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BACKGROUND: Rare bleeding disorder (RBDs) encompasses a deficiency of one or more of FXIII, FXI, FX, FVII, FV, FII, and FI clotting factors, leading to bleeding disorders with variable presentations and outcomes ranging from none or minimal to life-threatening events. RBDs are still underdiagnosed and underreported, especially in Saudi population with a high prevalence of consanguinity. OBJECTIVES: The study aimed to determine the frequency of RBDs, grading of their bleeding severity, and assessment of clinical manifestations and management of RBDs in tertiary Saudi Arabian hospitals. DESIGN AND SETTINGS: This retrospective study of RBDs describes the clinicopathological features of refereed cases to both Prince Sultan Military Medical City and King Khaled University Hospital in Riyadh, Saudi Arabia, from September 2018 to September 2021. Any patient who had already been diagnosed or suspected to have RBDs was enrolled in the study. PATIENTS AND METHODS: Patient's medical records were reviewed for demographic data, clinical presentations, bleeding and family history, consanguinity, treatment outcomes, and molecular testing. Samples were run in specialized coagulation laboratories. Patients with liver dysfunction or acquired factor deficiency were excluded. Patients were categorized into four groups according to the severity of bleeding episodes: asymptomatic, Grade I, Grade II, and Grade III. RESULTS: A total of 26 cases with RBDs were identified during the study period. Most of the included patients are males and pediatrics (

Objectives: The study aims to determine the knowledge of first-aid management in medical and non-medical students and how they will act in certain situations. Methods: A cross-sectional study has been conducted on a convenience sampling of 375 (medical and non-medical) students. The research proposal was approved by the IRB Committee of King Saud University. The data has been obtained randomly by a validated questionnaire in a sample of 381 participants. The questionnaire had items related to knowledge and management of first-aid skills. The study was conducted from August 2020 to May 2021 in King Saud University. Results: The participants in the current study were medical (53.02%) and non-medical students (46.98%). Overall results showed that all students possessed a good knowledge of first-aid management, but medical students possessed more knowledge in comparison to non-medical students. The awareness of students related to first-aid management was found to be 32.02% 'high', 56.43% 'middle' and 11.54% 'low'. Moreover, results illustrated that medical students are more interested to attend first-aid courses than non-medical students by 60.4% and 43.6% respectively. Conclusion: The study revealed the participants' knowledge and management were not adequate. A statistically significant association was found between being a medical student and having a high level of knowledge about first aid. Awareness campaigns must be conducted to increase awareness among the non-medical community about first-aid knowledge, and how it is essential for every individual.

Prime objective of the current research was to develop a stable nimesulide emulgel with the help of arabinoxylan, a natural gelling agent extracted from Plantago ovata. The response surface methodology was used by a Design Expert 10 software to formulate and optimize the emulgel. The experimental design approach evaluated the impact of independent and dependent variables. Independent variables were different concentrations of arabinoxylan, span 80 and tween 20, whereas, dependent variables were viscosity, pH, and content uniformity. FTIR demonstrated the compatibility of nimesulide with the excipients. Stability study indicated no phase separation and no change in pH for formulation F1, F3 and F4. The negative values of zeta potential revealed the excellent stability of emulgel. Viscosity, spreadability and extrudability values were in desired range. Ex-vivo permeation study illustrated 86%, 55% and 66% release of the drug over a period of 24 h from the formulations F1, F3 and F4, respectively. Analgesic effect of the optimized emulgel was significantly higher in test group as compared to control and did not produce any sort of irritation. Therefore, it can be concluded that the newly developed emulgel based on arabinoxylan, as gelling agent, appear to be an effective drug delivery system.

Shilajit is a natural substance found in the Himalayan region from Nepal to Pakistan. It is a decomposition product of Royle’s spurge , white clover , and different species of molds. The decomposition takes place over a time span of centuries by the action of microorganism. In the present study, shilajit samples from four different origins including siachen khaplu shilajit (SKS), kharmang pari saspolo shilajit (KPSS), kharmang ghandus shilajit (KGS), and kharmang shilajit center (KSC) of district Skardu, Pakistan were investigated. These samples were characterized using scanning electron microscopy (SEM), Energy-dispersive x-ray spectroscopy (EDS), x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV-visible spectroscopy (UV/vis). SEM revealed a notable difference in the shape and size of collected samples. All samples were found to possess crystalline nature, which is confirmed from XRD. The presence of multi-components and complex silicates confirmed the presence of humic substances (HS) in shilajit. A slight disparity in physiological properties of four samples were revealed due to geographical variations and ecological conditions, which determine the natural synthesis of shilajit. All samples exhibited antibacterial effects against Gram negative bacteria; Escherichia coli (E.coli) . About 76%, 98%, and 100% of bacteria were killed by SKS, both KPSS and KGS, and KSC samples, respectively. The cell viability analysis revealed that the KPSS (66%) and KGS (53%) were cyto-compatible as compared to the SKS (23%) and KSC (25%) samples. The Chick Chorionic Allantoic Membrane (CAM) assay was used to observe the angiogenic potential for SKS, KSC, and KGS samples. Hence, shilajit sample could be a potential candidate for the medicinal applications and offer a new approach to biomedical applications.

Abstract The increasing prevalence of chronic diseases and their associated morbidities demands a deeper understanding of underlying mechanism and causative factors, with the hope of developing novel therapeutic strategies. Autophagy, a conserved biological process, involves the degradation of damaged organelles or protein aggregates to maintain cellular homeostasis. Disruption of this crucial process leads to increased genomic instability, accumulation of reactive oxygen species (ROS), decreased mitochondrial functions, and suppression of ubiquitination, leading to overall decline in quality of intracellular components. Such deregulation has been implicated in a wide range of pathological conditions such as cancer, cardiovascular, inflammatory, and neurological disorders. This review explores the role of long non-coding RNAs (lncRNAs) as modulators of transcriptional and post-transcriptional gene expression, regulating diverse physiological process like proliferation, development, immunity, and metabolism. Moreover, lncRNAs are known to sequester autophagy related microRNAs by functioning as competing endogenous RNAs (ceRNAs), thereby regulating this vital process. In the present review, we delineate the multitiered regulation of lncRNAs in the autophagic dysfunction of various pathological diseases. Moreover, by highlighting recent findings on the modulation of lncRNAs in different stages of autophagy, and the emerging clinical landscape that recognizes lncRNAs in disease diagnosis and therapy, this review highlights the potential of lncRNAs as biomarkers and therapeutic targets in clinical settings of different stages of autophagic process by regulating ATG and its target genes. This focus on lncRNAs could lead to breakthroughs in personalized medicine, offering new avenues for diagnosis and treatment of complex diseases. Graphical Abstract

Abstract The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer (TNBC). Recent research indicates the aberrant expression of diverse tyrosine kinases (TKs) within this cancer, contributing significantly to tumor cell proliferation, survival, invasion, and migration. The contemporary paradigm shift towards precision medicine has highlighted TKs and their receptors as promising targets for pharmacotherapy against a range of malignancies, given their pivotal roles in tumor initiation, progression, and advancement. Intensive investigations have focused on various monoclonal antibodies (mAbs) and small molecule inhibitors that specifically target proteins such as epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor (VEGFR), cellular mesenchymal-epithelial transition factor (c-MET), human epidermal growth factor receptor 2 (HER2), among others, for combating TNBC. These agents have been studied both in monotherapy and in combination with other chemotherapeutic agents. Despite these advances, a substantial terrain of unexplored potential lies within the realm of TK targeted therapeutics, which hold promise in reshaping the therapeutic landscape. This review summarizes the various TK targeted therapeutics that have undergone scrutiny as potential therapeutic interventions for TNBC, dissecting the outcomes and revelations stemming from diverse clinical investigations. A key conclusion from the umbrella clinical trials evidences the necessity for in-depth molecular characterization of TNBCs for the maximum efficiency of TK targeted therapeutics, either as standalone treatments or a combination. Moreover, our observation highlights that the outcomes of TK targeted therapeutics in TNBC are substantially influenced by the diversity of the patient cohort, emphasizing the prioritization of individual patient genetic/molecular profiles for precise TNBC patient stratification for clinical studies.

Abstract Clinical biomedical applications of genomic technologies are extensive and provide possibilities to enhance healthcare covering the span of medical talents. Genome disorder prediction is an important issue in biomedical research. Genome disorders cause multivariate diseases such as cancer, dementia, diabetes, Leigh syndrome, etc. Existing machine and deep learning-based methods were introduced to forecast genome disorders. However, the genome prediction outcomes were not sufficient. To address this issue, propose a new method called Quadratic Feynman Polynomial Interpolated and Vector Nearest Centroid-based (QFPI-VNC) for acutely predicting the genome disorder with improved sensitivity and specificity. First, we utilized medical data about children from a public genomes dataset and applied it to Linear Quadratic and Feynman Kac Genome filtering to obtain computationally efficient filtered results. Next, the results are fed to the Concordance Correlated Polynomial Interpolation with the purpose of extracting genome wide data in an accurate manner. Finally, the features extracted are fused and fed to the Support Vector and Nearest Centroid model for genome disorder prediction. Experimental investigations of the proposed method employing the genome dataset confirm that the performance of the proposed method is prospective and in the scope of acceptance with relative to state-of-the-art methods in terms of convergence speed, recognition rate, sensitivity, and specificity. Results suggest that the QFPI-VNC method produces the best performance with a higher genome disease detection rate by 14%, accuracy by 11%, sensitivity by 14% specificity by 12%, and lesser convergence speed by 29% than compared to state-of-the-art methods.

Abstract Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.

Abstract Ovarian cancer is a formidable health challenge that demands accurate and timely survival predictions to guide clinical interventions. Existing methods, while commendable, suffer from limitations in harnessing the temporal evolution of patient data and capturing intricate interdependencies among different data elements. In this paper, we present a novel methodology which combines Temporal Analysis and Graph Neural Networks (GNNs) to significantly enhance ovarian cancer survival rate predictions. The shortcomings of current processes originate from their disability to correctly seize the complex interactions amongst diverse scientific information units in addition to the dynamic modifications that arise in a affected person`s nation over time. By combining temporal information evaluation and GNNs, our cautioned approach overcomes those drawbacks and, whilst as compared to preceding methods, yields a noteworthy 8.3% benefit in precision, 4.9% more accuracy, 5.5% more advantageous recall, and a considerable 2.9% reduction in prediction latency. Our method’s Temporal Analysis factor uses longitudinal affected person information to perceive good-sized styles and tendencies that offer precious insights into the direction of ovarian cancer. Through the combination of GNNs, we offer a robust framework able to shoot complicated interactions among exclusive capabilities of scientific data, permitting the version to realize diffused dependencies that would affect survival results. Our paintings have tremendous implications for scientific practice. Prompt and correct estimation of the survival price of ovarian most cancers allows scientific experts to customize remedy regimens, manipulate assets efficiently, and provide individualized care to patients. Additionally, the interpretability of our version`s predictions promotes a collaborative method for affected person care via way of means of strengthening agreement among scientific employees and the AI-driven selection help system. The proposed approach not only outperforms existing methods but also has the possible to develop ovarian cancer treatment by providing clinicians through a reliable tool for informed decision-making. Through a fusion of Temporal Analysis and Graph Neural Networks, we conduit the gap among data-driven insights and clinical practice, proposing a capable opportunity for refining patient outcomes in ovarian cancer management operations.

Abstract Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

Abstract A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

الملخص: أهداف البحث: السكتة الدماغية هي أحد الأسباب الرئيسية للوفاة وضعف الوظيفة الجسدية. أصبحت عملية استئصال الخثرة داخل الأوعية الدموية الآن هي المعيار المعترف به لعلاج السكتة الدماغية الإقفارية الحادة في الدورة الدموية الأمامية مع انسداد الأوعية الدموية الكبيرة، وخاصة عندما تكون صور الدماغ المواتية واضحة في غضون 24 ساعة من ظهور الأعراض. تهدف هذه الدراسة إلى توضيح الخصائص والملامح السريرية ونتائج المرضى الذين خضعوا لاستئصال الخثرة داخل الأوعية الدموية للسكتة الدماغية الإقفارية الحادة. طريقة البحث: في تصميم مجموعة مستندة إلى السجلات بأثر رجعي، قمنا بمراجعة السجلات الطبية للأفراد الذين خضعوا لاستئصال الخثرة داخل الأوعية الدموية بسبب السكتة الدماغية الإقفارية الحادة في مستشفى الملك فهد العام في جدة، المملكة العربية السعودية. شملت الدراسة، التي أجريت من يوليو 2022 إلى نهاية أكتوبر 2023، تحليلا شاملا للعوامل السريرية والإشعاعية والعوامل المرتبطة بالوقت والنتائج المتنوعة. النتائج: في مجموعة مكونة من 117 مريضا بمتوسط عمر 58.5 عاما، كان 81 مريضا (69.2%) من الذكور. كان ارتفاع ضغط الدم والسكري من أكثر عوامل الخطر شيوعا، حيث أثرا على 48.7% و41% من المجموعة على التوالي. كان لدى ما يقرب من نصف المرضى درجة مواتية وفق برنامج ألبرتا للسكتة الدماغية للتصوير المقطعي المبكر (7-10)، وتلقى 45.3% منهم منشط البلازمينوجين عن طريق الوريد. حقق ما يقرب من ثلثي المرضى درجة 2ب في العلاج المعدل للاحتشاء الدماغي أو أعلى، وأظهر 39.3% انسداد الشريان الدماغي الأوسط-إم1. كان معدل حدوث التحول النزفي المصحوب بأعراض حوالي 13%، بينما حقق ثلث المرضى نتيجة مواتية حسب مقياس رانكين المعدل (0-2). ارتبطت عوامل مثل التقدم في السن، وارتفاع درجات مقياس السكتة الدماغية للمعاهد الوطنية للصحة، والإقامة الطويلة في المستشفى بنتائج أسوأ. وعلى العكس من ذلك، فإن غياب ارتفاع ضغط الدم والرجفان الأذيني، إلى جانب ارتفاع مقياس انحلال الخثرة في احتشاء دماغي، زاد بشكل كبير من احتمالية تحقيق نتائج إيجابية. الاستنتاجات: في دراستنا، أظهر مرضى استئصال الخثرة من الأوعية الدموية الذين تم فحصهم فئة عمرية أصغر سنا، حيث أظهرت النتائج السريرية والشعاعية درجة معينة من التشابه مع المنشورات الأخرى. ومن الجدير بالذكر أن غياب ارتفاع ضغط الدم والرجفان الأذيني، جنبا إلى جنب مع درجة أعلى في اختبار انحلال الخثرة في احتشاء دماغي، كان مرتبطا بزيادة احتمالية تحقيق نتيجة إيجابية Abstract: Background: Stroke is a significant contributor to both mortality and compromised physical function. Endovascular thrombectomy (EVT) is now the recognized standard of care for anterior circulation acute ischemic stroke (AIS) with large vessel occlusion (LVO), especially when favorable brain images are evident within 24 h of symptom onset. This study elucidated the characteristics, clinical profiles, and outcomes of patients who underwent EVT for AIS. Methodology: In this retrospective record-based cohort study, we reviewed the medical records of individuals who underwent EVT for AIS at King Fahad General Hospital in Jeddah, Saudi Arabia. The study, conducted from July 2022 through October 2023, encompassed a comprehensive analysis of clinical, radiological, and time-related factors and diverse outcomes. Results: In a cohort of 117 patients with a mean age of 58.5 years, 81 (69.2%) were male. Hypertension and diabetes mellitus were the most common risk factors, affecting 48.7% and 41% of the cohort, respectively. Approximately half of the patients had a favorable ASPECT Score (7–10), and 45.3% received intravenous tissue plasminogen activator (tPA). Nearly two-thirds achieved a modified thrombolysis in cerebral infarction (mTICI) score of 2b or higher, and 39.3% exhibited M1-middle cerebral artery (MCA) occlusion. The incidence of symptomatic hemorrhagic transformation was about 13%, whereas one-third of the patients attained a favorable mRs outcome (0–2). Factors such as advanced age, higher NIH Stroke Scale (NIHSS) scores, and prolonged hospital stays were associated with poorer outcomes. Conversely, the absence of hypertension and atrial fibrillation, along with higher TICI scores, significantly increased the likelihood of favorable outcomes, with P values of 0.015, 0.0490, and 0.025, respectively. Conclusions: In our study, the examined patients who underwent EVT were younger in age, with clinical and radiological outcomes showing some degree of comparability to other publications. Notably, the absence of hypertension and atrial fibrillation, combined with a higher TICI score, was associated with an increased likelihood of achieving a favorable outcome.

Abstract Nuclear receptors (NRs) function as crucial transcription factors in orchestrating essential functions within the realms of development, host defense, and homeostasis of body. NRs have garnered increased attention due to their potential as therapeutic targets, with drugs directed at NRs demonstrating significant efficacy in impeding chronic disease progression. Consequently, these pharmacological agents hold promise for the treatment and management of various diseases. Accumulating evidence emphasizes the regulatory role of exosome-derived microRNAs (miRNAs) in chronic inflammation, disease progression, and therapy resistance, primarily by modulating transcription factors, particularly NRs. By exploiting inflammatory pathways such as protein kinase B (Akt)/mammalian target of rapamycin (mTOR), nuclear factor kappa-B (NF-κB), signal transducer and activator of transcription 3 (STAT3), and Wnt/β-catenin signaling, exosomes and NRs play a pivotal role in the panorama of development, physiology, and pathology. The internalization of exosomes modulates NRs and initiates diverse autocrine or paracrine signaling cascades, influencing various processes in recipient cells such as survival, proliferation, differentiation, metabolism, and cellular defense mechanisms. This comprehensive review meticulously examines the involvement of exosome-mediated NR regulation in the pathogenesis of chronic ailments, including atherosclerosis, cancer, diabetes, liver diseases, and respiratory conditions. Additionally, it elucidates the molecular intricacies of exosome-mediated communication between host and recipient cells via NRs, leading to immunomodulation. Furthermore, it outlines the implications of exosome-modulated NR pathways in the prophylaxis of chronic inflammation, delineates current limitations, and provides insights into future perspectives. This review also presents existing evidence on the role of exosomes and their components in the emergence of therapeutic resistance.

Abstract Hydroxamic acids represent a group of weak organic acids, both naturally occurring and synthetically derived, characterized by the general formula RC(= O)N(R’OH). In this study, we investigated the binding behavior of N-m-tolyl-4-chlorophenoxyaceto hydroxamic acid with calf thymus DNA (ct-DNA) and torula yeast RNA (t-RNA) through a combination of techniques including UV–visible spectroscopy, fluorescence emission analysis, viscometry, and computational simulations using AutoDock4 software. Our findings reveal that the mode of binding between the compound and the nucleic acids is consistent with intercalation. Competitive binding experiments demonstrated that the complex competes effectively with ethidium bromide (EB) for binding to ct-DNA/t-RNA, displacing EB from its binding sites. Additionally, the introduction of the compound into the DNA-EB system resulted in a quenching of fluorescence emission peaks. Analysis of absorption spectra indicated a red shift and hypochromic shift when the compound interacted with DNA, further supporting the intercalative binding mode. The calculated binding constant (Kb) value for the compound is 6.62 × 104 M−1 and 5.40 × 103 M−1 indicating a strong interaction with ct-DNA and t-RNA respectively. We determined the Stern–Volmer constants for ct-DNA and t-RNA as 9.96 × 104 M−1 and 8.13 × 105 M−1, respectively. The binding free energy values for ct-DNA/t-RNA were calculated to be − 3.741 × 107 and − 5.425 × 108 kcal/mol, respectively. Viscometric studies corroborated the UV results, showing a continuous increase in relative viscosity of ct-DNA/t-RNA solutions with the addition of the optimal hydroxamic acid concentration. Furthermore, we assessed the antioxidant activity of the compound using DPPH-radical scavenging and β-carotene linoleic acid assays. Gel electrophoresis results demonstrated the compound's remarkable efficacy in preventing DNA damage. Collectively, all experimental evidence supports the conclusion that N-m-tolyl-4-chlorophenoxyaceto hydroxamic acid binds to ct-DNA/t-RNA through an intercalative mechanism, which is consistent with our molecular docking simulations.

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)—CNN1, CNN2, and CNN3—each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal hyperparameters for the proposed CNN models were determined using the Grid Search Optimization technique. This approach determines optimal values for architectural and fine-tuning hyperparameters, which is essential for learning. Rigorous evaluations of loss, accuracy, and confusion matrix thoroughly assessed the performance of the CNN models. Three datasets from the International Skin Imaging Collaboration (ISIC) Archive were utilized for the classification tasks. The primary objective of this study is to create a robust CNN system that can accurately diagnose skin lesions. Three separate CNN models were developed using the labeled ISIC Archive datasets. These models were designed to accurately detect skin lesions, assess the malignancy of the lesions, and classify the different types of lesions. The results indicate that the proposed CNN models possess robust capabilities in identifying and categorizing skin lesions, aiding healthcare professionals in making prompt and precise diagnostic judgments. This strategy presents an optimistic avenue for enhancing the diagnosis of skin cancer, which could potentially decrease avoidable fatalities and extend the lifespan of people diagnosed with skin cancer. This research enhances the discipline of biomedical image processing for skin lesion identification by utilizing the capabilities of DL algorithms.

Abstract Purpose To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique. Background Advanced deep learning methods are becoming more crucial in contemporary medical diagnostics, particularly for detecting intricate and uncommon neurological illnesses such as MBD. This rare neurodegenerative disorder, sometimes associated with persistent alcoholism, is characterized by the loss of myelin or tissue death in the corpus callosum. It poses significant diagnostic difficulties owing to its infrequency and the subtle signs it exhibits in its first stages, both clinically and on radiological scans. Methods The novel method of Variational Autoencoders (VAEs) in conjunction with attention mechanisms is used to identify MBD peculiar diseases accurately. VAEs are well-known for their proficiency in unsupervised learning and anomaly detection. They excel at analyzing extensive brain imaging datasets to uncover subtle patterns and abnormalities that traditional diagnostic approaches may overlook, especially those related to specific diseases. The use of attention mechanisms enhances this technique, enabling the model to concentrate on the most crucial elements of the imaging data, similar to the discerning observation of a skilled radiologist. Thus, we utilized the VAE with attention mechanisms in this study to detect MBD. Such a combination enables the prompt identification of MBD and assists in formulating more customized and efficient treatment strategies. Results A significant breakthrough in this field is the creation of a VAE equipped with attention mechanisms, which has shown outstanding performance by achieving accuracy rates of over 90% in accurately differentiating MBD from other neurodegenerative disorders. Conclusion This model, which underwent training using a diverse range of MRI images, has shown a notable level of sensitivity and specificity, significantly minimizing the frequency of false positive results and strengthening the confidence and dependability of these sophisticated automated diagnostic tools.

Abstract Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.

Abstract Nanoparticulate systems have the prospect of accounting for a new making of drug delivery systems. Nanotechnology is manifested to traverse the hurdle of both physical and biological sciences by implementing nanostructures indistinct fields of science, particularly in nano-based drug delivery. The low delivery efficiency of nanoparticles is a critical obstacle in the field of tumor diagnosis. Several nano-based drug delivery studies are focused on for tumor diagnosis. But, the nano-based drug delivery efficiency was not increased for tumor diagnosis. This work proposes a method called point biserial correlation symbiotic organism search nanoengineering-based drug delivery (PBC-SOSN). The objective and aim of the PBC-SOSN method is to achieve higher drug delivery efficiency and lesser drug delivery time for tumor diagnosis. The contribution of the PBC-SOSN is to optimized nanonengineering-based drug delivery with higher r drug delivery detection rate and smaller drug delivery error detection rate. Initially, raw data acquired from the nano-tumor dataset, and nano-drugs for glioblastoma dataset, overhead improved preprocessed samples are evolved using nano variational model decomposition-based preprocessing. After that, the preprocessed samples as input are subjected to variance analysis and point biserial correlation-based feature selection model. Finally, the preprocessed samples and features selected are subjected to symbiotic organism search nanoengineering (SOSN) to corroborate the objective. Based on these findings, point biserial correlation-based feature selection and a symbiotic organism search nanoengineering were tested for their modeling performance with a nano-tumor dataset and nano-drugs for glioblastoma dataset, finding the latter the better algorithm. Incorporated into the method is the potential to adjust the drug delivery detection rate and drug delivery error detection rate of the learned method based on selected features determined by nano variational model decomposition for efficient drug delivery.

Different degrees of natural radioactivity found in quartz can have negative consequences on health. Quartz vein along the investigated Abu Ramad area, Egypt, had its natural radioactivity assessed. The HPGe spectrometer was used to determine the role played by the radionuclides 238U, 232Th, and 40K in the gamma radiation that was emitted, and the results showed that these concentrations are 484.64 ± 288.4, 36.8 ± 13.1 and 772.2 ± 134.6 Bq kg−1 were higher than the corresponding reported global limits of 33, 45, and 412 Bq kg−1 for each radionuclide (238U, 232Th, and 40K). Among the radiological hazard parameters, the excess lifetime cancer risk (ELCR) is estimated and it's mean value of ELCR (1.2) is higher than the permissible limit of 0.00029. The relationship between the radionuclides and the associated radiological hazard characteristics was investigated based on multivariate statistical methods including Pearson correlation, principal component analysis (PCA), and hierarchical cluster analysis (HCA). According to statistical research, the radioactive risk of quartz is primarily caused by the 238U, 232Thand 40K. Finally, applying quartz to building materials would pose a significant risk to the public.

Abstract Colorectal cancer (CRC) stands as one of the most prevalent form of cancer globally, causing a significant number of deaths, surpassing 0.9 million in the year 2020. According to GLOBOCAN 2020, CRC ranks third in incidence and second in mortality in both males and females. Despite extensive studies over the years, there is still a need to establish novel therapeutic targets to enhance the patients’ survival rate in CRC. Nuclear receptors (NRs) are ligand-activated transcription factors (TFs) that regulate numerous essential biological processes such as differentiation, development, physiology, reproduction, and cellular metabolism. Dysregulation and anomalous expression of different NRs has led to multiple alterations, such as impaired signaling cascades, mutations, and epigenetic changes, leading to various diseases, including cancer. It has been observed that differential expression of various NRs might lead to the initiation and progression of CRC, and are correlated with poor survival outcomes in CRC patients. Despite numerous studies on the mechanism and role of NRs in this cancer, it remains of significant scientific interest primarily due to the diverse functions that various NRs exhibit in regulating key hallmarks of this cancer. Thus, modulating the expression of NRs with their agonists and antagonists, based on their expression levels, holds an immense prospect in the diagnosis, prognosis, and therapeutical modalities of CRC. In this review, we primarily focus on the role and mechanism of NRs in the pathogenesis of CRC and emphasized the significance of targeting these NRs using a variety of agents, which may represent a novel and effective strategy for the prevention and treatment of this cancer. Graphical Abstract

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Ovarian cancer (OVC) is one of the most common causes of cancer-related deaths in women worldwide. Despite advancements in detection and therapy, the prognosis of OVC remains poor due to late diagnosis and the lack of effective therapeutic options at advanced stages. Therefore, a better understanding of the biology underlying OVC is essential for the development of effective strategies for early detection and targeted therapies. Nuclear receptors (NRs) are a superfamily of 48 transcription factors that, upon binding to their specific ligand, play a vital role in regulating various cellular processes such as growth, development, metabolism, and homeostasis. Accumulating evidence from several studies has shown that their aberrant expression is associated with multiple human diseases. Numerous NRs have shown significant effects in the development of various cancers, including OVC. This review summarizes the recent findings on the role of NRs in OVC, as well as their potential as prognostic and therapeutic markers. Further, the basic structure and signaling mechanism of NRs have also been discussed briefly. Moreover, this review highlights their cellular and molecular mechanisms in chemoresistance and chemosensitization. Further, the clinical trials targeting NRs for the treatment of OVC have also been discussed.

Nanotechnology has emerged as a promising solution for tackling antibiotic resistance in monogastric animals, providing innovative methods to enhance animal health and well-being. This review explores the novel use of nanotechnology-based nanomaterials as substitutes for antibiotics in monogastric animals. With growing global concerns about antibiotic resistance and the need for sustainable practices in animal husbandry, nanotechnology offers a compelling avenue to address these challenges. The objectives of this review are to find out the potential of nanomaterials in improving animal health while reducing reliance on conventional antibiotics. We examine various forms of nanomaterials and their roles in promoting gut health and also emphasize fresh perspectives brought by integrating nanotechnology into animal healthcare. Additionally, we delve into the mechanisms underlying the antibacterial properties of nanomaterials and their effectiveness in combating microbial resistance. By shedding light on the transformative role of nanotechnology in animal production systems. This review contributes to our understanding of how nanotechnology can provide safer and more sustainable alternatives to antibiotics.

Cancer has become a burgeoning global healthcare concern marked by its exponential growth and significant economic ramifications. Though advancements in the treatment modalities have increased the overall survival and quality of life, there are no definite treatments for the advanced stages of this malady. Hence, understanding the diseases etiologies and the underlying molecular complexities, will usher in the development of innovative therapeutics. Recently, YAP/TAZ transcriptional regulation has been of immense interest due to their role in development, tissue homeostasis and oncogenic transformations. YAP/TAZ axis functions as coactivators within the Hippo signaling cascade, exerting pivotal influence on processes such as proliferation, regeneration, development, and tissue renewal. In cancer, YAP is overexpressed in multiple tumor types and is associated with cancer stem cell attributes, chemoresistance, and metastasis. Activation of YAP/TAZ mirrors the cellular ''social'' behavior, encompassing factors such as cell adhesion and the mechanical signals transmitted to the cell from tissue structure and the surrounding extracellular matrix. Therefore, it presents a significant vulnerability in the clogs of tumors that could provide a wide window of therapeutic effectiveness. Natural compounds have been utilized extensively as successful interventions in the management of diverse chronic illnesses, including cancer. Owing to their capacity to influence multiple genes and pathways, natural compounds exhibit significant potential either as adjuvant therapy or in combination with conventional treatment options. In this review, we delineate the signaling nexus of YAP/TAZ axis, and present natural compounds as an alternate strategy to target cancer.

Abstract The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Abstract Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg–Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.

Abstract Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.

Abstract Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights. Deep learning, a subset of artificial intelligence, holds significant promise in revolutionizing the detection and diagnosis of lung pathologies. By leveraging expansive datasets, deep learning algorithms autonomously discern intricate patterns and features within medical images, such as chest X-rays and CT scans. These algorithms exhibit an exceptional capacity to recognize subtle markers indicative of lung diseases. Yet, while their potential is evident, inherent limitations persist. The demand for abundant labeled data during training and the susceptibility to data biases challenge their accuracy. To address these formidable challenges, this research introduces a tailored computer-assisted system designed for the automatic retrieval of annotated medical images that share similar content. At its core lies an intelligent deep learning-based features extractor, adept at simplifying the retrieval of analogous images from an extensive chest radiograph database. The crux of our innovation rests upon the fusion of YOLOv5 and EfficientNet within the features extractor module. This strategic fusion synergizes YOLOv5's rapid and efficient object detection capabilities with EfficientNet's proficiency in combating noisy predictions. The result is a distinctive amalgamation that redefines the efficiency and accuracy of features extraction. Through rigorous experimentation conducted on an extensive and diverse dataset, our proposed solution decisively surpasses conventional methodologies. The model's achievement of a mean average precision of 0.488 with a threshold of 0.9 stands as a testament to its effectiveness, overshadowing the results of YOLOv5 + ResNet and EfficientDet, which achieved 0.234 and 0.257 respectively. Furthermore, our model demonstrates a marked precision improvement, attaining a value of 0.864 across all pathologies—a noteworthy leap of approximately 0.352 compared to YOLOv5 + ResNet and EfficientDet. This research presents a significant stride toward enhancing radiologists' workflow efficiency, offering a refined and proficient tool for retrieving analogous annotated medical images.

Abstract Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.

Abstract An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.

Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets.

Abstract The most important challenge faced in designing orthopedic devices is to control the leaching of ions from the substrate material, and to prevent biofilm formation. Accordingly, the surgical grade stainless steel (316L SS) was electrophoretically deposited with functional composition of biopolymers and bioceramics. The composite coating consisted of: Bioglass (BG), hydroxyapatite (HA), and lawsone, that were loaded into a polymeric matrix of Xanthan Dialdehyde/Chondroitin Sulfate (XDA/CS). The parameters and final composition for electrophoretic deposition were optimized through trial-and-error approach. The composite coating exhibited significant adhesion strength of “4B” (ASTM D3359) with the substrate, suitable wettability of contact angle 48°, and an optimum average surface roughness of 0.32 µm. Thus, promoting proliferation and attachment of bone-forming cells, transcription factors, and proteins. Fourier transformed infrared spectroscopic analysis revealed a strong polymeric network formation between XDA and CS. scanning electron microscopy and energy dispersive X-ray spectroscopy analysis displayed a homogenous surface with invariable dispersion of HA and BG particles. The adhesion, hydrant behavior, and topography of said coatings was optimal to design orthopedic implant devices. The said coatings exhibited a clear inhibition zone of 21.65 mm and 21.04 mm with no bacterial growth against Staphylococcus aureus (S. Aureus) and Escherichia coli (E. Coli) respectively, confirming the antibacterial potential. Furthermore, the crystals related to calcium (Ca) and HA were seen after 28 days of submersion in simulated body fluid. The corrosion current density, of the above-mentioned coating was minimal as compared to the bare 316L SS substrate. The results infer that XDA/CS/BG/HA/lawsone based composite coating can be a candidate to design coatings for orthopedic implant devices.

Abstract Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain. However, during a seizure, there may be a sudden increase in the electrical activity in one hemisphere of the brain, causing asymmetry in the EEG signal. In patients with epilepsy, interictal EEG may show asymmetric spikes or sharp waves, indicating the presence of epileptic activity. Therefore, the detection of symmetry/asymmetry in EEG signals can be used as a useful tool in the diagnosis and management of epilepsy. However, it should be noted that EEG findings should always be interpreted in conjunction with the patient's clinical history and other diagnostic tests. In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA). The DNN model learns the characteristics of the EEG signals through nine different statistical and Hjorth parameters extracted from various levels of decomposed signals obtained by using the Stationary Wavelet Transform. Next, the extracted features were reduced using the BDFA which helps to train DNN faster and improve its performance. The results show that the extracted features help to differentiate the normal, interictal, and ictal signals effectively with 100% accuracy, sensitivity, specificity, and F1 score with a 13% selected feature subset when compared to the existing approaches.

Abstract The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.

SKP2 (S-phase kinase-associated protein 2) is a member of the F-box family of substrate-recognition subunits in the SCF ubiquitin-protein ligase complexes. It is associated with ubiquitin-mediated degradation in the mammalian cell cycle components and other target proteins involved in cell cycle progression, signal transduction, and transcription. Being an oncogene in solid tumors and hematological malignancies, it is frequently associated with drug resistance and poor disease outcomes. In the current review, we discussed the novel role of SKP2 in different hematological malignancies. Further, we performed a limited in-silico analysis to establish the involvement of SKP2 in a few publicly available cancer datasets. Interestingly, our study identified Skp2 expression to be altered in a cancer-specific manner. While it was found to be overexpressed in several cancer types, few cancer showed a down-regulation in SKP2. Our review provides evidence for developing novel SKP2 inhibitors in hematological malignancies. We also investigated the effect of SKP2 status on survival and disease progression. In addition, the role of miRNA and its associated families in regulating Skp2 expression was explored. Subsequently, we predicted common miRNAs against Skp2 genes by using miRNA-predication tools. Finally, we discussed current approaches and future prospective approaches to target the Skp2 gene by using different drugs and miRNA-based therapeutics applications in translational research.

Background and Objectives: Screening for type 2 diabetes mellitus (DM2) aims to identify asymptomatic individuals who may be at a higher risk, allowing proactive interventions. The objective of this study was to predict the incidence of DM2 and prediabetes in the Saudi population over the next five years. Materials and Methods: The study was conducted in the Aseer region through August 2023 using a cross-sectional survey for data collection. A multistage stratified random sampling technique was adopted, and data were collected through face-to-face interviews using the validated Arabic version of the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK). Results: In total, 652 individuals were included in the study. Their mean age was 32.0 ± 12.0 years; 53.8% were male, 89.6% were from urban areas, and 55.8% were single. There were statistically significant differences between males and females in AUSDRISK items, including age, history of high blood glucose, use of medications for high blood pressure, smoking, physical activity, and measurements of waist circumference (p < 0.05). Based on AUSDRISK scores, 46.2% of the included participants were predicted to develop impaired glucose tolerance within the coming five years (65.8% among females vs. 23.6%), and 21.9% were predicted to develop DM2 (35.6% among males vs. 6.0% among females); this difference was statistically significant (p = 0.0001). Conclusions: Urgent public health action is required to prevent the increasing epidemic of DM2 in Saudi Arabia.

Abstract Cancer stem cells (CSCs) constitute a minority cell population characterized by unbounded proliferative potential in both solid and hematological cancers. Despite sharing key stem cell attributes, CSCs possess unique traits, including the initiation and propagation of tumors and resistance to conventional therapies. The purpose of this review is to delve into the origins and fundamental characteristics of CSCs, emphasizing their role in tumor growth and metastasis. The focus extends to unraveling cellular signaling pathways driving oncogenic processes and understanding aberrant cellular crosstalk crucial for targeted cancer therapies. Beginning with an exploration of CSC properties and behavior, we progress to dissecting the cellular signaling network that fuels oncogenic pathways. The discussion spans the inception of CSCs, their survival strategies, and adaptation to new environments. We then transit to recent therapeutic advancements targeting CSCs, culminating in an exploration for precise therapeutic targeting. This review henceforth, underscores the vital significance of comprehending CSCs in cancer progression and treatment resistance. By unraveling the complex signaling pathways and survival mechanisms unique to CSCs, it paves the way for targeted therapeutic strategies that hold immense promise in enhancing cancer treatment efficacy while minimizing collateral damage.

Globally, there is an increase in a number of bone disorders including osteoarthritis (OA), osteomyelitis, bone cancer, and etc., which has led to a demand for bone tissue regeneration. In order to take use of the osteogenic potential of natural herbs, mesoporous bioactive glass nanoparticles (MBGNs) have the ability to deliver therapeutically active chemicals locally. MBGNs influence bioactivity and osteointegration of materials making them suitable for bone tissue engineering (BTE). In the present study, we developed Peganum Harmala (P. harmala) loaded MBGNs (PH-MBGNs) synthesized via modified Stöber process. The MBGNs were analyzed in terms of surface morphology, chemical make-up, amorphous nature, chemical interaction, pore size, and surface area before and after loading with P. harmala. A burst release of drug from PH-MBGNs was observed within 8 h immersion in phosphate buffer saline (PBS). PH-MBGNs effectively prevented Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) from spreading. Furthermore, PH-MBGNs developed a hydroxyapatite (HA) layer in the presence of simulated body fluid (SBF) after 21 days, which confirmed the in-vitro bioactivity of MBGNs. In conclusion, PH-MBGNs synthesized in this work are potential candidate for scaffolding or a constituent in the coatings for BTE applications.

The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network—Capsule Network (CapsNet)—and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model’s remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.

Head and neck cancers (HNC) encompass a broad spectrum of neoplastic disorders characterized by significant morbidity and mortality. While contemporary therapeutic interventions offer promise, challenges persist due to tumor recurrence and metastasis. Central to HNC pathogenesis is the aberration in numerous signaling cascades. Prominently, the Wnt signaling pathway has been critically implicated in the etiology of HNC, as supported by a plethora of research. Equally important, variations in the expression of non-coding RNAs (ncRNAs) have been identified to modulate key cancer phenotypes such as cellular proliferation, epithelial-mesenchymal transition, metastatic potential, recurrence, and treatment resistance. This review aims to provide an exhaustive insight into the multifaceted influence of ncRNAs on HNC, with specific emphasis on their interactions with the Wnt/β-catenin (WBC) signaling axis. We further delineate the effect of ncRNAs in either exacerbating or attenuating HNC progression via interference with WBC signaling. An overview of the mechanisms underlying the interplay between ncRNAs and WBC signaling is also presented. In addition, we described the potential of various ncRNAs in enhancing the efficacy of chemotherapeutic and radiotherapeutic modalities. In summary, this assessment posits the potential of ncRNAs as therapeutic agents targeting the WBC signaling pathway in HNC management.

With the rapid development of nanotechnology, the study of nanocomposites as electrode materials has significantly enhanced the scope of research towards energy storage applications. Exploring electrode materials with superior electrochemical properties is still a challenge for high-performance supercapacitors. In the present research article, we prepared a novel nanocomposite of tungsten trioxide nanoparticles grown over supported graphene oxide sheets and embedded with a poly(3,4-ethylenedioxythiophene) matrix to maximize its electrical double layer capacitance. The extensive characterization shows that the poly(3,4-ethylenedioxythiophene) matrix was homogeneously dispersed throughout the surface of the tungsten trioxide–graphene oxide. The poly(3,4-ethylenedioxythiophene)@tungsten trioxide–graphene oxide exhibits a higher specific capacitance of 478.3 F·g−1 at 10 mV·s−1 as compared to tungsten trioxide–graphene oxide (345.3 F·g−1). The retention capacity of 92.1% up to 5000 cycles at 0.1 A·g−1 shows that this ternary nanocomposite electrode also exhibits good cycling stability. The poly(3,4-ethylenedioxythiophene)@tungsten trioxide–graphene oxide energy density and power densities are observed to be 54.2 Wh·kg−1 and 971 W·kg−1. The poly(3,4-ethylenedioxythiophene)@tungsten trioxide–graphene oxide has been shown to be a superior anode material in supercapacitors because of the synergistic interaction of the poly(3,4-ethylenedioxythiophene) matrix and the tungsten trioxide–graphene oxide surface. These advantages reveal that the poly(3,4-ethylenedioxythiophene)@tungsten trioxide–graphene oxide electrode can be a promising electroactive material for supercapacitor applications.

Background: The emergence and spread of SARS-CoV-2 throughout the world has created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none is available clinically. Thus, the drug repurposing approach may help to overcome the current pandemic. Methods: The main protease (Mpro) of SARS-CoV-2 is crucial for cleaving nascent polypeptide chains. Here, FDA-approved antiviral and anti-infection drugs were screened by high-throughput virtual screening (HTVS) followed by re-docking with standard-precision (SP) and extra-precision (XP) molecular docking. The most potent drug's binding was further validated by free energy calculations (Prime/MM-GBSA) and molecular dynamics (MD) simulation. Results: Out of 1397 potential drugs, 157 showed considerable affinity toward Mpro. After HTVS, SP, and XP molecular docking, four high-affinity lead drugs (Iodixanol, Amikacin, Troxerutin, and Rutin) with docking energies −10.629 to −11.776 kcal/mol range were identified. Among them, Amikacin exhibited the lowest Prime/MM-GBSA energy (−73.800 kcal/mol). It led us to evaluate other aminoglycosides (Neomycin, Paramomycin, Gentamycin, Streptomycin, and Tobramycin) against Mpro. All aminoglycosides were bound to the substrate-binding site of Mpro and interacted with crucial residues. Altogether, Amikacin was found to be the most potent inhibitor of Mpro. MD simulations of the Amikacin-Mpro complex suggested the formation of a complex stabilized by hydrogen bonds, salt bridges, and van der Waals interactions. Conclusion: Aminoglycosides may serve as a scaffold to design potent drug molecules against COVID-19. However, further validation by in vitro and in vivo studies is required before using aminoglycosides as an anti-COVID-19 agent.

Heterocyclic compounds are significant lead drug candidates based on their various structure–activity relationships (SAR), and their use in pharmaceutics is constantly developing. Benzimidazole (BnZ) is synthesized by a condensation reaction between benzene and imidazole. The BnZ structure consists of two nitrogen atoms embedded in a five-membered imide ring which is fused with a benzene ring. This review examines the conventional and green synthesis of metallic and non-metallic BnZ and their derivatives, which have several potential SARs, along with a wide range of pharmacological properties, including anti-cancer, anti-inflammatory, anti-microbial, anti-tubercular, and anti-protozoal properties. These compounds have been proven by pharmacological investigations to be efficient against different strains of microbes. Therefore, in this review, the structural variations of BnZ are listed along with various applications, predominantly related to their biological activities.

The present study was conducted to fabricate and characterize mucilage-based polymeric networks of Aloe vera for controlled drug release. Aloe vera mucilage was used to develop a polymeric network via the free-radical polymerization method using potassium persulphate as the initiator, N′ N′-Methylene bisacrylamide as the crosslinker, and acrylamide as the monomer. Using varying concentrations of Aloe vera mucilage, crosslinker, and monomer, we developed different formulations. Swelling studies were conducted at pH 1.2 and 7.4. Concentrations of polymer, monomer, and crosslinker were optimized as a function of swelling. Porosity and gel content were calculated for all samples. FTIR, SEM, XRD, TGA, and DSC studies were conducted for the characterization of polymeric networks. Thiocolchicoside was used as a model drug to study the in vitro release in acidic and alkaline pH. Various kinetics models were applied by using a DD solver. Increasing content of monomer and crosslinker swelling, porosity, and drug release decreased while gel content increased. An increase in Aloe vera mucilage concentration promotes swelling, porosity, and drug release of the polymeric network but decreases gel content. The FTIR study confirmed the formation of crosslinked networks. SEM indicated that the polymeric network had a porous structure. DSC and XRD studies indicated the entrapment of drugs inside the polymeric networks in amorphous form. The analytical method was validated according to ICH guidelines in terms of linearity, range, LOD, LOQ, accuracy, precision, and robustness. Analysis of drug release mechanism revealed Fickian behavior of all formulations. All these results indicated that the M1 formulation was considered to be the best polymeric network formulation in terms of sustaining drug release patterns.

Composite structures have drawn growing attention for their outstanding characteristics benefiting from the combination of multi materials. This paper aims to investigate the crushing performance of a hybrid system involving an outer thin-walled tube made of carbon fibre-reinforced plastic (CFRP), a medial polyurethane (PU) foam and an inner thin-walled tube made of aluminium alloy (Al). The elastoplastic model of Al, the continuous damage model of CFRP and the compressible foam model of PU were established with assistance of universal material tests. The finite element model of the composite structure, namely CFRP-PU-Al, was validated by the experimental study. Based upon the reliable numerical modelling, the parametric study was carried out for exploring the effects of the wall thickness, diameter and ply orientation of the outer CFRP tube, namely CFRP-O, on the crushing behaviours of the composite structure. It was found that increasing the diameter or wall thickness of CFRP-O was helpful to increase the mean crushing force and energy absorption of CFRP-PU-Al, however, the overall specific energy absorption of the composite sandwich structure was limited by the characteristics of constitutive materials. Compared with the diameter, the wall thickness had a higher impact on the energy absorption characteristics, and increasing the diameter may decrease the specific energy absorption due to the increase of the overall structural mass. In terms of the ply orientation, the composite structure with stacking sequence of [0°/45°]3 possessed higher mean crushing force and energy absorption than the structure with stacking sequence of [0°/90°]3 under axial compression in this study.

Novel transparent glasses and glass-ceramics were fabricated by using melt-quenching technique with the following composition: TeO2–WO3–Nb2O5–KF–La2O3 doped with Tm2O3. Different properties and physical parameters of these glasses were investigated. As the concentration of Tm3+ ions increases, the linear refractive index increases while the optical energy gap decreases. With high Tm3+ ion concentration creates nucleation in the glass matrix this was investigated by x-ray diffraction. The Judd-Ofelt parameters used to calculate the electric and magnetic dipole transition probabilities, branching ratios, and radiative lifetimes of various excited states of Tm3+-doped parent glass. The emission cross section values for the 3F4→3H6 transition computed by using the McCumber hypothesis. The stimulated emission cross section σeFL(λ) and gain values of fabricated glass increase with Tm3+ ions. The spectroscopic quality and figure of merit (FOM) gain (σeFL(λ)×τR) factors were evaluated for the prepared transparent glass. Furthermore, the produced glasses were demonstrated to have low OH content and they have high fluoride ion content. These important properties of the glasses and glass-ceramics make them suitable for use in photonic applications.

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness. Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. Transfer learning has been applied to diabetic retinopathy classification with promising results. Pre-trained models, such as convolutional neural networks (CNNs), can be fine-tuned on a new dataset of retinal images to classify diabetic retinopathy. This manuscript aims at developing an automated scheme for diagnosing and grading DR and HR. The retinal image classification has been performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology has been proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques have been applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images has been performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification have been performed using transfer learning models. The segmented images were then classified using Modified ResNet 101 architecture. The performance for enhanced images has been evaluated on PSNR and shows better results as compared to the existing literature. The network is trained on more than 6000 images from MESSIDOR and ODIR datasets and achieves the classification accuracy of 98.72%.