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AbstractGestational diabetes is characterized by hyperglycemia diagnosed during pregnancy. High blood sugar levels are likely to affect both the mother and child. This disease frequently goes undiagnosed due to its fewer prominent symptoms, resulting in severe unmanaged hyperglycemia, obesity, childbirth complications and overt diabetes. Artificial Intelligence is increasingly deployed in the medical field, revolutionizing and automating data processing and decision-making. Machine learning is a subset of artificial intelligence that can create reliable healthcare screening and predictive systems. With the advent of machine learning, detecting gestational diabetes and getting more profound insights about the disease is possible. This study explores the development of a reliable clinical decision support system for gestational diabetes detection using multiple machine learning architectures using combinations of five data balancing methods to detect gestational diabetes. An ensemble stack trained on the synthetic minority oversampling technique with edited nearest neighbor obtained the highest performance with accuracy, sensitivity and precision of 96%, 95% and 99%, respectively. Additionally, a layer of explainable artificial intelligence was added to the best-performing model using libraries such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, Quantum lattice, Explain Like I’m 5 algorithm, Anchor and Feature importance. The importance of factors such as Visceral Adipose Deposit and its contribution toward the prediction of gestational diabetes is explored. This research aims to provide a meaningful and interpretable clinical decision support system to aid healthcare professionals in early gestational diabetes detection and improved patient management.

Abstract The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.

Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the “Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the “Liebowitz Social Anxiety Scale questionnaire” and “The fear of speaking in public” are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.

Osteoporosis is a metabolic bone condition that occurs when bone mineral density and mass decrease. This makes the bones weak and brittle. The disorder is often undiagnosed and untreated due to its asymptomatic nature until the manifestation of a fracture. Machine Learning (ML) is extensively used in diverse healthcare domains to analyze precise outcomes, provide timely risk scores, and allocate resources. Hence, we have designed multiple heterogeneous machine-learning frameworks to predict the risk of Osteoporosis. An open-source dataset of 1493 patients containing bone density, blood, and physical tests is utilized. Thirteen distinct feature selection techniques were leveraged to extract the most salient parameters. The best-performing pipeline consisted of a Forward Feature Selection algorithm followed by a custom multi-level ensemble learning-based stack, which achieved an accuracy of 89 %. Deploying a layer of explainable artificial intelligence using tools such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance provided interpretability and rationale behind classifier prediction. With this study, we aim to provide the holistic risk prediction of Osteoporosis and concurrently present a system for automated screening to assist physicians in making diagnostic decisions.

Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.

Magnesium and its alloys, with their unique properties such as high strength/density ratio, good castability, and machinability, have found several applications in the aerospace and automotive industries. One of the reasons that restrict their widespread applicability is their poor corrosion resistance since Mg is readily oxidized in the presence of oxygen and humidity. The oxide layer is pseudo-passive and non-protective. The present effort tried to improve the passivity of this layer with the addition of alloying elements such as Tin (Sn) and Yttrium (Y) to create phases that interact differently with the oxidizing environment, thereby improving the corrosion resistance. In this work, the corrosion behavior of pure magnesium and Mg-5Sn-xY (x = 0.5, 1, 2 wt.%) were evaluated using immersion and potentiodynamic polarization tests. XRD, SEM-EDS, and EBSD have investigated the microstructure and elemental composition of the alloys. The present study is focused on elucidating the microstructure-corrosion relationship in Mg-Sn-Y alloys.

Microbial contamination of medical devices and treatment rooms leads to several detrimental hospital and device-associated infections. Antimicrobial copper coatings are a new approach to control healthcare-associated infections (HAI’s). This review paper focuses on the efficient methods for depositing highly adherent copper-based antimicrobial coatings onto a variety of metal surfaces. Antimicrobial properties of the copper coatings produced by various deposition methods including thermal spray technique, electrodeposition, electroless plating, chemical vapor deposition (CVD), physical vapor deposition (PVD), and sputtering techniques are compared. The coating produced using different processes did not produce similar properties. Also, process parameters often could be varied for any given coating process to impart a change in structure, topography, wettability, hardness, surface roughness, and adhesion strength. In turn, all of them affect antimicrobial activity. Fundamental concepts of the coating process are described in detail by highlighting the influence of process parameters to increase antimicrobial activity. The strategies for developing antimicrobial surfaces could help in understanding the mechanism of killing the microbes.

In this study, the effect of calcium (Ca) and erbium (Er) on the microstructure, mechanical properties, and corrosion behavior of magnesium-zinc alloys is reported. The alloys were prepared using disintegrated melt deposition (DMD) technique using the alloying additions as Zn, Ca, and Mg-Er master alloys and followed by hot extrusion. Results show that alloying addition of Er has significantly reduced the grain sizes of Mg-Zn alloys and also when compared to pure magnesium base material. It also has substantially enhanced both the tensile and the compressive properties by favoring the formation of MgZn2 type secondary phases that are uniformly distributed during hot-extrusion. The quaternary Mg-Zn-Ca-Er alloy exhibited the highest strength due to lower grain size and particle strengthening due to the influence of the rare earth addition Er. The observed elongation was a result of extensive twinning observed in the alloys. Also, the degradation rates have been substantially reduced as a result of alloying additions and it is attributed to the barrier effect caused by the secondary phases.

Background: The theoretical knowledge of nursing underpins the practice, while the practice environment determines the circumstances within which the theoretical knowledge is applied. The biggest challenge facing nursing as an academic field is the theory-practice gap, which is a universal issue in nursing. This study aimed to raise solutions to close the gap between theory and practice in nursing education through the eyes of nursing students in UAE., Methods: A qualitative descriptive approach was followed; whereby 25 Emirati nursing students were interviewed., Results: Two main themes are discussed in this study: 'Clinical Culture Creation' and 'Curriculum Content Reformation'. The students suggested to decreased the loud and stress on their clinical educators. They also suggested creating synchronization between what is taught in classrooms and labs and what is offered in the clinical training. Moreover, some of the students expected to have more benefit if they get their clinical training in health institutions owned by their college. On the other side, many of the participants suggested to move from the integration system to the block system. Another interesting suggestion includes having the same college staff to teach the theory and the clinical. The final suggestion includes decreasing the paper work during clinical training., Conclusions: This study explored the solutions to bridge the theory-practice gap in nursing education in the UAE. The study has implications for nursing education and practice within the UAE and is imperative for graduating workplace ready professional nurses within the country., (© 2021. The Author(s).)

Aim: The aim of this study is to explore factors contributing to the theory-practice gap in nursing education in the United Arab Emirates., Background: The gap between what is taught in nursing classrooms and what is practised in clinical settings creates challenges for nursing students, practitioners, managers and educators. This has important implications for the United Arab Emirates and other developing countries as their healthcare systems require a permanent nursing taskforce that is well supplied with ready to practice graduates., Methods: A qualitative descriptive approach was used, whereby 25 senior student nurses were interviewed about their perceptions of the theory-practice gap., Results: The major theme that emerged in this study was the 'tripod of clinical practice', with three subthemes: prepared students, aware and supportive preceptors and qualified clinical faculty. The other theme that emerged was 'real life outside the simulated lab', with two subthemes: various and unfamiliar psychomotor skills and communication with real patients., Conclusion: The tripod of clinical practice must be achieved for quality clinical practice. Further, attention should be directed to development of skills (e.g., communicating with real patients) that are difficult to acquire while dealing with simulated patients., Implications for Nursing Management: Nursing students need longer immersion in simulated clinical settings, coupled with experienced clinical faculty and supportive, aware and prepared preceptors., (© 2021 John Wiley & Sons Ltd.)

The hexangular star building-like lutetium hexacyanoferrate (LuHCF) structure with an average size of ca. 8.0 ± 0.5 μm was synthesized using a simple, one-step electrochemical method, and it was highly dispersed on to a reduced graphene oxide (RGO) modified glassy carbon electrode (GCE) support for the first time. The size and shape of the as-synthesized LuHCF micro stars were controlled by the deposition time. The LuHCF/RGO samples were characterized by a variety of analytical and spectroscopy techniques, viz. scanning electron microscopy (SEM), infra-red spectroscopy (IR), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). In addition, LuHCF/RGO/GCE was adopted for the novel electrochemical detection of salicylic acid (SA) using cyclic voltammetry (CV) and amperometry methods. The charge transfer resistant value of LuHCF/RGO/GCE was smaller than LuHCF and bare GCE, which exhibit a remarkable electrocatalytic performance towards SA. Notably, the SA sensor was found to exhibit a lower detection limit and high sensitivity of ca. 0.49 μM and 77.2 μA mM -1 cm -2 , respectively. The reported SA sensor possesses an excellent real time application with commercially purchased aspirin tablets and salic ointment (which contains salicylic acid). The excellent analytical parameters of the reported sensor, surpasses the previously reported modified electrodes, rendering practical industrial applications.

Here in, we report the simultaneous voltammetric determination of epinephrine (EP) and p-acetoaminophenol (AP) on a poly curcumin (1,7 Bis ((4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5 dione), poly CM) modified glassy carbon electrode (GCE) for the first time. The CM was polymerized on to the GCE surface by simple electro polymerization process. A low peak to peak (ΔEp) separation of 60 mV was observed, indicating fast electron transfer between poly CM and the electrode surface. The electrochemical measurements and surface morphology of the as prepared poly CM film modified electrode were studied using cyclic voltammetry (CV), and field emission scanning electron microscopy (SEM), respectively. Moreover, poly CM modified GCE exhibits enhanced electro catalytic activity toward EP and AP in the linear range of 4.97-230.76 μM and 0.99-230.76 μM and with very low detection limit (LOD) of 0.05 μM and 0.1 μM. The sensitivity is 0.621 and 0.303 μAμM(-1)cm(2) for EP and AP, respectively. The practical feasibility of the proposed poly CM/GCE was evaluated in adrenaline injection (1 mg mL(-1)) solutions and paracetamol tablets for the simultaneous determination of EP and AP. We found maximum recovery of 99.2% for adrenaline injection and 97.4% for paracetamol tablets. Finally the modified electrode exhibit excellent repeatability, reproducibility and stability for the selective and simultaneous determination of EP and AP., (Copyright © 2013 Elsevier B.V. All rights reserved.)

In this current study we used electrochemically active film which contains poly-L-methionine (PMET) and electrochemically reduced graphene oxide (ERGO) on glassy carbon electrode (GCE) for pyrazinamide (PZM) detection. The electrocatalytic response of analyte at PMET/ERGO/GCE film was measured using both cyclic voltammetry (CV) and differential pulse voltammetry (DPV). In addition, electrochemical impedance studies revealed that the smaller R(ct) value observed at PMET/ERGO film modified GCE which authenticates its good conductivity and faster electron transfer rate. The prepared PMET/ERGO/GCE film exhibits excellent DPV response towards PZM and the reduction peak current increased linearly with respect to PZM concentration in the linear range between 0.4 μM to 1129 μM with a sensitivity of 0.266 μA μM(-1) cm(-2). Real sample studies were carried out in human blood plasma and urine samples, which offered good recovery and revealed the promising practicality of the sensor for PZM detection. The proposed sensor displayed a good selectivity, repeatability, sensitivity with appreciable consistency and good reproducibility. In addition, the proposed electrochemical sensor showed good results towards the commercial pharmaceutical PZM samples., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Direct electrochemistry of glucose oxidase (GOx) at an electrochemically reduced graphene oxide-multiwalled carbon nanotubes hybrid (ERGO-MWCNT) modified glassy carbon electrode (GCE) has been reported. The π-π stacking interaction operating between the MWCNT and graphene oxide (GO) has been revealed by UV-Vis absorption spectroscopy. GOx was well immobilized onto the ERGO-MWCNT hybrid film, as a result direct electrochemistry of GOx has been achieved. Compared with pristine MWCNT, 2.1 fold higher peak current and very low peak to peak separation (ΔE(p)) of 26 mV were observed at the hybrid film, demonstrating faster electron transfer between GOx and the modified electrode surface. Moreover, the modified film exhibited high electrocatalytic activity towards glucose via reductive detection of oxygen consumption and in the presence of mediator. The proposed biosensor exhibits low detection limit of 4.7 μM with wide linear range of 0.01-6.5mM and acquires excellent storage and operational stabilities. The accurate glucose determination in human blood serum and good recoveries achieved in spiked urine samples revealed their great potential in the practical applications., (Copyright © 2012 Elsevier B.V. All rights reserved.)

PH-797804 ((aS)-3-{3-bromo-4-[(2,4-difluorobenzyl)oxy]-6-methyl-2-oxopyridin-1(2H)-yl}-N,4-dimethylbenzamde) is a diarylpyridinone inhibitor of p38 mitogen-activated protein (MAP) kinase derived from a racemic mixture as the more potent atropisomer (aS), first proposed by molecular modeling and subsequently confirmed by experiments. Due to steric constraints imposed by the pyridinone carbonyl group and the 6- and 6'-methyl substituents of PH-797804, rotation around the connecting bond of the pyridinone and the N-phenyl ring is restricted. Density functional theory predicts a remarkably high rotational energy barrier of >30 kcal mol(-1), corresponding to a half-life of more than one hundred years at room temperature. This gives rise to discrete conformational spaces for the N-phenylpyridinone group, and as a result, two atropic isomers that do not interconvert under ambient conditions. Molecular modeling studies predict that the two isomers should differ in their binding affinity for p38α kinase; whereas the atropic S (aS) isomer binds favorably, the opposite aR isomer incurs significant steric interference with p38α kinase. The two isomers were subsequently identified and separated by chiral chromatography. IC50 values from p38α kinase assays confirm that one atropisomer is >100-fold more potent than the other. It was ultimately confirmed by small-molecule X-ray diffraction that the more potent atropisomer, PH-797804, is the aS isomer of the racemic pair. Extensive pharmacological characterization supports that PH-797804 carries most activity both in vitro and in vivo, and it has a stability profile compatible with oral formulation and delivery options., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A novel series of highly potent and selective p38 MAP kinase inhibitors was developed originating from a substituted N-aryl-6-pyrimidinone scaffold. SAR studies coupled with in vivo evaluations in rat arthritis model culminated in the identification of 10 with excellent oral efficacy. Compound 10 exhibited a significantly enhanced dissolution rate compared to 1, translating to a high oral bioavailability (>90%) in rat. In animal studies 10 inhibited LPS-stimulated production of tumor necrosis factor-α in a dose-dependent manner and demonstrated robust efficacy comparable to dexamethasone in a rat streptococcal cell wall-induced arthritis model., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

A series of N-aryl pyridinone inhibitors of p38 mitogen activated protein (MAP) kinase were designed and prepared based on the screening hit SC-25028 (1) and structural comparisons to VX-745 (5). The focus of the investigation targeted the dependence of potency and metabolic stability on the benzyloxy connectivity, the role of the C-6 position and the substitution pattern on the N-phenyl ring. Further optimization produced the highly selective and potent pyridinones 2 and 3. These inhibitors exhibited activity in both acute and chronic models of inflammation., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The synthesis and SAR studies of a novel N-aryl pyridinone class of p38 kinase inhibitors are described. Systematic structural modifications to the HTS lead, 5, led to the identification of (-)-4a as a clinical candidate for the treatment of inflammatory diseases. Additionally, the chiral synthesis and properties of (-)-4a are described., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The identification and evolution of a series of potent and selective p38 inhibitors is described. p38 inhibitors based on a N-benzyl pyridinone high-throughput screening hit were prepared and their SAR explored. Their design was guided by ligand bound co-crystals of p38alpha. These efforts resulted in the identification of 12r and 19 as orally active inhibitors of p38 with significant efficacy in both acute and chronic models of inflammation.

PH-797804 is a diarylpyridinone inhibitor of p38alpha mitogen-activated protein (MAP) kinase derived from a racemic mixture as the more potent atropisomer (aS), first proposed by molecular modeling and subsequently confirmed by experiments. On the basis of structural comparison with a different biaryl pyrazole template and supported by dozens of high-resolution crystal structures of p38alpha inhibitor complexes, PH-797804 is predicted to possess a high level of specificity across the broad human kinase genome. We used a structural bioinformatics approach to identify two selectivity elements encoded by the TXXXG sequence motif on the p38alpha kinase hinge: (i) Thr106 that serves as the gatekeeper to the buried hydrophobic pocket occupied by 2,4-difluorophenyl of PH-797804 and (ii) the bidentate hydrogen bonds formed by the pyridinone moiety with the kinase hinge requiring an induced 180 degrees rotation of the Met109-Gly110 peptide bond. The peptide flip occurs in p38alpha kinase due to the critical glycine residue marked by its conformational flexibility. Kinome-wide sequence mining revealed rare presentation of the selectivity motif. Corroboratively, PH-797804 exhibited exceptionally high specificity against MAP kinases and the related kinases. No cross-reactivity was observed in large panels of kinase screens (selectivity ratio of >500-fold). In cellular assays, PH-797804 demonstrated superior potency and selectivity consistent with the biochemical measurements. PH-797804 has met safety criteria in human phase I studies and is under clinical development for several inflammatory conditions. Understanding the rationale for selectivity at the molecular level helps elucidate the biological function and design of specific p38alpha kinase inhibitors.