLHIV, with a distribution of 36 (61.1%) and viral load ranging from 34 to 3000 IU/mL. HCV coinfection is frequent in the Nepalese population of people living with HIV, particularly due to HCV genotypic variant 3. The findings of this study could be useful for the management and clearance of the HCV co-infection in PLHIV, aiming to provide a good quality of life.

[This corrects the article DOI: 10.1371/journal.pone.0157831.].

This article provides a comprehensive examination of sustainable 6G wireless communication systems, addressing the urgent need for environmentally friendly and energy-efficient networks. The background establishes the broader context and significance of the study, emphasizing the escalating concerns surrounding the environmental impact and energy consumption of wireless communication systems. The purpose of this study is to explore and propose sustainable solutions for 6G networks. The methods employed in this research encompass an analysis of various strategies and technologies, including energy-aware network design, dynamic power management, energy harvesting, and green infrastructure deployment. The main findings of this article highlight the effectiveness of these approaches in enhancing energy efficiency, reducing carbon footprint, and optimizing resource management in 6G networks. The conclusions drawn from this study emphasize the importance of sustainable 6G wireless communication systems in achieving a more eco-friendly and energy-efficient future. It is crucial to adopt these sustainable practices to minimize environmental impact and address the increasing energy demands of wireless communication networks. The article provides valuable insights to researchers, industry practitioners, and policymakers, aiding in the development and implementation of sustainable practices for 6G wireless communication systems.

The maxillary sinus is frequently implicated in facial pain syndromes arising from infection, neoplasia, dental procedures, and, importantly, migraine, which can mimic “sinus headache” and contribute to misdiagnosis and inappropriate antibiotic use. Despite the clinical burden of chronic maxillary sinus pain, the sensory neuron subtypes that convey nociceptive and mechanosensory signals from the sinus mucosa remain incompletely defined. In this study, trigeminal ganglion (TG) neurons innervating the maxillary sinus (maxillary sinus TG neurons) were retrogradely labeled with the fluorescent dye DiD in mice and characterized using ex vivo patch-clamp electrophysiology and single-cell RT-PCR. Maxillary sinus TG neurons were found to be predominantly small-diameter, C-afferent nociceptors with electrophysiologic features including high thresholds, repetitive firing, and broad action potentials. Notably, maxillary sinus TG neurons formed a distinct molecular and functional subgroup: they expressed Nav1.9, while showing minimal Nav1.8 expression and limited overlap with Nav1.8-positive nociceptor populations. A majority of maxillary sinus TG neurons were mechanically responsive, generating mechanically activated currents with heterogeneous adaptation profiles, and a subset expressed the mechanoreceptor Piezo2. Collectively, these findings identify maxillary sinus TG neurons as a specialized population of Nav1.9-enriched C-afferent nociceptors with mechanosensitive properties, providing a mechanistic framework for pressure-evoked sinus pain. This work advances the neurobiological basis of sinus-related pain and suggests that Nav1.9 and mechanoreceptor pathways may be potential therapeutic targets for conditions in which sinus symptoms overlap with migraine and other craniofacial pain disorders.

Open-source software (OSS) ecosystems, defined as environments composed of package managers and programming languages (e.g., NPM for JavaScript), are essential for software development and foster collaboration and innovation. Although their significance is acknowledged, understanding what makes OSS communities healthy and sustainable requires further exploration. This thesis quantitatively assesses the health of OSS projects and communities within the NPM, PyPI, Maven, and RubyGems ecosystems. We explore five research questions addressing project standards, community responsiveness, contribution distribution, contributor retention, and newcomer integration strategies. Our analysis shows varied documentation practices, insider engagement levels, and contribution patterns. Our findings highlight both strengths and different areas for improvement across ecosystems. For example, RubyGems excels in the adoption of project documentation and exhibits the most even distribution of contributions among all contributors, including highly active contributors. and a very responsive community, but it needs to improve contribution retention and attract newcomers to the projects. Meanwhile, NPM and Maven show a trend toward getting new contributors, characterized by a high ratio of individual contributions. They need to better adopt a code of conduct, pull request templates, and increase the number of active contributors in a project. This thesis offers insights to developers and maintainers on how to strengthen ecosystems and support vibrant communities effectively.