Search Results (19,271)

Introduction: The broader determinants of health including a wide range of community assets are extremely important in relation to public health outcomes. Multiple health conditions, multimorbidity, is a growing problem in many populations worldwide. Methods: This paper quantified the effect of community assets on major health conditions for the population of England over six years, at a fine spatial scale using a data analytic approach. Community assets, which included indices of the health system, green space, pollution, poverty, urban environment, safety, and sport and leisure facilities, were quantified in relation to major health conditions. The health conditions examined included high blood pressure, obesity, dementia, diabetes, mental health, cardiovascular conditions, musculoskeletal conditions, respiratory conditions, kidney and liver disease, and cancer. Cluster analysis and dendrograms were calculated for the community assets and major health conditions. For each health condition, a statistical model with all community assets was fitted, and model selection was performed. The number of significant community assets for each health condition was recorded. The unique variance, explained by each significant community asset per health condition, was quantified using hierarchical variance partitioning within an analysis of variance model. Results: The resulting data indicate major health conditions are often clustered, as are community assets. The results suggest that diversity and richness of community assets are key to major health condition outcomes. Primary care service waiting times and distance to public parks were significant predictors of all health conditions examined. Primary care waiting times explained the vast majority of the variances across health conditions, with the exception of obesity, which was better explained by absolute poverty. Conclusions: The implications of the combined findings of the health condition clusters and explanatory power of community assets are discussed. The vast majority of determinants of health could be accounted for by healthcare system performance and distance to public green space, with important covariate socioeconomic factors. Emphases on community approaches, significant relationships, and asset strengths and deficits are needed alongside targeted interventions. Whilst the performance of the public health system remains of key importance, community assets and local infrastructure remain paramount to the broader determinants of health. Full article

The SARS-CoV-2 nucleocapsid protein (N protein) is critical in viral replication by undergoing liquid–liquid phase separation to seed the formation of a ribonucleoprotein (RNP) complex to drive viral genomic RNA (gRNA) translation and in suppressing both stress granules and processing bodies, which is postulated to increase uncoated gRNA availability. The N protein can also form biomolecular condensates with a broad range of host endogenous proteins including RNA binding proteins (RBPs). Amongst these RBPs are proteins that are associated with pathological, neuronal, and glial cytoplasmic inclusions across several adult-onset neurodegenerative disorders, including TAR DNA binding protein 43 kDa (TDP-43) which forms pathological inclusions in over 95% of amyotrophic lateral sclerosis cases. In this study, we demonstrate that the N protein can form biomolecular condensates with TDP-43 and that this is dependent on the N protein C-terminus domain (N-CTD) and the intrinsically disordered C-terminus domain of TDP-43. This process is markedly accelerated in the presence of RNA. In silico modeling suggests that the biomolecular condensate that forms in the presence of RNA is composed of an N protein quadriplex in which the intrinsically disordered TDP-43 C terminus domain is incorporated. Full article

Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687–0.781) and 0.747 (95% CI 0.701–0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features. Full article

(1) Background: Data on persisting symptoms after SARS-CoV-2 infection in children and adolescents are conflicting. Due to the absence of a clear pathophysiological correlate and a definitive diagnostic test, the diagnosis of Long COVID currently rests on consensus definitions only. This review aims to summarise the evidence regarding Long COVID in children and adolescents, incorporating the latest studies on this topic. (2) Methods: We designed a comprehensive search strategy to capture all relevant publications using Medline via the PubMed interface, with the initial literature search conducted in April 2023. To be included, publications had to present original data and include >50 participants with Long COVID symptoms aged between 0 and18 years. (3) Results: A total of 51 studies met the inclusion criteria, with most studies originating from Europe (n = 34; 66.7%), followed by the Americas (n = 8; 15.7%) and Asia (n = 7; 13.7%). Various study designs were employed, including retrospective, cross-sectional, prospective, or ambispective approaches. Study sizes varied significantly, with 18/51 studies having fewer than 500 participants. Many studies had methodological limitations: 23/51 (45.1%) studies did not include a control group without prior COVID-19 infection. Additionally, a considerable number of papers (33/51; 64.7%) did not include a clear definition of Long COVID. Other limitations included the lack of PCR- or serology-based confirmation of SARS-CoV-2 infection in the study group. Across different studies, there was high variability in the reported prevalence of Long COVID symptoms, ranging from 0.3% to 66.5%, with the majority of studies included in this review reporting prevalences of approximately 10–30%. Notably, the two studies with the highest prevalences also reported very high prevalences of Long COVID symptoms in the control group. There was a relatively consistent trend for Long COVID prevalence to decline substantially over time. The prevalence of Long COVID appeared to differ across different paediatric age groups, with teenagers being more commonly affected than younger children. Furthermore, data suggest that children and adolescents are less commonly affected by Long COVID compared to adults. In children and adolescents, Long COVID is associated with a very broad range of symptoms and signs affecting almost every organ system, with the respiratory, cardiovascular, and neuropsychiatric systems being most commonly affected. (4) Conclusions: The heterogeneity and limitations of published studies on Long COVID in children and adolescents complicate the interpretation of the existing data. Future studies should be rigorously designed to address unanswered questions regarding this complex disease. Full article

A peptide-based hydrogel sequence was computationally predicted from the Ala-rich cross-linked domains of elastin. Three candidate peptides were subsequently synthesised and characterised as potential drug delivery vehicles. The elastin-derived peptides are Fmoc-FFAAAAKAA-NH₂, Fmoc-FFAAAKAA-NH₂, and Fmoc-FFAAAKAAA-NH₂. All three peptide sequences were able to self-assemble into nanofibers. However, only the first two could form hydrogels, which are preferred as delivery systems compared to solutions. Both of these peptides also exhibited favourable nanofiber lengths of at least 1.86 and 4.57 µm, respectively, which are beneficial for the successful delivery and stability of drugs. The shorter fibre lengths of the third peptide (maximum 0.649 µm) could have inhibited their self-assembly into the three-dimensional networks crucial to hydrogel formation. Full article

The ongoing amplification of climate change necessitates the exploration and implementation of effective strategies to mitigate ecological issues while simultaneously preserving economic and social well-being. Renewable power systems offer a way to reduce adverse anthropogenic effects without hindering economic growth. This study aims to conduct a comprehensive bibliometric analysis of renewable power systems to explore their historical context, identify influential studies, and uncover research gaps, hypothesizing that global contributions and policy support significantly influence the field’s dynamics. Following Preferred Reporting Items For Systematic Reviews And Meta-Analyses guidelines, this study utilized Scopus tools analysis and VOSviewer 1.6.20 software to examine the metadata sourced from scientific databases in Scopus. The outcomes of this investigation facilitate the identification of the most prolific countries and authors, as well as collaborative efforts that enrich the theoretical landscape of renewable power systems. The study also traces the evolution of research on renewable power systems. Furthermore, the results reveal key scientific clusters in the analysis: the first cluster concentrates on renewable energy and sustainable development, the second on the relationship between government policies and renewable power systems, and the third on the role of incentives that catalyse the advancement of renewable power systems. The findings of this meta-analysis not only contribute valuable insights to existing research but also enable the identification of emerging research areas related to renewable power system development. Full article

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model’s performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings. Full article

Girsu, the modern site of Tello (southern Iraq), represents one of the earliest known urban centres of the ancient world, along with Uruk, Eridu, and Ur. During the 3rd millennium BCE (3000–2000 BCE), Girsu was revered as the sanctuary of the Sumerian heroic deity Ningirsu, who fought with supernatural beasts and made possible the introduction of irrigation and agriculture in Sumer. While much is known about the gods, their roles, and rituals inside the temples, there is little textual or archaeological evidence concerning the rituals that took place in the large open-air plazas adjacent to the temples. These areas within the sacred precinct were where the general population would gather to participate in festivals and ceremonies to honour the gods. To better understand the ancient cultic realm in southern Mesopotamia, an in-depth investigation of a favissa (ritual pit) discovered within the sacred precinct at Girsu was undertaken. The excavations recovered a large quantity of ceramics and animal remains that had been used for ritual purposes. Through the study of archaeological remains of cultic spaces at Girsu, information on ritual behaviour such as sacrificial animal slaughtering and consumption for the purpose of feasting, the types of libations provided to quench the thirst of the gods, and the distance travelled to take part in the annual festivals to pay homage to the patron god of their sacred city were explored. Analysis of the associated ceramics, cuneiform texts, and zooarchaeological remains (including stable isotope data), allowed a multi-faceted and integrative approach to better understand ceremonial behaviour and ritual feasting in this sacred city. New insights into communal and performative participation in ceremonies, especially by non-elite individuals, are generated. These data increase our knowledge not only of how Girsu’s citizens organised their sacred spaces and religious festivals, but also of how they behaved in order to satisfy the ever-demanding needs of their gods. Full article

Clinical trials with treatments inhibiting myostatin pathways to increase muscle mass are currently ongoing in spinal muscular atrophy. Given evidence of potential myostatin pathway downregulation in Spinal Muscular Atrophy (SMA), restoring sufficient myostatin levels using disease-modifying treatments (DMTs) might arguably be necessary prior to considering myostatin inhibitors as an add-on treatment. This retrospective study assessed pre-treatment myostatin and follistatin levels’ correlation with disease severity and explored their alteration by disease-modifying treatment in SMA. We retrospectively collected clinical characteristics, motor scores, and mysotatin and follistatin levels between 2018 and 2020 in 25 Belgian patients with SMA (SMA1 (n = 13), SMA2 (n = 6), SMA 3 (n = 6)) and treated by nusinersen. Data were collected prior to treatment and after 2, 6, 10, 18, and 30 months of treatment. Myostatin levels correlated with patients’ age, weight, SMA type, and motor function before treatment initiation. After treatment, we observed correlations between myostatin levels and some motor function scores (i.e., MFM32, HFMSE, 6MWT), but no major effect of nusinersen on myostatin or follistatin levels over time. In conclusion, further research is needed to determine if DMTs can impact myostatin and follistatin levels in SMA, and how this could potentially influence patient selection for ongoing myostatin inhibitor trials. Full article

Background: Adenosine triphosphate-citrate lyase (ACLY) inhibition has proven clinically efficacious for low-density lipoprotein cholesterol (LDL-c) lowering and cardiovascular disease (CVD) risk reduction. Clinical and genetic evidence suggests that some LDL-c lowering strategies, such as 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibition with statin therapy increase body weight and the risk of developing type 2 diabetes mellitus (T2DM). However, whether ACLY inhibition affects metabolic risk factors is currently unknown. We aimed to investigate the effects of ACLY inhibition on glycaemic and anthropometric traits using Mendelian randomization (MR). Methods: As genetic instruments for ACLY inhibition, we selected weakly correlated single-nucleotide polymorphisms at the ACLY gene associated with lower ACLY gene expression in the eQTLGen study (N = 31,684) and lower LDL-c levels in the Global Lipid Genetic Consortium study (N = 1.65 million). Two-sample Mendelian randomization was employed to investigate the effects of ACLY inhibition on T2DM risk, and glycaemic and anthropometric traits using summary data from large consortia, with sample sizes ranging from 151,013 to 806,834 individuals. Findings for genetically predicted ACLY inhibition were compared to those obtained for genetically predicted HMGCR inhibition using the same instrument selection strategy and outcome data. Results: Primary MR analyses showed that genetically predicted ACLY inhibition was associated with lower waist-to-hip ratio (β per 1 standard deviation lower LDL-c: −1.17; 95% confidence interval (CI): −1.61 to −0.73; p < 0.001) but not with risk of T2DM (odds ratio (OR) per standard deviation lower LDL-c: 0.74, 95% CI = 0.25 to 2.19, p = 0.59). In contrast, genetically predicted HMGCR inhibition was associated with higher waist-to-hip ratio (β = 0.15; 95%CI = 0.04 to 0.26; p = 0.008) and T2DM risk (OR = 1.73, 95% CI = 1.27 to 2.36, p < 0.001). The MR analyses considering secondary outcomes showed that genetically predicted ACLY inhibition was associated with a lower waist-to-hip ratio adjusted for body mass index (BMI) (β = −1.41; 95%CI = −1.81 to −1.02; p < 0.001). In contrast, genetically predicted HMGCR inhibition was associated with higher HbA1c (β = 0.19; 95%CI = 0.23 to 0.49; p < 0.001) and BMI (β = 0.36; 95%CI = 0.23 to 0.49; p < 0.001). Conclusions: Human genetic evidence supports the metabolically favourable effects of ACLY inhibition on body weight distribution, in contrast to HMGCR inhibition. These findings should be used to guide and prioritize ongoing clinical development efforts. Full article

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects. Full article

Pseudocirrhosis is a diffuse nodularity of the liver that radiologically mimics cirrhosis but is a distinct pathological process. It is seen almost exclusively in patients with liver metastases and may represent a response to systemic treatment. Data on the risk factors for pseudocirrhosis and outcomes are limited. In total, 170 patients with a diagnosis of breast cancer and pseudocirrhosis in a 10-year period were identified and retrospectively analysed. Data were collected on baseline patient characteristics, treatments received, and outcomes. Median time between diagnosis of liver metastases and diagnosis of pseudocirrhosis was 17.1 months (range, 0–149 months). In total, 89.4% of patients received chemotherapy between their diagnosis of breast cancer liver metastases and their diagnosis of pseudocirrhosis, most commonly a taxane (74.7%) or capecitabine (67.1%), and the median treatment lines received was 3. Median OS from first diagnosis of pseudocirrhosis was 7.6 months (95% CI: 6.1–9.6 months) and was longer in patients with HER2+ disease at 16.7 months (95% CI: 6.4–32.9 months), which was statistically significant. In our study, pseudocirrhosis occurred in the presence of liver metastases and was associated with a poor prognosis. HER2+ patients with pseudocirrhosis had a better prognosis than other subtypes, but we did not identify other significant predictors of survival. Chemotherapy was not a prerequisite for pseudocirrhosis development, although the majority of patients had received at least one line of chemotherapy before pseudocirrhosis was diagnosed. Full article

Background: The presenting symptoms of patients with Hirschsprung’s disease (HD) are a failure to pass meconium, abdominal distension, and bilious vomiting. The gold standard diagnosis is a rectal biopsy to confirm aganglionosis. The aim of our study was to describe the diagnostic pathway of Hirschsprung’s disease at our institution and document the indication for a rectal biopsy. Methods: We have performed a prospective collection of all patients who underwent a rectal biopsy to exclude HD from December 2022 until September 2023 including. The following data were collected: patient’s age, presenting symptoms, type of biopsy, failure rate, complications, and histopathological results. Results: We identified 33 patients who underwent 34 rectal biopsies at 0.6 years of age. A total of 17 patients had a rectal suction biopsy (RSB), and 17 patients underwent a partial thickness under general anaesthesia (GA). 1/17 (6%) patients had an inconclusive RSB and subsequently underwent a biopsy under GA. Constipation and chronic abdominal distension plus vomiting were the most common presenting symptoms throughout all ages. Five patients (15%) had a rectal biopsy that was positive for HD. Conclusion: A protocolised approach to the assessment of infants and children with suspected HD ensures the appropriate utilisation of invasive procedures such as biopsy. Full article

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model. Full article

Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneously; therefore, a single model should be capable of detecting defects in all kinds of fabric. So training a robust deep learning model that detects defects in fabric datasets generated during production with high accuracy and lower computational costs is required. This study uses an indigenous dataset directly sourced from Chenab Textiles, providing authentic and diverse images representative of actual manufacturing conditions. The dataset is used to train a computationally faster but lighter state-of-the-art network, i.e., YOLOv8. For comparison, YOLOv5 and MobileNetV2-SSD FPN-Lite models are also trained on the same dataset. YOLOv8n achieved the highest performance, with a mAP of 84.8%, precision of 0.818, and recall of 0.839 across seven different defect classes. Full article