Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.

Accurate and efficient segmentation of coronary arteries from CTA images is crucial for diagnosing and treating cardiovascular diseases. This study proposes a structured approach that combines vesselness enhancement, heart region of interest (ROI) extraction, and the ResUNet deep learning method to accurately and efficiently extract coronary artery vessels. Vesselness enhancement and heart ROI extraction significantly improve the accuracy and efficiency of the segmentation process, while ResUNet enables the model to capture both local and global features. The proposed method outperformed other state-of-the-art methods, achieving a Dice similarity coefficient (DSC) of 0.867, a Recall of 0.881, and a Precision of 0.892. The exceptional results for segmenting coronary arteries from CTA images demonstrate the potential of this method to significantly contribute to accurate diagnosis and effective treatment of cardiovascular diseases.

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique.

Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

The burden of persistent symptoms after COVID-19 (long COVID): a meta-analysis of controlled studies in children and adults.

BACKGROUND: Previous meta-analyses estimating the prevalence of the post-COVID-19 condition (PCC) were confounded by the lack of negative control groups. This may result in an overestimation of the prevalence of those experiencing PCC, as these symptoms are non-specific and common in the general population. In this study, we aimed to compare the burden of persistent symptoms among COVID-19 survivors relative to COVID-19-negative controls. METHODS: A systematic literature search was conducted using the following databases (PubMed, Web of Science, and Scopus) until July 2023 for comparative studies that examined the prevalence of persistent symptoms in COVID-19 survivors. Given that many of the symptoms among COVID-19 survivors overlap with post-hospitalization syndrome and post-intensive care syndrome, we included studies that compare the prevalence of persistent symptoms in hospitalized COVID-19 patients relative to non-COVID-19 hospitalized patients and in non-hospitalized COVID-19 patients relative to healthy controls that reported outcomes after at least 3 months since infection. The results of the meta-analysis were reported as odds ratios with a 95% confidence interval based on the random effects model. RESULTS: Twenty articles were included in this study. Our analysis of symptomatology in non-hospitalized COVID-19 patients compared to negative controls revealed that the majority of symptoms examined were not related to COVID-19 infection and appeared equally prevalent in both cohorts. However, non-COVID-19 hospitalized patients had higher odds of occurrence of certain symptoms like anosmia, ageusia, fatigue, dyspnea, and brain fog (P < 0.05). Particularly, anosmia and ageusia showed substantially elevated odds relative to the negative control group at 11.27 and 9.76, respectively, P < 0.05. In contrast, analysis of hospitalized COVID-19 patients compared to those hospitalized for other indications did not demonstrate significantly higher odds for the tested symptoms. CONCLUSIONS: The persistent symptoms in COVID-19 survivors may result from hospitalization for causes unrelated to COVID-19 and are commonly reported among the general population. Although certain symptoms exhibited higher odds in non-hospitalized COVID-19 patients relative to controls, these symptoms are common post-viral illnesses. Therefore, the persistent symptoms after COVID-19 may not be unique to SARS-CoV-2. Future studies including well-matched control groups when investigating persistent symptoms in COVID-19 survivors are warranted to draw a firm conclusion.

Relationship between bad oral habits, signs, and symptoms of temporomandibular joint disorders among Saudi population: A cross-sectional study.

Background: According to the American Dental Association, temporomandibular disorders (TMD) refer to a group of disorders characterised by pain in the temporomandibular joint (TMJ), the periauricular area, or the muscles of mastication. TMJ noise/sounds, and any deviations or restriction during jaw movements. Many oral habits are common and usually do not harm the TMJ and associated structures. However, these habits may result in TMJ disorders, if the activity goes beyond someone's physiological tolerance. The causes of degenerative changes of TMJ are believed to be multifactorial and controversial too. Objectives: This study aims to evaluate the prevalence of oral habits and its relationship with temporomandibular disorders in the Saudi population of Taif city. Materials and Methods: This questionnaire-based cross-sectional study was conducted from March 2021 to July 2021 at Taif city, Kingdom of Saudi Arabia (KSA). The Arabic version of a standardized questionnaire (recommended by the American Academy of Orofacial Pain) was used and distributed randomly among 441 citizens of Taif city. Results: Our study showed that many respondents had different TMJ disorders, such as pain during eating, sound in jaw joint, pain around ear, temples, and cheek, headache and neck pain, change in bite, and pain during opening and closing of the mouth. On the other hand, many respondents answered that they had TMD, pain associated with nail biting/object biting, lip biting, clenching teeth, and chewing gum. Conclusion: In the present study, a relationship between harmful oral habits and the development of signs and symptoms of TMDs among adolescents who lived in Taif city, KSA was noted. In the present study, no clinical examinations were conducted and only consisted of closed-ended questions, all of which may have a lower the validity rate. An effort was made to overcome these limitations by using a well-designed standardized questionnaire by the American Academy of the Orofacial Pain. We conclude that further studies are required and using of clinical examination to measure the severity of the signs and symptoms for better understanding the association of oral habits with TMJ disorders.

Hepatic vessels segmentation using deep learning and preprocessing enhancement.

PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. METHODS: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. RESULTS: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. CONCLUSIONS: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

Seroprevalence of hepatitis B virus surface antigen (HBsAg) in Egypt (2000-2022): a systematic review with meta-analysis.

BACKGROUND: Hepatitis B infection seriously threatens global public health, especially in developing nations. Despite several investigations on HBV incidence, the national pooled prevalence remains unknown, particularly in populations at-risk at whom interventions should be primarily aimed. METHODS: A comprehensive literature search of the following databases: Medline [PubMed], Scopus, Google Scholar, and Web of Science was conducted following the PRISMA guidelines. I-squared and Cochran's Q were used to measure the heterogeneity between the studies. Publications that matched the following were included: Primary studies published in Egypt from 2000 to 2022 reported HBV prevalence based on HBsAg. We excluded any studies that were not performed on Egyptians or that were performed on patients suspected of acute viral hepatitis or studies focusing on occult hepatitis or vaccination evaluation studies, or national surveys. RESULTS: The systematic review included 68 eligible studies reporting a total of 82 incidences of HBV infection based on hepatitis B surface antigen with a total sample size of 862,037. The pooled national prevalence among studies was estimated to be 3.67% [95% CI; 3: 4.39]. Children under 20 with a history of HBV vaccination during infancy had the lowest prevalence of 0.69%. The pooled prevalence of HBV infection among pregnant women, blood donors, and healthcare workers was 2.95%, 1.8%, and 1.1%, respectively. While patients with hemolytic anemia and hemodialysis patients, patients with malignancies, HCC patients, and chronic liver disease patients had the highest prevalences at 6.34%, 25.5%, 18.6%, and 34%, respectively. Studies reporting HBV prevalence in urban settings compared to rural settings revealed a similar HBV prevalence of 2.43% and 2.15%, respectively. Studies comparing HBV prevalence in males and females revealed a higher prevalence among males (3.75%) than females (2.2%). CONCLUSION: In Egypt, hepatitis B infection is a significant public health issue. The blocking of mother-to-infant hepatitis B transmission, the scaling up of the scope of the existing vaccination program, and implementing new strategies, including screen-and-treat, may reduce the prevalence of the disease.

Interventions for reducing late-onset sepsis in neonates: an umbrella review.

OBJECTIVES: Neonatal sepsis is one of the leading causes of neonatal deaths in neonatal intensive care units. Hence, it is essential to review the evidence from systematic reviews on interventions for reducing late-onset sepsis (LOS) in neonates. METHODS: PubMed and the Cochrane Central were searched from inception through August 2020 without any language restriction. Cochrane reviews of randomized clinical trials (RCTs) assessing any intervention in the neonatal period and including one or more RCTs reporting LOS. Two authors independently performed screening, data extraction, assessed the quality of evidence using Cochrane Grading of Recommendations Assessment, Development and Evaluation, and assessed the quality of reviews using a measurement tool to assess of multiple systematic reviews 2 tool. RESULTS: A total of 101 high-quality Cochrane reviews involving 612 RCTs and 193,713 neonates, evaluating 141 interventions were included. High-quality evidence showed a reduction in any or culture-proven LOS using antibiotic lock therapy for neonates with central venous catheters (CVC). Moderate-quality evidence showed a decrease in any LOS with antibiotic prophylaxis or vancomycin prophylaxis for neonates with CVC, chlorhexidine for skin or cord care, and kangaroo care for low birth weight babies. Similarly, moderate-quality evidence showed reduced culture-proven LOS with intravenous immunoglobulin prophylaxis for preterm infants and probiotic supplementation for very low birth weight (VLBW) infants. Lastly, moderate-quality evidence showed a reduction in fungal LOS with the use of systemic antifungal prophylaxis in VLBW infants. CONCLUSIONS: The overview summarizes the evidence from the Cochrane reviews assessing interventions for reducing LOS in neonates, and can be utilized by clinicians, researchers, policymakers, and consumers for decision-making and translating evidence into clinical practice.

Effect of an early occupational therapy intervention on length of stay in moderate and severe traumatic brain injury patients.

PURPOSE: This study aimed to determine how an early occupational therapy (OT) intervention affected hospital length of stay (LOS) in a sample of patients with a moderate to severe traumatic brain injury (TBI). METHODS: This quasi-experimental study was conducted with 2018-2020 data from a rehabilitation center at the King Saud Medical City in Riyadh, Kingdom of Saudi Arabia. The sample of 29 TBI patients included 15 experimental (prospective) group participants who received an early OT intervention and 14 control group (retrospective) participants who did not receive the intervention. The intervention provided patients with daily OT therapy based on their needs and was divided into two phases: the intensive care unit (ICU) phase and the general ward phase. The following measures were used: Glasgow Coma Scale score at admission (both groups), hospital LOS (from admission until discharge; both groups), and functional independence measures (FIM) at admission and discharge (experimental group). RESULTS: Experimental group patients had a much shorter LOS (average 61.53 days) compared with the control group (mean 108.86 days). Additionally, the experimental group had a statistically significant increase in FIM scores from admission to discharge. CONCLUSIONS: These results suggest that providing early OT interventions to patients with moderate and severe TBIs can help decrease their LOS, which can contribute to reduced treatment costs.

Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app

Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups. Here, we use machine learning to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended. We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout. Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.

Systemic LSD1 Inhibition Prevents Aberrant Remodeling of Metabolism in Obesity.

The transition from lean to obese states involves systemic metabolic remodeling that impacts insulin sensitivity, lipid partitioning, inflammation, and glycemic control. Here, we have taken a pharmacological approach to test the role of a nutrient-regulated chromatin modifier, lysine-specific demethylase (LSD1), in obesity-associated metabolic reprogramming. We show that systemic administration of an LSD1 inhibitor (GSK-LSD1) reduces food intake and body weight, ameliorates nonalcoholic fatty liver disease (NAFLD), and improves insulin sensitivity and glycemic control in mouse models of obesity. GSK-LSD1 has little effect on systemic metabolism of lean mice, suggesting that LSD1 has a context-dependent role in promoting maladaptive changes in obesity. In analysis of insulin target tissues we identified white adipose tissue as the major site of insulin sensitization by GSK-LSD1, where it reduces adipocyte inflammation and lipolysis. We demonstrate that GSK-LSD1 reverses NAFLD in a non-hepatocyte-autonomous manner, suggesting an indirect mechanism potentially via inhibition of adipocyte lipolysis and subsequent effects on lipid partitioning. Pair-feeding experiments further revealed that effects of GSK-LSD1 on hyperglycemia and NAFLD are not a consequence of reduced food intake and weight loss. These findings suggest that targeting LSD1 could be a strategy for treatment of obesity and its associated complications including type 2 diabetes and NAFLD.

Gene-drive mosquitoes: a prospect for future malaria control.

Despite major developments in malaria control over the past two decades, the disease continues to scourge the human population across the globe. Rising concerns such as insecticide resistance amongst vector mosquitoes are a cause of huge fear amongst healthcare providers and policymakers. Amidst such dire circumstances, a recent development may form the blueprint for future malaria control as for the first time ever researchers were able to decimate an entire mosquito population using gene-drive technology within a span of one year in a multi-generation, ecologically challenging study. Despite some concerns, the technology displayed a high potential of becoming a powerful tool in malaria control.

Genome-Wide Association Study-Guided Exome Rare Variant Burden Analysis Identifies IL1R1 and CD3E as Potential Autoimmunity Risk Genes for Celiac Disease.

Celiac disease (CeD) is a multifactorial autoimmune enteropathy characterized by the overactivation of the immune system in response to dietary gluten. The molecular etiology of CeD is still not well-understood. Therefore, this study aims to identify potential candidate genes involved in CeD pathogenesis by applying multilayered system biology approaches. Initially, we identified rare coding variants shared between the affected siblings in two rare Arab CeD families by whole-exome sequencing (WES). Then we used the STRING database to construct a protein network of rare variants and genome-wide association study (GWAS) loci to explore their molecular interactions in CeD. Furthermore, the hub genes identified based on network topology parameters were subjected to a series of computational validation analyses like pathway enrichment, gene expression, knockout mouse model, and variant pathogenicity predictions. Our findings have shown the absence of rare variants showing classical Mendelian inheritance in both families. However, interactome analysis of rare WES variants and GWAS loci has identified a total of 11 hub genes. The multidimensional computational analysis of hub genes has prioritized IL1R1 for family A and CD3E for family B as potential genes. These genes were connected to CeD pathogenesis pathways of T-cell selection, cytokine signaling, and adaptive immune response. Future multi-omics studies may uncover the roles of IL1R1 and CD3E in gluten sensitivity. The present investigation lays forth a novel approach integrating next-generation sequencing (NGS) of familial cases, GWAS, and computational analysis for solving the complex genetic architecture of CeD.

Surgical Reconstruction of a Traumatic Superior Sagittal Sinus Injury Using Synthetic Vascular Graft in a Resource-Limited Civilian Field Hospital During the Syrian Civil War.

BACKGROUND: Traumatic dural sinus injuries following penetrating brain injury are rare but are often associated with significant morbidity and mortality. The management of significant defects is operative and can be challenging. In modern civilian practice, patients with these injuries typically receive extensive preoperative investigation and advanced perioperative monitoring. METHODS: We report a case of successful operative management in a frontline civilian field hospital of a 33-year-old man who sustained a shrapnel injury to the junction of the middle and posterior thirds of the superior sagittal sinus during the Syrian Civil War. RESULTS: The injury was repaired successfully with a polytetrafluoroethylene graft. The postoperative course was uncomplicated. Anticoagulation was commenced 2 days postoperatively. The patient was discharged 5 days postoperatively with a GCS of 15 and mild weakness of the limbs bilaterally. CONCLUSIONS: Immediate repair of dural sinus injuries is feasible in the context of a low-resource wartime environment and should be attempted without delay. To our knowledge, this is the first case of successful use of a synthetic vascular graft for repair of a dural venous sinus injury in an ill-equipped hospital.

Automatic deep learning system for COVID-19 infection quantification in chest CT.

The paper proposes an automatic deep learning system for COVID-19 infection areas segmentation in chest CT scans. CT imaging proved its ability to detect the COVID-19 disease even for asymptotic patients, which make it a trustworthy alternative for PCR. Coronavirus disease spread globally and PCR screening is the adopted diagnostic testing method for COVID-19 detection. However, PCR is criticized due its low sensitivity ratios, also, it is time-consuming and manual complicated process. The proposed framework includes different steps; it starts to prepare the region of interest by segmenting the lung organ, which then undergoes edge enhancing diffusion filtering (EED) to improve the infection areas contrast and intensity homogeneity. The proposed FCN is implemented using U-net architecture with modified residual block to include concatenation skip connection. The block improves the learning of gradient values by forwarding the infection area features through the network. The proposed system is evaluated using different measures and achieved dice overlapping score of 0.961 and 0.780 for lung and infection areas segmentation, respectively. The proposed system is trained and tested using many 2D CT slices extracted from diverse datasets from different sources, which demonstrate the system generalization and effectiveness. The use of more datasets from different sources helps to enhance the system accuracy and generalization, which can be accomplished based on the data availability in in the future.

Prevalence estimates of drug addiction among high-school students and its association with violence, and school behaviors: A cross-sectional study from Jordan.

INTRODUCTION: This study aims to estimate the prevalence of drug addiction among high-school students and its association with violence and school behavior. The present study provides baseline data focusing specifically on the extent of drug addiction and violence. METHODS: A cross-sectional study design was used. The questionnaire covered students' demographic profiles, including age, gender, and academic branch, and statements related to violent behaviors and school-related factors. A urine sample was collected from each participant to test for drugs and was analyzed by Multi-Drug Rapid Test Cup. RESULTS: Out of 440 students, a total of 414 were included in this study. Positive drug use via urine sample was confirmed in 70 students (16.9%), with 80% being males. Violent behaviors were common, including used to write on the walls (118, 28.5%), had problems with teachers (107, 25.9%) or peers (267, 64.5%), or using abusive language with school staff (63, 15.2%). There were no significant associations between drug addiction and these violent behaviors (p > 0.05). Students who had no respect for the teachers were significantly more in the addicted group (31.4%) than the non-addicted group (14%) (p = 0.004). CONCLUSION: High school should be identified as a critical period at risk for drug use and violent actions. This study points to the importance of early detection of drug addiction among vulnerable young people, enabling the development of educational programs to prevent abuse and dependence on substances.

The impact of a gluten-free diet on the growth pattern of Saudi children with coeliac disease.

OBJECTIVE: To assess the growth pattern of children with coeliac disease after the introduction of a gluten-free diet. METHOD: The retrospective study was conducted at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, and comprised data from January 2015 to December 2018 of children aged 2-16 years with biopsy-proven coeliac disease. Serial measurements of height-for-age and weight-for-age z-scores were recorded at 0, 4, 8, 12 and 16 months. Data on insulin-like growth factor-1 and insulin-like growth factor binding protein-3 obtained at diagnosis and during follow-up was retrieved. Clinical, demographic, and laboratory data was extracted from the patients' medical files. Data was analysed using SPSS 22. RESULTS: Of the 47 patients, 25(53.2%) were boys and 22(46.8%) were girls. The overall mean age was 8.7±3.4 years. There was a significant time effect for weight-for-age and height-for-age z-scores (p<0.001). There was significant increase in the secretion of insulin-like growth factor-1 and insulin-like growth factor binding protein-3 (p<0.05) during the first 8 months of a gluten-free diet. CONCLUSIONS: The administration of gluten-free diet for Saudi children with coeliac disease normalized growth parameters and improved the endogenous secretion of growth factors.

Metabolic bone disease in children and adolescent patients with ulcerative colitis

Abstract Objective: Metabolic bone disease concerns a broad spectrum of conditions related to reduced bone density. Metabolic bone disease has been linked to chronic inflammatory diseases, such as ulcerative colitis. This study examines the prevalence of metabolic bone disease in ulcerative colitis patients and explores possible clinical predictors. Method: The authors performed a retrospective study involving children and adolescents with confirmed ulcerative colitis between January 2013 and December 2018. Bone density was evaluated through a dual-energy X-ray absorptiometry scan of the spine and total body. Osteoporosis was defined as a bone mineral density Z-score of <−2 and osteopenia as a Z-score of between −1.0 and −2. Results: A total of 37 patients were included in this analysis, with a mean age of 13.4 ± 3.9 years and a mean duration of illness of 2.1 ± 2.4 years. Using lumbar spine Z-scores and total body Z-scores, osteoporosis and osteopenia were identified by dual-energy X-ray absorptiometry scan measurements in 11 patients (29.7%) and 15 patients (40.5%), and in ten patients (27%) and 13 patients (35%), respectively. Lumbar spine Z-scores were significantly positively associated with male gender (B = 2.02; p = 0.0001), and negatively associated with the presence of extraintestinal manifestations (B = −1.51, p = 0.009) and the use of biologics (B = −1.33, p = 0.004). However, total body Z-scores were positively associated with body mass index Z-scores (B = 0.26, p = 0.004) and duration of illness in years (B = 0.35, p = 0.003). Conclusions: Metabolic bone disease is very common in this cohort of Saudi Arabian children and adolescents with ulcerative colitis and its occurrence appears to increase in female patients who suffer from extraintestinal manifestations.

Metabolic bone disease in children and adolescent patients with ulcerative colitis.

OBJECTIVE: Metabolic bone disease concerns a broad spectrum of conditions related to reduced bone density. Metabolic bone disease has been linked to chronic inflammatory diseases, such as ulcerative colitis. This study examines the prevalence of metabolic bone disease in ulcerative colitis patients and explores possible clinical predictors. METHOD: The authors performed a retrospective study involving children and adolescents with confirmed ulcerative colitis between January 2013 and December 2018. Bone density was evaluated through a dual-energy X-ray absorptiometry scan of the spine and total body. Osteoporosis was defined as a bone mineral density Z-score of <-2 and osteopenia as a Z-score of between -1.0 and -2. RESULTS: A total of 37 patients were included in this analysis, with a mean age of 13.4±3.9 years and a mean duration of illness of 2.1±2.4 years. Using lumbar spine Z-scores and total body Z-scores, osteoporosis and osteopenia were identified by dual-energy X-ray absorptiometry scan measurements in 11 patients (29.7%) and 15 patients (40.5%), and in ten patients (27%) and 13 patients (35%), respectively. Lumbar spine Z-scores were significantly positively associated with male gender (B=2.02; p=0.0001), and negatively associated with the presence of extraintestinal manifestations (B=-1.51, p=0.009) and the use of biologics (B=-1.33, p=0.004). However, total body Z-scores were positively associated with body mass index Z-scores (B=0.26, p=0.004) and duration of illness in years (B=0.35, p=0.003). CONCLUSIONS: Metabolic bone disease is very common in this cohort of Saudi Arabian children and adolescents with ulcerative colitis and its occurrence appears to increase in female patients who suffer from extraintestinal manifestations.

Systemic hypoxia inhibits T cell response by limiting mitobiogenesis via matrix substrate-level phosphorylation arrest.

Systemic oxygen restriction (SOR) is prevalent in numerous clinical conditions, including chronic obstructive pulmonary disease (COPD), and is associated with increased susceptibility to viral infections. However, the influence of SOR on T cell immunity remains uncharacterized. Here we show the detrimental effect of hypoxia on mitochondrial-biogenesis in activated mouse CD8+ T cells. We find that low oxygen level diminishes CD8+ T cell anti-viral response in vivo. We reveal that respiratory restriction inhibits ATP-dependent matrix processes that are critical for mitochondrial-biogenesis. This respiratory restriction-mediated effect could be rescued by TCA cycle re-stimulation, which yielded increased mitochondrial matrix-localized ATP via substrate-level phosphorylation. Finally, we demonstrate that the hypoxia-arrested CD8+ T cell anti-viral response could be rescued in vivo through brief exposure to atmospheric oxygen pressure. Overall, these findings elucidate the detrimental effect of hypoxia on mitochondrial-biogenesis in activated CD8+ T cells, and suggest a new approach for reducing viral infections in COPD.

Deep learning and level set approach for liver and tumor segmentation from CT scans.

PURPOSE: Segmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time-consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow-up assessment. METHOD: This work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region-based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region-based level aims to refine the predicted segmentation to find the correct final segmentation. RESULTS: The effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively. CONCLUSION: The proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.