Actus Santé

Menstruation is a natural process for women that shows a healthy female reproductive system, yet women are stigmatized during this experience. A woman's physical, mental, and social well-being are significantly impacted by menstruation. Thus, menstrual disorders are widespread and are a major social problem. There have been variations in people's perceptions of menstruation and gaps in research comparing gender and beliefs about menstruation and its impact on academic performance. This study investigates gender and beliefs about menstruation and how it affects academic performance and social life using a self-administered online questionnaire that inquires about general knowledge of menstruation, participants' beliefs and experiences with menstruation, participants' academic performance and demographic questions. Participants responded using a 5-point Likert scale, showing that 40.44% of the respondents' knowledge and understanding of menstruation have positively influenced over the years.Furthermore, the results from the chi-square analysis revealed an association between men's and women's perceptions of menstruation. Interestingly, women stated that menstruation has no effect on their academic performances, but on the other hand, men were indecisive about its impact on women's intellectual tasks. Also, 28.72% of respondents reported that they find studying and excelling during menstruation uneasy. The result shows that women can excel at any task, even during menstruation. Knowledge of menstruation is essential in furthering a better understanding of women's health, demystifying menstruation myths, and advancing the social well-being of this gender.

Background Pulsta valve is increasingly used for percutaneous pulmonary valve implantation (PPVI) in patients with large native right ventricular outflow tract (RVOT). The aim of this study is to reveal Pulsta valve implantation outcomes within the native RVOT and to evaluate the adaptability of Pulsta valve to diverse native main pulmonary artery (PA). Methods A multicenter retrospective study encompassing 183 patients with moderate to severe pulmonary regurgitation (PR) in the native RVOT who underwent PPVI with Pulsta valves(R) between February 2016 and August 2023 at the five Korean and Taiwanese tertiary referral centers. Results Successful implantation of the Pulsta valves was achieved in 180 out of 183 patients (98.4 %) with an average age of 26.6 {+/-} 11.0 years. Mean follow-up duration was 29 months. Baseline assessments revealed enlarged right ventricle (RV) volume (mean indexed RV end-diastolic volume: 163.1(Interquartile range, IQR, 152.2-179.9) mL/m2), which significantly decreased to 123.0(IQR: 106.9-137.2) mL/m2 after one year. In this study, the main PA types were classified as follows: pyramidal (3.8%), straight (38.3%), reverse pyramidal (13.7%), convex (26.2%), and concave (18.0%) shapes. Pulsta valve placement was adapted, with distal main PA for pyramidal shapes and proximal or mid-PA for reverse pyramidal shapes. The remaining patients underwent Pulsta valve implantation in the proximal or mid part of the main PA, depending on the anatomical features and size of the main PA. Two patients experienced Pulsta valve embolization to RV, necessitating surgical removal, and one patient encountered valve migration to the distal main PA, necessitating surgical fixation. Conclusions Customized valve insertion sites are pivotal in self-expandable PPVI considering diverse native RVOT shape. Rather soft and compact structure of Pulsta valve has characteristics to be adaptable to diverse native RVOT geometries. Key words percutaneous pulmonary valve implantation; native right ventricular outflow tract type; congenital heart disease; pulmonary regurgitation

Background: Population-level prevention efforts in the US have yet to correct the upward trends in increasing US obesity prevalence. Italy, France, and Japan are high-income countries that have employed approaches aimed at obesity prevention that may have contributed to their favorable obesity trends. It is plausible that these efforts could be culturally adapted with similar effectiveness to the US population. This paper outlines the protocol of a scoping review that will identify and characterize the range of obesity prevention efforts implemented in Italy, France, and Japan to inform potential strategies for obesity prevention in the US. Methods and analysis: We will conduct a systematic search in PubMed, Embase, and Cochrane. We will assess articles that describe any national, regional, or local public health and/or clinical guideline, recommendation, intervention, campaign, policy or other population-level effort targeting obesity prevention in Italy, France, and Japan. Two independent reviewers will screen the articles, and a third independent reviewer will resolve conflicts that arise upon screening. Descriptive synthesis will be performed to describe the country-specific prevention strategies and summarize the available evidence for their effectiveness. Discussion: The purpose of this paper is to describe the study protocol for a scoping review that will provide a comprehensive summary of available evidence for obesity prevention strategies that have been implemented in Italy, France, and Japan. These strategies will inform potential prevention efforts for consideration in the US. Scoping review registration DOI: https://doi.org/10.17605/OSF.IO/M35YX

BACKGROUND: Studies in ostensibly healthy male master athletes have revealed a greater prevalence of coronary artery calcification (CAC) and coronary plaques compared with relatively sedentary counterparts. In contrast, data relating to potentially adverse coronary remodelling in female master athletes is sparse and conflicting. We investigated the prevalence of coronary atherosclerosis in a cohort of predominantly post-menopausal female master athletes with a low atherosclerotic risk profile. METHODS: 196 female athletes with a mean age of 55{+/-}7 years-old and median exercise duration of 33 years (25-39) and 59 relatively sedentary females of similar age underwent cardiovascular investigations including a coronary computed tomogram angiography with assessment of CAC scores, coronary plaques, and pericoronary adipose tissue attenuation. 70% of the athletes and 68% of the control group were post-menopausal. RESULTS: Athletes and controls had a similarly low Framingham 10-year risk (1.49% versus 2.1%; P=0.68), but body mass index and blood pressure were lower and HDL-C was higher in the athletes. The prevalence of CAC score >0 Agatston units (AU) was low and did not differ between athletes and controls (21% versus 32%;P=0.073). Female athletes had a lower prevalence of a CAC score >50th centile (19% versus 32%;P<0.03) and >75th centile (14% versus 25%;P=0.045) for age compared with controls, but the prevalence of a CAC score >100 AU did not differ between the groups (3.6% versus 8.5%;P=0.12). There were also no differences between the groups in the prevalence of individuals with coronary plaques (21% versus 32%;P=0.09), total plaque volume (16 mm3 [IQR 3-56] versus 49 [5-142] mm3;P=0.08), or plaque burden (10.8% [2.8-21] versus 15.4% [4.6-28];P=0.46). Coronary plaques were predominantly calcified in both athletes and controls (80% versus 63%;P=0.08). Age, blood pressure and HDL-C were independent predictors for a CAC score >0 AU among athletes. CONCLUSIONS: In contrast with previous studies in male master athletes, lifelong exercise in female counterparts does not appear to be associated with increased CAC score, coronary plaque burden or any qualitative differences in coronary plaque compared with relatively sedentary healthy counterparts. Coronary atherosclerosis in master female athletes is mainly driven by traditional risk factors.

Background: The aim of this single-center retrospective study is to evaluate the long-term outcome after convergent procedure (CP) for patients with paroxysmal atrial fibrillation (AF), persistent AF and long-standing persistent AF. Methods and results: We analyzed outcomes of patients that underwent CP from January 2009 until July 2020. 119 patients with paroxysmal AF (23.5%), persistent AF (5.9%) or long-standing persistent AF (70.6%) that attended long-term follow up were included. The outcome was assessed at 1-year after CP and at long-term follow up. At 1-year follow up rhythm and daily AF burden were assessed for patients with implantable loop recorder (61.2%). For others rhythm was assessed by clinical presentation and 12-lead ECG recording. At long-term follow up patients having sinus rhythm or unclear history of AF were monitored with 7-day ECG Holter and AF burden was determined. Long-term success was defined as freedom from AF/atrial flutter (AFL) with sinus rhythm on 12-lead ECG recording and AF/AFL burden <1% on 7-day Holter ECG. Repeat catheter ablations (RFA) prior to long-term follow up were documented. At 1-year follow up 91.4% of patients had sinus rhythm and 76.1% of patients had AF/AFL burden <1%. At long-term follow up (8.3 {+/-} 2.8 years) 65.5% of patients had sinus rhythm and 53.8% patients had AF/AFL burden <1% on 7-day holter ECG. Additional RFAs were performed in 32.8% of patients who had AF or AFL burden <1%. At long-term follow up age, body mass index and left atrial volume index were associated with increased risk of AF recurrence. Conclusions: CP resulted in high long-term probability of sinus rhythm maintenance. During long-term follow-up additional RFAs were required to maintain sinus rhythm in a substantial number of patients.

Meaningful metrics of antiviral activity are essential for determining the efficacy of therapeutics in human clinical trials. Molnupiravir (MOV) is a broadly acting antiviral nucleoside analog prodrug that acts as a competitive alternative substrate for the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We developed an assay, Culture-PCR, to better understand the impact of MOV therapy on infectious SARS-CoV-2. Culture-PCR revealed MOV eliminated infectious virus within 48 hours in the nasopharyngeal compartment, the upper airway location with the greatest levels of infectious virus. MOV therapy was associated with increases in mutations across the viral genome but select regions were completely unaffected, thus identifying regions where mutation likely abrogates infectivity. MOV therapy did not alter the magnitude or neutralization capacity of the humoral immune response, a documented correlate of protection. Thus, we provide holistic insights into the function of MOV in adults with COVID-19.

Background Postoperative delirium (POD) contributes to severe outcomes such as death or development of dementia. Thus, it is desirable to identify vulnerable patients in advance during the perioperative phase. Previous studies mainly investigated risk factors for delirium during hospitalization and further used a linear logistic regression (LR) approach with time-invariant data. Studies have not investigated patients fluctuating conditions to support POD precautions. Objective In this single-center study, we aimed to predict POD in a recovery room setting with a non-linear machine learning (ML) technique using pre-, intra-, and postoperative data. Methods The target variable POD was defined with the Nursing Screening Delirium Scale (Nu-DESC) [≥] 1. Feature selection was conducted based on robust univariate test statistics and L1 regularization. Non-linear multi-layer perceptron (MLP) as well as tree-based models were trained and evaluated - with the receiver operating characteristics curve (AUROC), the area under precision recall curve (AUPRC), and additional metrics - against LR and published models on bootstrapped testing data. Results The prevalence of POD was 8.2% in a sample of 73,181 surgeries performed between 2017 and 2020. Significant univariate impact factors were the preoperative ASA status, the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The tree-based model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418 outperforming linear LR, well as best applied and retrained baseline models. Conclusions Overall, non-linear machine learning models using data from multiple perioperative time phases were superior to traditional ones in predicting POD in the recovery room. Class imbalance was seen as a main impediment for model application in clinical practice.

While large language models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. A medical knowledge graph (KG) houses a wealth of structured medical concept relations sourced from authoritative references, such as UMLS, making it a valuable resource to ground LLM diagnostic process in knowledge. In this paper, we examine the synergistic potential of LLMs and medical KG in predicting diagnoses given electronic health records (EHR), under the framework of Retrieval-augmented generation (RAG). We proposed a novel graph model: DR.KNOWS, that selects the most relevant pathology knowledge paths based on the medical problem descriptions. In order to evaluate DR.KNOWS, we developed the first comprehensive human evaluation approach to assess the performance of LLMs for diagnosis prediction and examine the rationale behind their decision-making processes, aimed at improving diagnostic safety. Using real-world hospital datasets, our study serves to enrich the discourse on the role of medical KGs in grounding medical knowledge into LLMs, revealing both challenges and opportunities in harnessing external knowledge for explainable diagnostic pathway and the realization of AI-augmented diagnostic decision support systems.

Background: The aging population faces numerous health challenges, with sedentary behavior and decreased physical activity being paramount. We explore the physical behaviour of older adults in the GOTO combined lifestyle intervention study and its related immuno-metabolic health effects. Methods: The research utilized accelerometers and machine learning to assess physical activity behaviours during a 13-week program of increased physical activity and decreased calorie intake. Subsequently, the association of variation in physical behaviour with immuno-metabolic health parameters is investigated cross-sectionally at baseline and longitudinally using sex-stratified linear regression and linear mixed regression respectively. Results: Participants exhibited physical behaviors similar to their age-matched peers from the UK-Biobank. Interestingly, gender-based differences were evident, with men and women showing distinct daily physical behavioural patterns. At baseline, a positive correlation was found between higher physical behavior and a healthier immune-metabolic profile, particularly in men. The longitudinal changes depict an overall boost in activity levels, predominantly among women. While increasing general activity and engaging in intense exercises proved advantageous for physical health, the immune-metabolic health benefits were more pronounced in men. Conclusion: The short-term GOTO intervention underscores the significance of regular physical activity in promoting healthy aging even in middle to older age. Gender differences in behavior and health benefits deserve much more attention though. Our results advocate the broader implementation of such programs and emphasize the utility of technology, like accelerometers and machine learning, in both monitoring and promoting active lifestyles among older adults.

Metabolites are important indicators of individual health and can serve as crucial targets for therapy. However, the genetic basis of many metabolites remains largely unexplored, especially among underrepresented East Asians and during critical periods such as pregnancy. In this study, we utilized genetic information obtained from non-invasive prenatal testing to conduct a genome-wide association analysis of 84 metabolites, including 37 amino acids, 10 vitamins, 24 metal elements, and 13 hormones, among 34,394 Chinese pregnant women. Of these metabolites, 52 and 11 had not previously been studied in East Asians or globally. We identified 30 novel metabolite-gene associations. We also observed substantial differences in the genetic effects on hormones between pregnancy and non-pregnancy periods, suggesting effect modifications in response to physiological changes. Furthermore, we uncovered pervasive pleiotropic effects for 50.94% of the genetic associations among metabolites, as well as between six metabolites and eight pregnancy biomarkers. Using mendelian randomization, we identified potential causal relationships between plasma folate and ischemic stroke, vitamin D3 and Graves' disease, copper and open-angle glaucoma, and androstenedione and rheumatoid arthritis. These discoveries provide invaluable genetic insights into human metabolism, laying the foundation for future mechanistic studies and the development of new therapeutic targets, particularly for underrepresented East Asians.

A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 360k Finns spanning up to 50 years of individuals' lifetimes. Individuals with a high genetic generalized epilepsy PRS (PRS-GGE) in FinnGen had an increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.55 per PRS-GGE standard deviation [SD]) across their lifetime and after unspecified seizure events. Effect sizes of epilepsy PRSs were comparable to effect sizes in clinically curated data supporting our EHR-derived epilepsy diagnoses. Within 10 years after an unspecified seizure, the GGE rate was 37% when PRS-GGE > 2 SD compared to 5.6% when PRS-GGE < -2 SD. The effect of PRS-GGE was even larger on GGE subtypes of idiopathic generalized epilepsy (IGE) (HR 2.1 per SD PRS-GGE). We further report significantly larger effects of PRS-GGE on epilepsy in females and in younger age groups. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). We found PRS-GGE specifically associated with GGE in comparison with >2000 independent diseases while PRS-NAFE was also associated with other diseases than NAFE such as back pain. Here, we show that epilepsy specific PRSs have good discriminative ability after a first seizure event i.e. in circumstances where the prior probability of epilepsy is high outlining a potential to serve as biomarkers for an epilepsy diagnosis.

Objective Recent advancements in GPT-4 have enabled analysis of text with visual data. Diagnosis in ophthalmology is often based on ocular examinations and imaging, alongside the clinical context. The aim of this study was to evaluate the performance of multimodal GPT-4 (GPT-4V) in an integrated analysis of ocular images and clinical text. Methods This retrospective study included 40 patients seen in our institution with ocular pathologies. Cases were selected by a board certified ophthalmologist, to represent various pathologies and match the level for ophthalmology residents. We provided the model with each image, without and then with the clinical context. We also asked two non-ophthalmology physicians to write diagnoses for each image, without and then with the clinical context. Answers for both GPT-4V and the non-ophthalmologists were evaluated by two board-certified ophthalmologists. Performance accuracies were calculated and compared. Results GPT-4V provided the correct diagnosis in 19/40 (47.5%) cases based on images without clinical context, and in 27/40 (67.5%) cases when clinical context was provided. Non-ophthalmologists physicians provided the correct diagnoses in 24/40 (60.0%), and 23/40 (57.5%) of cases without clinical context, and in 29/40 (72.5%) and 27/40 (67.5%) with clinical context. Conclusion GPT-4V at its current stage is not yet suitable for clinical application in ophthalmology. Nonetheless, its ability to simultaneously analyze and integrate visual and textual data, and arrive at accurate clinical diagnoses in the majority of cases, is impressive. Multimodal large language models like GPT-4V have significant potential to advance both patient care and research in ophthalmology.

Biological pathways, including individual gut microbiome are potential barriers for maternal nutritional supplementation to improvement in infant growth. We evaluated the impact of balanced energy-protein (BEP) supplementation during pregnancy and the first six months of lactation on the composition and functionality of gut microbiome in mothers and their infants in rural Burkina Faso. Our findings reveal that BEP supplementation led to a significant increase in microbiome diversity during pregnancy. In the second trimester, there was a notable decrease in the abundance of an Oscillospiraceae species, while postpartum, the abundance of Bacteroides fragilis increased. We identified concerted enriched or depleted microbial pathways associated with BEP supplementation, including the phosphotransferase system, a critical mechanism for bacterial carbohydrates uptake, which exhibited enrichment in infants born to BEP-supplemented mothers. Despite these observations, the intricate biological connections with other omics necessitate further analysis to fully elucidate the underlying comprehensive biological pathways.

Dementia probably due to Alzheimer's disease (AD) is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments. In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into 'slow' and 'fast' progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 +- 0.01 AUC in external validation. By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared to conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.

Objective This retrospective chart review aims to quantify the rate of patients with intellectual disability (ID) accessing an Australian ambulatory EEG service, and understand the implications of discontinuing studies against medical advice. Methods Electronic records of referrals, patient monitoring notes, and EEG reports were accessed retrospectively. Each referral was assessed to determine whether the patient had an ID. For each study where patients were discharged against medical advice, the outcomes of their EEG report were assessed and compared between the ID and non-ID groups. Exploratory analysis was performed assessing the effects of age, the percentage of the requested monitoring undertaken, and outcome rates as a function of monitoring duration. Results There were significantly more patients in the ID group with early disconnection than the non-ID group (Chi squared test, p=0.000). There was no significant difference in the rates of clinical outcomes between the ID and non-ID groups amongst patients who disconnected early. Conclusions Although rates of early disconnection are higher in those with ID, study outcomes are largely similar between patients with and without ID in this retrospective analysis of an ambulatory EEG service. Significance Ambulatory EEG is a viable alternative to inpatient monitoring for patients with ID.

Introduction: Posterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease. Methods: We compared the language samples of 25 patients with PCA with age-matched cognitively normal (CN) individuals across two distinct tasks: a visually-dependent picture description and a visually-independent job description task. We extracted word frequency, word utterance latency, and spatial relational words for this comparison. We then conducted an in-depth analysis of the language used in the picture description task to identify specific linguistic indicators that reflect the visuospatial processing deficits of PCA. Results: Patients with PCA showed significant language deficits in the visually-dependent task, characterized by higher word frequency, prolonged utterance latency, and fewer spatial relational words, but not in the visually-independent task. An in-depth analysis of the picture description task further showed that PCA patients struggled to identify certain visual elements as well as the overall theme of the picture. A predictive model based on these language features distinguished PCA patients from CN individuals with high classification accuracy. Discussion: The findings indicate that language is a sensitive behavioral construct to detect visuospatial processing abnormalities of PCA. These insights offer theoretical and clinical avenues for understanding and managing PCA, underscoring language as a crucial marker for the visuospatial deficits of this atypical variant of Alzheimer's disease.

Emerging evidence highlights the association of reactive astrocytes with neurodegenerative diseases. Although astrocytic pathology is a pathological hallmark of progressive supranuclear palsy (PSP), its role in the pathophysiology of the disease is not fully understood. In this study, we aimed to assess astrocyte reactivity in vivo in patients with PSP using magnetic resonance spectroscopy and plasma biomarkers. Furthermore, given the central role of astrocytes in brain energy metabolism and their glycolytic profile, which implies a preference for lactate production, we investigated alterations in brain energy metabolism by measuring brain lactate levels and examined their relationship with astrocyte reactivity. We included 30 patients with PSP-Richardson's syndrome and 30 healthy controls; in patients, tau deposition was confirmed via 18F-florzolotau-PET. Myo-inositol, an astroglial marker, and lactate were quantified in the anterior cingulate cortex (ACC) via magnetic resonance spectroscopy. The ACC was studied because previous functional imaging studies revealed its involvement as a distinctive feature of PSP. It involves in cognitive processes, such as executive function, which often exhibit deficits in patients with PSP. We measured plasma concentrations of glial fibrillary acidic protein (GFAP) as another astrocytic marker, neurofilament light chain (NfL), and tau phosphorylated at threonine 181. Reactive astrocytes, tau deposition, and synaptic loss in the ACC were assessed in post-mortem brain samples from another three patients with PSP with comparable disease durations to those of participants. The level of myo-inositol in the ACC and the plasma GFAP were significantly higher in patients than in healthy controls; these increases were significantly associated with Frontal Assessment Battery and Mini-Mental State Examination scores, respectively. The lactate level in the ACC was high in patients and correlated significantly with high myo-inositol levels. The plasma NfL outperformed other biomarkers in discriminating patients from controls (area under the curve [AUC] = 0.95), followed by lactate and myo-inositol (AUC = 0.88 and 0.78, respectively). Histological analysis of the ACC in patients revealed evident reactive astrocytes despite mild tau deposition and no marked synaptic loss. We found high levels of astrocyte biomarkers (myo-inositol in the ACC and plasma GFAP) in patients with PSP, suggesting astrocyte reactivity, and these biomarkers correlated with cognitive decline. Elevated myo-inositol levels were associated with high lactate levels, suggesting a link between reactive astrocytes and brain energy metabolism changes. Our results indicate that astrocyte reactivity in the ACC precedes pronounced tau pathology and neurodegenerative processes in the region and affects brain function in PSP.

Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use -- thresholded activity counts (TAC) and the gross movement (GM) score suffer from high sensitivity and specificity, respectively. We had previously proposed a hybrid version of these two measures -- the GMAC -- that showed better overall detection performance. However, the previously proposed GMAC used both accelerometer and gyroscope data and used the same parameter values from the TAC and GM measures. In this paper, we aim to answer two important questions to improve the usefulness of the GMAC measure: (a) can the GMAC measure be implemented using only the accelerometer data? (b) what are the optimal parameter values for the GMAC measure? We propose a modified version of the GMAC that works with only accelerometer data, and optimize this measure's parameters. This optimized GMAC showed better detection performance than the previously proposed GMAC and surprisingly had comparable performance to that of the best-performing machine learning-based measure (random forest inter-subject model). Although intra-subject machine learning-based measures perform better than the optimized GMAC, the latter is simpler, well suited for real-time upper-limb use detection, and is the best option when a trained machine learning-based intra-subject model or labeled data is unavailable. The optimized GMAC measure can be a useful measure for either offline detection or for real-time detection and feedback of upper limb use.

Prostate cancer (PCa) is the fourth most prevalent cancer globally, and the most common among men. Most PCa patients in Oman are presented during the advanced stages of the disease with widespread metastatic disease reducing their overall rates of survival. Characterisation of the Omanis PCa population could be beneficial to develop a clinical profile demonstrating specific characteristics to better classify and derive radiomics signatures. These could help in developing artificial intelligence methods to assist with earlier and quicker diagnosis of possible prostate lesions. A retrospective, cross sectional study has been designed to determine the pathological and radiological characteristics based on multi sequence 3 dimensional Magnetic Resonance Imaging (MRI). The MRI records are maintained within the existing electronic healthcare records of the Sultan Qaboos University Hospital Department of Radiology and Molecular Imaging. Data will be extracted based on a confirmed diagnosis reported between the 1st of January 2010 and October 2023. All patients included within the study will be aged between 18-99 years. A study specific data extraction template has been devised to gather demographic details, clinical parameters and radiological findings based on existing imaging reports within the HIS and PACS systems. Ethics approval Research Ethics approval reference for this study is (MREC 3176 REF. NO. SQUEC 2832023) The data analysis will be conducted using statistical software to conduct a Joinpoint regression analysis and linear regression modelling. We will also conduct a descriptive analysis.

Background: Community treatment orders (CTOs) are used to manage community-based care for individuals with severe mental health conditions who have been discharged from inpatient care. Evidence examining whether CTOs are successful at reducing rehospitalisation has been mixed. Methods: Using deidentified electronic health records from 2009-21, we compared patients who had ever been placed on a CTO (n=836) and two other groups of patients who had never been placed on CTO: patients admitted under Section 3 of the Mental Health Act (n=1,182) and outpatients with severe mental health issues (n=7,651). We examined the association between CTOs and rehospitalisation using within-individual stratified multivariable Cox regression. Results: Patients on CTO were more likely to be male, single, of Black or Mixed ethnicity, and have a severe mental illness diagnosis than patients in the comparison groups. Time spent on CTO was associated with a lower risk of hospitalisation compared to time spent off CTO for the same individual (HR 0.60; 95% CI 0.56-0.64). This decreased risk of hospitalisation remained when we restricted analysis to individuals with a single CTO episode (HR 0.05; 95% CI 0.02-0.11) and when we restricted follow-up time to a patient's first CTO episode (HR 0.20; 95% CI 0.17-0.25). However, there was no difference in re-hospitalisations when we observed patients starting from the first CTO (HR 1.07; 95% CI 1.00-1.16). Conclusions: We found that patients on CTO were at lower risk of hospitalisation, though this pattern was not observed when we excluded time prior to the first CTO. Further research should consider whether CTOs provide genuine clinical benefit. Keywords: community treatment orders, compulsory community treatment, hospitalisation rates, within individual analysis, electronic health records, clinical informatics