Fair and Diverse Group Formation Based on Multidimensional Features

The goal of group formation is to build a team to accomplish a specific task. Algorithms are being developed to improve the team\u27s effectiveness so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals’ expertise for expert recommendation and/or team formation, there has been relatively little prior work on modeling demographics and incorporating demographics into the group formation process. We propose a novel method to represent experts’ demographic profiles based on multidimensional demographic features. Moreover, we introduce three diversity ranking algorithms that form a group by considering demographic features along with the minimum required skills. Unlike many ranking algorithms that consider one Boolean demographic feature (e.g., gender or race), our diversity ranking algorithms consider multiple demographic features simultaneously. Finally, we introduce a fair team formation algorithm that balances each candidate\u27s demographic information and expertise. We evaluate our proposed algorithms using real datasets based on members of a computer science program committee. The result shows that our algorithms form a program committee that is more diverse with an acceptable loss in utility

Extramedullary Hematopoiesis in a Patient with Beta Thalassemia: A Rare Case Report

Extramedullary hematopoiesis (EMH) is a rare disorder, defined as the appearance of hematopoietic elements outside the bone marrow or peripheral blood due to ineffective erythropoiesis or inadequate bone marrow activity in a variety of hematological diseases. EMH often manifests as hemopoietic masses in a variety of normal and abnormal bodily sites. We present a 21-year-old man with a medical history of beta thalassemia since he was nine months old. The primary clinical symptom was mild abdominal pain. In this case, we describe a rare instance of small bowel obstruction due to EMH and portal hypertension. Surgery solved the clinical problems, and the patient was discharged home

PREVALENCE OF MAXILLARY SECOND PREMOLAR TEETH WITH 2 CANALS IN SAUDI POPULATION OF EASTERN AND SOUTHERN REGIONS

Aim: The aim of this study is to increase the practitioner’s awareness of the possibility of 2 canals of upper second premolar teeth during endodontic treatment that helps to avoid failure of RCT caused by missing one of the main canals. Methodology: It is a cross-sectional study was conducted in 2 different regions east and south regions of Saudi Arabia. Data collected from randomized patients sample of armed forces hospital southern region and from Dammam medical complex- dental department (ministry of health) in eastern region including male and female with age between 15-45 y/o, by using Chi-square test. 140 patients were selected for the study (95 patients in the southern region and 45 patients in eastern region). They were examined clinically and radiographically. Results: Out of 140 patients, 79 patients (56%) have 2 canals in the upper second premolars and 61 patients (44%) have 1 canal in the upper second premolars In eastern region, out of 45 patients, 26 patients (57.77%) have 2 canals in the upper second premolars and 19 patients (42.33%) have 1 canal in the upper second premolars. In the southern region, out of 95 patients, 53 patients (55.78%) have 2 canals in the upper second premolars and 40 patients (44.22%) have 1 canal in the upper second premolars. Conclusion: This study showed close proportions in the probability of two canals of upper second premolar teeth in both eastern and southern regions of Saudi Arabia. So, the dentists should be aware that the percentage of two canals in upper second premolar teeth almost reaches more than 50% while doing root canal treatment

Relationship between Knowledge and Awareness of Risk of Oral Healthcare\u27s in Elderly Patients Attending in the Primary Health Care at Saudi Arabia 2022

Background We aimed to investigate the relationship between Relationship between knowledge and awareness of risk of oral health cares in elderly patients attending in the Primary health care at Saudi Arabia. Despite the high prevalence of risk of oral health cares, oral dryness and awareness of its complications, there is limited research on the clinical management of patients with risk of oral healthcare\u27s and oral dryness in general dental care. Saliva has several important functions essential to maintaining overall health, as well as oral health. It is important for oral homeostasis with numerous functions, which include lubricating soft tissues, regulating pH levels, clearing food particles, antimicrobial function, and facilitating tooth mineralization. Oral frailty, as defined by the Saudi Arabia  Dental Association, is a series of phenomena and processes characterized by vulnerability of oral health status due to age-related changes in different oral health conditions (number of teeth, oral hygiene, and oral functions). Oral frailty provides a warning to avoid the following negative repercussions. neglecting slight declines in oral function. Aim of the study: To investigate the relationship between Relationship between knowledge and awareness of risk of oral health cares in elderly patients attending in the Primary health care at Saudi Arabia 2022. Method: cross sectional study conducted at outpatient dental clinics in primary health care center at Saudi Arabia in Sample population consists of Saudi out patients aged 60 <80 years attending. Our total participants were (200). Results:. Show among the elderly patients regarding age majority of the study groups from the ≥75 years were (44.0%), regarding the relationships with their grandparents the majority of the respondents they are not alive were (41.0%), the education status the majority of the respondents medium were (29.0%), the you smoke the most of participant answer No were (63.0%) while Yes were (37.0%) .Conclusion: The oral health status of elderly people was found to be poor. Hence, it is concluded from this study that tooth loss is higher among the geriatric group,  eating is the foundation of human life. The ability of older people to eat is supported by a wide variety of factors related to tooth and oral functions, such as the number of teeth present, masticatory strength, swallowing function, and occlusal support. To increase awareness of the importance of oral function in the Saudi Arabia population, the concept of oral frailty has been introduced

Efficacy of Pluronic F-127 gel containing green tea catechin extract on chronic periodontitis – A clinical study

Purpose: To evaluate the efficacy of pluronic F-127 gel containing green tea catechin extract as a local drug delivery system in the treatment of chronic periodontitis. Methods: A total of 20 chronic periodontitis patients participated as per the set inclusion and exclusion criteria. Complete scaling and root planing (SRP) was done for all subjects and pluronic F-127 gel containing green tea catechin was applied on one site. The contralateral site received SRP alone. The plaque index (PI), gingival index (GI), and probing pocket depth (PPD) were recorded at baseline and on the 28th day. Results: At the 28th-day follow-up, green tea catechin tooth sites showed significantly lower mean scores (GI = 0.55, p = 0.30 and PPD = 3.35 mm) than the corresponding SRP tooth sites (GI = 1.25, PI = 1.15, and PPD = 4.40 mm) (p < 0.05). Conclusion: When compared to scaling and root planing alone, the local drug delivery gel containing green tea catechin as an adjuvant was more effective in reducing the clinical parameters of periodontitis. Keywords: Adjuvant therapy, Camellia sinensis, Local drug delivery, Periodontal pocke

Assessment of Knowledge Among the Physicians Regarding Dental Screening Prior to Chemotherapy and Radiotherapy

Objective: To evaluate the physicians\u27 knowledge regarding the referral for dental screening prior to chemotherapy and radiotherapy. Material and Methods: We conducted a cross-sectional study using simple random sampling among 468 physicians from various specialties with diverse experience levels from different regions in Saudi Arabia. A self-reporting questionnaire was distributed among the physicians, which consisted of questions assessing the physicians\u27 knowledge about oral health and complications in patients prior to chemotherapy and radiotherapy. Statistical analysis was done after the data was collected employing SPSS, and p<0.05 was taken as significant. Results: Residents were more as expected (39.3%), followed by specialists (2.31%). The majority had a practice experience for more than five years (67.8%). The scores for the knowledge assessment showed that 51.3%, nearly half of the participants, had lower scores. The scores were statistically significant (p<0.05). Conclusion: General physicians and specialists should be aware of the dental complications and associated diseases in patients with malignancies and those undergoing chemo and radiotherapy. It is proposed that more awareness should be raised among physicians to rectify this lapse

Knowledge, Attitude, and Practice of Complementary and Alternative Medicine among Program’s Residents in Tabuk, Saudi Arabia

BACKGROUND: Complementary and alternative medicine (CAM) focuses on stimulating the body’s ability to heal itself through energy alignment, herbal supplementation, and other balancing techniques. AIM: The objective of the study was to investigate and compare the Knowledge Attitude Practice (KAP) of CAM among program’s residents in Tabuk region. METHODS: A cross-sectional CAP study was conducted among program’s residents in Tabuk region. All program’s residents of all specialties in Tabuk region were included in the study. Data were collected by predesigned electronic questionnaire covering the needed items. Collected data were coded and analyzed using SPSS Inc., Chicago, Illinois, USA. The Chi-square test was used as a test of significance and p = 0.05 or less was considered statistically significant. RESULTS: Most (95.8%) of the participants have heard about CAM, 25% have used CAM in treatment before, and 72.3% of them reported beneficial outcome, 25.7% strongly agree and 48.6% agree that CAM is a useful complement to pharmacological medicine, 38.2% believed that the results of CAM are usually due to the placebo effect, and 52.1% recommended using CAM. On the other hand, 79.9% have knowledge about acupuncture, 54.9% spiritual healing and herbal medicine, 43.1% massage, 41% yoga, 70.8% bloodletting cupping, and 56.3% about cauterization. More than third (38.2%) of the participants agreed that the use of herbal products is a valid form of drugs which can be used for the treatment of variety of diseases, 48.6% agreed that CAM is a useful complement to pharmacological medicine, while 36.1% strongly agreed that CAM treatments are not tested in a scientifically recognized manner. CONCLUSION: In our study, the majority of program’s residents in Tabuk region agree that CAM is a useful complement to pharmacological medicine and recommended using CAM while reasonable percentage of them believed that the results of CAM are usually due to the placebo effect

Exploring the Efficacy of Deep Learning Techniques in Detecting and Diagnosing Alzheimer’s Disease: A Comparative Study

Transfer learning has become extremely popular in recent years for tackling issues from various sectors, including the analysis of medical images. Medical image analysis has transformed medical care in recent years, enabling physicians to identify diseases early and accelerate patient recovery. Alzheimer’s disease (AD) diagnosis has been greatly aided by imaging. AD is a degenerative neurological condition that slowly deprives patients of their memory and cognitive abilities. Computed tomography (CT) and brain magnetic resonance imaging (MRI) scans are used to detect dementia in AD patients. This research primarily aims to classify AD patients into multiple classes using ResNet50, VGG16, and DenseNet121 as transfer learning along with convolutional neural networks on a large dataset as compared to existing approaches as it improves classification accuracy. The methods employed utilize CT and brain MRI scans for AD patient classification, considering various stages of AD. The study demonstrates promising results in predicting AD phases with MRI, yet challenges persist, including processing large datasets and cognitive workload involved in interpreting scans. Addressing image quality variations is crucial, necessitating advancements in imaging technology and analysis techniques. The different stages of AD are early mental retardation, mild mental impairment, late mild cognitive impairment, and final AD stage. The novel approach gives results with an accuracy of 96.6% and significantly improved outcomes compared to existing models

Exploring the Potential of Convolutional Neural Networks in Classifying Alzheimer’s Stages with Multi-biomarker Approach

Multiple studies have attempted to use a single type of data to predict various stages of Alzheimer’s disease (AD). However, combining multiple data modalities can improve prediction accuracy. In this study, we utilized a combination of biomarkers, including magnetic resonance imaging (MRI), electronic health records, and cerebrospinal fluid (CSF), to classify subjects into three groups based on clinical tests—normal cognitive controls (CN), mild cognitive impairment (MCI), and AD. To determine the significant parameters, we employ a novel technique that utilizes sparse autoencoders to extract features from CSF, clinical data, and convolutional neural networks’ (CNN’s) MRI imaging data. Our results indicate that deep learning methods outperform traditional machine learning models such as decision trees, support vector machines, random forests and K-nearest neighbors. The proposed method significantly outperforms traditional models, achieving an accuracy of 0.87 for CN versus AD, a precision of 0.93 for CN, and a recall of 0.88 for AD on the external test set. The integration of various data modalities and the application of deep learning techniques enhance the prediction accuracy, demonstrating the potential for improved diagnostic tools in clinical settings