An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. METHODS: In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. RESULTS: Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. CONCLUSION: This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Potassium Spraying Preharvest and Nanocoating Postharvest Improve the Quality and Extend the Storage Period for Acid Lime (Citrus aurantifolia Swingle) Fruits.

Citrus fruits are one of the most abundant crops globally in more than 140 countries throughout the world. Acid lime (Citrus aurantifolia swingle) is one of the citrus fruits which popularly has rich nutritional and therapeutic features. The storage period is the important factor that affects the economic and quality properties of this fruit. This study aims to demonstrate the enhancing effect of preharvest spraying with potassium, in addition to the postharvest dipping of fruits in some edible coatings, on the quality and storability of acid lime fruits. Preharvest spraying with organic and mineral forms of potassium, namely, potassium thiosulfate 1.75 g/L (S) and potassium tartrate 2 g/L (T), were carried out at three different times, in May, June, and July. On the other hand, postharvest treatments were carried out via dipping fruits in different types of biopolymers (carboxymethyl cellulose (E2) and gum arabic (E3)) and carboxymethyl cellulose/gum arabic composite (E4) as well as nanocoating formulation based on both biopolymers and doped zinc oxide nanoparticles (ZnONPs) (E1), which were prepared via acid lime peel waste extract. Herein, the physiochemical and morphological characterizations confirmed that the nanocoating was prepared at the nanoscale and doped with green synthesis ZnONPs, with recorded sizes of around 80 and 20 nm, respectively. Preharvest spraying with potassium tartrate enhanced fruit traits (Spraying with potassium tartrate at pre-harvest and nanocoating dipping at post-harvest (TE1), spraying with potassium tartrate at pre-harvest and carboxy methyl cellulose dipping at post-harvest (TE2), spraying with potassium tartrate at pre-harvest and gum arabic dipping at post-harvest (TE3) and spraying with potassium tartrate at pre-harvest and carboxymethyl cellulose/gum arabic composite dipping at post-harvest (TE4)), followed by potassium thiosulfate (spraying with potassium thiosulfate at pre-harvest and nanocoating dipping at post-harvest (SE1), spraying with potassium thiosulfate at pre-harvest and carboxy methyl cellulose dipping at post-harvest (SE2), spraying with potassium thiosulfate at pre-harvest and gum arabic dipping at post-harvest (SE3) and spraying with potassium thiosulfate at pre-harvest and carboxymethyl cellulose/gum arabic dipping at post-harvest (SE4)), compared to control. For postharvest treatments, E1 improved fruit quality, followed by E2, E4, and E3, respectively. The integration between pre- and postharvest treatments showed a clear superiority of TE2, followed by TE4, SE1, and SE2, respectively.

A scoping review of artificial intelligence-based methods for diabetes risk prediction.

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners.

Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players' physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players' performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders' role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15-30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.

In silico screening of SARS-CoV2 helicase using African natural products: Docking and molecular dynamics approaches.

In the current medical era, there is an urgent necessity to identify new effective drugs to enrich the COVID-19's therapeutic arsenal. The SARS-COV-2 NSP13/helicase enzyme has been identified as a potential target for developing novel COVID-19 inhibitors. In this work, we aimed at endorsing effective natural products with potential inhibitory action towards the NSP13 through the virtual screening of 1012 natural products of botanical and marine origin from the South African Natural Compounds Database (SANCDB). The molecules were docked into the NTPase active site, and the best twelve compounds were chosen for further analysis. Thereafter, a combination of molecular dynamics simulations and MM-GBSA free energy calculations were carried out for a subset of best hits complexed with NSP13 helicase. We believe that the findings of this work will pave the way for additional research and experimental validation of some natural products as viable NSP13 helicase inhibitors.

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities-DXA and retinal images)-to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.

Novel Wastewater Treatment by Using Newly Prepared Green Seaweed-Zeolite Nanocomposite.

A dependent step-by-step study that included experimental and field study was applied to explore the simplest and most effective system that could be applied for adsorption of Congo Red (CR) dye from the effluent of wastewater that comes out from different industries. Zeolite (Z) surface and pores were subjected to a modification process using green seaweed (GS) algae. Thereafter, each Z, GS, and composite from both were evaluated based on the adsorption efficacy to clean up CR dyes from aqueous solutions. A wet impregnation method was followed to fabricate the zeolite/algae (ZGS) nanocomposite which was characterized using the most appropriate characterization techniques. Batch experiments were selected to be the method of choice in order to follow up the performance of the adsorption process versus different practical variables. Moreover, dye adsorption kinetics and isotherms were investigated as well. At lowered concentrations of CR, the novel nanocomposite ZGS revealed more efficacy than its counterparts, Z and GS, in terms of the adsorption capacity. The maximum adsorption capacities were found to be 8.10, 10.30, and 19.70 mg/g for Z, GS, and ZGS, respectively. Laboratory tests confirmed that the novel nanocomposite ZGS could be introduced as a new and economical nanoadsorbent to capture and remove negatively charged dyes from wastewater effluents that come out from industries at lower concentrations of CR dye and analogous compounds. The dye adsorption on GS, Z, and ZGS coincide with the pseudo-first, Langmuir isotherm, and second-order models. Evaluation for the sorption mechanism was conducted using a diffusion model known as Weber's intraparticle. Depending on the last findings, field experiments on removing dyes from industrial wastewater revealed optimistic findings as the efficiency of our modern and eco-friendly nanoadsorbent reached 91.11%, which helps in the reuse of industrial wastewater.

The Linkage Between Bone Densitometry and Cardiovascular Disease.

Dual-energy X-ray absorptiometry (DXA) has been traditionally used to assess body composition covering bone, fat and muscle content. Cardiovascular disease (CVD) has deleterious effects on bone health and fat composition. Therefore, early detection of bone health, fat and muscle composition would help to anticipate a proper diagnosis and treatment plan for CVD patients. In this study, we leveraged machine learning (ML)-based models to predict CVD using DXA, demonstrating that it can be considered an innovative approach for early detection of CVD. We leveraged state-of-the-art ML models to classify the CVD group from non-CVD group. The proposed logistic regression-based model achieved nearly 80% accuracy. Overall, the bone mineral density, fat content, muscle mass and bone surface area measurements were elevated in the CVD group compared to non-CVD group. Ablation study revealed a more successful discriminatory power of fat content and bone mineral density than muscle mass and bone areas. To the best of our knowledge, this work is the first ML model to reveal the association between DXA measurements and CVD in the Qatari population. We believe this study will open new avenues of introducing DXA in creating the diagnosis and treatment plan of cardiovascular diseases.

Development of novel benzofuran-isatin conjugates as potential antiproliferative agents with apoptosis inducing mechanism in Colon cancer.

In the current work, a new set of carbohydrazide linked benzofuran-isatin conjugates (5a-e and 7a-i) was designed and synthesised. The anticancer activity for compounds (5b-d, 7a, 7b, 7d and 7g) was measured against NCI-55 human cancer cell lines. Compound 5d was the most efficient, and thus subjected to the five-dose screen where it showed excellent broad activity against almost all tested cancer subpanels. Furthermore, all conjugates (5a-e and 7a-i) showed a good anti-proliferative activity towards colorectal cancer SW-620 and HT-29 cell lines, with an excellent inhibitory effect for compounds 5a and 5d (IC50 = 8.7 and 9.4 µM (5a), and 6.5 and 9.8 µM for (5d), respectively). Both compounds displayed selective cytotoxicity with good safety profile. In addition, both compounds provoked apoptosis in a dose dependent manner in SW-620 cells. Also, they significantly inhibited the anti-apoptotic Bcl2 protein expression and increased the cleaved PARP level that resulted in SW-620 cells apoptosis.

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.

Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome.

Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. OBJECTIVE: The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. METHODS: We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. RESULTS: Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. CONCLUSIONS: Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

Understanding the Food Habits and Physical Activities of Diabetes Cohort in Qatar.

In this study, we analyze the food and lifestyle-related factors for a Diabetic cohort from Qatar, where the prevalence of diabetes is among the top in the Middle East region. Statistical analysis shows that the diabetic group is consuming a lower amount of fast foods, soft drinks and meats as a meal but a higher amount of vegetables and fruits compared to the control group. Though the diabetic cohort consumes a lower number of snacks and desserts, they consume a higher amount of sugar for tea. Interestingly, we find the diabetes cohort is spending a lower amount of time in sedentary life but their involvement in different physical activities is lower than the control group. Overall, we conclude that the Qatari diabetic cohort, considered in this study, is following standard guidelines for food and drinks but they may need to improve the physical activity level following physician guidelines.

Cardiovascular Diseases in Qatar: Smoking, Food Habits and Physical Activities Perspectives.

Cardiovascular diseases (CVDs) trigger a high number of deaths across the world. In this study, we investigate the food, drinking, smoking, and lifestyle-related habits for a Qatari CVD cohort to understand the implication of these factors on CVD. Statistical analysis shows that the CVD group is consuming a lower amount of fast foods, soft drinks, snacks, and meats compared to the control group. Alarmingly, the level of smoking is still higher in the CVD group, and the consumption level of healthy items (e.g., cereal, cornflakes) in breakfast is relatively lower compared to the control group. Interestingly, the CVD cohort is spending more time walking and avoiding heavy sports, compared to the control group, but their involvement in moderate physical activities is lower than the control group. Overall, we conclude that the Qatari CVD cohort is following most of the standard guidelines related to food items and heavy sports; however, the cohort should reduce smoking habits, and may modify the moderate level of physical activity based on physician guidelines.

MITOCHONDRIAL MEMBRANE POTENTIAL, MORPHOLOGY AND ATP PRODUCTION IN MAMMALIAN CELLS EXPOSED TO X-RAYS.

This study aimed to reveal the effect of X-irradiation on mitochondrial function in terms of mitochondrial membrane potential (ΔΨm) and ATP productivity. At the cellular level, ΔΨm was quantified using JC-1, a mitochondrial probe that emits red or green fluorescence at high or low ΔΨm sites, respectively. The fluorescence area was quantified for both colors together relative to the whole-cell area of the same cell. The fluorescence areas versus whole-cell areas varied widely among the irradiated cells depending on the X-ray doses received (6 and 10 Gy) and incubation time, although the relative red area to total mitochondrial area was rather constant. Average ATP concentrations temporarily increased and showed a maximum at 48 h after irradiation for largely G1-arrested cells. These results indicate that an increase of mitochondrial volume per cell, not simply an increase in their active sites, is induced by irradiation, resulting in enhanced ATP production.

Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model.

Over the past decades, Ionic liquids (ILs) have gained considerable attention from the scientific community in reason of their versatility and performance in many fields. However, they nowadays remain mainly for laboratory scale use. The main barrier hampering their use in a larger scale is their questionable ecological toxicity. This study investigated the effect of hydrophobic and hydrophilic cyclic cation-based ILs against four pathogenic bacteria that infect humans. For that, cations, either of aromatic character (imidazolium or pyridinium) or of non-aromatic nature, (pyrrolidinium or piperidinium), were selected with different alkyl chain lengths and combined with both hydrophilic and hydrophobic anionic moieties. The results clearly demonstrated that introducing of hydrophobic anion namely bis((trifluoromethyl)sulfonyl)amide, [NTF2] and the elongation of the cations substitutions dramatically affect ILs toxicity behaviour. The established toxicity data [50% effective concentration (EC50)] along with similar endpoint collected from previous work against Aeromonas hydrophila were combined to developed quantitative structure-activity relationship (QSAR) model for toxicity prediction. The model was developed and validated in the light of Organization for Economic Co-operation and Development (OECD) guidelines strategy, producing good correlation coefficient R2 of 0.904 and small mean square error (MSE) of 0.095. The reliability of the QSAR model was further determined using k-fold cross validation.

On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs.

This paper investigates the combination of multiresolution methods for feature extraction for lung cancer. The focus is on the impact of combining wavelet and curvelet on the accuracy of the disease diagnosis. The paper investigates feature extraction with two different levels of wavelet, two different wavelet functions and the combination of wavelet and curvelet to obtain a high classification rate. The findings suggest the potential of combining different multiresolution methods in achieving high accuracy rates.

Development of rapid oral bacteria detection apparatus based on dielectrophoretic impedance measurement method.

In this study, a bacteria detection apparatus based on dielectrophoretic impedance measurement (DEPIM) method was demonstrated for rapid evaluation of oral hygiene. The authors integrated a micro electrode chip on which bacteria were captured by dielectrophoresis (DEP), an AC voltage source to induce DEP force, and an impedance measurement circuit to a portable instrument that enables rapid and automated oral bacterial inspection in hospitals and clinics. Special considerations have been made on effects of high electrical conductivity of oral samples on DEP force and DEPIM results. It was shown experimentally and theoretically that using a higher electric field frequency for the DEP bacteria trap and the impedance measurement could realise DEPIM application to bacteria inspection from oral samples with higher conductivity. Based on these investigations, the authors optimised the frequency condition of the DEPIM suitable for inspecting an oral sample along with the design and development of a portable DEPIM apparatus for on-site inspection of oral bacteria. Under the optimised frequency condition, DEPIM results were in good agreement with the conventional culture method showing significant applicability of the DEPIM apparatus for practical rapid oral bacteria inspection.

Isolated medial medullary infarction due to vertebral artery dissection.

A 54-year-old man developed left hemiparesis and tactile and deep sensory disturbance following onset of rightside cervical pain. These symptoms resulted from an isolated infarct in the right medial area of the upper medulla oblongata and intracranial vertebral artery (VA) dissection. Atherosclerotic disease of the VA is the most common cause of medial medullary infarction. In past reports of isolated medial medullary infarction, only a few cases involved VA dissection.