Assessing the relationship between burnout syndrome and irritable bowel syndrome among medical health providers and medical students in Saudi Arabia.

Burnout syndrome, characterized by chronic unmanageable workplace stress, has been linked to lower gastrointestinal disorders, including irritable bowel syndrome. However, the relationship between burnout syndrome and irritable bowel syndrome among medical health providers and medical students in Saudi Arabia has not been fully explored. This cross-sectional correlational study was conducted in Southern Saudi Arabia from 2021 to 2022 and involved 931 medical health providers and medical students who completed an electronic questionnaire. The study assessed the presence and severity of burnout and irritable bowel syndrome and examined their relationship. Burnout syndrome was evaluated using the Maslach Burnout Inventory-Student Survey (MBI-SS), while irritable bowel syndrome criteria and severity were assessed using validated tools. The study found that 85% of medical health providers and medical students experienced high levels of burnout and irritable bowel syndrome severity, with physicians and nurses mainly affected. Occupational exhaustion was high in 44.4% of participants, while depersonalization was high in 53% of participants. Personal accomplishment was low in 73.5% of participants. Mild, moderate, and severe irritable bowel syndrome was reported in 25.6%, 23.8%, and 12% of participants, respectively. The study highlights a significant association between burnout syndrome and irritable bowel syndrome severity among medical health providers and medical students in Saudi Arabia. These findings underscore the importance of developing effective interventions to prevent and manage burnout syndrome and related health issues among healthcare professionals and medical students in the region.

Factors influencing the quality of life of GERD patients in the Aseer Region, Saudi Arabia.

Gastroesophageal reflux disease (GERD) is a common upper gastrointestinal disorder characterized by troublesome symptoms, including heartburn and acid regurgitation. GERD is associated with complications such as peptic stricture, Barrett's esophagus, and esophageal adenocarcinoma, and it negatively affects quality of life (QoL). Aims: To assess the factors influencing the QoL of GERD patients in the Aseer region of Saudi Arabia. Settings and Design: This descriptive cross-sectional study used self-administered questionnaires in a study population of patients aged ≥18 years from Aseer, Saudi Arabia, between January 15, 2023-February 15, 2023. Materials and Methods: A previously validated GERD health-related QoL (GERD-HRQoL) questionnaire was used to assess the patients' sociodemographic data, GERD, and GERD-HRQoL. Statistical Analysis Used: Descriptive analysis included describing the frequency distribution and percentage for study variables, including demographic data, GERD-related QoL symptoms, and QoL, which were graphed. Cross-tabulation presented the distribution of GERD-HRQoL scores by their personal data and other factors using the Pearson Chi-square and exact probability tests. Results: Overall, 502 participants previously diagnosed with GERD completed the questionnaire. Participants' were aged 18-65 years (mean age of 31.5 ± 14.6 years), and 384 (76.5%) were male. The most frequent symptom affecting QoL was heartburn (85.9%), followed by postprandial heartburn (84.3%), heartburn while lying down (82.7%), bloating or gassy feelings (79.9%), and heartburn while standing up (77.3%). Conclusions: Our study showed that patients with GERD had a poor QoL due to GERD-related symptoms, mainly heartburn. Younger age, male sex, and lower educational status were associated with lower GERD-HRQoL scores.

Incremental Learning for Panoramic Radiograph Segmentation.

This study aimed to determine a fundamental method for the automated detection and treatment of dental and orthodontic problems. Manual intervention is inefficient and prone to human error in detecting anomalies. Deep learning was used to identify a solution to this problem. We proposed leveraging incremental learning approaches using Mask RCNN as backbone networks on small datasets to construct a more accurate model from automatically labeled data. The knowledge acquired at one stage of education is carried over to the subsequent stage. By incorporating newly annotated data, transfer learning improved the model's performance. Despite the data scarcity issues inherent in radiograph image collection, the findings for filling and tooth segmentation tasks were encouraging and adequate. We compared our results to prior research to optimize the performance of our proposed method.

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs.

Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. Recent advancements in deep learning-based segmentation and object detection algorithms have enabled the provision of predictable and practical identification to assist in the evaluation of a patient's mineralized oral health, enabling dentists to construct a more successful treatment plan. However, there has been a lack of efforts to develop collaborative models that enhance learning performance by leveraging individual models. The article describes a novel technique for enabling collaborative learning by incorporating tooth segmentation and identification models created independently from panoramic radiographs. This collaborative technique permits the aggregation of tooth segmentation and identification to produce enhanced results by recognizing and numbering existing teeth (up to 32 teeth). The experimental findings indicate that the proposed collaborative model is significantly more effective than individual learning models (e.g., 98.77% vs. 96% and 98.44% vs.91% for tooth segmentation and recognition, respectively). Additionally, our models outperform the state-of-the-art segmentation and identification research. We demonstrated the effectiveness of collaborative learning in detecting and segmenting teeth in a variety of complex situations, including healthy dentition, missing teeth, orthodontic treatment in progress, and dentition with dental implants.

Isolated tumour microparticles induce endothelial microparticle release in vitro.

: Cancer induces a hypercoagulable state, resulting in an increased risk of venous thromboembolism. One of the mechanisms driving this is tissue factor (TF) production by the tumour, released in small lipid bound microparticles. We have previously demonstrated that tumour cell line media-induced procoagulant changes in HUVEC. The aim of this study was to investigate the effect of tumour microparticles and recombinant human TF (rhTF) on the endothelium. Procoagulant microparticles from the PANC-1 cell line were harvested by ultrafiltration. HUVEC were then incubated with these procoagulant microparticles or rhTF. Flow cytometry was used to investigate the effect of endothelial cell surface protein expression and microparticle release. Microparticles but not soluble TF was responsible for the procoagulant activity of cell-free tumour media. We also demonstrated an increase in endothelial microparticle release with exposure to tumour microparticles, with a positive linear relationship observed (R = 0.6630 P ≤ 0.0001). rhTF did not induce any of the changes observed with microparticles. Here we demonstrate that procoagulant activity of tumour cell line media is dependent on microparticles, and that exposure of endothelial cells to these microparticles results in an increase in microparticle release from HUVEC. This suggests a mechanism of transfer of procoagulant potential from the cancer to the remote endothelium.

The CD105:CD106 microparticle ratio is CD106 dominant in polycystic ovary syndrome compared to type 2 diabetes and healthy subjects.

BACKGROUND: A retrospective analysis was carried out from patients and controls during the past 5 years from a series of studies investigating endothelial microparticles (MP). METHODS: In total, 319 samples from 207 individuals were included in this analysis, from patients with type 2 diabetes (T2D, n = 105), women with polycystic ovary syndrome (PCOS, n = 145) and healthy volunteers (n = 69). All data were generated via the same flow cytometry protocol with the same antibody clones. Endothelial markers CD105 (Endoglin) and CD106 (Vascular cell adhesion molecule-1) were used to enumerate MP in venous blood. RESULTS: The ratio of CD105MP:CD106MP was significantly different between groups (F = 63.43, p < 0.0001). Women with PCOS were found to have a median CD105MP:CD106MP ratio of 0.40 (IQR 0.24-0.57), suggesting approximately two CD106MP were found per CD105MP. The T2D group showed a median ratio of 2.32 (1.51-3.69) whereas in healthy volunteers the ratio was 2.21 (1.63-3.55). Serum intercellular adhesion molecule-1 was also shown to be significantly increased in PCOS when compared with control or T2D groups (F = 14.5, p < 0.001). CONCLUSION: These data suggest that women with PCOS have an altered endothelial MP release in favour of CD106. Thus a potential activated endothelial state exists in women with PCOS with a shift towards a predominantly CD106MP profile.