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Multimorbidity in people with cardiovascular disease (CVD) is common, but large-scale contemporary reports of patterns and trends in patients with incident CVD are limited. We investigated the burden of comorbidities in patients with incident CVD, how it changed between 2000 and 2014, and how it varied by age, sex, and socioeconomic status (SES). We used the UK Clinical Practice Research Datalink with linkage to Hospital Episode Statistics, a population-based dataset from 674 UK general practices covering approximately 7% of the current UK population. We estimated crude and age/sex-standardised (to the 2013 European Standard Population) prevalence and 95% confidence intervals for 56 major comorbidities in individuals with incident non-fatal CVD. We further assessed temporal trends and patterns by age, sex, and SES groups, between 2000 and 2014. Among a total of 4,198,039 people aged 16 to 113 years, 229,205 incident cases of non-fatal CVD, defined as first diagnosis of ischaemic heart disease, stroke, or transient ischaemic attack, were identified. Although the age/sex-standardised incidence of CVD decreased by 34% between 2000 to 2014, the proportion of CVD patients with higher numbers of comorbidities increased. The prevalence of having 5 or more comorbidities increased 4-fold, rising from 6.3% (95% CI 5.6%-17.0%) in 2000 to 24.3% (22.1%-34.8%) in 2014 in age/sex-standardised models. The most common comorbidities in age/sex-standardised models were hypertension (28.9% [95% CI 27.7%-31.4%]), depression (23.0% [21.3%-26.0%]), arthritis (20.9% [19.5%-23.5%]), asthma (17.7% [15.8%-20.8%]), and anxiety (15.0% [13.7%-17.6%]). Cardiometabolic conditions and arthritis were highly prevalent among patients aged over 40 years, and mental illnesses were highly prevalent in patients aged 30-59 years. The age-standardised prevalence of having 5 or more comorbidities was 19.1% (95% CI 17.2%-22.7%) in women and 12.5% (12.0%-13.9%) in men, and women had twice the age-standardised prevalence of depression (31.1% [28.3%-35.5%] versus 15.0% [14.3%-16.5%]) and anxiety (19.6% [17.6%-23.3%] versus 10.4% [9.8%-11.8%]). The prevalence of depression was 46% higher in the most deprived fifth of SES compared with the least deprived fifth (age/sex-standardised prevalence of 38.4% [31.2%-62.0%] versus 26.3% [23.1%-34.5%], respectively). This is a descriptive study of routine electronic health records in the UK, which might underestimate the true prevalence of diseases. The burden of multimorbidity and comorbidity in patients with incident non-fatal CVD increased between 2000 and 2014. On average, older patients, women, and socioeconomically deprived groups had higher numbers of comorbidities, but the type of comorbidities varied by age and sex. Cardiometabolic conditions contributed substantially to the burden, but 4 out of the 10 top comorbidities were non-cardiometabolic. The current single-disease paradigm in CVD management needs to broaden and incorporate the large and increasing burden of comorbidities.

Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one. We used longitudinal data from linked electronic health records of 4.6 million patients aged 18-100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis). We also varied the prediction windows to 12, 36, 48, and 60 months after baseline and compared model performances. For internal validation, we used 5-fold cross-validation. When the initial set of variables was used, GBC outperformed RF and CPH, with an area under the receiver operating characteristic curve (AUC) of 0.779 (95% CI 0.777, 0.781), compared to 0.752 (95% CI 0.751, 0.753) and 0.740 (95% CI 0.739, 0.741), respectively. In external validation, we observed an AUC of 0.796, 0.736, and 0.736 for GBC, RF, and CPH, respectively. The addition of temporal information improved AUC across all models. In internal validation, the AUC rose to 0.848 (95% CI 0.847, 0.849), 0.825 (95% CI 0.824, 0.826), and 0.805 (95% CI 0.804, 0.806) for GBC, RF, and CPH, respectively, while the AUC in external validation rose to 0.826, 0.810, and 0.788, respectively. This enhancement also resulted in robust predictions for longer time horizons, with AUC values remaining at similar levels across all models. Overall, compared to the baseline reference CPH model, the final GBC model showed a 10.8% higher AUC (0.848 compared to 0.740) for prediction of risk of emergency admission within 24 months. GBC also showed the best calibration throughout the risk spectrum. Despite the wide range of variables included in models, our study was still limited by the number of variables included; inclusion of more variables could have further improved model performances. The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning.

Background The COVID-19 pandemic has triggered a global mental health crisis. Yet, we know little about the lasting effects of COVID-19 infection on mental health. This prospective longitudinal study aimed to investigate the trajectories of mental health changes in individuals infected with COVID-19 and to identify potential predictors that may influence these changes. Methods A web-survey that targeted individuals that had been infected with COVID-19 was used at three time-points: T0 (baseline), T1 (six months), and T2 (twelve months). The survey included demographics, questions related to COVID-19 status, previous psychiatric diagnosis, post-COVID impairments, fatigue, and standardized measures of depression, anxiety, insomnia. Linear mixed models were used to examine changes in depression, anxiety, and insomnia over time and identify factors that impacted trajectories of mental health outcomes. Results A total of 236 individuals completed assessments and was included in the longitudinal sample. The participants’ age ranged between 19 and 81 years old (M = 48.71, SD = 10.74). The results revealed notable changes in mental health outcomes over time. The trajectory of depression showed significant improvement over time while the trends in anxiety and insomnia did not exhibit significant changes over time. Younger participants and individuals who experienced severe COVID-19 infection in the acute phase were identified as high-risk groups with worst mental ill-health. The main predictors of the changes in the mental health outcomes were fatigue and post-COVID impairments. Conclusions The findings of our study suggest that mental health outcomes following COVID-19 infection exhibit a dynamic pattern over time. The study provides valuable insights into the mental health trajectory following COVID-19 infection, emphasizing the need for ongoing assessment, support, and interventions tailored to the evolving mental health needs of this population.

Peer-to-peer live media streaming over the Internet is becoming increasingly more popular, though it is still a challenging problem. Nodes should receive the stream with respect to intrinsic timing constraints, while the overlay should adapt to the changes in the network and the nodes should be incentivized to contribute their resources. In this work, we meet these contradictory requirements simultaneously, by introducing a distributed market model to build an efficient overlay for live media streaming. Using our market model, we construct two different overlay topologies, tree-based and mesh-based, which are the two dominant approaches to the media distribution. First, we build an approximately minimal height multiple-tree data dissemination overlay, called Sepidar. Next, we extend our model, in GLive, to make it more robust in dynamic networks by replacing the tree structure with a mesh. We show in simulation that the mesh-based overlay outperforms the multiple-tree overlay. We compare the performance of our two systems with the state-of-the-art NewCoolstreaming, and observe that they provide better playback continuity and lower playback latency than that of NewCoolstreaming under a variety of experimental scenarios. Although our distributed market model can be run against a random sample of nodes, we improve its convergence time by executing it against a sample of nodes taken from the Gradient overlay. The evaluations show that the streaming overlays converge faster when our market model works on top of the Gradient overlay.

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (ii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities and provide a survey of on-device training from a systems perspective.

In the ever-evolving adversarial machine learning landscape, developing effective defenses against patch attacks has become a critical challenge, necessitating reliable solutions to safeguard real-world AI systems. Although diffusion models have shown remarkable capacity in image synthesis and have been recently utilized to counter \\(\\ell_p\\)-norm bounded attacks, their potential in mitigating localized patch attacks remains largely underexplored. In this work, we propose DiffPAD, a novel framework that harnesses the power of diffusion models for adversarial patch decontamination. DiffPAD first performs super-resolution restoration on downsampled input images, then adopts binarization, dynamic thresholding scheme and sliding window for effective localization of adversarial patches. Such a design is inspired by the theoretically derived correlation between patch size and diffusion restoration error that is generalized across diverse patch attack scenarios. Finally, DiffPAD applies inpainting techniques to the original input images with the estimated patch region being masked. By integrating closed-form solutions for super-resolution restoration and image inpainting into the conditional reverse sampling process of a pre-trained diffusion model, DiffPAD obviates the need for text guidance or fine-tuning. Through comprehensive experiments, we demonstrate that DiffPAD not only achieves state-of-the-art adversarial robustness against patch attacks but also excels in recovering naturalistic images without patch remnants. The source code is available at https://github.com/JasonFu1998/DiffPAD.

Background Students are among the groups that use smartphones for long periods throughout the day and night. Therefore, this study aimed to examine the relationship between smartphone characteristics and the prevalence of hand discomfort among university students. Methods This study included 204 university students, selected based on their willingness to participate and inclusion criteria. Participants reported hand pain and discomfort by completing the Cornell Hand Discomfort Questionnaire (CHDQ). Personal information was collected through a demographic questionnaire. Smartphone characteristics were obtained from the Internet based on the smartphone model self-reported by students. Results According to the Cornell questionnaire, 59.3% of students reported experiencing discomfort in their right hand, while 38.2% reported discomfort in their left hand due to smartphone use. Furthermore, 36.3% of students reported experiencing pain in two or more regions on their right hand, while 20.1% reported pain in two or more areas on their left hand. More than half of the students in the right hand (53.5%) and more than one-third (33.3%) in the left hand obtained pain scores of more than 1.5. The chi-square test indicated a statistically significant relationship between the weight of the smartphone and the prevalence of discomfort in the right hand (χ 2  = 4.80, p  = 0.03). Furthermore, a statistically significant relationship was found between the discomfort or pain scores experienced in both hands and the number of painful areas in those hands (right hand: χ 2  = 219.04, p  = 0.00; left hand: χ 2  = 213.13, p  = 0.00). Conclusions Smartphone use can cause discomfort and pain in the hands of university students. The physical characteristics of the smartphone, such as its weight, play a significant role in contributing to right-hand-related pain among students. It is important to consider ergonomic factors in smartphone design and usage to reduce musculoskeletal problems among users, especially students.

Background Excessive smartphone usage among students can lead to discomfort in their hands and fingers. This study investigates the impact of smartphone holding posture, duration of usage, and the prevalence of wrist and finger pain among university students. Methods This cross-sectional study involved 213 university students who were selected based on inclusion criteria. Data was collected through a demographic information questionnaire. Participants self-reported five different postures for holding and interacting with a smartphone. The prevalence, frequency, severity, and interference of wrist and finger discomfort were assessed using the Cornell Hand Discomfort Questionnaires (CHDQ). Results The study revealed that the average age of participants was 21.3 ± 2.2 years. On average, they had been using smartphones for 7.9 ± 3.1 years and spent an average of 4.9 ± 2.5 h daily holding them in their hands. In terms of discomfort, more than 25% of students reported pain in areas C (thumb finger), E (Palm Pollicis), and F (wrist) of the right hand, which was significantly related to the duration of holding the smartphone in that hand. Additionally, smartphone holding duration significantly affected areas D (palm) and F of the left hand, with over 11% of students experiencing discomfort. The most prevalent posture among students (41% of participants) involved holding the smartphone with the right hand only, with the thumb touching the screen. Notably, areas B (χ 2  = 21.7), C (χ 2  = 10.27), D (χ 2  = 65.54), and E (χ 2  = 59.49) of the right hand, as well as areas C (χ 2  = 6.58) and E (χ 2  = 44.28) of the left hand, exhibited significant associations with the postures of holding the smartphone. Conclusions The duration of smartphone use and the postures in which it is held contribute to the prevalence of discomfort in the thumb area and related muscles among right-handed students.

Helicobacter pylori (H. pylori) infection is generally acquired during early childhood; therefore, the immune response which usually takes place at this age may influence or even determine susceptibility to the infection contributing to the clinical outcomes in adulthood. Several cytokines including IL-6, IL-10, and TGF-β1 as well as Foxp3+ cell numbers have been shown to be higher; however, some other cytokines consisting of IL-1β, IL-17A, and IL-23 are lower in infected children than in infected adults. Immune response to H. pylori infection in children is predominant Treg instead of Th17 cell response. These results indicate that immune system responses probably play a role in persistent H. pylori infection. Childhood H. pylori infection is also associated with significantly lower levels of inflammation and ulceration compared with adults. This review, therefore, aimed to provide critical findings of the available literature about comparative immune system in children and adults with H. pylori infection.

Helicobacter pylori (H. pylori)-induced gastric inflammation in the gastric mucosa and significantly increases the risk of developing gastritis and peptic ulcer disease (PUD). The objective of this research is to determine the role of tumor necrosis factor-α (TNF-α) expression in the gastric mucosa of patients with H. pylori-associated gastritis and PUD compared to uninfected patients, and we determined the relation between TNF-α expression and Th1/Th17/Th22, and Treg cells. Fifty-five patients with H. pylori-associated gastritis, 47 patients with H. pylori-associated PUD, and 48 uninfected patients were in this research. Antrum biopsy was used to detect H. pylori, virulence factors and histopathological assessments. Expression of TNF-α in the infected group was significantly higher than the uninfected group. Also, cagA/oipA-positive infected patients induce significantly more TNF-α expression than do cagA/oipA-negative infected patients. Expression of TNF-α was significantly increased in the PUD group than the gastritis group. Notably, TNF-α expression had a significant positive correlation with the frequency of Th1/Th17/Th22 lymphocytes in the PUD group. These findings indicate the importance of increasing TNF-α with Th1, Th17, Th22 responses increase as an important risk factor for PUD in context of H. pylori infection.Key pointsExpression of TNF-α was significantly increased in the PUD group than the gastritis group.Notably, TNF-α expression had a significant positive correlation with the frequency of Th1/Th17/Th22 lymphocytes in the PUD group.These findings indicate the importance of increasing TNF-α with Th1, Th17, Th22 responses increase as an important risk factor for PUD in context of H. pylori infection.