Miracle-dz/UniFAR-FTUs huggingface dataset:
Common disease-associated gene variants in a Saudi Arabian population

BACKGROUND: Screening programs for the most prevalent conditions occurring in a country is an evidence-based prevention strategy. The burden of autosomal recessive disease variations in Saudi Arabia is high because of the highly consanguineous population. The optimal solution for estimating the carrier frequency of the most prevalent diseases is carrier screening. OBJECTIVES: Identify the most influential recessive alleles associated with disease in the Saudi population. DESIGN: We used clinical whole-exome sequencing data from an in-house familial database to evaluate the most prevalent genetic variations associated with disease in a Saudi population. SETTINGS: King Abdullah International Medical Research Center (KAIMRC) and King Abdulaziz Medical City. METHODS: Whole exome sequencing data obtained from clinical studies of family members, a cohort of 1314 affected and unaffected individuals, were filtered using the in-house pipeline to extract the most prevalent variant in the dataset. MAIN OUTCOME MEASURES: Most prevalent genetic variations associated with disease in the Saudi population. SAMPLE SIZE: 1314 affected and unaffected individuals. RESULTS: We identified 37 autosomal recessive variants and two heterozygous X-linked variants in 35 genes associated with the most prevalent disorders, which included hematologic (32%), endocrine (21%), metabolic (11%) and immunological (10%) diseases. CONCLUSION: This study provides an update of the most frequently occurring alleles, which support future carrier screening programs. LIMITATIONS: Single center that might represent the different regions but may be biased. In addition, most of the families included in the database are part of the proband's genetic identification for specific phenotypes. CONFLICT OF INTEREST: None.
Biomechanical variations in children who are overweight and obese during high‐impact activities: A systematic review and meta‐analysis

Youth who are obese or overweight demonstrate evidence of poor lower extremity joint health and alterations in gait characteristics compared with youth who are healthy weight. However, there is no consensus if altered movement patterns are still present during high‐impact activities. The purpose of this review was to determine if spatiotemporal and kinematic and kinetic variables during high‐impact activities were significantly different between youth who are overweight and obese compared with youth who are healthy weight. An electronic search of five databases was conducted, and a meta‐analysis and qualitative evidence synthesis was performed to determine the level of evidence, analyzing three tasks: running, jumping, and hopping. The findings of this review include the following: (1) overweight/obese (OW/OB) had higher stance phase time during running, (2) OW/OB had decreased hip flexion angles during running and stationary running, (3) OW/OB had decreased knee flexion angles during landing phase of jumping and hopping, and (4) OW/OB had increased hip abduction moments during running and jumping. These altered kinematic and kinetic variables at the hip and knee may result in mechanical inefficiency with high‐impact activities, as well as potentially increased risk of joint degradation and poor joint health into adulthood.
Who is the hardest to wake up from sleep? An investigation of self‐reported sleep inertia using a latent profile analysis

Few studies have assessed the overall nature and profiles of subjective sleep inertia (SI) within the general population. This study was designed to identify subjective SI profiles and examine the associations between profiles of subjective SI with sociodemographic and sleep‐related characteristics. A total of 11 colleges and universities were surveyed from May 30 to June 17, 2021, by convenience sampling. A total of 1,240 participants provided usable data regarding sociodemographic information, Sleep Inertia Questionnaire, and sleep‐related characteristics via an online platform. Latent profile analysis was utilised to identify profiles of SI. Multinomial logistic regression was further performed to examine the predisposing factors of profiles of SI. Four profiles of SI were identified: (1) “Low SI”, 20%; (2) “Mild SI”, 31%; (3) “Moderate SI”, 33%; and (4) “Severe SI”, 16%. Compared to a Low SI profile, younger, individuals with an evening chronotype, and individuals who had <6 h sleep/night, experienced poor sleep quality, and moderate‐to‐severe sleep disturbance were at increased risk of experiencing severe SI. Individuals with more languid types tended to show more severe SI, while individuals reporting greater flexibility experienced less SI. This study is the first effort to examine the profiles of subjective SI using latent profile analysis and identified four profiles of SI in the general population. This effort may contribute to a greater understanding of SI, including the development of a screening tool and interventions to reduce SI.
Examining implementation of an intervention to reduce psychological distress and intimate partner violence in a refugee camp setting

ABSTRACT An integrated approach to reduce intimate partner violence and improve mental health in humanitarian settings requires coordination across health and protection services. We developed and tested the Nguvu intervention, which combined evidence-based interventions for psychological distress and intimate partner violence among Congolese refugee women in Nyarugusu refugee camp (Tanzania). We conducted 29 semi-structured interviews with Nguvu participants and stakeholders to explore the relevance, acceptability, feasibility, and impact of this intervention. Participants reported that the intervention aligned with needs and filled a gap in programming, yet further adaptations may improve the fit of the intervention. The Nguvu intervention was acceptable to participants, including group discussion of sensitive topics. Confidentiality was highly regarded among staff and participants, which improved safety and acceptability. It was feasible to train non-specialist refugee workers to deliver the intervention with adequate supervision. Facilitators noted contextual challenges that made it difficult to implement the intervention: limited infrastructure, competing priorities, and population mobility. The intervention was perceived to improve awareness of the association between violence and mental health, reduce self-blame, and build skills to improve wellbeing. Recommended adaptations reveal promising, yet challenging future directions for addressing social determinants of mental health and implementing multi-sectoral programmes in complex humanitarian settings.
Dexmedetomidine suppresses serum syndecan-1 elevation and improves survival in a rat hemorrhagic shock model

Hemorrhagic shock causes vascular endothelial glycocalyx (EGCX) damage and systemic inflammation. Dexmedetomidine (DEX) has anti-inflammatory and EGCX-protective effects, but its effect on hemorrhagic shock has not been investigated. Therefore, we investigated whether DEX reduces inflammation and protects EGCX during hemorrhagic shock. Anesthetized Sprague-Dawley rats were randomly assigned to five groups (n=7 per group): no shock (SHAM), hemorrhagic shock (HS), hemorrhagic shock with DEX (HS+DEX), hemorrhagic shock with DEX and the α7 nicotinic type acetylcholine receptor antagonist methyllycaconitine citrate (HS+DEX/MLA), and hemorrhagic shock with MLA (HS+MLA). HS was induced by shedding blood to a mean blood pressure of 25–30 mmHg, which was maintained for 30 min, after which rats were resuscitated with Ringer’s lactate solution at three times the bleeding volume. The survival rate was assessed up to 3 h after the start of fluid resuscitation. Serum tumor necrosis factor-alpha (TNF-α) and syndecan-1 concentrations, and wet-to-dry ratio of the heart were measured 90 min after the start of fluid resuscitation. The survival rate after 3 h was significantly higher in the HS+DEX group than in the HS group. Serum TNF-α and syndecan-1 concentrations, and the wet-to-dry ratio of heart were elevated by HS, but significantly decreased by DEX. These effects were antagonized by MLA. DEX suppressed the inflammatory response and serum syndecan-1 elevation, and prolonged survival in rats with HS.
A transcriptome-based association study of growth, wood quality, and oleoresin traits in a slash pine breeding population

Slash pine (Pinus elliottii Engelm.) is an important timber and resin species in the United States, China, Brazil and other countries. Understanding the genetic basis of these traits will accelerate its breeding progress. We carried out a genome-wide association study (GWAS), transcriptome-wide association study (TWAS) and weighted gene co-expression network analysis (WGCNA) for growth, wood quality, and oleoresin traits using 240 unrelated individuals from a Chinese slash pine breeding population. We developed high quality 53,229 single nucleotide polymorphisms (SNPs). Our analysis reveals three main results: (1) the Chinese breeding population can be divided into three genetic groups with a mean inbreeding coefficient of 0.137; (2) 32 SNPs significantly were associated with growth and oleoresin traits, accounting for the phenotypic variance ranging from 12.3% to 21.8% and from 10.6% to 16.7%, respectively; and (3) six genes encoding PeTLP, PeAP2/ERF, PePUP9, PeSLP, PeHSP, and PeOCT1 proteins were identified and validated by quantitative real time polymerase chain reaction for their association with growth and oleoresin traits. These results could be useful for tree breeding and functional studies in advanced slash pine breeding program.
Interprofessional communication in the hospital setting : a systematic review of the qualitative literature

ABSTRACT Communication plays a key role in the provision of safe patient care, and miscommunication in healthcare can lead to avoidable patient harm or mortality. Interprofessional communication (IPCom) can be challenging due to differences in training, education and roles between healthcare professions. The aim of this systematic review was to synthesize the qualitative evidence regarding healthcare providers’ perceptions of interprofessional communication in the hospital setting. Four databases (PubMed, CINAHL, Web of Science, and Embase) were searched for studies that met the inclusion criteria. Eighteen studies were identified as suitable for inclusion in the review and were examined using thematic synthesis. Thematic synthesis led to the development of five descriptive themes: 1) ‘Hierarchy”, 2) “Interprofessional Ethos,” 3) “Healthcare Environment,” 4) “Personal Factors” and 5) “Methods of Communication,” and two overarching analytical themes: “Barriers to Communication” and “Facilitators to Communication. ” Personal factors, such as strong interprofessional relationships, were found to be important facilitators to IPCom, while organizational factors, such as challenging and hierarchical working environments, were found to pose barriers to IPCom.
Quantitative evaluation of liver function with gadoxetic acid enhanced MRI: Comparison among signal intensity-, T1-relaxometry-, and dynamic-hepatocyte-specific-contrast-enhanced MRI- derived parameters

Abstract Aims Three types of gadoxetic acid enhanced MRI parameters have been proposed to quantify liver function. However, until now there is no consensus on which one that has the greatest potential for use in clinical practice. This study was conducted to compare the efficacy of three types of gadoxetic acid enhanced MR parameters for quantitative assessment of liver function. Methods Imaging data of 10 patients with chronic liver disease and 20 healthy volunteers were analyzed. Parameters based on signal intensity(SI), T1 changes or dynamic-hepatocyte-specific-contrast-enhancement MR were calculated. Their mutual correlations, discriminatory capacity between cirrhotic and healthy liver and correlations with Child-Pugh score and Model for end-stage liver-disease (MELD) were estimated. Results The strongest correlations were observed between relative enhancement of the liver and T1 time at 20 min after contrast agent injection, and between liver-spleen contrast ratio at 20 min after contrast agent injection and hepatic uptake rate (|r|> 0.90, p < .05, both). All parameters but input-relative blood flow (p = 0.17) were significantly different between patient and control group (p < .05), with AUROCs of liver-to-muscle ratio (LMR), increase of LMR and hepatic extraction fraction greater than 0.90 (p < .05). Liver-to-spleen ratio, LMR and hepatic uptake index presented a strong correlation with Child-Pugh score and MELD (|r|> 0.8, p < .05). Conclusion Simple SI-based parameters were as good as more complex parameters in evaluating liver function at gadoxetic acid enhanced MR. In clinical routine LMR seems to be the easiest-to-use parameter for quantitative evaluation of liver function.
Mapping oncogenic protein interactions for precision medicine

Normal protein‐protein interactions (normPPIs) occur with high fidelity to regulate almost every physiological process. In cancer, this highly organised and precisely regulated network is disrupted, hijacked or reprogrammed resulting in oncogenic protein‐protein interactions (oncoPPIs). OncoPPIs, which can result from genomic alterations, are a hallmark of many types of cancers. Recent technological advances in the field of mass spectrometry (MS)‐based interactomics, structural biology and drug discovery have prompted scientists to identify and characterise oncoPPIs. Disruption of oncoPPI interfaces has become a major focus of drug discovery programs and has resulted in the use of PPI‐specific drugs clinically. However, due to several technical hurdles, studies to build a reference oncoPPI map for various cancer types have not been undertaken. Therefore, there is an urgent need for experimental workflows to overcome the existing challenges in studying oncoPPIs in various cancers and to build comprehensive reference maps. Here, we discuss the important hurdles for characterising oncoPPIs and propose a three‐phase multidisciplinary workflow to identify and characterise oncoPPIs. Systematic identification of cancer‐type‐specific oncogenic interactions will spur new opportunities for PPI‐focused drug discovery projects and precision medicine.
Polyphenol-rich tart cherries (Prunus Cerasus, cv Montmorency) improve sustained attention, feelings of alertness and mental fatigue and influence the plasma metabolome in middle-aged adults: a randomised, placebo-controlled trial

Abstract Tart Montmorency cherries (MC) are a particularly rich source of anthocyanins and other polyphenols that have been shown to elicit antioxidant, anti-inflammatory and vasomodulatory actions. The current study aimed to determine the influence of chronic MC supplementation on cognitive function and mood. In a 3-month double-blinded, placebo-controlled parallel study, middle-aged adults (mean ± sd: 48 ± 6 years) were randomly assigned to either 30 ml twice daily of MC (n 25) or the same amount of an isoenergetic placebo (n 25). Cognitive function and mood were assessed before and after supplementation using a computerised cognitive task battery and visual analogue scales. Cerebral blood flow was also monitored by near-infrared spectroscopy during the task battery, and questionnaires were administered to determine subjective sleep and health status and plasma metabolomics were analysed before and after supplementation. After 3 months, the MC resulted in higher accuracy in digit vigilance (mean difference: 3·3, 95 % CI: 0·2, 6·4 %) with lower number of false alarms (mean difference: −1·2, 95 % CI: −2·0, −0·4) compared with the placebo. There was also a treatment effect for higher alertness (mean difference: 5·9, 95 % CI: 1·3, 10·5 %) and lower mental fatigue ratings (mean difference −9·5, 95 % CI: −16·5, −2·5 %) with MC. Plasma metabolomics revealed an increase in a number of amino acids in response to MC intake, but not placebo. These data suggest an anti-fatiguing effect of MC supplementation as well as the ability to improve sustained attention during times of high cognitive demand, this could be related to changes in amino acid metabolism.
Structural, biocompatibility, and antibacterial properties of Ge-DLC nanocomposite for biomedical applications.

Integrative production of new nanocomposites has been used to enhance favorable features of biomaterials for unlocking ultimate potential of different molecules. In the present study, advantageous properties of diamond like carbons (DLC) and germanium (Ge) like greater biocompatibility and antibacterial attributes were aimed to combined into a thin film. For this purpose, 400 nm DLC-Ge nanocomposite was coated on the borosilicate glasses via the magnetron sputtering and surface characteristics was analyzed by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and The Raman spectrum. Biocompatibility analysis were performed by 3-(4,5-Dimethylthiazol-2-yl) (MTT) cell viability assay and Hoechst 33258 fluorescent staining genotoxicity assessments on the human fibroblast cell line (HDFa). Finally, antibacterial properties of DLC-Ge nanocomposite coatings were investigated by Pseudomonas aeruginosa (ATCC 27853) and Staphylococcus aureus (ATCC 25923) bacterial attachment analysis. As a result of magnetron sputtering coating, nearly 400 nm thick DLC-Ge nanocomposite film showed a smooth, a non-porous, and a dense characteristic. Cell viability analysis showed that Ge-DLC coatings permits %95 cell surface growth of fibroblast cells. Also, there were no significant difference in aspect of nuclear abnormalities compared to the (-) control which showed nonmutagenic features of the thin film. Finally, antibacterial attachment analysis put forth that Ge-DLC coatings inhibits bacterial adhesion as %40 and %25 rates for P. aeruginosa and S. aureus bacterial strains, respectively. From these results, DLC-Ge nanocomposites could be proposed as a potential new biomaterial for various biomedical applications.
Predicting personalised risk of disability worsening in multiple sclerosis with machine learning

Multiple sclerosis is a heterogeneous disease with an unpredictable course. We applied machine learning to generate individualised risk scores of disability worsening and stratify patients into subgroups with different prognosis. Clinical data and MRI scans from published randomised clinical trials in patients with relapsing-remitting and progressive MS were divided into training (n=5,483) and external validation data sets (n=2,668). We processed brain MRI scans to obtain 18 measures for lobar grey matter, deep grey matter and lesion volumes, and T1-/T2-weighted ratio of the normal-appearing white matter regions. We developed a machine learning model, called subpopulation risk stratification (SunRiSe), that combines multi-parametric clinical and MRI data to estimate individualised risk scores and stratify patients into subgroups on the basis of this risk. In particular, we entered MRI measures, the Expanded Disability Status Scale, age and gender to generate risk scores of disability worsening (i.e., the time to confirmed disability worsening). Based on SunRiSe risk scores, high-, medium-, and low-risk subpopulations were defined at study entry. We assessed whether selecting patients at high risk of disability worsening reduces sample size compared to when all risk groups were sampled together. In both the training and external validation data sets, SunRiSe-stratified patients in three groups associated with different levels of risk of disability worsening. In the external validation data set, patients at high risk were mainly progressive MS and had more disability events compared to those at medium-risk (hazard ratio [HR]=1.34, p<0.0001) and low-risk (HR=1.51, p<0.0001). At study entry, male gender, older age, higher lesion load, higher disability, lower lobar cortical grey matter, lower normal-appearing white matter T1/T2 ratio and lower deep grey matter volumes, were the most important variables in defining the SunRiSe risk score. The inclusion of patients predicted to be at high risk, reduced (i) duration of an event-driven trial by an average of 4.5 months; (ii) the number of participants in a randomised trial by approximately 200, with 80% statistical power to detect a 30% treatment effect. Machine learning provides a personalised risk score that can identify patients who have the greatest risk of disability worsening and therefore should be treated with the most effective medications and monitored more closely. Risk stratification allows the enrichment of clinical trials with patients more likely to worsen, and thereby reduces trial duration and sample size.
PPRP: Preserving-Privacy Route Planning Scheme in VANETs

Route planning helps a vehicle to share a message with the roadside units (RSUs) on its path in advance, which greatly speeds the authentication between the vehicle and the RSUs when the vehicle enters the RSUs’ coverage. In addition, since only a small amount of necessary information needs to be shared between the vehicle and the RSUs, route planning can reduce the storage overhead of the vehicle’s on-board unit (OBU) and the RSUs. However, the message sharing requires the assistance of the certification authority (CA), which will lead CA easily to obtain the vehicle’s planning route. Although CA knows the vehicle’s registration information and helps the vehicle to communicate with RSUs, it is unacceptable that the path of their vehicle is obtained by CA for most drivers. In fact, vehicle’s sensitive information such as planning route, starting time, stop place, should be privacy for others including CA. Inspired with the method of oblivious transfer, a preserving-privacy route planning scheme in VANETs is proposed in this article, in which, a vehicle deduces the information of RSUs on its path with the help of CA, while CA knows nothing about which RSUs’ information has been deduced by the vehicle. Later, fast authentication or other service is easily achieved between the vehicle and the RSUs (V2R) with the pre-shared information. After V2R authentication, vehicles could easily communicate with adjacent vehicles with the help of RSUs (V2V). Finally, compared with related schemes, performance evaluation illustrates the proposed scheme is better in terms of time consumption.
Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services, and provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a huge volume of data from the various sensors, devices, and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze, and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.