Search Results (893)

This research explores the intersection of health informatics and behavioral science through the lens of fitness technologies, specifically Fitbit products. Grounded in the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT), this study examines how these technologies influence user acceptance and physical activity motivation. Employing a qualitative approach, the paper analyzed Fitbit user reviews to reveal insights into real-world interactions and perceptions, thereby deepening the understanding of technology adoption behaviors in health contexts. The findings highlight the significance of perceived ease of use and usefulness, as well as the integration of health consciousness in technology acceptance, enriching the TAM framework. Additionally, the study confirms Self-Determination Theory’s proposition of intrinsic motivation being more effective for lasting behavior change, as seen in users’ evolving interactions with Fitbit features. Furthermore, this study contributes to health behavior theories by demonstrating the role of technological devices in altering health routines. Full article

Multidrug-resistant (MDR) Escherichia coli is a major foodborne pathogen posing a critical threat to public health, particularly through the contamination of animal products. The increasing prevalence and virulence of MDR E. coli strains underscore the urgent need for alternative antimicrobial strategies. This study aimed to synthesize and characterize zinc oxide nanoparticles (ZnO-NPs) using Stevia rebaudiana as a sustainable capping and reducing agent, aligning with green chemistry principles. Of the 120 fish samples, 74.2% (89/120) were positive for E. coli contamination. Among the identified E. coli strains, 77.8% (119/153) were classified as MDR. Resistance profiling revealed 22 distinct patterns, and seven highly resistant and virulent strains were selected for further analyses. The eco-friendly auto-combustion synthesis of ZnO-NPs produced nanoparticles with semi-spherical to hexagonal shapes and an average size ranging from 12 to 25 nm. Scanning Electron Microscope–Energy Dispersive X-ray analysis (SEM-EDS) confirms that ZnO-NPs primarily consist of zinc (37.5%) and oxygen (19.9%), with carbon (42.6%) indicating the green synthesis process. ZnO-NPs demonstrated potent, dose-dependent antibacterial and antibiofilm activity against the selected MDR E. coli strains. Additionally, mechanistic studies revealed that ZnO-NPs disrupt bacterial cell membranes, alter cellular morphology, and interfere with DNA integrity. These findings highlight the potential of eco-friendly ZnO-NPs as a promising nanomaterial for enhancing food safety and addressing the growing challenge of MDR foodborne bacteria. Full article

Background/Objectives: This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers. Methods: The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers. Results: forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool. Conclusions: AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models. Full article

The sustainable reuse of batteries after their first life in electric vehicles requires accurate state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies the systematic ProKnow-C methodology to analyze the state of the art in SoH estimation using machine learning (ML). A bibliographic portfolio of 534 papers (from 2018 onward) was constructed, revealing key research trends. Public datasets are increasingly favored, appearing in 60% of the studies and reaching 76% in 2023. Among 12 identified sources covering 20 datasets from different lithium battery technologies, NASA’s Prognostics Center of Excellence contributes 51% of them. Deep learning (DL) dominates the field, comprising 57.5% of the implementations, with LSTM networks used in 22% of the cases. This study also explores hybrid models and the emerging role of transfer learning (TL) in improving SoH prediction accuracy. This study also highlights the potential applications of SoH predictions in energy informatics and smart systems, such as smart grids and Internet-of-Things (IoT) devices. By integrating accurate SoH estimates into real-time monitoring systems and wireless sensor networks, it is possible to enhance energy efficiency, optimize battery management, and promote sustainable energy practices. These applications reinforce the relevance of machine-learning-based SoH predictions in improving the resilience and sustainability of energy systems. Finally, an assessment of implemented algorithms and their performances provides a structured overview of the field, identifying opportunities for future advancements. Full article

Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses—a ranked list of symptom–disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert–patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts. Full article

Objective: Since the lifting of the COVID-19 restrictions in China in November 2022, there has been a notable surge in the COVID-19 infection rate. Little is known about the prevalence of long COVID among the general adult population and its impact on COVID-19-related worries and behaviors after the policy change. Methods: This cross-sectional study recruited 1530 adults with prior COVID-19 infection in Guangzhou from February to March 2023. Logistic regression analyses and trend analyses were performed to investigate the associations between long COVID- and COVID-19-related worries and preventive behaviors. Results: The estimated prevalence of long COVID among adults in China was 18.0% (95% confidence interval: 16.1% to 20.0%). Common long COVID symptoms included cough (60.7%), fatigue (47.6%), dyspnea (34.5%), palpitation (26.2%), and insomnia (25.1%). Adjusted for background variables, individuals with long COVID exhibited higher level of COVID-19-related worries compared to those who had fully recovered from the infection (reference: without long COVID; adjusted odds ratios ranged from 1.87 to 2.55, all p values < 0.001). Participants primarily expressed worries regarding the potential for COVID-19 reinfection, the impact of the pandemic on daily life, the increasing number of COVID-19 cases and deaths, and the capacity of the healthcare system. While long COVID did not statistically significantly affect their preventive behaviors. Conclusions: Long COVID was prevalent among the general adult population in China after lifting the COVID-19 restrictions, and it had a significant impact on COVID-19-related worries. This study highlights the importance of monitoring the mental health of individuals with long COVID and developing targeted intervention strategies to improve their adherence to preventive measures. Full article

Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. A relevant application is the investigation of patient-reported outcome measures (PROMs) after spine surgery. Although a multitude of PROMs have been described and validated, there is currently no consensus regarding which questionnaires should be utilized. Additionally, studies have reported varying degrees of accuracy in predicting patient outcomes based on questionnaire responses. PROMs currently lack standardization, which renders them difficult to compare across studies. The purpose of this manuscript is to identify applications of machine learning to predict PROMs after spine surgery. Full article

Introduction: Acute kidney injury (AKI) among the pediatric population is considered a risk factor for mortality and morbidities during pediatric intensive care unit (PICU) admission. The association between AKI and increased mortality risk and length of stay (LOS) is still inconclusive. This retrospective cohort study aimed to evaluate the impact of AKI severity upon critical management and clinical parameters with an evaluation of severity progression among AKI patients admitted to the PICU at a tertiary care hospital. Methods: AKI, defined with the Kidney Disease Improving Global Outcomes (KDIGO) classification, was determined based on serum creatinine and urine output with respect to the patient’s baseline value. The following outcomes were examined: mortality, mechanical ventilation use, use of non-invasive ventilation, recovery at discharge, and LOS in the hospital and PICU stratified by type of AKI upon admission. Medical records of the 165 included patients were reviewed for clinical data and study outcomes. Results: The median age of the patients was 7 years (IQR 1.5–10 years), and 58% were boys; 78 (47.2%) had stage 1 AKI, 49 (29.71%) had stage 2 AKI, and 38 (23%) had stage 3 AKI at admission. The mortality rate was 26%. The median LOS in the PICU was statistically significant between AKI stages, with a higher median LOS among patients with AKI stage 3 at admission. Using the maximum KDIGO stage, there was no association between AKI and mortality (adjusted OR 1.91, 95% CI 0.05), whereas for the mechanical ventilation outcome, the adjusted OR was 1.84 (95% CI 0.42–8.1). Conclusions: The severity of AKI is not associated solely with mortality and clinical outcomes as the numbers of comorbidities and organ failures contribute to mortality. However, improving awareness of AKI and understanding the disease progression course would reduce acute and long-term morbidity and mortality. Full article

Background/Objectives: Improvements in catheter connection design intended to increase safety have resulted in connections that are difficult to release manually. No medical device exists to safely disconnect catheter connections. Nurses and other users have developed workarounds including use of hemostats, tourniquets, and wrenches. These workarounds are not always successful for performing this task and can break catheters and catheter connections. This study aimed to evaluate a disconnection device to safely disconnect catheter connections. Methods: This is a mixed-methods study using a user-centered design approach with triangulation of quantitative and qualitative data mapped to Valdez’s sociotechnical framework. Nurses (N = 139) from units across two academic medical centers encompassing diverse patient populations engaged in usability testing and surveys. Data about users’ past catheter disconnection experiences and usability of the specialized disconnection device were collected and analyzed. Triangulation of quantitative data and qualitative themes was mapped using Valdez’s socio-technical framework to complement and strengthen the final design generated for nurses’ user requirements. Results: Ninety-five percent of nurses reported previous difficulty with disconnecting luer connections; 93% of those reporting difficulty improvised with readily available medical devices or products to better grip the connected parts. Over 85% of nurses reported positive experiences using the specialized disconnection device; others suggested design improvements for better performance. Conclusions: The nurses who tested the developed disconnection device reported high acceptability, accessibility, ease of use, and improved task performance. Moreover, as workarounds develop at points of practice where no systematic solution exists, aiming product development activities at these points help close gaps in achieving and maintaining patient safety. This study was not registered. Full article

Background: Migraine, a complex neurological condition, poses significant challenges for both sufferers and healthcare providers. While prescription medications play a vital role in managing migraine attacks, the quest for natural, non-pharmacological alternatives has garnered increasing interest. This review explores the efficacy and safety of natural supplements as treatments for migraine relief, comparing them with conventional prescription medications. Methods: The review delves into herbal supplements, clinical studies on natural remedies, aromatherapy, dietary influences, and lifestyle modifications in the context of migraine management in several databases. Results: The findings shed light on the potential of natural supplements as complementary or alternative approaches to traditional migraine therapies, offering insights into a holistic and personalized treatment paradigm for migraine sufferers. Conclusions: Natural supplements have gained attention as potential treatments for migraine relief, often perceived as safer alternatives to conventional medications. Full article

Obstructive Sleep Apnea (OSA) is a prevalent chronic sleep-related breathing disorder characterized by partial or complete airway obstruction. The expensive, time-consuming, and labor-intensive nature of the gold-standard approach, polysomnography (PSG), and the lack of regular monitoring of patients’ daily lives with existing solutions motivates the development of clinical support for enhanced prognosis. In this study, we utilize image representations of sleep stages and contextual patient-specific data, including medical history and stage durations, to investigate the use of wearable devices for OSA screening and comorbid conditions. For this purpose, we leverage the publicly available Wisconsin Sleep Cohort (WSC) dataset. Given that wearable devices are adept at detecting sleep stages (often using proprietary algorithms), and medical history data can be efficiently captured through simple binary (yes/no) responses, we seek to explore neural network models with this. Without needing access to the raw physiological signals and using epoch-wise sleep scores and demographic data, we attempt to validate the effectiveness of screening capabilities and assess the interplay between sleep stages, OSA, insomnia, and depression. Our findings reveal that sleep stage representations combined with demographic data enhance the precision of OSA screening, achieving F1 scores of up to 69.40. This approach holds potential for broader applications in population health management as a plausible alternative to traditional diagnostic approaches. However, we find that purely modality-agnostic sleep stages for a single night and routine lifestyle information by themselves may be insufficient for clinical utility, and further work accommodating individual variability and longitudinal data is needed for real-world applicability. Full article

Background: Approximately two to three children die from pneumonia every hour, and pneumonia is the leading cause of hospitalization for children under five in Bangladesh. Bangladesh has adopted the Pocket Book guidelines by the World Health Organization (WHO) for hospital management of childhood pneumonia. These guidelines recommend the proper use of injectable antibiotic administration. Objectives: We assessed and compared the prescription drugs for treating childhood pneumonia following WHO guidelines in a secondary and tertiary hospital in Bangladesh. Methods: We conducted a cross-sectional comparative study among children under five years who were admitted to a tertiary hospital, Dhaka Medical College Hospital (DMCH), and a secondary-level hospital, Kushtia District Hospital (KDH), with pneumonia between May 2021 and May 2022. A structured questionnaire was administered to the eligible participants. Additionally, we reviewed the hospital records related to the patient’s treatment. SPSS (Version 28) was used to conduct statistical analysis. Results: 316 children were enrolled during the study period, of whom 66.4% were collected from DMCH. There were 65.8% and 24.6% of patients who were classified with severe pneumonia and very severe pneumonia, respectively. In DMCH, the severity of pneumonia percentage was 57.6%, while in KDH, the percentage was 82%. A significant difference was found between the two facilities in diagnosing complicated pneumonia, prescribing the appropriate antibiotics, and ensuring oxygen availability. Amoxicillin was prescribed to 83.5% of the participants, and ceftriaxone was used at a high rate (64.5–70.9%). Combining injections of ceftriaxone with oral amoxicillin or other combinations of antibiotics, both facilities used high frequencies of non-antibiotic corticosteroids. Conclusions: Antibiotics were overprescribed, and injections were prescribed at higher levels than WHO recommended. This could pose a threat to antibiotic resistance. There is a need to enforce standard prescribing policies and treatment guidelines to reduce morbidity and mortality among hospitalized children with pneumonia. Full article

Dengue fever is caused by four common serotypes of the dengue virus (DENV-1, DENV-2, DENV-3, and DENV-4). Patients infected with one serotype may develop lifelong serotype-specific protective immunity. However, they remain susceptible to reinfection with the other serotypes, often increasing the risk of severe forms of dengue. This cross-sectional study investigates the prevalence of the four dengue serotypes in patients who presented with dengue fever at Makkah hospitals between April 2023 and May 2024. Data were collected from the medical records of the Regional Laboratory in Makkah, Saudi Arabia. The 238 positive dengue samples included 185 samples (77.73%) from male patients. The average age of the patients was 37.65 years (SD = 15.05). Dengue type 2 was the most common serotype, followed by type 1, type 3, and type 4. Most of the dengue patients were Saudi nationals, followed by Egyptians. There were 11 dengue-positive samples that were not diagnosed with any of the four dengue serotypes. Since Makkah receives numerous international travelers, these samples might contain novel dengue serotypes circulating in different parts of the world. This study underscores the need for the continuous monitoring of dengue serotypes to predict potential outbreaks and mitigate the risk of severe dengue in susceptible populations. Full article

Pathogen infection in animals and plants is recognized in a relatively similar manner by the interaction of pattern recognition receptors on the host cell surface with pathogen-associated molecular patterns on the pathogen surface. Previous work demonstrates that animal pathogenic bacteria can be recognized by plant receptors and alter transcriptome. In this work, we have hypothesized that exposure to human parasites, Cryptosporidium parvum and Giardia lamblia, would also trigger pathogen response in plants, leading to changes in transcriptome. Detached Arabidopsis leaves were exposed for one hour to heat-inactivated Cryptosporidia or Giardia. The transcriptome profile showed large changes in gene expression with significant overlap between two parasites, including upregulated GO terms “cellular response to chitin”, “response to wounding”, “response to oomycetes”, “defense response to fungus”, “incompatible interaction”, and “activation of innate immune response”, and downregulated GO terms “positive regulation of development”, “cell surface”, “regulation of organ growth”, “wax biosynthetic process”, “leaf and shoot morphogenesis”. Uniquely downregulated GO terms in response to Cryptosporidia were GO terms related to chromatin remodelling, something that was not reported before. To conclude, it appears that while Cryptosporidia or Giardia are not pathogens of Arabidopsis, this plant possesses various mechanisms of recognition of pathogenic components of parasites. Full article

C-reactive protein (CRP) and estimated glomerular filtration rate (eGFR) are key biomarkers reflecting systemic inflammation and metabolic dysfunction. This study explored systemic and oral health indicators, including CRP and eGFR, as potential factors associated with periodontitis, using a longitudinal clinical dataset comprising 23,742 records from patients identified by ICD-10 codes between 2015 and 2022. Univariate Cox analysis and Gompertz models, selected based on AIC and BIC after evaluating alternative models, were employed to assess the predictive roles of CRP and eGFR in periodontitis incidence, adjusting for oral and systemic health factors. Elevated CRP (>15 mg/L) and reduced eGFR (<60 mL/min/1.73 m²) were significant predictors of periodontitis, with hazard ratios (HR) of 1.36 [1.05–1.77] and 1.39 [1.08–1.78], respectively. Atherosclerosis (HR: 2.12 [1.11–4.06]), diseases of the hard tissues of the teeth (HR: 7.30 [5.45–9.78]), and disorders of the teeth and supporting structures (HR: 3.02 [2.05–4.43]) also demonstrated strong predictive associations. CRP and eGFR emerged as potential biomarkers for predicting periodontitis, enabling early interventions to prevent tooth loss and systemic complications. Patients with chronic kidney disease, atherosclerotic heart disease, and lipid metabolism disorders are at higher risk, emphasizing the need for integrated care addressing both systemic and oral health factors. Full article