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Background: Induction of labor (IOL) initiates labor artificially, aiming to prevent potential risks for both mother and fetus. However, data on IOL outcomes for parous women in the developing countries are scarce. Objectives: This study evaluates maternal and neonatal outcomes in parous women undergoing IOL at a Sudanese hospital. Methods: A descriptive cross-sectional study included 75 parous women undergoing IOL at Haj-Alsafi Teaching Hospital. Demographic data, reasons for IOL, and maternal and neonatal outcomes were recorded. Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) software, version 26, with P values < 0.05 considered statistically significant. Results: Of 600 women, 75 (12.5%) underwent IOL over 6 months. Main indications were hypertensive disorders (21.3%) and postterm pregnancy (17.3%). Successful vaginal delivery (88%) predominated, with 12% requiring instrumental delivery. Maternal complications included postpartum hemorrhage (8%) and ruptured uterus (1.3%), while 69.7% had no complications. The neonatal survival rate was 91%, with 30.7% admitted to the Neonatal Intensive Care Unit (NICU) for further care. Among the NICU admissions, the mortality rate was 17.4%, all were preterm neonates. Conclusion: IOL is associated with successful VD in 88% and low rate of complications among the parous women; however, greater efforts are needed to prevent serious outcomes among the neonates.

Abstract Soybean oil, medium-chain triglycerides, olive oil, and fish oil (SMOFlipid) is used without evidence of benefits. We investigated the relationship between lipid emulsions and brain injury in term-equivalent age magnetic resonance imaging (MRI) in 148 very preterm infants with a birth weight of

Background: Pediatric malignant bone tumors constitute an important subgroup of solid tumors and need comprehensive multidisciplinary care for optimal management. We aimed to review our local practice of managing malignant bone tumors and identify gaps to improve service.Methods: We retrospectively reviewed disease characteristics and management of twenty-five pediatric patients with malignant bone tumors from Jan 2000 to Dec 2020.Results: Median age at diagnosis was 8.75 years, male to female ratio of 0.8:1, and median follow-up was 3.6 years. Pain and swelling were the predominant symptoms at presentation, while lung was the most common metastatic site in Osteosarcoma (OS) and Ewing’s Sarcoma Family Tumors (ESFT). Metastatic disease was not associated with developing an event in OS (P=0.26) but was significantly associated with developing an event in the ESFT subgroup (P=0.002). There was no association of tumor necrosis of

Neuroblastoma is an aggressive malignancy commonly found in children, with adult patients being quite rare. Pancreatic neuroblastoma, even among adult cases, is especially rare. We present a case of a 26-year-old woman presented with mild abdominal pain and a palpable mass diagnosed as neuroblastoma affecting the pancreas. After initial diagnostic challenges, a biopsy confirmed the diagnosis of neuroblastoma. The patient thereafter underwent a course of neoadjuvant chemotherapy, followed by a complex surgical resection. This case highlights the importance of a comprehensive diagnostic approach, including repeated biopsies and the involvement of a multidisciplinary team in the management of rare presentations of adult neuroblastoma.

Lumbar pedicular agenesis is a rare clinical and radiological finding. Therefore, it is commonly misdiagnosed, particularly in trauma cases, with the first case being reported by Hadley in 1946.Pedicle agenesis has specific radiographic features: a falsely appearing enlarged neural foramen; a dysplastic, dorsally displaced ipsilateral articular pillar and lamina; and a dysplastic ipsilateral transverse process.Computed tomography (CT) is the preferred diagnostic modality for pedicle agenesis and it is important to note that pedicle agenesis is a stable congenital anomaly. Typically, these patients do not exhibit neurological deficits and are managed conservatively or receive no treatment.In this case report, we will be reporting a case involving the absence of L5 left pedicle.

Hanan Awad Alkozi, Hanin Abdullah Alhudhayf,&ast; Norah Mohammed Abdulrahman Alawad&ast; Department of Optometry, College of Applied Medical Sciences, Qassim University, Buraydah, Qassim, Saudi Arabia&ast;These authors contributed equally to this workCorrespondence: Hanan Awad Alkozi, Department of Optometry, College of Applied Medical Sciences, Qassim University, Buraydah, 52571, Saudi Arabia, Tel +966163015475, Email h.alkozi@qu.edu.saPurpose: This study aimed to investigate the relationships between anxiety, depression, and ocular surface health. Cortisol levels were detected in human tears, and their relationship with anxiety levels was determined using a validated questionnaire.Patients and Methods: In total, 112 participants were recruited for this study. All participants were healthy medical students at the Qassim University. Each participant signed an informed consent form after receiving detailed information about the study. Visual acuity examination, TBUT, Shirmer1 test were performed. Participants were asked to fill out three questionnaires: Taylor Manifest Anxiety Scale, Beck Depression Inventory, and The Ocular Surface Disease Index. Tear samples were extracted from the Schirmer strips and cortisol level was measured using ELISA kits.Results: A total of 112 college students were included in the study, 58.9% of whom were females. The mean age was 21.9 ± 1.7 years. Subjective reported symptoms of anxiety levels were significantly correlated with depression scores, the OSDI, and reduced Schirmer test measurements. Moreover, cortisol levels detected in tears were positively associated with higher anxiety scores (r=0.328, P< 0.05).Conclusion: Ocular surface health is associated with symptoms of anxiety and depression. The use of tears to measure cortisol levels could be an interesting way to serve as an anxiety biomarker.Keywords: anxiety, cortisol, depression, dry eye, OSDI

Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer’s disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks’ intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman’s correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into ‘Positive’ and ‘Negative’ categories. ‘Positive’ subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. ‘Negative’ subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease.

Recent developments in the fields of communications, smart transportation systems and computer systems have significantly expanded the potential for intelligent solutions in the domains of traffic safety, convenience and efficiency. The utilization of Artificial Intelligence (AI) is presently prevalent across diverse sectors of application due to its significant capacity to augment conventional data-driven methodologies. In the domain of Vehicular Ad hoc NETworks (VANETs), data is regularly gathered from several sources. The collected data serves multiple goals, such as facilitating efficient routing, enhancing driver awareness, forecasting mobility patterns to prevent potential risks, and ultimately enhancing passenger comfort, safety and overall road experience. Internet of thing (IoT) can be a good solution fro many issues that of VANETs. In order to make a complete system of VANETs communications are very essential like 5G and 6G. This study provides a detailed examination of AI, IoT and 5G/6G methodologies currently being investigated by multiple research endeavors in the field of VANETs. The merits and demerits of the AI-based methodologies suggested for the VANET domain have been analyzed with the using of IoT and 5G/6G. In conclusion, forthcoming research prospects in the field of VANETs are ascertained.

Introduction: Autism Spectrum Disorder (ASD) is a lifelong hereditary neurodevelopmental disorder characterised by a lack of social communication and restricted and repetitive behaviours. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) classified symptoms of autism into two domains: the social communication and interaction domain. However, studies on autism in Saudi Arabia are still insufficient. Therefore, awareness and knowledge about autism can facilitate early detection and treatment, resulting in better outcomes. Aim: To evaluate parents’ knowledge, awareness, and attitude towards their autistic children in a tertiary care hospital in Saudi Arabia. Materials and Methods: This hospital-based cross-sectional study was conducted at Baljurashi Mental Health Hospital, AL-Baha region, Saudi Arabia, from January 2023 to November 2023. A total of 69 parents with autistic children who attended the outpatient clinic at Baljurashi Mental Health Hospital were included in the study. A questionnaire was administered among parents to assess their knowledge, awareness, and attitude regarding autism. Data was statistically analysed using descriptive statistics and the Chi-square test. Results: The age of the parents ranged from 16 to over 50 years, with 47 (68.1%) being males and 22 (31.9%) being females. Almost 29 (42.0%) of the parents were postgraduates, and 21 (30.4%) had a university-level education. About 24 (34.8%) of the participants believed that consanguineous marriage increases the risk of autism, 19 (27.5%) believed that some vaccines increase the risk of autism, and 39 (56.5%) knew that autism is associated with learning disabilities. Three-quarters of the participants, 54 (78.3%), stated that autism was known to them, 50 (72.5%) thought that an autistic child could be affected by a normal child, and 53 (76.8%) had good knowledge about autism. Conclusion: The study showed that the majority of parents had good knowledge about autism disorder. The findings reflect the positive impact of focused training initiatives and public awareness efforts aimed at improving parents’ knowledge, awareness, and attitude toward autism.

Many deaf and hard of hearing children face significant challenges in language, literacy, social, and academic development. These challenges increase for deaf and hard of hearing children who are from culturally and linguistically diverse backgrounds (Cannon & Luckner, 2016). To address these challenges, philosophies, policies, and practices should support full inclusion. Deaf and hard of hearing children from ethnically, racially, and linguistically diverse backgrounds enrich many public school classrooms. However, addressing inequities based on disability, race, or ethnicity is about more than placement. Questions still remain about teachers' practices, and few teacher preparation programs in the United States provide training on how to work with deaf and hard of hearing children from diverse racial, ethnic, and language backgrounds (Cannon & Luckner, 2016). Yet teachers need tools for addressing inequities and dismantling systemic racism, and effective tools may be found in children's literature.

A Blast loading history can be represented by the peak pressure value and the impulse. Combinations of pressure and impulse of various blast loading scenarios can be collected to draw a pressure-impulse (P-I) curve that corresponds to a level of damage of a blast-resistant structural component. The P-I curve is typically used for a preliminary design or assessment of blast resistance structural components. Generating a P I curve for a given structural component and level of protection requires repeatedly ru nning a single degree of freedom system (SDOF) model, which can be tedious for early design stages. Hence, a simplified approach (i.e., the normalization approach) was utilized to promptly generate the P- I curve for insulated precast concrete wall panels, which relies on shifting a control P I curve using normalization factors. The generated P-I curves using the normalization approach are validated with the traditional SDOF approach curves; 95% of the examined insulted panel configurations have an error between +/-7%. As a result of the reached low error percentages, a spreadsheet- design tool was developed using this approach. The tool can further enable rapid evaluation of the performance of insulated precast concrete panels under blast loading during the preliminary design phase.

Oral malignant lesions are the most common malignancy of which majority are diagnosed in advanced stages in Saudi Arabia. Patients with oral malignant lesion are first seen by general dental practitioners and dental students. Multiple surveys have been conducted and published to assess the students, interns, and dentists’ awareness and attitude toward oral premalignant lesions worldwide. However, little is known about this issue in Saudi Arabia. We aimed to assess knowledge and awareness of detecting oral premalignant lesions among dental students, dental interns, and practitioners in Saudi Arabia. A cross-sectional questionnaire-based survey was conducted and electronically distributed. The questionnaire comprised of three sections including demographic variables, knowledge of risk factors of oral malignancy, and six clinical case scenarios. Descriptive statistics were used, and the results were reported using cross-tabulation and the Chi-square test. A 300 participants answered to the questionnaire, and 83% of respondents strongly agreed that smoking is a risk factor for oral malignancy. However, there was some uncertainty in responses for some more specific oral-related factors, such as a sharp-edged tooth and irritation from dental restoration. Furthermore, the knowledge and awareness of detecting frankly benign and frankly malignant lesions such as fibroma and squamous cell carcinoma are higher comparing to potentially malignant lesion, i.e. lichen planus. Knowledge and awareness of detecting oral premalignant lesions vary based on malignancy factors and by participants academic level. Fibroma and SCC are detected easily among all participants. However, lichen planus was the hardest to diagnose among oral lesions.

Abstract The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

The impact of learning enjoyment on motivation, enthusiasm, and overall learning experiences is significant. Previous studies, lacking an unbiased tool for measuring enjoyment and confronting various influencing factors, produced conflicting results regarding enjoyment levels in different instructional methods. Hence, we developed a learning enjoyment scale for evaluating both active and passive educational activities. We applied the developed scale to 112 first-year medical and dental students to assess their enjoyment during didactic physiology lectures and explored possible associated factors. Within this data note, we present students’ responses to the developed LES. The LES encompasses six dimensions: knowledge, comprehension, application, analysis, concentration, and enjoyment. Students provided ratings for each dimension on a five-point Likert scale, spanning from 1 (strongly disagree) to 5 (strongly agree). The cumulative scores across the six dimensions range from a minimum of 6 to a maximum of 30. These total scores can be categorized as excellent (> 24), acceptable (18-24), or low (< 18). The second section of the dataset examines specific factors influencing overall enjoyment, such as teacher proficiency, topic difficulty, active student participation, objectives fulfillment, low stress levels, and self-perceived acquisition of skills. In addition to objective measurement of students’ enjoyment level, the LES can be utilized for quantitative cross-comparisons between different teaching activities. By employing this dataset, we will undertake an analysis to determine the internal consistency of the Learning Enjoyment Scale (LES), with the anticipation that the outcomes will be published in another venue.

IntroductionThe prevalence of diet-related non-communicable diseases has increased. A low-carbohydrate diet (LCDs) is one of the most popular interventions. Several systematic reviews and meta-analyses of randomised clinical trials (RCTs) and non-RCTs have linked LCDs to the management of obesity, diabetes, cardiovascular disease, epilepsy, and cancer. However, there has been limited appraisal of the strength and quality of this evidence.ObjectiveTo systematically appraise existing meta-analyses and systematic reviews of RCTs and non-RCTs on the effects of LCDs on different health conditions. To understand their potential efficacy, we summarised the studies' findings and assessed the strength of the evidence.MethodsA search was conducted using the PubMed database from inception to October 2021 for systematic reviews and meta-analyses of RCTs and non-RCTs investigating the association between LCDs and multiple health outcomes in humans. The Academy of Nutrition and Dietetics Quality Criteria was used for the quality assessment. In addition, the evolution of heterogeneity, strength of the included studies, and effect sizes were extracted from each systematic review and meta-analysis.ResultsTen systematic reviews and meta-analyses were included. Of the included reviews, 70% were of positive quality, 30% were neutral, and none were negative. The majority of the studies included strength in each systematic review, and the meta-analyses were of low to medium strength. The existing literature indicates that LCDs may help promote weight reduction in adults who are obese or overweight. This conclusion is supported by the findings of studies included in the analysis, which were of low to moderate strength. Furthermore, compelling data indicates a significant association between low-carbohydrate diets (LCDs) and a reduction in haemoglobin A1c levels among those diagnosed with type 2 diabetes mellitus. In contrast, there was a lack of evidence of this correlation in type 1 diabetes mellitus patients or those with cardiovascular diseases. Additionally, there was limited evidence regarding the effectiveness of LCDs in epilepsy and adult cancer patients.ConclusionThis review thoroughly examines the current body of information on how LCDs affect various health outcomes. Studies have presented evidence to support the idea that incorporating LCDs can positively influence weight management and HbA1c levels. However, there is a lack of information regarding the association between LCDs and individuals with Type 1 diabetes mellitus and cardiovascular diseases. Additionally, there is limited empirical evidence to substantiate the effectiveness of LCDs in the treatment of epilepsy and adult cancer patients. The long-term effects of LCDs on mortality and other chronic diseases that account for different carbohydrate subtypes is unclear. Further longitudinal cohort studies are required to reach definitive conclusions.

The impact of learning enjoyment on motivation, enthusiasm, and overall learning experiences is significant. Previous studies, lacking an unbiased tool for measuring enjoyment and confronting various influencing factors, produced conflicting results regarding enjoyment levels in different instructional methods. Hence, we developed a learning enjoyment scale for evaluating both active and passive educational activities. We applied the developed scale to 112 first-year medical and dental students to assess their enjoyment during didactic physiology lectures and explored possible associated factors. Within this data note, we present students’ responses to the developed LES. The LES encompasses six dimensions: knowledge, comprehension, application, analysis, concentration, and enjoyment. Students provided ratings for each dimension on a five-point Likert scale, spanning from 1 (strongly disagree) to 5 (strongly agree). The cumulative scores across the six dimensions range from a minimum of 6 to a maximum of 30. These total scores can be categorized as excellent (> 24), acceptable (18-24), or low (< 18). The second section of the dataset examines specific factors influencing overall enjoyment, such as teacher proficiency, topic difficulty, active student participation, objectives fulfillment, low stress levels, and self-perceived acquisition of skills. In addition to objective measurement of students’ enjoyment level, the LES can be utilized for quantitative cross-comparisons between different teaching activities. By employing this dataset, we will undertake an analysis to determine the internal consistency of the Learning Enjoyment Scale (LES), with the anticipation that the outcomes will be published in another venue.

Persistent primitive hypoglossal artery (PPHA) is a rare embryological carotid–basilar anastomosis. Diagnosis is by imaging and computed tomography angiography is the most common methods employed. Herein we present a case of a 51 years-old Indian male, who admitted with midnight nonwitnessed syncope. CT angiogram of the brain revealed a very rare caroto-basilar anastomoses anomaly with PPHA.

Abstract Background The ophthalmology workforce is an integral component of any health care system. However, the demand for eye care has imposed a heavy burden on this system. Hence, this study aimed to estimate the trends, demographic characteristics, distribution, and variation between Saudi and non-Saudi ophthalmologists and the future challenges of the ophthalmology workforce in the Kingdom of Saudi Arabia (KSA). Methods This study was conducted in the KSA and included ophthalmologists practicing from 2010 to 2023. From the Saudi Commission for Health Specialties, we obtained the number, gender, nationality, and rank of ophthalmologists. The geographic distribution of ophthalmologists in the KSA was obtained from the Ministry of Health Statistical Yearbook 2021. Results As of January 2023, the KSA had a total of 2608 registered ophthalmologists, with approximately 81.06 ophthalmologists per 1,000,000 people. Only 38% of all ophthalmologists in the country were Saudis. The percentage of Saudi female graduates increased from 13.3% to 37.2% over 12 years [Sen’s estimator of slope for median increase per year = 1.33 (95% CI 1.22–1.57) graduates; trend test P

Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques proposed in the literature successfully reduce model sizes and maintain comparable accuracy. However, the robustness of compressed DL models against class imbalance, a natural phenomenon in real-life datasets, is still under-explored. We present a comprehensive experimental study of the performance and robustness of compressed DL models when trained on class-imbalanced datasets. We investigate the robustness of compressed DL models using three popular compression techniques (pruning, quantization, and knowledge distillation) with class-imbalanced variants of the CIFAR-10 dataset and show that compressed DL models are not robust against class imbalance in training datasets. We also show that different compression techniques have varying degrees of impact on the robustness of compressed DL models.