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Lung cancer is the most common cause of cancer death in the world. Effective early detection and appropriate medications can help treat this deadly cancer. Therefore, early detection of lung cancer is of utmost importance, especially in screening high-risk populations (such as smokers) with an urgent need to identify new biomarkers. The present study aimed to demonstrate the potential of using the panel of DNA methylation as a biomarker for the early diagnosis of lung cancer from sputum samples. The methylated promoter of p16INK4a, RASSF1A, and MGMT genes was estimated by the methylation-specific polymerase chain reaction in a sample of 84 lung cancer patients (65 smokers and 19 non-smokers). Based on the results, p16INK4a promoter methylation was significantly associated with smoking habit and lung cancer progression in terms of histological grading and patient staging. The sensitivity and specificity of the p16INK4a gene as a biomarker for lung cancer were 71% and 90%, respectively. The methylated promoter of RASSF1A was less sensitive (48%) as a biomarker for lung cancer with 83%. The results demonstrated a strong association between promoter methylation of RASSF1A and late stages of lung cancer (P=0.0007). The sensitivity of the MGMT gene as a biomarker for lung cancer was 61% with high specificity (92%), compared to other candidate genes in this study. The epigenetic alteration in the promoter region of p16INK4a, RASSF1A, and MGMT genes is highly associated with cancer cell development. It is suggested that the use of these candidate biomarkers can be used as an adjunct to computed tomography screening to diagnose patients at high risk for lung cancer after validation.

Abstract Arsenic (As) contamination in drinking water has been highlighted for its environmental significance and potential health implications. Iron-based filters are cost-effective and sustainable solutions for As removal from contaminated water. Applying Machine Learning (ML) models to investigate and optimize As removal using iron-based filters is limited. The present study developed Deep Learning Neural Network (DLNN) models for predicting the removal of As and other contaminants by iron-based filters from groundwater. A small Original Dataset (ODS) consisting of 20 data points and 13 groundwater parameters was obtained from the field performances of 20 individual iron-amended ceramic filters. Cubic-spline interpolation (CSI) expanded the ODS, generating 1600 interpolated data points (IDPs) without duplication. The Bayesian optimization algorithm tuned the model hyper-parameters and IDPs in a Stratified fivefold Cross-Validation (CV) setup trained all the models. The models demonstrated reliable performances with the coefficient of determination (R2) 0.990–0.999 for As, 0.774–0.976 for Iron (Fe), 0.934–0.954 for Phosphorus (P), and 0.878–0.998 for predicting manganese (Mn) in the effluent. Sobol sensitivity analysis revealed that As (total order index (ST) = 0.563), P (ST = 0.441), Eh (ST = 0.712), and Temp (ST = 0.371) are the most sensitive parameters for the removal of As, Fe, P, and Mn. The comprehensive approach, from data expansion through DLNN model development, provides a valuable tool for estimating optimal As removal conditions from groundwater.

Abstract Background Veterans of the 1990–1991 Gulf War have experienced excess health problems, most prominently the multisymptom condition Gulf War illness (GWI). The Department of Veterans Affairs (VA) Cooperative Studies Program #2006 “Genomics of Gulf War Illness in Veterans” project was established to address important questions concerning pathobiological and genetic aspects of GWI. The current study evaluated patterns of chronic ill health/GWI in the VA Million Veteran Program (MVP) Gulf War veteran cohort in relation to wartime exposures and key features of deployment, 27–30 years after Gulf War service. Methods MVP participants who served in the 1990–1991 Gulf War completed the MVP Gulf War Era Survey in 2018–2020. Survey responses provided detailed information on veterans’ health, Gulf War exposures, and deployment time periods and locations. Analyses determined associations of three defined GWI/ill health outcomes with Gulf War deployment characteristics and exposures. Results The final cohort included 14,103 veterans; demographic and military characteristics of the sample were similar to the full population of U.S. 1990–1991 Gulf War veterans. Overall, a substantial number of veterans experienced chronic ill health, as indicated by three defined outcomes: 49% reported their health as fair or poor, 31% met Centers for Disease Control and Prevention criteria for severe GWI, and 20% had been diagnosed with GWI by a healthcare provider. Health outcomes varied consistently with veterans’ demographic and military characteristics, and with exposures during deployment. All outcomes were most prevalent among youngest veterans (

Abstract Purpose To evaluate the diagnosis and management of bacterial meningitis in adult Sudanese patients in accordance with the Infectious Diseases Society of America (IDSA) guidelines for bacterial meningitis management. Patients and methods A cross-sectional, retrospective study design was used to recruit all patients aged > 18 years who were diagnosed with or suspected of having bacterial meningitis and admitted to Wad Medani Teaching Hospital, Gezira State, Sudan, between January 2017 and October 2022. Results In total, 201 patients were included in the analysis. The mean age of the participants was 44.1 ± 21.4 years, and 107 (53.2%) were male. Community-acquired bacterial meningitis accounted for 193 (96%) of the studied patients, and only 8 (4%) of the patients had healthcare-associated meningitis. Neuroimaging was utilized appropriately in 148 (73.6%) patients, blood cultures were not performed entirely, and lumbar puncture was seldom performed in 1 (0.5%) patient. Corticosteroids were appropriately administered to 65 (32.3%) patients, and antibiotics were administered appropriately to only 5 (2.5%) patients. Ceftriaxone 185 (76.1%) was the most frequently utilized antibiotic, followed by vancomycin 23 (9.5%). In terms of overall adherence, this study demonstrated that the IDSA guidelines were not followed at all in the treatment of patients with suspected bacterial meningitis. Conclusion The results of this study contradict the IDSA guidelines for the standard of care for bacterial meningitis. Antibiotic regimens are often incorrect, corticosteroids are administered appropriately in approximately one-third of patients, and neuroimaging is reasonably utilized. This study raises attention to several important issues regarding the diagnosis of bacterial meningitis, including the lack of confirming microbiological tests and the reliance of the diagnosis primarily on CT and clinical examination.

Abstract Background Mesenchymal stem cells (MSCs) have emerged as living biodrugs for myocardial repair and regeneration. Recent randomized controlled trials (RCTs) have reported that MSC-based therapy is safe and effective in heart failure patients; however, its dose-response relationship has yet to be established. We aimed to determine the optimal MSC dose for treating HF patients with reduced ejection fraction (EF) (HFrEF). Methods The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Cochrane Handbook guidelines were followed. Four databases and registries, i.e., PubMed, EBSCO, clinicaltrials.gov, ICTRP, and other websites, were searched for RCTs. Eleven RCTs with 1098 participants (treatment group, n = 606; control group, n = 492) were selected based on our inclusion/exclusion criteria. Two independent assessors extracted the data and performed quality assessments. The data from all eligible studies were plotted for death, major adverse cardiac events (MACE), left ventricular ejection fraction (LVEF), left ventricular end-systolic volume (LVESV), and 6-minute walk distance (6-MWD) as safety, efficacy, and performance parameters. For dose-escalation assessment, studies were categorized as low-dose (

Understanding the digital skills of young people around the world is key to informing research as well as enabling us to examine future trends in industry and society. There has been much celebratory discourse on how younger people have some inherent expertise in using devices, navigating online spaces, creating content and engaging with social media. A more realistic analysis shows that even those born into a digital world need guidance and training in honing their digital abilities. Until recently, different types of literacies have often been examined separately. The reality though is that they are used in interconnected and overlapping ways. This paper reflects on how information, digital, media and news literacies can combine under the multi- or metaliterary framework to promote digital agency. The data obtained in three different studies at the same higher education institute is used to consider the digital practices of students and how these constitute digital agency. As a case study, the findings can be used to reflect on how a holistic approach to academic and media literacies will enhance the knowledge, skills and outlook of young digital consumers.

IntroductionActivated vascular endothelial growth factor receptors (VEGFRs), platelet-derived growth factor receptors (PDGFRs) and c-Kit have been shown to be involved in the growth, invasion and metastasis of non-rhabdomyosarcoma soft tissue sarcoma tumor (NRSTS) with promising results for targeted therapy. Our aim was to assess the expression of these markers among different histological types and correlate with outcomes.Material and methodsThis retrospective study included pediatric patients aged ≤ 18 years diagnosed with high-grade NRSTS who were treated at Children Cancer Hospital Egypt 57357 as per the COG NRSTS protocol (ARST0332). Expression of VEGFR2, PDGFRs (α and β) and c-Kit in tumor tissue was assessed by immunohistochemistry and correlated with clinical outcome.ResultsOf 113 patients, 96 were eligible for the analysis with a median age of 11 years. Overall, 32.3% demonstrated high expression of PDGFRα, 17.7% for PDGFRβ, 19.8% for VEGFR2 and 8.3% exhibited positive expression for c-kit on the tumor cells. Most cases of synovial sarcoma (45.8%) and 43.7% of patients with undifferentiated sarcoma exhibited high expression of PDGFRα while 41.6% of MPNST showed high expression to PDGFRβ. The 5-year overall survival (OS), event free survival and relapse free survival (RFS) for the whole cohort were 59%, 54% and 60% respectively. In univariate analyses, only PDGFRα had a negative prognostic impact on relapse free survival (RFS) (p=0.03). In multivariate analyses, VEGFR2 was found to have a negative prognostic impact for OS (p = 0.02).ConclusionOur findings indicated that tyrosine kinase receptors are upregulated in NRSTS and exhibited a distinct expression pattern within various subgroups. High expression of VEGFR2 and PDGFRα significantly correlated with reduced survival and may guide targeted therapy approaches for this poor prognosis group of patients.

To determine the prevalence of type 2 diabetes (T2DM) and prediabetes - impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) and associated risk determinants in Bangladeshi factory w orkers. A total of 791 male factory w orkers aged ≥20 years in the capital Dhaka city of Bangladesh w ere investigated. Anthropometric details, blood pressure and metabolic parameters w ere studied. We used the 2006 WHO diagnostic criteria for the diagnosis of T2DM and prediabetes. The age - standardized prev alence of T2DM, IFG and IGT w ere 15.4, 17.7, and 25.8%, respectively. The prevalence of IFG and IGT increased w ith age. Among the study population, 38.9% had family history of diabetes (F/H DM), 27.9% hypertension (HTN), general obesity defined by body mas s index (BMI) was 43.7% and central obesity defined by w aist circumference (WC) and w aist -hip ratio (WHR) w ere 35.3% and 78.3%, respectively. Poor agreement w as observed betw een fasting and 2 hr blood glucose. Employing logistic regression, it w as found that increased age, F/H DM, higher BMI and WHR, and HTN w ere independent risk indicators for T2DM. Increased age and higher BMI w ere also independent risk indicators for prediabetes. The prevalence of T2DM and prediabetes w ere found relatively high in these factory w orkers. Obesity and HTN w ere also observed high in this study population.

IntroductionDuring general anesthesia, frontal electroencephalogram (EEG) activity in the alpha frequency band (8–12 Hz) correlates with the adequacy of analgesia. Transcranial direct current stimulation (tDCS) and auditory stimulation, two noninvasive neuromodulation techniques, can entrain alpha activity in awake or sleeping patients. This study evaluates their effects on alpha oscillations in patients under general anesthesia.Methods30 patients receiving general anesthesia for surgery were enrolled in this two-by-two randomized clinical trial. Each participant received active or sham tDCS followed by auditory stimulation or silence according to assigned group (TDCS/AUD, TDCS/SIL, SHAM/AUD, SHAM/SIL). Frontal EEG was recorded before and after neuromodulation. Patients with burst suppression, mid-study changes in anesthetic, or incomplete EEG recordings were excluded from analysis. The primary outcome was post-stimulation change in oscillatory alpha power, compared in each intervention group against the change in the control group SHAM/SIL by Wilcoxon Rank Sum testing.ResultsAll 30 enrolled participants completed the study. Of the 22 included for analysis, 8 were in TDCS/AUD, 4 were in TDCS/SIL, 5 were in SHAM/AUD, and 5 were in SHAM/SIL. The median change in oscillatory alpha power was +4.7 dB (IQR 4.4, 5.8 dB) in SHAM/SIL, +2.8 dB (IQR 1.5, 8.9 dB) in TDCS/SIL (p = 0.730), +5.5 dB in SHAM/AUD (p = 0.421), and -6.1 dB (IQR -10.2, -2.2 dB) in TDCS/AUD (p = 0.045).ConclusiontDCS and auditory stimulation can be administered safely intraoperatively. However, these interventions did not increase alpha power as administered and measured in this pilot study.

The purpose of this study was to evaluate the impact of the Distance Learning Program (DLP) on provider capacity to provide comprehensive diabetes care and improvement on patient outcomes in Bangladesh. Study was conducted among 1974 DLP trained physicians, 1876 Non-DLP physicians and 2617 diabetic patients. In addition, quality of patient care and clinical outcomes before and after the implementation of DLP were also studied. Historical documents including patient case records from registered patient guidebooks and biochemical profiles from laboratory database were documented and analyzed. DLP physicians had more knowledge in each clinical practice guideline of diabetes care than Non-DLP physicians (odds ratio [OR] range: 2.2 to 11.7). DLP-trained physicians performed more diabetes-related examinations including blood pressure (OR=3.0), waist and hip circumference (OR= 2.6), BMI (OR= 2.6), foot (OR= 2.2), eye (OR= 2.0) and investigated blood glucose (OR= 2.2), hemoglobin A1c (A1C) (OR= 2.1), lipids (OR=1.9), creatinine (OR=1.9), micro albumin (OR=1.8) and ECG (OR=1.6) more than non-DLP physicians. DLP physicians felt more confident to give advice on key areas of diabetes care including diet (OR=2.9), physical activity (OR=3.0), smoking cessation (OR=2.5), foot care (OR=2.9) and complications related to diabetes (OR=2.8) than non-DLP doctors. Significant improvement of patients care including dietary advice (91 to 96%), frequency of clinic visits (3 to 3.6 time), foot care referral (3 to 8%), measurement of blood pressure (73.1 to 89.2%) and blood glucose (58 to 75%) were observed from pre to post DLP period. Average values of fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) significantly decreased in post DLP period. Patient satisfaction also improved between pre to post DLP period. DLP is feasible and acceptable to physicians in Bangladesh. It has made highly positive impact on patients care. We recommend that other resource-poor settings should consider the adoption of a DLP.

In this investigation, the effect of replacing the conventional solar absorber with a new solar absorber on the thermal performance of a double-pass solar air heater has been studied experimentally and numerically. Three configurations have been introduced, the first configuration is a double pass solar air heater with a flat plate solar absorber (DPSAHWFP) for the aim of comparison, and the second configuration is a double pass solar air heater with a tubular absorber that includes a set of tubes which are fitted perpendicularly to the direction of airflow (DPSAHWT-1), and the third configuration is double-pass solar air heater with a tubular absorber that involves set of tubes which are fitted in parallel to the direction of airflow (DPSAHWT-2). The experiments have been carried out under indoor conditions at a constant heat flux equal to 1000 W/m2 and different air mass flow rates (0.01– 0.03 kg/s). The results revealed that the air mass flow rate has a substantial impact compared to the rise in air temperature, hence, the thermal performance of solar air heater is directly proportional to increase air mass flow rate. In addition, the experimental and numerical outcomes indicated that for all air flow rates. The (DPSAHWT-2) offers higher thermal performance as compared to other models, where the maximum effective efficiency has been obtained at 0.03 kg/s equal to 80.9 %. Moreover, (DPSAHWT-2) is more efficient than DPSAHWFP and DPSAHWT-1 by 4.2 % and 9.8 % respectively.

Background Approximately 30% of the 700 000 Gulf War veterans report a chronic symptom‐based illness of varying severity referred to as Gulf War illness (GWI). Toxic deployment‐related exposures have been implicated in the cause of GWI, some of which contribute to metabolic dysregulation and lipid abnormalities. As this cohort ages, the relationship between GWI and atherosclerotic cardiovascular disease (ASCVD) is a growing concern. We evaluated associations between GWI and ASCVD, diabetes, hyperlipidemia, and hypertension in veterans of the Gulf War (1990–1991). Methods and Results Analysis of survey data collected in 2014 to 2016 from a national sample of deployed Gulf War veterans (n=942) and Veterans Health Administration electronic health record data (n=669). Multivariable logistic regression models tested for associations of GWI with self‐reported ASCVD, diabetes, hyperlipidemia, and hypertension, controlling for confounding factors. Separate models tested for GWI associations with ASCVD and risk factors documented in the electronic health record. GWI was associated with self‐reported hypertension (adjusted odds ratio [aOR], 1.67 [95% CI, 1.18–2.36]), hyperlipidemia (aOR, 1.46 [95% CI, 1.03–2.05]), and ASCVD (aOR, 2.65 [95% CI, 1.56–4.51]). In the subset of veterans with electronic health record data, GWI was associated with documented diabetes (aOR, 2.34 [95% CI, 1.43–3.82]) and hypertension (aOR, 2.84 [95% CI, 1.92–4.20]). Hyperlipidemia and hypertension served as partial mediators of the association between GWI and self‐reported ASCVD. Conclusions Gulf War veterans with GWI had higher odds of hyperlipidemia, hypertension, diabetes, and ASCVD compared with Gulf War veterans without GWI. Further examination of the mechanisms underlying this association, including a possible shared exposure‐related mechanism, is necessary.

In this research, Principal Component Analysis (PCA) is utilized for evaluation of causes of insecurity and crime rate investigation in Niger State, Nigeria. Data obtained based on the Principal Component Analysis reveals 76.74% total variation. The outcome extracted three PCs out of the 13 original variables, which implies a great dimensionality reduction, namely: Population, Population Density and Sex Ratio. The paper suggested that, addressing the root causes of crime such as population, drug/arrest, domestic product and sex-ratio will mitigate the insecurity and as well as crime rate in Niger state, Nigeria.

The present study was carried out in November 2019 to investigate the impact of Kani-qrzhala landfill on soil pollution of the surrounding area caused by the disposed of refuses from houses, factories, and hospitals. Soil samples were collected from 6 sites in Kani-qrzhala landfill on Erbil-Mosul Road ((Latitude 36˚13̍ N, Longitude 43˚.58̍̍ E). Soil samples were taken randomly selected at different depths (0-10), (10-20), (20-30) cm, were collected randomly at the dumpsite field in the study area with a control sample. The different parameters of soil samples were analyzed of contaminated metals such as Iron (Fe), Manganese (Mn), Nickel (Ni), Lead (Pb), and Zinc (Zn) were determined in the study soil samples by using portable x-ray fluorescence (XRF). Additionally, specialization index Geoaccumulation (I geo) was calculated and used for evaluating the soil pollution status: In the depth (0-10) cm, the heavy metals concentration was recorded as follow: Fe > Pb > Zn > Ni > Mn. Also in the depth (10-20) cm Fe > Pb > Zn > Mn > Ni. In the depth (20-30) cm depth Fe > Pb > Mn > Zn > Ni (0 mg kg-1). Measuring Geoaccumulation index (I geo): The results show that most the most location of the study area of soil surrounding the dumpsite were polluted by heavy metals followed by series: Ni, Pb > Zn > Fe >Mn.

The study was carried out in October 2021 nearby oil refineries which are located near Gwer road and 15 km far away from Erbil city (36° 8'32.31"N, 43°46'32.61"E), to investigate the impact of residual crude oil discharged from surrounding oil refineries on soil Physico-chemical properties and heavy metals contamination. A total of 108 soil samples were collected from (8) different locations, (4) distances, and (3) depths alongside the residual crude oil stream. By using (XRF) technique to determine total heavy metals concentration from collected soil samples. The range of soil pH is between (7.23- 8.7) and an increase in crude oil quantity causes increases in soil pH. (Fe) has the highest concentration among the studied metals ranging between (11925.09 - 22394.37 mgkg-1) followed by (Mn) which ranged between (127.89 - 528.12 mgkg-1) and (Zn) ranged between (41.68 - 107.58 mgkg-1). According to Pearson’s correlation, there is a significant positive correlation between (Fe and Mn) at (p ≤ 0.01, r=.753**) which indicates both metals are derived from the same source. Moreover, there are a significant positive correlation between (Zn and Mn) at (p ≤ 0.05, r=.338*) However, the correlation is less strong compared to (Fe and Mn) this mean (Zn) derived from multiple sources other than crude oil like automobile and truck tire which is one of the major sources for (Zn), since the study area was located next to the busy road.

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis as a pivotal precursor to effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology and deep learning algorithms, is investigated for its potential to discriminate between children with ASD and their typically developing (TD) peers. By employing these methods, the research aims to contribute to refining early detection strategies and support mechanisms. This study introduces innovative deep learning models grounded in convolutional neural network (CNN) and recurrent neural network (RNN) architectures, employing an eye-tracking dataset for training. Of note, performance outcomes have been realised, with the bidirectional long short-term memory (BiLSTM) achieving an accuracy of 96.44%, the gated recurrent unit (GRU) attaining 97.49%, the CNN-LSTM hybridising to 97.94%, and the LSTM achieving the most remarkable accuracy result of 98.33%. These outcomes underscore the efficacy of the applied methodologies and the potential of advanced computational frameworks in achieving substantial accuracy levels in ASD detection and classification.

This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD.

Autism spectrum disorder (ASD), or autism, can be diagnosed based on a lack of behavioral skills and social communication. The most prominent method of diagnosing ASD in children is observing the child’s behavior, including some of the signs that the child repeats. Hand flapping is a common stimming behavior in children with ASD. This research paper aims to identify children’s abnormal behavior, which might be a sign of autism, using videos recorded in a natural setting during the children’s regular activities. Specifically, this study seeks to classify self-stimulatory activities, such as hand flapping, as well as normal behavior in real-time. Two deep learning video classification methods are used to be trained on the publicly available Self-Stimulatory Behavior Dataset (SSBD). The first method is VGG-16-LSTM; VGG-16 to spatial feature extraction and long short-term memory networks (LSTM) for temporal features. The second method is a long-term recurrent convolutional network (LRCN) that learns spatial and temporal features immediately in end-to-end training. The VGG-16-LSTM achieved 0.93% on the testing set, while the LRCN model achieved an accuracy of 0.96% on the testing set.

Water tunnels are one of the oldest hydro-technologies for extracting water resources and/or transmitting them through water distribution systems. In the past, human societies have used tunneling for various purposes, including development, as a measure to enable underground resource extraction and the construction of transportation networks in challenging landscapes and topographies. The development of hydro-technology potentially involves the construction of tunnels to feed aqueducts, irrigation and waste water systems. Thus, the ability to make and maintain tunnels became an important component in creating lasting and sustainable water systems, which increased water supply and security, minimized construction costs, and reduced environmental impact. Thus, this review asks how, when and why human societies of the past included tunneling for the development of lasting water supply systems. This review presents a comprehensive overview across time and space, covering the history of tunneling in hydro technology from antiquity to the present, and it ponders how past experiences could impact on future hydro-technological projects involving tunneling. A historical review of tunnel systems enhances our understanding of the potential, performance, challenges, and prospects associated with the use of hydro-techniques. In the past, as the different examples in time and space demonstrate, tunneling was often dedicated to solving local problems of supply and disposal. However, across the world, some features were repeated, including the need for carving through the living rock or digging to create tunnels covered with stone slabs. Also, the world-wide use of extensive and costly tunnel systems indicates the high level of investment which human societies are willing to make for securing control over and with its water resources. This study helps us to gather inspiration from proven technologies of the past and more recent knowledge of water tunnel design and construction. As we face global warming and its derivate problems, including problems of water scarcity and flooding, the ability to create and maintain tunnels remains an important technology for the future.

CD45 is a transmembrane glycoprotein that is located on the surface of all leukocytes and modulates both innate and adaptive immune system functions. In a recent study, inflammation modulated the CD45 expression in leukocytes, but the effect on the expression of CD45 subtypes is unknown. In the present study, we therefore investigated the effect of inflammatory conditions in humans (surgery, sepsis) and ex vivo incubation with lipopolysaccharides (LPS) on the expression of the subtypes CD45RA and CD45RO in granulocytes, lymphocytes, and monocytes. Whole blood samples were obtained from healthy volunteers, postoperative patients, and patients with sepsis at day 1 of diagnosis, respectively. Samples were incubated with fluorescent antibodies directed against CD45, CD45RA and CD45RO in the absence and presence of lipopolysaccharide and subjected to flow cytometry. In comparison to volunteers, CD45RA surface expression in postoperative and septic patients was reduced by 89% exclusively on granulocytes, but not on lymphocytes or monocytes. In contrast, CD45RO was exclusively reduced on lymphocytes, by 82%, but not on other cell types. Receiver operating characteristic curve analyses demonstrated that CD45RA (on granulocytes) and CD45RO (on lymphocytes) allow a good differentiation of volunteers and patients with sepsis (AUC = 0.9; p = 0.0001). The addition of LPS to the whole blood samples obtained from volunteers, postoperative patients, and septic patients markedly increased the CD45RO expression in granulocytes, lymphocytes, and monocytes. In contrast, LPS reduced CD45RA exclusively on monocytes. In conclusion, the surface expression of CD45RA and CD45RO is regulated in inflammation in a cell-type- and stimulus-specific manner. Considering that CD45 subtypes are critically involved in immune system signaling, the pathophysiologic and diagnostic implications warrant further investigation.

Floods are one of the most dangerous natural disasters, causing great destruction, damage, and even fatalities worldwide. Flooding is the phenomenon of a sudden increase or even slow increase in the volume of water in a river or stream bed as the result of several possible factors: heavy or very long precipitation, melting snowpack, strong winds over the water, unusually high tides, tsunamis, or the failure of dams, gages, detention basins, or other structures that hold back water. To gain a better understanding of flooding, it is necessary to examine evidence, search for ancient wisdom, and compare flood-management practices in different regions in a chronological perspective. This study reviews flood events caused by rising sea levels and erratic weather from ancient times to the present. In addition, this review contemplates concerns about future flood challenges and possible countermeasures. Thus, it presents a catalogue of past examples in order to present a point of departure for the study of ancient floods and to learn lessons for preparation for future flood incidents including heavy rainfalls, particularly in urbanized areas. The study results show that ancient societies developed multifaceted technologies to cope with floods and many of them are still usable now and may even represent solutions and measures to counter the changing and increasingly more erratic weather of the present.

Introduction Many factors determine the outcomes of renal transplant, including characteristics of both donors and recipients. Antihuman leukocyte antigen donor-specific antibodies are strongly linked to antibody-mediated rejection and late allograft loss. Aim The aim was to determine the effect of recipient and donor characteristics on kidney transplant graft survival, with special stress on panel-reactive antibodies. Participants and methods This study included 168 adult recipient and donor couples. The authors analyzed data of kidney transplants performed between the years of 2008 and 2018 at National Institute of Urology and Nephrology. Results The authors found a significant increase in renal failure in recipients transplanted with unrelated donor kidney and in positive hepatitis C virus antibodies (HCV Abs) recipients (P = 0.002 and 0.001, respectively) and renal complications (P ≤ 0.001). There was also a significant increase in panel-reactive antibody-positive recipients to get renal complications (P = 0.03). There was a significant increase in renal failure with old-aged recipients (P = 0.008) and with increased duration of transplant (P = 0.003). By multivariate regression, the authors deduced that young age decreases graft loss risk (hazard ratio (HR): 0.88; 95% confidence interval (95% CI): 0.79–0.97; P = 0.017). Recipients of unrelated donor graft and HCV infection were associated with higher hazard ratio graft loss (HR: 13.56; 95% CI: 2.58–71.22; P = 0.002, and HR: 7.96; 95% CI: 1.04–60.44; P = 0.45). Unrelated donors graft were associated with 4 times higher hazard ratio renal complication (HR: 4.18; 95% CI: 2.01–8.72; P = 0.003). HCV infection increases risk three times (HR: 3.24; 95% CI: 1.31–7.99; P = 0.010). Unrelated donor was a significant independent predictor for recipient graft loss risk in a period of more than 5 years after transplantation. Conclusion The authors concluded that recipient and donor characteristics have important roles on kidney transplant graft survival and also on renal complications.

Selecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right person for the right job. In addition, artificial intelligence contributes to the selection of harmonious working teams capable of achieving an organization’s strategy and goals. This study aimed to contribute to the development of machine-learning models to analyze and cluster personality traits and classify applicants to conduct correct hiring decisions for particular jobs and identify their weaknesses and strengths. Helping applicants to succeed while managing work and training employees with weaknesses is necessary to achieving an organization’s goals. Applying the proposed methodology, we used a publicly available Big-Five-personality-traits-test dataset to conduct the analyses. Preprocessing techniques were adopted to clean the dataset. Moreover, hypothesis testing was performed using Pearson’s correlation approach. Based on the testing results, we concluded that a positive relationship exists between four personality traits (agreeableness, conscientiousness, extraversion, and openness), and a negative correlation occurred between neuroticism traits and the four traits. This dataset was unlabeled. However, we applied the K-mean clustering algorithm to the data-labeling task. Furthermore, various supervised machine-learning models, such as random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and AdaBoost, were used for classification purposes. The experimental results revealed that the SVM attained the highest results, with an accuracy of 98%, outperforming the other classification models. This study adds to the current literature and body of knowledge through examining the extent of the application of artificial intelligence in the present and, potentially, the future of human-resource management. Our results may be of significance to companies, organizations and their leaders and human-resource executives, in addition to human-resource professionals.

Healthy coastal sabkhas (sabkha is an Arabic term for a salt flat) offer plenty of ecosystem services including climate change mitigation. However, fewer research studies were conducted at coastal sabkhas compared to other coastal marshes. This study was conducted in a total of ten coastal sabkha sites with different vegetation covers along the southern Red Sea coast of Saudi Arabia. The main objectives were to model and predict the distribution of volumetric soil organic carbon (SOC) density (kg C/m3) and cumulative SOC stocks (kg C/m2) using three different mathematic functions (allometric, exponential, and sigmoid) based on sampled and observed soil carbon (C) data (total of 125 soil cores = 1250 soil samples). Sigmoid function showed the greatest fit for predicting the distribution of volumetric SOC density over soil profile depth with mean Adj. R2 = 0.9978, 0.9611, and 0.9623 for vegetation cover of >25–50, >50–75, and >75–100%, respectively. For modeling the cumulative SOC stocks, both validation indices and p of the t-test confirmed that using the exponential function is the most appropriate to be used for predicting the SOC stock among different vegetation covers. Moreover, assessing the topsoil concentration factors (TCFs) showed that the distribution of the SOC content is impacted to a great extent by the vegetation cover at coastal sabkhas. Sampling the soil parameter of interest to estimate the SOC stocks is constrained by time and cost. Therefore, using the exponential function for predicting the distribution of cumulative SOC stocks at coastal sabkhas over soil profile depth is appropriate and promising for mapping SOC stocks at both regional and global spatial scales.

There has been increasing focus on conserving coastal ecosystems as they have been recognized as high ecosystem carbon stocks and are in the process of land conversion. The present study aims to examine how different vegetation covers impact the soil organic carbon (SOC) stock in coastal sabkhas. To this end, the study was carried out at ten sampling sites represent ten sabkhas in Saudi Arabia’s southern Red Sea coast for two main goals: (1) to examine the vertical distribution of SOC content, SOC density, and the soil bulk density (SBD) based on different vegetation covers, and (2) to assess these locations’ SOC stocks. This study posits that sabkhas with different vegetation covers had distinct parameters specified above. Significant SBD differences were observed in sabkhas with different vegetation covers, with the lowest mean values of sabkhas having >75–100% vegetation cover and the highest mean values of sabkhas having 0–25% vegetation cover. The studied sabkhas also showed significant difference in the total means of SOC density, SOC content, and SOC stock in terms of different vegetation covers, with the highest mean values of sabkhas having >75–100% vegetation cover and the lowest mean of sabkhas having 0–25% vegetation cover. The present study is the first to focus on Saudi Arabia’s sabkha blue carbon stocks and its results can help add to the literature on sabkhas carbon stock, thus aiding relevant government agencies working towards sabkhas management, encouraging public awareness regarding sabkhas conservation stocks, and their part in climate change mitigation.

Introduction Several ADHD teenagers had difficult behavioral problems during countries closing down due to Covid-19 pandemic. One of these negative outcomes that parents cannot control children’s behavior toward desired unhealthy food and the impulsive consequences. It was a great opportunity to convention a teletherapy program as a tool of intervention seeking for help to reduce uncontrolled self- management and nutrition, which may affect all sorts of childhood growth, development, health and behavior. Furthermore, it can affects daily life and academic success. Objectives We tried through our study to enhance the teletherapy as a therapeutic tool, during the first and second phase of Covid-19 pandemic, trying to help parents and patient to overcome the impulsive behavior by using a specific therapy technique based on nutrition and behavioral therapy Methods Our case study is a young girl aged 12:4 Yrs. In middle bilingual Arabic/ American School. The therapeutic program designed via teletherapy program using multi-media and thru multi phases sessions, to increase focus attention, emotional control and reduce impulsivity. Results The outcomes of the enhancing nutrition and behavior teletherapy program, showed significant improvement for the specific goal. Sensible change in the girl’s impulsive behavior, more focusing, emotional control and more accepting about health nutrition habits. Conclusions The important finding that intensive, focused nutation and self-management techniques provided via teletherapy as solitary program brought benefits to individual’s, family and reduced impulsivity outcomes. In addition, family education to become an expert at learning simple techniques in daily life can brining a sense of pleasure for long life wellbeing. Disclosure No significant relationships.

The Arabian Peninsula is an arid zone with a hot desert climate and severe water scarcity. The low humidity, elevated ambient temperatures, and high evaporation rates in the region deemed conventional surface irrigation unsustainable. The IoT-based subsurface smart irrigation systems can be essentially developed for these regions to avoid surface evaporation losses. In this research, the sandy soil conditions of western Saudi Arabia have been considered in numerical simulations to evaluate the performance of a subsurface smart irrigation system. The influence zone of saturation generated by subsurface diffusers in the target root region has been analysed for two different types of sandy soils. The simulation results generated by the COMSOL Multiphysics program reveal that the subsurface smart irrigation system can be effectively applied to simultaneously manage the target root zone at the ideal saturated conditions and prevent surface evaporation losses.

Background: Candida spp. is the yeast caused infection termed candidiasis; this is naturally found on the skin and inner the body, in an area like the gut, intestines, throat, vagina, and mouth, with the absence inciting any problems. Candida albicans is yeast naturally found in the skin; intestinal tract; vaginal and mouth. This is a polymorphic yeast (that capable of growing either as avoid shaped budding yeast, when it affect the mouth, it is typically termed oral thrush, and when it affect the dermal (diaper area) called diaper rash. clinical signs and symptoms involve white patches on the or other areas of the mouth and throat, `other signs may involve soreness and problems of swallowing. Objective: To study detection, isolation, and identification of C. albicans from infants in Al-Battol Teaching Hospital in Diyala Province by routine laboratory procedure, and identification of other species of Candida. Patients and Methods: Two hundred samples were taken from infants less than 2 years who attended Al-Battol teaching Hospital in Diyala, province during the period from October 2019 to February 2020. Method of this study by making a routine and confirmative diagnosis processes involve a microscopic examination, culture on Sabouraud’s dextrose agar for diagnosis of primary invasive candidiasis, and culture on chrome agar for differentiating between C. albicans and other spp. Results: The results of this study showed the isolates of oral samples were 12 (24%) C. albicans and 18(36.0%) C. non albicans; for dermal isolates were 10(20.0%) C. albicans and 17(34.0%) C. non albicans; and for both isolates oral and dermal samples were 13(26.0%) C. albicans and 21 (42.0) C. non albicans. Conclusion: In this study found that the male are more infected by Candida than female. And also we concluded that small age children more infected than large age.

The objective of the present study was to evaluate the leaves of plane tree (Platanus orientalis L.) impaired by traffic pollution. Trees from four locations of Erbil City were investigated. Leaf morphology, leaf and soil heavy metal concentration and leaf pigments were measured from the collected leave samples. One way ANOVA was used to compare means and determine significant variation among the studied parameters. The highest increase in chlorophyll a and total chlorophyll were obtained in Sami Abul-Rahman Park whereas, the highest reduction was observed in Ainkawa road. Likewise in case of carotenoid contents, non significant effect was obtained from samples. The study obviously points out that the motor vehicles stimulate air pollution may result in decreases the concentration of photosynthetic pigments in the plane trees exposed to road side air pollution. It is shown that the lowest metal concentrations were detected in Sami Abul-Rahman Park in comparing with the other locations. It is shown that the lowest metal concentrations such as Ni, Zn, Mn and Fe in soil and plant samples were detected in Gulan Street When compared with the other locations. According to the geoaccumulation index (Igeo), the soils were classified into uncontaminated and uncontaminated to moderately contaminate.

Veterans with difficult-to-diagnose conditions who receive care in the Department of Veterans Affairs (VA) healthcare system can be referred for evaluation at one of three specialty VA War-Related Illness and Injury Study Centers (WRIISC). Veterans of the 1990–1991 Gulf War have long experienced excess rates of chronic symptoms associated with the condition known as Gulf War Illness (GWI), with hundreds evaluated at the WRIISC. Here we provide the first report from a cohort of 608 Gulf War Veterans seen at the WRIISC who completed questionnaires on chronic symptoms (>6 months) consistent with GWI as well as prominent exposures during Gulf War deployment. These included veterans’ reports of hearing chemical alarms/donning Military-Ordered Protective Posture Level 4 (MOPP4) gear, pesticide use, and use of pyridostigmine bromide (PB) pills as prophylaxis against the effects of nerve agents. Overall, veterans in the cohort were highly symptomatic and reported a high degree of exposures. In multivariable models, these exposures were significantly associated with moderate-to-severe chronic symptoms in neurocognitive/mood, fatigue/sleep, and pain domains. Specifically, exposure to pesticides was associated with problems with concentration and memory, problems sleeping, unrefreshing sleep, and joint pain. Use of MOPP4 was associated with light sensitivity and unrefreshing sleep and use of PB was associated with depression. We also evaluated the association of exposures with symptom summary scores based on veterans’ severity of symptoms in four domains and overall. In multivariable modeling, the pain symptom severity score was significantly associated with pesticide use (Odds ratio (OR): 4.13, 95% confidence intervals (CI): 1.78–9.57) and taking PB pills (OR: 2.28, 95% CI: 1.02–5.09), and overall symptom severity was significantly associated with use of PB pills (OR: 2.41, 95% CI: 1.01–5.75). Conclusion: Decades after deployment, Gulf War veterans referred to a VA tertiary evaluation center report a high burden of chronic symptoms, many of which were associated with reported neurotoxicant exposures during the war.