Search Results (15,248)

Produce prescription programs can benefit both individuals and health systems; however, best practices for integrating such programs into the Australian health system are yet unknown. This study explored stakeholders’ perspectives on the acceptability, potential design and integration of produce prescription programs for adults with type 2 diabetes in Australia. Purposive sampling was used to recruit 22 participants for an online workshop, representing six stakeholder groups (government, healthcare service, clinician, food retailer, consumer, non-government organisation). Participant responses were gathered through workshop discussions and a virtual collaboration tool (Mural). The workshop was video-recorded and transcribed verbatim, and thematic analysis was conducted using a deductive–inductive approach. Stakeholders recognised produce prescription as an acceptable intervention; however, they identified challenges to implementation related to contextuality, accessibility, and sustainability. Stakeholders were vocal about the approach (e.g., community-led) and infrastructure (e.g., screening tools) needed to support program design and implementation but expressed diverse views about potential funding models, indicating a need for further investigation. Aligning evaluation outcomes with existing measures in local, State and Federal initiatives was recommended, and entry points for integration were identified within and outside of the Australian health sector. Our findings provide clear considerations for future produce prescription interventions for people with type 2 diabetes. Full article

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease’s presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field. Full article

The COVID-19 pandemic has overwhelmed healthcare systems and triggered global economic downturns. While vaccines have reduced the lethality rate of SARS-CoV-2 to 0.9% as of October 2024, the continuous evolution of variants remains a significant public health challenge. Next-generation medical therapies offer hope in addressing this threat, especially for immunocompromised individuals who experience prolonged infections and severe illnesses, contributing to viral evolution. These cases increase the risk of new variants emerging. This study explores miniACE2 decoys as a novel strategy to counteract SARS-CoV-2 variants. Using in silico design and molecular dynamics, blocking proteins (BPs) were developed with stronger binding affinity for the receptor-binding domain of multiple variants than naturally soluble human ACE2. The BPs were expressed in E. coli and tested in vitro, showing promising neutralizing effects. Notably, miniACE2 BP9 exhibited an average IC₅₀ of 4.9 µg/mL across several variants, including the Wuhan strain, Mu, Omicron BA.1, and BA.2 This low IC50 demonstrates the potent neutralizing ability of BP9, indicating its efficacy at low concentrations.Based on these findings, BP9 has emerged as a promising therapeutic candidate for combating SARS-CoV-2 and its evolving variants, thereby positioning it as a potential emergency biopharmaceutical. Full article

The popularity of ChatGPT has raised questions surrounding AI’s potential for health use cases. Since the release of ChatGPT in 2022, social media users have shared their prompts and ChatGPT responses on different topics such as health. Despite editorials and opinion articles discussing the potential uses of ChatGPT, there is a lack of a systematic approach to identify and analyze the use cases of ChatGPT in health. This study establishes a framework for gathering and identifying tweets (i.e., posts on social media site “X”, formerly known as Twitter) that discuss health use cases of ChatGPT, integrating topic modeling with constructivist grounded theory (CGT) to organize these topics into common categories. Using this framework, nine topics were identified, which were further grouped into four categories: (1) Clinical Workflow, (2) Wellness, (3), Diseases, and (4) Gender Identity. The Clinical Workflow category was the most popular category, and included four topics: (1) Seeking Advice, (2) Clinical Documentation, (3) Medical Diagnosis, and (4) Medical Treatment. Among the identified topics, “Diet and Workout Plans” was the most popular topic. This research highlights the potential of social media to identify the health use cases and potential health applications of an AI-based chatbot such as ChatGPT. The identified topics and categories can be beneficial for researchers, professionals, companies, and policymakers working on health use cases of AI chatbots. Full article

Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy. Full article

Fish allergy is a significant health concern, with diagnosis and management complicated by diverse fish species and allergens. We conducted a comprehensive RNA-seq analysis of eight fish species to identify allergen profiles, integrating ImmunoCAP sIgE data to explore associations with allergen expression and diagnostic performance. Over 30 putative fish allergens were identified, with varying sequence similarities and expression levels, roughly classifying fish into two groups based on parvalbumin (PV) expression. Higher similarities in allergen expression correlated with stronger sIgE data relationships among fish extracts. High PV expression and conserved PV sequences were linked to elevated sIgE measurements, potentially indicating higher allergenicity. For diagnosis, species-specific extract sIgE remained the best indicator of corresponding fish allergy diagnosis, while incorporating multiple sIgE data enhanced performance. In component-resolved diagnosis (CRD), the current panel with PV alone showed comparable performance to fish extract for PV-high fish allergy, while PV-low fish may require the inclusion of more minor allergens for improved CRD accuracy. This RNA-seq allergen analysis helps reveal fish allergen profiles, classify fish groups, and predict allergenicity, potentially improving CRD design and food management in fish allergy. Full article

Introduction: The environmentally acquired aerobic spore-forming (EAS-Fs) bacteria that are ubiquitous in nature (e.g., soil) are transient colonisers of the mammalian gastro-intestinal tract. Without regular exposure, their numbers quickly diminish. These species of bacteria have been suggested to be essential to the normal functioning of metabolic and immunogenic health. The modern Western lifestyle restricts exposure to these EAS-Fs, possibly explaining part of the pathogenesis of many Western diseases. To date, the only animal studies that address specific microbiome modelling are based around germ-free animals. We have designed a new animal model that specifically restricts exposure to environmental sources of bacteria. Methodology: A new protocol, termed Super Clean, which involves housing mice in autoclaved individually ventilated cages (IVCs), with autoclaved food/water and strict ascetic handling practice was first experimentally validated. The quantification of EAS-Fs was assessed by heat-treating faecal samples and measuring colony-forming units (CFUs). This was then compared to mice in standard conditions. Mice were housed in their respective groups from birth until 18 months. Stool samples were taken throughout the experiment to assess for abundance in transiently acquired environmental bacteria. Clinical, biochemical, histological, and gene expression markers were analysed for diabetes, hypercholesterolaemia, obesity, inflammatory bowel disease, and non-alcoholic fatty liver disease (the “diseases of the West”). Results: Our results show that stringent adherence to the Super Clean protocol produces a significantly decreased abundance of aerobic spore-forming Bacillota after 21 days. This microbiomic shift was correlated with significantly increased levels of obesity and impaired glucose metabolism. There was no evidence of colitis, liver disease or hypercholesterolaemia. Conclusions: This new murine model successfully isolates EAS-Fs and has potential utility for future research, allowing for an investigation into the clinical impact of living in relative hygienic conditions. Full article

Antimicrobial resistance (AMR) poses a significant global health threat, necessitating the development of novel antibacterial strategies. Serratiopeptidase (SP), a metalloprotease produced by bacteria such as Serratia marcescens, has gained attention not only for its anti-inflammatory properties but also for its potential antibacterial activity. However, its protein nature makes it susceptible to pH changes and self-proteolysis, limiting its effectiveness. This study aimed to increase both the enzymatic stability and antibacterial activity of serratiopeptidase through immobilization on titanium oxide nanoparticles (TiO₂-NPs), leveraging the biocompatibility and stability of these nanomaterials. Commercial TiO₂-NPs were characterized using TGA/DTG, FT-IR, UV–Vis, and XRD analyses, and their biocompatibility was assessed through cytotoxicity studies. Serratiopeptidase was produced via fermentation using the C8 isolate of Serratia marcescens obtained from the intestine of Bombyx mori L., purified chromatographically, and immobilized on carboxylated nanoparticles via EDC/NHS coupling at various pH conditions. The optimal enzymatic activity was achieved by using pH 5.1 for nanoparticle activation and pH 5.5 for enzyme coupling. The resulting bioconjugate demonstrated stable proteolytic activity at 25 °C for 48 h. Immobilization was confirmed by FT-IR spectroscopy, and the Michaelis–Menten kinetics were determined. Notably, the bioconjugate exhibited two-fold greater antibacterial activity against E. coli than the free enzyme or TiO₂-NPs at 1000 µg/mL. This study successfully developed a serratiopeptidase–TiO₂ bioconjugate with enhanced enzymatic stability and antibacterial properties. The improved antibacterial activity of the immobilized enzyme presents a promising approach for developing new tools to combat antimicrobial resistance, with potential applications in healthcare, food safety, and environmental protection. Full article

Background/Objectives: The COVID-19 pandemic has profoundly impacted healthcare systems worldwide, including the delivery of ophthalmic emergency services. This study examines the impact of the COVID-19 pandemic on the clinical presentation of emergencies and the accessibility of healthcare in ophthalmology. Methods: The study employed a single-center, consecutive case series design with historical controls to examine electronic health records over a 21-day period during the COVID-19 pandemic and a matched period from the preceding year. Records were analyzed for demographic variables, diagnosis, length of stay, travel distance, and referral status. The urgency of cases was evaluated by three independent graders using the BaSe SCOrE (BAsic SEverity Score for Common OculaR Emergencies). Results: A total of 1229 patients were included in the study, with 786 patients in the 2019 cohort and 443 patients in the 2020 cohort. During the pandemic period, there was a significant decrease in the number of patients and the duration of their visits (p < 0.0001, p < 0.0001, respectively). There was an increase in walk-in patients (p = 0.03), who took significantly longer journeys to be treated as compared to referred patients (p < 0.01). At the same time, the severity of emergencies increased (p = 0.02). The 2019 logistic regression model found that age (p = 0.003), referral status (p < 0.001), distance (p = 0.009), and first presentation (p = 0.02) were significant predictors of the severity, while gender was not (p = 0.78). The 2020 model found that only age (p < 0.001) and referral status (p < 0.001) were significant predictors of severity. Conclusions: The observed decline in patient volume, increased severity of emergencies, and shifts in predictive variables within the logistic regression models are indicative of significant barriers to healthcare access. Therefore, enhancing health literacy and ensuring low-threshold access to emergency services are crucial, especially during crises. Full article

There is evidence that anxiety and avoidance toward close relationships (i.e., insecure attachment orientations), as well as posttraumatic stress symptoms (PTSSs), are linked to a poor quality of life. The current study aimed to investigate the potential mediating effects of PTSSs on the associations between insecure attachment orientations and domains of quality of life. A convenience sample of 497 adults (375 females, 75.5%), ranging in age between 18 and 65 years old (M = 32.48, SD = 13.26), was recruited. Participants were administered self-report instruments assessing attachment anxiety and avoidance, PTSSs, and domains of quality of life, including physical health, psychological status, social relationships, and environment. A series of mediation analyses were performed to test the mediating role of PTSSs in the relationships between attachment orientations and domains of quality of life. Results showed that attachment anxiety was related to decreased levels of quality of life in all domains, and that their associations were mediated by PTSSs. Also, attachment avoidance was related to a worse quality of psychological status and social relationships, and PTSSs were a significant mediating variable in these associations. Prevention programs and clinical interventions focused on promoting effective strategies for managing distress might be critical in reducing the impact of distressing events on the quality of life of individuals with insecure attachment. Full article

Background: Mental healthcare for children and adolescents with a history of childhood abuse constitutes a major public health issue. Indeed, abuse exposes children to severe and complex post-traumatic stress disorder (cPTSD) but also to neurodevelopmental and psychological repercussions impacting the developmental trajectory. Trauma-focused care is essential to avoid the chronicization of symptoms and disorders. Objective: The aim of this prospective case series study was to investigate the efficacy of eye movement desensitization and reprocessing (EMDR) on complex post-traumatic symptoms and associated psychiatric disorders in adolescents with a history of abuse. Method: Twenty-two adolescents, aged 12 to 17, who had been abused during childhood were included. All adolescents met ICD-11 criteria for complex PTSD. Subjective measures of PTSD and associated psychiatric disorders were taken before (T0) and after 3 months of EMDR therapy (T1). Results: The average PTSD symptom score on the CPTS-RI significantly decreased from 40.2 to 34.4 after EMDR, indicating improvement in post-traumatic symptoms. A significant decrease in the average depression score (CDI from 18.2 at T0 to 10.6 at T1), anxiety score (R–CMAS from 21.3 at T0 to 13.3 at T1), emotional regulation score (ALS from 29 at T0 to 10.8 at T1), insomnia score (ISI from 18.5 at T0 to T1 of 9.2 at T1), and harmful use of alcohol and drugs score (ADOSPA from 2.3 at T0 to 0.3 at T1) was observed after EMDR therapy, as well as an increase in quality of life (CBCL 4–16 score from 57.9 at T0 to 77.4 at T1). Conclusions: The results of this study are encouraging and suggest that EMDR may be effective in the symptom management reducing post-traumatic symptoms and certain comorbid disorders frequently seen in adolescents who have experienced childhood abuse. Further research is needed on adolescent populations suffering from cPTSD (e.g., randomized controlled trials with control groups and other therapies or evaluating the action of the different phases of the study). Full article

This study aimed to investigate the bioaccessibility and biostability of carotenoids, vitamin E isomers, and individual polyphenolic compounds after the in vitro gastrointestinal digestion of two types of berries (raspberry and blackberry fruits). The results of the polyphenols analysis showed that raspberry fruits contained higher concentrations of hydroxybenzoic acids, hydroxycinnamic acids, flavanols, and flavonols compared to blackberry fruits, but exhibited the lowest bioaccessibility values for all the studied polyphenol classes. Ellagic acid represented 13.63% and 2.65% of the hydroxybenzoic acids in raspberry and blackberry fruits. The hydroxybenzoic acids exhibited the highest bioaccessibility index in the intestinal phase of both types of berries, and gallic acid emerged as one of the most bioaccessible phenolic compounds. The bioaccessibility of carotenoids ranged between 15.7 and 17.30% for lutein, 5.52 and 7.56% for astaxanthin, and 7.85 and 9.93% for canthaxanthin, with elevated values being observed in raspberry fruits. Although vitamin E and carotenoids follow a similar path for absorption, the bioaccessibility of vitamin E isomers was higher than that of carotenoids, with γ-tocopherol being the most bioaccessible isomer in both raspberries and blackberries. Knowing the bioaccessibility of food constituents during digestion is crucial, as the potential effectiveness of bioactives for human health largely depends on the bioavailability of these molecules. Full article

Haze refers to an atmospheric phenomenon with extremely low visibility, which has significant impacts on human health and safety. Water vapor alters the scattering properties of atmospheric particulate matter, thus affecting visibility. A comprehensive analysis of the role of water vapor in haze formation is of great scientific significance for forecasting severe pollution weather events. This study investigates the distribution characteristics and variations of water vapor during haze weather in Changchun City (44°N, 125.5°E) in autumn and winter seasons, aiming to reveal the relationship between haze and atmospheric water vapor content. Analysis of observational results for a period of two months (October to November 2023) from a three-wavelength Raman lidar deployed at the site reveals that atmospheric water vapor content is mainly concentrated below 5 km, accounting for 64% to 99% of the total water vapor below 10 km. Furthermore, water vapor content in air pollution exhibits distinct stratification characteristics with altitude, especially within the height range of 1–3 km, where significant water vapor variation layers exist, showing spatial consistency with inversion layers. Statistical analysis of haze events at the site indicates a high correlation between the concentration variations of PM_2.5 and PM₁₀ and the variations in average water vapor mixing ratio (WVMR). During haze episodes, the average WVMR within 3 km altitude is 3–4 times higher than that during clear weather. Analysis of spatiotemporal height maps of aerosols and water vapor during a typical haze event suggests that the relative stability of the atmospheric boundary layer may hinder the vertical transport and diffusion of aerosols. This, in turn, could lead to a sharp increase in aerosol extinction coefficients through hygroscopic growth, thereby possibly exacerbating haze processes. These observational findings indicate that water vapor might play a significant role in haze formation, emphasizing the potential importance of observing the vertical distribution of water vapor for better simulation and prediction of haze events. Full article

In recent years, nitrate has emerged as a significant groundwater pollutant due to its potential ecotoxicity. In particular, nitrate contamination of brackish groundwater poses a serious threat to both ecosystems and human health and remains difficult to treat. A promising, sustainable, and environmentally friendly solution when biological treatments are not applicable is the conversion of nitrate to harmless nitrogen (N₂) or ammonia (NH₃) as a nutrient by electrocatalytic nitrate reduction (eNO₃R) using solar photovoltaic energy. This review provides a comprehensive overview of the current advances in eNO₃R for the production of nitrogen and ammonia. The discussion begins with fundamental concepts, including a detailed examination of the mechanisms and pathways involved, supported by Density Functional Theory (DFT) to elucidate specific aspects of ammonium and nitrogen formation during the process. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) offers promising advancements in enhancing the predictive power of DFT, accelerating the discovery and optimization of novel catalysts. In this review, we also explore various electrode preparation methods and emphasize the importance of in situ characterization techniques to investigate surface phenomena during the reaction process. The review highlights numerous examples of copper-based catalysts and analyses their feasibility and effectiveness in ammonia production. It also explores strategies for the conversion of nitrate to N₂, focusing on nanoscale zerovalent iron as a selective material and the subsequent oxidation of the produced ammonia. Finally, this review addresses the implementation of the eNO₃R process for the treatment of brackish groundwater, discussing various challenges and providing reasonable opinions on how to overcome these obstacles. By synthesizing current research and practical examples, this review highlights the potential of eNO₃R as a viable solution to mitigate nitrate pollution and improve water quality. Full article

Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the data gathered from IMU readings while the patient is performing the TUG test can be used to build a predictive model that would provide an estimate of the probability of suffering a fall in the near future, i.e., assessing prospective fall risk. This study applies deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) to build such predictive models based on features extracted from IMU data acquired during TUG test realizations. Data were obtained from a cohort of 106 older adults wearing wireless IMU sensors with sampling frequencies of 100 Hz while performing the TUG test. The dependent variable is a binary variable that is true if the patient suffered a fall in the six-month follow-up period. This variable was used as the output variable for the supervised training and validations of the deep learning architectures and competing machine learning approaches. A hold-out validation process using 75 subjects for training and 31 subjects for testing was repeated one hundred times to obtain robust estimations of model performances At each repetition, 5-fold cross-validation was carried out to select the best model over the training subset. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values. Full article