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This work presents an analysis methodology based on the use of the Finite Element Method (FEM) nowadays considered one of the main numerical tools for solving Boundary Value Problems (BVPs). The proposed methodology, so-called cg-FEM (Cartesian grid FEM), has been implemented for fast and accurate numerical analysis of 2D linear elasticity problems. The traditional FEM uses geometry-conforming meshes; however, in cg-FEM the analysis mesh is not conformal to the geometry. This allows for defining very efficient mesh generation techniques and using a robust integration procedure, to accurately integrate the domain’s geometry. The hierarchical data structure used in cg-FEM together with the Cartesian meshes allow for trivial data sharing between similar entities. The cg-FEM methodology uses advanced recovery techniques to obtain an improved solution of the displacement and stress fields (for which a discretization error estimator in energy norm is available) that will be the output of the analysis. All this results in a substantial increase in accuracy and computational efficiency with respect to the standard FEM. cg-FEM has been applied in structural shape optimization showing robustness and computational efficiency in comparison with FEM solutions obtained with a commercial code, despite the fact that cg-FEM has been fully implemented in MATLAB.

The unique positioning of mangrove ecosystems between land and sea makes them vital in the nitrogen cycle. The role of nitrification in the nitrogen cycle is important to provide nitrogen compounds readily absorbed by mangrove plants. Nevertheless, the nitrification process and nitrifying bacteria in mangrove areas have yet to be comprehensively understood. The primary objective of this study is to provide comprehensive analysis of nitrifying bacteria in mangrove sediments by conducting a systematic review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses method is used as a guide to help report reviews systematically and has a flow chart to show the process of selecting relevant studies. Data collection was carried out by utilizing 6 databases and journal search engines including Scopus, PubMed, ResearchGate, Google Scholar, and Springer in order to achieve more comprehensive findings. This study employed the widely recognized and commonly used technique of defining the review's scope in a focused manner by first identifying the population, intervention, comparison, and outcome. This study identified 358 studies, and 31 studies were included in the review after screening. Based on the screening results, research on nitrifying bacteria in mangrove sediments is geographically limited to several countries such as Indonesia, Vietnam, Thailand, China, Mexico, the United States, India, and Saudi Arabia. This study vealed that there is a high level of diversity among nitrifying bacteria in mangrove sediment, with five distinct groups identified: ammonia oxidizing bacteria, nitrite oxidizing bacteria, anammox bacteria, and comammox bacteria, a recently identified group. In carrying out changes in nitrogen compounds, nitrifying bacteria use functional genes from different steps of the nitrification process, such as nitrogenase, ammonia monooxygenase subunit A, nitrite oxidoreductase alpha subunit, nitrate reductase alpha chain, nitrite reductase, nitric oxide reductase, nitrous oxide reductase, hydrazine synthase, hydrazine oxidoreductase and hydroxylamine oxidoreductase genes. Ammonia-oxidizing bacteria were the predominant group in general, but various nitrifying bacteria groups were distributed diversely across different mangrove environments. This study also indicated the vegetation type and the distribution of nitrifying bacteria in mangrove sediments. The depth of these sediments typically varies from 0 to 60 centimeters, with most samples taken at a depth of 0 to 20 centimeters. The type of vegetation at the sampling location is dominated by species of Kandelia candel, Avicennia marina, Kandelia obovata, and Rhizophora mangle. Limitations regarding research on nitrifying bacteria in mangrove sediments provide opportunities for in-depth study. This comprehensive review provides an in-depth overview of the variety and spread of nitrifying bacteria, highlighting their role in nitrogen cycling and emphasizing the potential for discovering new nitrifying bacteria in mangrove sediments.

Abstract The search for biomarkers for the early diagnosis of neurodegenerative diseases is a growing area. Numerous investigations are exploring minimally invasive and cost-effective biomarkers, with the detection of phosphorylated Tau (pTau) protein emerging as one of the most promising fields. pTau is the main component of the paired helical filaments found in the brains of Alzheimer’s disease cases and serves as a precursor in the formation of neurofibrillary tangles (NFTs). Recent research has revealed that analysis of p-Tau181, p-Tau217 and p-Tau231 in blood may be an option for detecting the preclinical stage of Alzheimer’s disease. In this study, we have analyzed the values of pTau 181 in the serum of Syrian hamsters during hibernation. Naturally, over the course of hibernation, these animals exhibit a reversible accumulation of pTau in the brain tissue, which rapidly disappears upon awakening. A biosensing system based on the interferometric optical detection method was used to measure the concentration of pTau181 protein in serum samples from Syrian hamsters. This method eliminates the matrix effect and amplifies the signal obtained by using silicon dioxide nanoparticles (SiO2 NPs) biofunctionalized with the αpTau181 antibody. Our results indicate a substantial increase in the serum concentration of pTau in threonine-181 during hibernation, which disappears completely 2–3 h after awakening. Investigating the mechanism by which pTau protein appears in the blood non-pathologically may enhance current diagnostic techniques. Furthermore, since this process is reversible, and no tangles are detected in the brains of hibernating hamsters, additional analysis may contribute to the discovery of improved biomarkers. Additionally, exploring drugs targeting pTau to prevent the formation of tangles or studying the outcomes of any pTau-targeted treatment could be valuable.

Abstract In this work, the effect of moderate electromagnetic fields (2.5, 10, and 15 mT) was studied using an immersed coil inserted directly into a bioreactor on batch cultivation of yeast under both aerobic and anaerobic conditions. Throughout the cultivation, parameters, including CO2 levels, O2 saturation, nitrogen consumption, glucose uptake, ethanol production, and yeast growth (using OD 600 measurements at 1-h intervals), were analysed. The results showed that 10 and 15 mT magnetic fields not only statistically significantly boosted and sped up biomass production (by 38–70%), but also accelerated overall metabolism, accelerating glucose, oxygen, and nitrogen consumption, by 1–2 h. The carbon balance analysis revealed an acceleration in ethanol and glycerol production, albeit with final concentrations by 22–28% lower, with a more pronounced effect in aerobic cultivation. These findings suggest that magnetic fields shift the metabolic balance toward biomass formation rather than ethanol production, showcasing their potential to modulate yeast metabolism. Considering coil heating, opting for the 10 mT magnetic field is preferable due to its lower heat generation. In these terms, we propose that magnetic field can be used as novel tool to increase biomass yield and accelerate yeast metabolism.

Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (https://doi.org/10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023).

Abstract Background Patients with incurable cancer are frequently hospitalized within their last 30 days of life (DOL) due to numerous symptoms and concerns. These hospitalizations can be burdensome for the patient and the caregivers and are therefore considered a quality indicator of end-of-life care. This retrospective cohort study aims to investigate the rates and potential predictors of hospitalizations and re-hospitalizations within the last 30 DOL. Methods This register data study included 383 patients with non-curable cancer who died in the pre-covid period between July 2018 and December 2019. Descriptive statistics with Chi-squared tests for the categorical data and logistic regression analysis were used to identify factors associated with hospitalization within the last 30 DOL. Results A total of 272 (71%) had hospitalizations within the last 30 days of life and 93 (24%) had > 1 hospitalizations. Hospitalization was associated with shorter time from palliative care unit (PCU) referral to death, male gender, age

Globally, pests and plant diseases severely threaten forestry and agriculture. Plant protection could be substantially enhanced by using noncontact, extremely effective, and reasonably priced techniques for identifying and tracking pests and plant diseases across large geographic areas. Precision agriculture is the study of using other technologies, such as hyperspectral remote sensing, to increase cultivation instead of traditional agricultural methods with less negative environmental effects. In this article, we proposed a novel deep-learning architecture and optimization algorithm for crop leaf disease recognition. In the initial step, a multilevel contrast enhancement technique is proposed for a better visual of the disease on the leaves of cotton and wheat. After that, we proposed three novel residual block and self-attention mechanisms, named 3-residual block-deep convolutional neural network (RBNet) Self, 5-RBNet Self, and 9-RBNet Self. After that, the proposed models are trained on enhanced images and later extracted deep features from the self-attention layer. The 5-RBNET Self and 9-RBNET Self performed well in terms of accuracy and precision rate; therefore, we did not consider the 3-RBNET Self for the next process. The dragonfly optimization algorithm is proposed for the best feature selection and applied to the self-attention features of 5-RBNET Self and 9-RBNET Self models to improve the classification performance further and reduce the computational cost. The proposed method is evaluated on two publically available crop disease images, such as the cotton, wheat, and EuroSAT datasets. For both crops, the proposed method obtained a maximum accuracy of 98.60% and 93.90%, respectively, whereas for the EuroSAT, the proposed method obtained an accuracy of 83.10%. Compared to the results with recent techniques, the proposed method shows improved accuracy and precision rate.

In the present digital era, being skilled and updated on modern software development practices has become of crucial importance for software engineering graduates. Moreover, the freelancing industry has grown immensely in recent years, and individuals, more than ever before, are fascinated by the opportunities it offers and have greater assurance that it can be a successful and satisfying alternative to regular employment. Unlike others, in the case of software, industry is leading the education. This makes Software Engineering Education (SEE) additionally responsible for minimizing the gap between the skills of the graduating students and the skills needed by the employers out there. There is not any previous work available in this that focuses on the skills required to cope with the freelancing industry by graduate students and recommendations for improvements to Pakistan higher education curriculum that help produce graduates who are capable enough to get themselves employed in freelancing platforms. This study aims to dissect the software industry needs and trends related to the freelancing industry and to uncover suggestions for training in this dynamic field. The data was extracted through different freelancing platforms using the Scrapy framework of Python, and then LDA analysis was performed on the scraped data using Python to find the most trending topics in the SE field and better analyze the situation. Using LDA analysis, the dataset extracted at two distinct time periods is investigated to describe how the software industry changes from time to time. For validity, the updated data was scraped on runtime from freelancing websites. The results of the analysis are shown in different formats, and empirical findings are discussed with reference to two different time periods and in relation to previous studies.

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections.

Abstract Measurement of the ultra-rare K + → π + ν ν ¯ $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10 −5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10 −5.

Introduction Patients with schizophrenia have shown a high mortality rate, and life expectancy is shortened by 10-20 years. This seems to be mainly caused by metabolic and cardiovascular diseases. Several risk factors are identified, including sedentary lifestyle, poor diet, low socioeconomic status, cognitive dysfunction, and antipsychotics iatrogenicity. Objectives We aimed to explore somatic pathologies reported in patients with schizophrenia, and to assess risk factors predisposing to these impairments. Methods We conducted a retrospective descriptive and analytical study, based on clinical and psychiatric observations of 60 patients with schizophrenia, hospitalized in psychiatry “B” department, Hedi Chaker university hospital (Sfax, Tunisia), during the period between 2015 and 2017. Results Among our patients, 38.3% suffered from somatic comorbidities: diabetes (21.7%), hypertension (15%), coronary disease (15%), hyperlipidaemia (15%), respiratory diseases (6.7%). Tobacco consumption was reported in 53.3% of patients. It was significantly associated with the occurrence of cardiovascular diseases (p=0.036). Alcohol abuse was noted in 16.7%, while obesity was reported in 6.7% of patients. Significant associations were found between obesity and diabetes (p=0.001), and between organic diseases and cognitive disorganisation (p=0.022). Somatic comorbidities were more frequent in patients with low socio-economic level (p=0.015). Among our patients, 83.3% were treated with conventional antipsychotics while 38.3% were treated with atypical antipsychotics (AAP). We showed that AAP were associated with the occurrence of organic diseases (p=0.037). Conclusions Physical health of patients with schizophrenia requires a serious attention. Coordinated care between psychiatrists and other healthcare professionals should monitor the physical health of these patients to prevent a premature death. Disclosure of Interest None Declared

Introduction COVID-19 affected humankind worldwide in different aspects of life. Survivors still report the effects of the pandemic on daily life, physical health, and mental health. Objectives To assess effects of the pandemic on the mood and the quality of life of the survivors. Methods We conducted a prospective cohort study including 121 Tunisian COVID-19 inpatients who had been discharged alive from hospital. Each enrolled patient was asked about the period before the hospital stay, and the 6-9 month-period after hospital discharge, using several scales: the validated Arabic version of “Patient Health Questionnaire” (PHQ-9) to screen for depressive symptoms, and “EuroQol five-dimension three-level” (EQ-5D-3L) to assess the quality of life. Results The median age of participants was 59 years, with extreme values ranging from 18 to 80. Among them, 51.2% were females. As compared with baseline statue of patients, the depressive dimension assessed through PHQ was significantly impaired (7.05 vs 1.12; p

Introduction Child maltreatment (CM) refers to all forms of physical or psychological violence, sexual abuse, and neglect of a person under the age of 18, resulting in actual or potential harm to their health, survival, development, or dignity. It is recognized as a predictor of psychological difficulties in adulthood, such as bulimic behavior. Objectives The aim of our study was to assess the link between CM and bulimic behaviors in the Tunisian general population. Methods We conducted a cross-sectional, descriptive, and analytical study among Facebook group members, using an online questionnaire, from February 17, 2023, to May 26, 2023. All respondents over the age of 18 were included in the study. CM was assessed using the Childhood Trauma Questionnaire (CTQ), which provides information on five types of maltreatment: emotional abuse (EA), physical abuse (PA), sexual abuse (SA), emotional neglect (EN), and physical neglect (PN). The Bulimic Investigatory Test, Edinburgh (BITE) was used to screen and assess the intensity of bulimic behavior. Results A total of 528 responses were included in the study. The mean age of the sample was 33.3±11.95 years. Mean AE, AP, AS, NE, NP, and overall CTQ scores were 8.30; 6.58; 6.38; 10.14; 7.26, and 49.72, respectively. A history of severe AE, AP, AS, NE, or NP was reported by 13.1%, 10.8%, 8.5%, 11.6% and 8.3% of respondents, respectively. The mean BITE score was 10.76 ±6.85 and 6.6% of our population were at high risk of developing bulimic behavior. In the bivariate study, the BITE score was significantly correlated with all forms of MI. The strongest correlation was with AE (r=0.310; p

Introduction relapse and frequent rehospitalizations. The length of stay (LOS) of these patients has been a concern of researchers. The ability to identify determinants of LOS at admission – and, thus, identify patients who are likely to need a longer stay early on – may help treatment planning. Objectives We aimed to investigate socio-demographic and clinical profile of inpatients with schizophrenia, and to identify factors associated with LOS. Methods It was a retrospective study carried out among 90 inpatients with schizophrenia admitted to the psychiatry “B” department, Hedi Chaker university hospital (Sfax, Tunisia), during the period between January 2015 and December 2019. Data collection was performed through the patients’ medical records. Statistical analysis was performed using SPSS v.25. Results The mean age of our patients was 32 years. Among them, 57.78% were women. The mean LOS was 28 days. Factors found to be significantly associated with LOS were: the number of admissions (p

Introduction Adverse effects (AEs) of psychotropic drugs are more frequent and potentially more dangerous in elderly subjects (ES), probably due to a greater frequency of somatic comorbidities, as well as polymedication. Objectives The aims of this study were to determine the prevalence of AEs of psychotropic treatment among ES hospitalized in psychiatry, and to identify the associated sociodemographic and clinical factors. Methods We conducted a retrospective and descriptive study. It concerned male patients aged at least 60 years, hospitalized in the psychiatry B department at CHU Hedi Chaker (Sfax, Tunisia) between 2018 and 2022. We collected demographic and clinical data from their medical records using a pre-established form. Results We included 30 patients. The average age was 64 years. Addictive behaviors were reported in 60%, and somatic histories were noted in 53.3% of patients. The three most frequent psychiatric diagnoses were schizophrenia (43.3%), bipolar disorder (33.3%) and depressive disorder (13.3%). Among our patients, 10% experienced adverse psychotropic drug reactions: orthostatic hypotension 6.7%; neurological AEs 3.3%. Univariate analysis showed no significant relationship between sociodemographic variables and psychotropic drug AEs. Patients with bipolar disorder were more likely to develop AEs of psychotropic treatment (p=0.04). Conclusions Our results suggest that special attention should be paid to avoiding psychotropic medication AEs in psychiatric inpatients ES. Indeed, extra precautions need to be taken in this population due to their reduced ability to report their symptoms. Disclosure of Interest None Declared

Introduction Virtual Reality (VR) is a transformative technology that facilitates the development of immersive virtual environments. Its application is steadily growing within Cognitive Behavioral Therapy (CBT) techniques, notably in virtuo exposure therapy. This is particularly evident in the treatment of specific phobias, with a specific focus on addressing blood-injection-injury phobias. Objectives The objective of our study is to design a treatment protocol for patients suffering from blood phobia based on VR. Methods We used the following scales: - Fear Survey Schedule-III (FSS-III) and the Injection Phobia Scale (IPS) for psychometric evaluation of the intensity of avoidance fear. - Questionnaire on cybersickness: to identify potential adverse effects of exposure to virtual reality. To conduct a functional analysis of phobias, we used the SECCA grid and the SORC grid. Results The therapeutic protocol stages of VR for a patient suffering from Blood-Injection-Injury Phobia (BIIP) are as follows: 1. Collection of sociodemographic and clinical data. 2. Functional analysis to identify triggering factors, contributing factors, and consequences of behavior. The SECCA or SORC grid can help in conducting this functional analysis. 3. Psychometric evaluation of the intensity of avoidance fear using the three scales: FSS-III, IPS, and the cybersickness scale. 4. Patient education on the mechanisms of the phobia. 5. Setting of objectives. 6. Therapeutic contract. 7. The Protocol : 8. Cognitive approach: identification of automatic thoughts and replacement with more rational thoughts. 9. Behavioral approach: Progressive exposure, controlled immersion of the patient in virtual environments corresponding to situations that trigger their phobia. This exposure is coupled with relaxation. The treatment continues with regular follow-up to ensure the consolidation of progress and to adjust strategies. For relapse prevention, simple measures ,like personalized exercises to be done by the patient, can favor the long-term maintenance of the acquired skills. Conclusions Virtual reality exposure therapies (in virtuo) are as effective as in-vivo therapies. Besides, they offer a significant advantage over the latter as they facilitate access to stimuli or anxiety-provoking situations that are difficult to access or control in the real world. Disclosure of Interest None Declared

Introduction The interface between dermatology and psychiatry is complex and of clinical importance. Skin disorders in psychiatric inpatients are common, serious and under diagnosed. Objectives The aim of our study was to assess the prevalence and profile of several skin diseases observed in psychiatric inpatients. Methods We conducted a cross-sectional study in the period from october,13 2023 to october,20 2023, among psychiatric male inpatients, hospitalized in psychiatry B department of the Hedi Chaker University Hospital (Sfax, Tunisia). We collected sociodemographic and clinical data using a pre-established form. Results Over a period of a week, 35 patients were included in our study. The mean age of patients was 39,97 years. Among them, 80% were single and 14,3% were married. Addictive behaviors were reported in 74,3% of cases. The level of hygiene was good in 74,3% of patients. The three most common psychiatric diagnoses were schizophrenia (31,4%), followed by bipolar disorder (28,6%) and schizoaffective disorder (25,7%). We recorded 13 cases of skin diseases (37,2% of patients). Dermatological lesions were dominated by traumatic origin in 14,3% of cases. They were of infectious origin in 11,4% of cases, immunoallergic in 8,6% and parasitic in 2.9%. Conclusions The prevalence of skin diseases is high in psychiatric inpatients, for whom proper skin care is necessary to improve their quality of life. Disclosure of Interest None Declared

Introduction In the medical field, work addiction is a double-edged phenomenon. It can be regarded as a positive addiction leading to high motivation to work, but it can also have adverse mental, physical, and social consequences. Objectives To assess the relationship between work addiction and perfectionism in trainee doctors. Methods We conducted a cross-sectional descriptive and analytical study among trainee doctors. We used the “Work Addiction Risk Test” (WART), and “The Big Three perfectionism scale short form”. Results A total of 99 doctors were included. The mean age of participants was 27.6 years, with a sex ratio (M/F) of 0.33. The doctors in our study worked 5.39±1.6 hours a day and were on call 3.84±2.87 times a month. Their average number of hours of sleep was less than 7 hours in 43.4 % of participants. The mean score of the WART was 61.2±14.83. Among the trainee doctors surveyed 39% were considered at high risk of workaholism. The mean WART score was significantly higher among female physicians and those who slept less than 7 hours per day on average. In addition, the average score on the WART scale was significantly associated with the number of calls per month. We found a statistically significant association between perfectionism scores and work addiction scores. Conclusions Our study showed that work addiction is common among doctors in training and is favored by high levels of perfectionism. It is therefore essential to explore and define preventive measures to help them find a balance allowing them to aim for high standards and be able to progress, without setting unrealistic expectations, which can lead to work addiction. Disclosure of Interest None Declared

Introduction Eating disorders (ED) negatively affect physical, mental, and social well-being. The exact psychopathology of ED is still unknown, with research suggesting the interplay of a combination of factors. Objectives The aim of our study was to estimate the prevalence of ED in the Tunisian general population, and to identify associated risk factors. Methods We conducted a cross-sectional, descriptive and analytical study among Facebook group members, using an online questionnaire, over the period from February 17, 2023 to May 26, 2023. All respondents over the age of 18 were included in the study. All participants filled a socio-demographic questionnaire. The Eating Attitudes Test (EAT-26) was used to screen for those at risk of eating disorders. Results A total of 528 responses were included in the study. 33.3±11.95 years. The subjects were unmarried in 63.4% of cases, of low socio-economic level in 19.5%, with a university education in 75.2% and with a regular occupation in 56.1% of cases. The mean EAT-26 score was 12.36±10.34. according to this scale, 12.3% of our population were at high risk of developing an ED. In a multivariate analysis, the female gender (p = 0.006), the low economic status (p = 0.012), a psychiatric comorbidity (p < 0.001), and physical activity (p= 0,037) were strongly associated with ED. Conclusions This study highlighted the magnitude of the risk of disordered eating attitudes in the Tunisian population and the need for programs to prevent and control these disorders. Disclosure of Interest None Declared