Natural compounds targeting YAP/TAZ axis in cancer: Current state of art and challenges

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.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer

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%.

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis

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.

Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery

Nanotechnology has emerged as a promising frontier in revolutionizing the early diagnosis and surgical management of gastric cancers. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high surgical and pharmacological therapy recurrence rates. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for detecting and treating cancer. Considering the limitations of GIC MRI and endoscopy and the complexity of gastric surgery, the early diagnosis and prompt treatment of gastric illnesses by nanotechnology has been a promising development. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. In this research, the boosting technique of machine learning was utilized to capture nonlinear interactions between a large number of input variables and outputs by using XGBoost and RNN-CNN as a classification method. The research sample included 350 patients, comprising 200 males and 150 females. The patients' mean ± SD was 50.34 ± 13.04 with a mean age of 50.34 ± 13.04. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.034), distant metastasis (P = 0.004), and tumor stage (P = 0.014) were shown to have a statistically significant link with GC patient survival. AUC was 93.54%, Accuracy 93.54%, F1-score 93.57%, Precision 93.65%, and Recall 93.87% when analyzing stomach pictures. Integrating nanotechnology with advanced machine learning techniques holds promise for improving the diagnosis and treatment of gastric cancer, providing new avenues for precision medicine and better patient outcomes.

SKping cell cycle regulation: role of ubiquitin ligase SKP2 in hematological malignancies

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.

Dynamic interplay of nuclear receptors in tumor cell plasticity and drug resistance: Shifting gears in malignant transformations and applications in cancer therapeutics

Recent advances have brought forth the complex interplay between tumor cell plasticity and its consequential impact on drug resistance and tumor recurrence, both of which are critical determinants of neoplastic progression and therapeutic efficacy. Various forms of tumor cell plasticity, instrumental in facilitating neoplastic cells to develop drug resistance, include epithelial-mesenchymal transition (EMT) alternatively termed epithelial-mesenchymal plasticity, the acquisition of cancer stem cell (CSC) attributes, and transdifferentiation into diverse cell lineages. Nuclear receptors (NRs) are a superfamily of transcription factors (TFs) that play an essential role in regulating a multitude of cellular processes, including cell proliferation, differentiation, and apoptosis. NRs have been implicated to play a critical role in modulating gene expression associated with tumor cell plasticity and drug resistance. This review aims to provide a comprehensive overview of the current understanding of how NRs regulate these key aspects of cancer biology. We discuss the diverse mechanisms through which NRs influence tumor cell plasticity, including EMT, stemness, and metastasis. Further, we explore the intricate relationship between NRs and drug resistance, highlighting the impact of NR signaling on chemotherapy, radiotherapy and targeted therapies. We also discuss the emerging therapeutic strategies targeting NRs to overcome tumor cell plasticity and drug resistance. This review also provides valuable insights into the current clinical trials that involve agonists or antagonists of NRs modulating various aspects of tumor cell plasticity, thereby delineating the potential of NRs as therapeutic targets for improved cancer treatment outcomes.

Lipid metabolism and its implications in tumor cell plasticity and drug resistance: what we learned thus far?

Metabolic reprogramming, a hallmark of cancer, allows cancer cells to adapt to their specific energy needs. The Warburg effect benefits cancer cells in both hypoxic and normoxic conditions and is a well-studied reprogramming of metabolism in cancer. Interestingly, the alteration of other metabolic pathways, especially lipid metabolism has also grabbed the attention of scientists worldwide. Lipids, primarily consisting of fatty acids, phospholipids and cholesterol, play essential roles as structural component of cell membrane, signalling molecule and energy reserves. This reprogramming primarily involves aberrations in the uptake, synthesis and breakdown of lipids, thereby contributing to the survival, proliferation, invasion, migration and metastasis of cancer cells. The development of resistance to the existing treatment modalities poses a major challenge in the field of cancer therapy. Also, the plasticity of tumor cells was reported to be a contributing factor for the development of resistance. A number of studies implicated that dysregulated lipid metabolism contributes to tumor cell plasticity and associated drug resistance. Therefore, it is important to understand the intricate reprogramming of lipid metabolism in cancer cells. In this review, we mainly focused on the implication of disturbed lipid metabolic events on inducing tumor cell plasticity-mediated drug resistance. In addition, we also discussed the concept of lipid peroxidation and its crucial role in phenotypic switching and resistance to ferroptosis in cancer cells. Elucidating the relationship between lipid metabolism, tumor cell plasticity and emergence of resistance will open new opportunities to develop innovative strategies and combinatorial approaches for the treatment of cancer.

Delineating the role of nuclear receptors in colorectal cancer, a focused review

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.

Exploring the nexus of nuclear receptors in hematological malignancies

Hematological malignancies (HM) represent a subset of neoplasms affecting the blood, bone marrow, and lymphatic systems, categorized primarily into leukemia, lymphoma, and multiple myeloma. Their prognosis varies considerably, with a frequent risk of relapse despite ongoing treatments. While contemporary therapeutic strategies have extended overall patient survival, they do not offer cures for advanced stages and often lead to challenges such as acquisition of drug resistance, recurrence, and severe side effects. The need for innovative therapeutic targets is vital to elevate both survival rates and patients' quality of life. Recent research has pivoted towards nuclear receptors (NRs) due to their role in modulating tumor cell characteristics including uncontrolled proliferation, differentiation, apoptosis evasion, invasion and migration. Existing evidence emphasizes NRs' critical role in HM. The regulation of NR expression through agonists, antagonists, or selective modulators, contingent upon their levels, offers promising clinical implications in HM management. Moreover, several anticancer agents targeting NRs have been approved by the Food and Drug Administration (FDA). This review highlights the integral function of NRs in HM's pathophysiology and the potential benefits of therapeutically targeting these receptors, suggesting a prospective avenue for more efficient therapeutic interventions against HM.

Spinal Cord Termination and Lumbar Puncture Safety in Spinal Deformities

Background Lumbar puncture, a common diagnostic and therapeutic procedure, is performed regardless of individual spinal alignment variations. However, the impact of kyphosis, scoliosis, and kyphoscoliosis on spinal cord termination level and lumbar puncture safety remains unclear. Objectives This study aimed to determine if the termination level of the spinal cord is different in individuals with spinal deformities and to assess the necessity of routine neuroimaging for safe lumbar puncture localization. Study design and settings This single-center retrospective study was conducted at a university hospital using patients' electronic medical records. The study was focused on patients diagnosed with kyphosis, scoliosis, or kyphoscoliosis using spinal magnetic resonance imaging from January 2010 to December 2022. Participants We evaluated 240 patients: 120 with diagnosed spinal deformities (kyphosis, scoliosis, or kyphoscoliosis) and 120 without deformities, categorized by sex (deformed: 92 females, 28 males; non-deformed: 72 females, 48 males). Patients with spinal trauma, bleeding, or tumors were excluded. Results No statistically significant correlation was found between spinal deformities and spinal cord termination, with L1 remaining the most common endpoint in all groups. Conclusion Routine neuroimaging prior to lumbar puncture in patients with spinal deformities was not associated with a safer procedure due to no observed impact on the termination level of the spinal cord.

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

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.

Exploring the therapeutic potential of layered double hydroxides and transition metal dichalcogenides through the convergence of rheumatology and nanotechnology using generative adversarial network

Two-dimensional Layered double hydroxides (LDHs) are highly used in the biomedical domain due to their biocompatibility, biodegradability, controlled drug loading and release capabilities, and improved cellular permeability. The interaction of LDHs with biological systems could facilitate targeted drug delivery and make them an attractive option for various biomedical applications. Rheumatoid Arthritis (RA) requires targeted drug delivery for optimum therapeutic outcomes. In this study, stacked double hydroxide nanocomposites with dextran sulphate modification (LDH-DS) were developed while exhibiting both targeting and pH-sensitivity for rheumatological conditions. This research examines the loading, release kinetics, and efficiency of the therapeutics of interest in the LDH-based drug delivery system. The mean size of LDH-DS particles (300.1 ± 8.12 nm) is -12.11 ± 0.4 mV. The encapsulation efficiency was 48.52%, and the loading efficacy was 16.81%. In vitro release tests indicate that the drug's discharge is modified more rapidly in PBS at pH 5.4 compared to pH 5.6, which later reached 7.3, showing the case sensitivity to pH. A generative adversarial network (GAN) is used to analyze the drug delivery system in rheumatology. The GAN model achieved high accuracy and classification rates of 99.3% and 99.0%, respectively, and a validity of 99.5%. The second and third administrations resulted in a significant change with p-values of 0.001 and 0.05, respectively. This investigation unequivocally demonstrated that LDH functions as a biocompatible drug delivery matrix, significantly improving delivery effectiveness.

Harnessing Sulforaphane Potential as a Chemosensitizing Agent: A Comprehensive Review

Recent advances in oncological research have highlighted the potential of naturally derived compounds in cancer prevention and treatment. Notably, sulforaphane (SFN), an isothiocyanate derived from cruciferous vegetables including broccoli and cabbage, has exhibited potent chemosensitizing capabilities across diverse cancer types of bone, brain, breast, lung, skin, etc. Chemosensitization refers to the enhancement of cancer cell sensitivity to chemotherapy agents, counteracting the chemoresistance often developed by tumor cells. Mechanistically, SFN orchestrates this sensitization by modulating an array of cellular signaling pathways (e.g., Akt/mTOR, NF-κB, Wnt/β-catenin), and regulating the expression and activity of pivotal genes, proteins, and enzymes (e.g., p53, p21, survivin, Bcl-2, caspases). When combined with conventional chemotherapeutic agents, SFN synergistically inhibits cancer cell proliferation, invasion, migration, and metastasis while potentiating drug-induced apoptosis. This positions SFN as a potential adjunct in cancer therapy to augment the efficacy of standard treatments. Ongoing preclinical and clinical investigations aim to further delineate the therapeutic potential of SFN in oncology. This review illuminates the multifaceted role of this phytochemical, emphasizing its potential to enhance the therapeutic efficacy of anti-cancer agents, suggesting its prospective contributions to cancer chemosensitization and management.

Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application

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.

Radiation attenuation of boro-tellurite glasses for efficient shielding applications

The borotellurite glasses whose chemical structure is (29.5-0.4x)CaO + 10CaF2 + (60-0.6x)B2O3 + xTeO2+ 0.5Yb2O3 (where x=10, 16, 22, 31, and 54 % mole. represent TCCBY1-TCCBY5, respectively) are Pb-free, thermally stable, and transparent glasses with attractive optical features for technological applications. The gamma-photons, electrons, protons, neutrons, carbon ions, fast neutrons, and fast neutron interaction parameters of these glasses are presented in this study to better understand the role of TeO2 in influencing their radiation shielding properties and radiation protection applications. The photon mass attenuation coefficient was evaluated by XCOM computation and simulation using the FLUKA code. The FLUKA code was also used to evaluate the mass stopping powers of the charged radiations, while neutrons' cross sections were evaluated using standard expressions. For 0.015 MeV-15 MeV photons, the mass attenuation coefficients of the glasses fell from 17.9499 to 0.0246 cm2/g for TCCBY1, 20.5628 to 0.0263 cm2/g for TCCBY2, 23.2756 to 0.079 cm2/g for TCCBY3, 26.7487 to 0.0298 cm2/g for TCCBY4, and 33.3591 to 0.0335 cm2/g for TCCBY5. The photon half-value layer at 15 keV is reduced by about 19.57%, 32.68%, 48.84%, and 63.89% when the TeO2 content increases from 10 mol to 16, 22, 31, and 54 mol, respectively. TeO2 was found to suppress photon buildup in the glasses. The mass stopping powers of charged radiation increased as glass density decreased. The addition of TeO2 into the glass structure increased the ability of the TCCBY glass to absorb fast neutrons by up to 54 % mole. The gamma radiation and fast neutron moderating ability of TCCBY5 glass compared to common shields and other materials is exceptional. The glass is recommended for the design of Pb-free, transparent, and efficient radiation protection structures.

Application of lipid and polymeric-based nanoparticles for treatment of inner ear infections via XGBoost

Taking hearing loss as a prevalent sensory disorder, the restricted permeability of blood flow and the blood-labyrinth barrier in the inner ear pose significant challenges to transporting drugs to the inner ear tissues. The current options for hear loss consist of cochlear surgery, medication, and hearing devices. There are some restrictions to the conventional drug delivery methods to treat inner ear illnesses, however, different smart nanoparticles, including inorganic-based nanoparticles, have been presented to regulate drug administration, enhance the targeting of particular cells, and decrease systemic adverse effects. Zinc oxide nanoparticles possess distinct characteristics that facilitate accurate drug delivery, improved targeting of specific cells, and minimized systemic adverse effects. Zinc oxide nanoparticles was studied for targeted delivery and controlled release of therapeutic drugs within specific cells. XGBoost model is used on the Wideband Absorbance Immittance (WAI) measuring test after cochlear surgery. There were 90 middle ear effusion samples (ages = 1-10 years, mean = 34.9 months) had chronic middle ear effusion for four months and verified effusion for seven weeks. In this research, 400 sets underwent wideband absorbance imaging (WAI) to assess inner ear performance after surgery. Among them, 60 patients had effusion Otitis Media with Effusion (OME), while 30 ones had normal ears (control). OME ears showed significantly lower absorbance at 250, 500, and 1000 Hz than controls (p < 0.001). Absorbance thresholds >0.252 (1000 Hz) and >0.330 (2000 Hz) predicted a favorable prognosis (p < 0.05, odds ratio: 6). It means that cochlear surgery and WAI showed high function in diagnosis and treatment of inner ear infections. Regarding the R2 0.899 and RMSE 1.223, XGBoost shows excellent specificity and sensitivity for categorizing ears as having effusions absent or present or partial or complete flows present, with areas under the curve (1-0.944).

Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach

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.

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

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.

Network of Extracellular Traps in the Pathogenesis of Sterile Chronic Inflammatory Diseases: Role of Oxidative Stress and Potential Clinical Applications

Significance: Extracellular traps (ETs) represent structured frameworks that comprised DNA embellished with histones and granular proteins extruded by immune cells in response to various stimuli. Immune cells contribute to adverse effects of chronic inflammation via ET generation, promoting the release of nuclear chromatin, reactive oxygen species (ROS), and bioactive proteins into the extracellular matrix. Recent Advances: The occurrence of ET formation has been documented across diverse immune cell types. The excessive production of ROS during the activation of these cells has the potential to initiate substantial DNA damage, culminating in chromosome decondensation. The inflammatory microenvironment fosters ROS and ET generation, impacting tissue microenvironment remodeling. Recent studies reveal ET involvement in sustaining persistent inflammation, promoting angiogenesis, and initiating thrombotic processes. Critical Issues: This review elucidates ET participation in chronic inflammatory disease etiology, detailing ROS-dependent and ROS-independent ET formation mechanisms and their contextual manifestations. It discusses diverse immune cell-derived ETs in the inflammatory milieu and their responses to therapies. Furthermore, the review emphasizes the significance of ETs as potential biomarkers and envisions prophylactic strategies against ET-associated chronic inflammation. Future Directions: Subsequent investigations are warranted to uncover the intricate mechanisms governing the resolution of inflammation through ETs in normal physiological processes. Moreover, a comprehensive understanding of the aberrant pathways driving ET formation in persistent inflammation is imperative. Prospective research endeavors should focus on executing expansive clinical studies to discern the involvement of ETs in both the diagnostic and prognostic facets of inflammatory diseases, thereby shedding light on their prospective utility as biomarkers.

Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm

The purpose of this paper is to analyze the heat transfer behavior of the electromagnetic 3D micropolar tri-hybrid nanofluid flow of a solar radiative slendering sheet with non-Fourier heat flux model. The conversion of solar radiation into thermal energy is an area of significant interest as the demand for renewable heat and power continues to grow. Due to their enhanced ability to promote heat transmission, nanofluids can significantly contribute to enhancing the efficiency of solar-thermal systems. The combination of silicon oil-based silicon (Si), magnesium oxide (MgO), and titanium (Ti) nanofluids has attracted attention for their ability to improve the performance of solar-thermal systems. The present study discloses a new approach for intelligent numerical computing solving, which utilizes an MLP feed-forward back-propagation ANN and the Levenberg-Marquard algorithm. The collection of data was conducted for the purpose of testing, certifying, and training the ANN model. The Bvp4c solver in MATLAB is utilized to solve the nonlinear equations governing the momentum, temperature, skin-friction coefficient, and Nusselt number. The characteristics of numerous dimensionless parameters such as porosity parameter [Formula: see text], vortex viscosity parameter [Formula: see text], electric field parameter [Formula: see text], thermal relaxation time [Formula: see text], heat source/sink parameter, [Formula: see text] thermal radiation parameter [Formula: see text], temperature ratio parameter [Formula: see text],nanoparticle volume fraction [Formula: see text] on Si + MgO + Ti/silicon oil micropolar tri-hybrid nanofluida are analyzed. The ANN model engages in a process of data selection, network construction, training, and evaluation of its effectiveness through the utilization of mean square error. Tables and graphs are used to show how essential parameters affect fluid transport properties. The velocity profile is decreased by higher values of the porosity parameter, whereas the temperature profile is increased. The temperature profile is inversely proportional to higher values of the electric field parameter. The micro-rotation profiles reduced by expanding values vortex viscosity parameter. It has been determined that entropy generation and Bejan number intensifications for enlarged nanoparticle volume fraction.

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.

Synthesis and characterization of mesoporous bioactive glass nanoparticles loaded with peganum harmala for bone tissue engineering

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.

Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers

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.

A study on the effect of bioactive glass and hydroxyapatite-loaded Xanthan dialdehyde-based composite coatings for potential orthopedic applications

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.

EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

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.

Cobalt Ferrite Surface-Modified Carbon Nanotube Fibers as an Efficient and Flexible Electrode for Overall Electrochemical Water Splitting Reactions

One of the most practical and environmentally friendly ways to deal with the energy crises and global warming is to produce hydrogen as clean fuel by splitting water. The central obstacle for electrochemical water splitting is the use of expensive metal-based catalysts. For electrocatalytic hydrogen production, it is essential to fabricate an efficient catalyst for the counterpart oxygen evolution reaction (OER), which is a four-electron-transfer sluggish process. Here in this study, we have successfully fabricated cobalt-based ferrite nanoparticles over the surface of carbon nanotube fiber (CNTF) that was utilized as flexible anode materials for the OER and overall electrochemical water splitting reactions. Scanning electron microscopy images with elemental mapping showed the growth of nanoparticles over CNTF, while electrochemical characterization exhibited excellent electrocatalytic performance. Linear sweep voltammetry revealed the reduced overpotential value (260 mV@η10mAcm-2) with a small Tafel slope of 149 mV dec-1. Boosted electrochemical double layer capacitance (0.87 mF cm-2) for the modified electrode also reflects the higher surface area as compared to pristine CNTF (Cdl = 0.022 mF cm-2). Charge transfer resistance for the surface-modified CNTF showed the lower diameter in the Nyquist plot and was consequently associated with the better Faradaic process at the electrode/electrolyte interface. Overall, the as-fabricated electrode could be a promising alternative for the efficient electrochemical water splitting reaction as compared to expensive metal-based electrocatalysts.

Crosstalk between Non-Coding RNAs and Wnt/β-Catenin Signaling in Head and Neck Cancer: Identification of Novel Biomarkers and Therapeutic Agents

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.

Multimodal hybrid convolutional neural network based brain tumor grade classification

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.

Theoretical insights into the structural, optoelectronic, thermoelectric, and thermodynamic behavior of novel quaternary LiZrCoX (X = Ge, Sn) compounds based on first-principles study

The structural, magnetic, electronic, elastic, vibrational, optical, thermodynamic as well as thermoelectric properties of newly predicted quaternary LiZrCoX (X = Ge, Sn) Heusler compounds are evaluated intricately with the aid of ab initio techniques developed under the framework of density functional theory. The computed structural properties are found to be in tandem with the existing analogous theoretical and experimental facts. Structural optimization has been carried out in three different structural arrangements, i.e., Type-1, Type-2, and Type-3. Further analysis of the optimization curves reveals that the Type-3 phase, which has the least amount of energy, is the most stable structure for the compounds under consideration. The tabulated cohesive energy and formation energy of these compounds depict their chemical as well as thermodynamic stability. The absence of negative phonon frequencies in the phonon band spectrum of the studied compounds depicts their dynamic stability. Similarly, the tabulated second-order elastic constants (Cij) and the linked elastic moduli show their stability in the cubic phase. The calculated value of Pugh's ratio and Cauchy pressure reveal that LiZrCoGe is brittle whereas LiZrCoSn is ductile. Additionally, the optical characteristics of the compounds are studied in terms of the dielectric function, refractive index, extinction coefficient, absorption coefficient, reflectivity, energy loss function, and optical conductivity. The obtained high value of power factor and figure of merit of the studied lithium-based quaternary compounds predict good thermoelectric behavior in these compounds. Thus, LiZrCoX (X = Ge, Sn) compounds can therefore be used to create innovative and intriguing thermoelectric materials as well as optoelectronic and energy-harvesting equipment.

Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach

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.

TVFx - CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique

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.

Exosomal noncoding RNA-mediated spatiotemporal regulation of lipid metabolism: Implications in immune evasion and chronic inflammation

The hallmark of chronic inflammatory diseases is immune evasion. Successful immune evasion involves numerous mechanisms to suppress both adaptive and innate immune responses. Either direct contact between cells or paracrine signaling triggers these responses. Exosomes are critical drivers of these interactions and exhibit both immunogenic and immune evasion properties during the development and progression of various chronic inflammatory diseases. Exosomes carry diverse molecular cargo, including lipids, proteins, and RNAs that are crucial for immunomodulation. Moreover, recent studies have revealed that exosomes and their cargo-loaded molecules are extensively involved in lipid remodeling and metabolism during immune surveillance and disease. Many studies have also shown the involvement of lipids in controlling immune cell activities and their crucial upstream functions in regulating inflammasome activation, suggesting that any perturbation in lipid metabolism results in abnormal immune responses. Strikingly, the expanded immunometabolic reprogramming capacities of exosomes and their contents provided insights into the novel mechanisms behind the prophylaxis of inflammatory diseases. By summarizing the tremendous therapeutic potential of exosomes, this review emphasizes the role of exosome-derived noncoding RNAs in regulating immune responses through the modulation of lipid metabolism and their promising therapeutic applications.

Incidence and risk factors of contrast-induced nephropathy in acute stroke patients undergoing computed tomography angiography: A single-center study

OBJECTIVES

To investigate the prevalence and risk factors linked to contrast-induced nephropathy in this specific patient population, aiming to ensure the highest quality of clinical care.

METHODS

In a retrospective analysis, all patients who presented with an acute stroke to King Fahad Hospital, Jeddah, Emergency Department from March until November 2022 and underwent Computed Tomography Angiography (CTA) brain, Inclusion criteria were as follows: a baseline creatinine results and CTA examination performed within 24 hours of symptom onset and an available early (<5 days after CTA) follow-up creatinine result.

RESULTS

Among 246 stroke patients in the emergency, 182 underwent brain CTA and 8.24% had Contrast-Induced Nephropathy (CIN). intracerebral hemorrhage (ICH) increased CIN risk 7-fold (OR=6.7; 95% CI: 1.23-33.3). Abnormal baseline raised CIN risk 8-fold (OR=7.8; 95% CI: 1.74-35.1). hypertension doubled the risk for CIN (OR=2.1; 95% CI: 1.26-6.98) CONCLUSION: The incidence of CIN was 8.2%, particularly elevated in patients with ICH, hypertension, tissue plasminogen administration, and abnormal baseline, necessitating vigilance in managing acute stroke cases.

Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS

Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.

Acute Popliteal Artery Thrombotic Occlusion Post Total Knee Arthroplasty in a Patient With Antiphospholipid Syndrome: A Report of a Rare Complication

Acute popliteal arterial thrombotic occlusion following total knee arthroplasty is a rare but serious complication, most of which happens due to blunt trauma during surgery in patients with preexisting peripheral vascular disease. Historically, popliteal artery thrombosis has been approached only by open surgery. In this report, we describe a case of acute thrombotic occlusion of the popliteal artery occurring immediately after total knee arthroplasty in a patient who was presumed healthy and found to have antiphospholipid syndrome and was successfully managed by mechanical endovascular thrombectomy.

Fabrication and Characterization of a Poly(3,4-ethylenedioxythiophene)@Tungsten Trioxide-Graphene Oxide Hybrid Electrode Nanocomposite for Supercapacitor Applications

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.

Enhanced brain tumor classification using graph convolutional neural network architecture

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.

Natural compounds targeting nuclear receptors for effective cancer therapy

Human nuclear receptors (NRs) are a family of forty-eight transcription factors that modulate gene expression both spatially and temporally. Numerous biochemical, physiological, and pathological processes including cell survival, proliferation, differentiation, metabolism, immune modulation, development, reproduction, and aging are extensively orchestrated by different NRs. The involvement of dysregulated NRs and NR-mediated signaling pathways in driving cancer cell hallmarks has been thoroughly investigated. Targeting NRs has been one of the major focuses of drug development strategies for cancer interventions. Interestingly, rapid progress in molecular biology and drug screening reveals that the naturally occurring compounds are promising modern oncology drugs which are free of potentially inevitable repercussions that are associated with synthetic compounds. Therefore, the purpose of this review is to draw our attention to the potential therapeutic effects of various classes of natural compounds that target NRs such as phytochemicals, dietary components, venom constituents, royal jelly-derived compounds, and microbial derivatives in the establishment of novel and safe medications for cancer treatment. This review also emphasizes molecular mechanisms and signaling pathways that are leveraged to promote the anti-cancer effects of these natural compounds. We have also critically reviewed and assessed the advantages and limitations of current preclinical and clinical studies on this subject for cancer prophylaxis. This might subsequently pave the way for new paradigms in the discovery of drugs that target specific cancer types.

Exploring the gelling properties of Plantago ovata-based Arabinoxylan: Fabrication and optimization of a topical emulgel using response surface methodology

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.

Growth, studies of milled and irradiated crystalline samples of DBNT for macro-photonic and electro-mechano functionalities

Single crystals of organic type of NLO crystalline material of DBNT - 8, 9-Dimethoxybenzo[b]naphtho [2,3-d] thiophene are proceeded to be grown by slow evaporation procedure and milled to micro scale and irradiated of Co-60 source of 100 Gy, 500 Gy and 5000 Gy for better scope of classification of system of the monoclinic type of DBNT-pure, micro and irradiated ones. The hardness study specifies the reverse indentation size effect (RISE) with work hardening coefficient above two of all DBNT specimen leads to the micro-tribological workings for springs with proper elastic parameters; the transmittance of DBNT of 5 specimens are 321 nm, 323 nm, 341 nm, 351 nm, and 352 nm for macro, micro, 100 Gy, 500 Gy, 5000 Gy. The photonic utility of identity for 3.86 eV and is 3.8629 eV by the transmittance data. The Non Linear Optical - NLO component this of 1.9, 1.94, 1.95, 1.96, 1.99 times that of KDP from which phase matching provision is enabled the influx property for the DBNT specimens is in the order of microns the (110) and (111) indexing represent for display device configuration. The dielectric behaviour of DBNT shows that polarization properly enabled for all categories by electrical performance, the abnormal variation is due to the vacancies created in the molecule by irradiation.

A Review of Approaches to the Metallic and Non-Metallic Synthesis of Benzimidazole (BnZ) and Their Derivatives for Biological Efficacy

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.

Towards further understanding the applications of endophytes: enriched source of bioactive compounds and bio factories for nanoparticles

The most significant issues that humans face today include a growing population, an altering climate, an growing reliance on pesticides, the appearance of novel infectious agents, and an accumulation of industrial waste. The production of agricultural goods has also been subject to a great number of significant shifts, often known as agricultural revolutions, which have been influenced by the progression of civilization, technology, and general human advancement. Sustainable measures that can be applied in agriculture, the environment, medicine, and industry are needed to lessen the harmful effects of the aforementioned problems. Endophytes, which might be bacterial or fungal, could be a successful solution. They protect plants and promote growth by producing phytohormones and by providing biotic and abiotic stress tolerance. Endophytes produce the diverse type of bioactive compounds such as alkaloids, saponins, flavonoids, tannins, terpenoids, quinones, chinones, phenolic acids etc. and are known for various therapeutic advantages such as anticancer, antitumor, antidiabetic, antifungal, antiviral, antimicrobial, antimalarial, antioxidant activity. Proteases, pectinases, amylases, cellulases, xylanases, laccases, lipases, and other types of enzymes that are vital for many different industries can also be produced by endophytes. Due to the presence of all these bioactive compounds in endophytes, they have preferred sources for the green synthesis of nanoparticles. This review aims to comprehend the contributions and uses of endophytes in agriculture, medicinal, industrial sectors and bio-nanotechnology with their mechanism of action.

Heavy metal contamination levels, source distribution, and risk assessment in fine sand of urban surface deposited sediments of Ekaterinburg, Russia

Urban surface deposited sediments (USDS) are unique indicators of local pollution that pose a potential threat to the living environment and human health. Ekaterinburg is a highly populated metropolitan area in Russia with rapid urbanization and industrialization activities. In Ekaterinburg's residential areas, about 35, 12, and 16 samples are represented by green zones, roads, driveways, and sidewalks, respectively. The total concentrations of heavy metals was detected using a chemical analyzer inductively coupled plasma mass spectrometry (ICP-MS). Zn, Sn, Sb, and Pb have the highest concentrations in the green zone, while V, Fe, Co, and Cu represent the utmost values on roads. Moreover, Mn and Ni are the prevailing metals in the fine sand fraction of driveways along with sidewalks. Broadly, the high pollution in the studied zones is generated by anthropogenic activities and traffic emissions. The potential ecological risk (RI) was observed in high risk (IR > 600), even though the results of all heavy metals reveal no adverse health effects from the considered noncarcinogenic metal for adults and children by different exposure pathways except the children's exposure to Co in case of the dermal contact, where the HI values of Co for children in the studied zones are higher than the proposed level (> 1). In all urban zones, the total carcinogenic risk (TLCR) values are predicted as a high potential inhalation exposure.

A comprehensive appraisal of mechanism of anti-CRISPR proteins: an advanced genome editor to amend the CRISPR gene editing

The development of precise and controlled CRISPR-Cas tools has been made possible by the discovery of protein inhibitors of CRISPR-Cas systems, called anti-CRISPRs (Acrs). The Acr protein has the ability to control off-targeted mutations and impede Cas protein-editing operations. Acr can help with selective breeding, which could help plants and animals improve their valuable features. In this review, the Acr protein-based inhibitory mechanisms that have been adopted by several Acrs, such as (a) the interruption of CRISPR-Cas complex assembly, (b) interference with target DNA binding, (c) blocking of target DNA/RNA cleavage, and (d) enzymatic modification or degradation of signalling molecules, were discussed. In addition, this review emphasizes the applications of Acr proteins in the plant research.

Aloe vera-Based Polymeric Network: A Promising Approach for Sustained Drug Delivery, Development, Characterization, and In Vitro Evaluation

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.

Neutron attenuation features and elastic properties of silicate glasses containing Ta2O5, and Li2O

In this investigation, the elastic properties and neutrons attenuation factors for some optical glasses containing Ta₂O₅, SiO₂, and Li₂O were reported. The present glasses were also consisted of ZrO₂ and Nb₂O₅ in very small concentrations. The glasses are chemically defined as 26.47Li₂O-5.88ZrO₂-(20-x)Ta₂O₅-xNb₂O₅-47.06SiO2, where, x takes the values: 0, 2.94, 5.88, and 11.77 mol%. The elastic properties of these glassy specimens were determined by employing Makishima-Mackenzie's theory (M.M.T). By using the same method, moreover, the micro-hardness and Poisson's ratio were assessed. Cross sections for slow, moderated, and fissile neutrons were computed through standard expressions and models. In addition, the influence of the partial replacement of Ta₂O₅ by Nb₂O₅ on the parameters were also analysed. The glass with the lowest Nb₂O₅ content presented the highest cross sections for fast, moderated, and slow neutrons. The neutron-absorption ability of included glasses declined as glass density declined and Nb₂O₅ molar concentration increased in the glasses. Therefore, the sample with the highest Ta₂O₅ content is recommended for neutron absorption applications.

Recovery of W(VI) from Wolframite Ore Using New Synthetic Schiff Base Derivative

A new synthetic material, namely, (3-(((4-((5-(((S)-hydroxyhydrophosphoryl)oxy)-2-nitrobenzylidene) amino) phenyl) imino) methyl)-4-nitrophenyl hydrogen (R)-phosphonate)), was subjected to a quaternary ammonium salt and named (HNAP/QA). Several characterizations, such as FTIR spectrometry, ¹H-NMR analysis, ¹³C-NMR analysis, ³¹P-NMR Analysis, TGA analysis, and GC-MS analysis, were performed to ensure its felicitous preparation. HNAP/QA is capable of the selective adsorption of W(VI) ions from its solutions and from its rock leachate. The optimum factors controlling the adsorption of W(VI) ions on the new adsorbent were studied in detail. Furthermore, kinetics and thermodynamics were studied. The adsorption reaction fits the Langmuir model. The sorption process of the W(VI) ions is spontaneous due to the negative value of ∆G° calculated for all temperatures, while the positive value of ∆H° proves that the adsorption of the W(VI) ions adsorption on HNAP/QA is endothermic. The positive value of ∆S° suggests that the adsorption occurs randomly. Ultimately, the recovery of W(IV) from wolframite ore was conducted successfully.

NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets

Spinal cord segmentation is the process of identifying and delineating the boundaries of the spinal cord in medical images such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. This process is important for many medical applications, including the diagnosis, treatment planning, and monitoring of spinal cord injuries and diseases. The segmentation process involves using image processing techniques to identify the spinal cord in the medical image and differentiate it from other structures, such as the vertebrae, cerebrospinal fluid, and tumors. There are several approaches to spinal cord segmentation, including manual segmentation by a trained expert, semi-automated segmentation using software tools that require some user input, and fully automated segmentation using deep learning algorithms. Researchers have proposed a wide range of system models for segmentation and tumor classification in spinal cord scans, but the majority of these models are designed for a specific segment of the spine. As a result, their performance is limited when applied to the entire lead, limiting their deployment scalability. This paper proposes a novel augmented model for spinal cord segmentation and tumor classification using deep nets to overcome this limitation. The model initially segments all five spinal cord regions and stores them as separate datasets. These datasets are manually tagged with cancer status and stage based on observations from multiple radiologist experts. Multiple Mask Regional Convolutional Neural Networks (MRCNNs) were trained on various datasets for region segmentation. The results of these segmentations were combined using a combination of VGGNet 19, YoLo V2, ResNet 101, and GoogLeNet models. These models were selected via performance validation on each segment. It was observed that VGGNet-19 was capable of classifying the thoracic and cervical regions, while YoLo V2 was able to efficiently classify the lumbar region, ResNet 101 exhibited better accuracy for sacral-region classification, and GoogLeNet was able to classify the coccygeal region with high performance accuracy. Due to use of specialized CNN models for different spinal cord segments, the proposed model was able to achieve a 14.5% better segmentation efficiency, 98.9% tumor classification accuracy, and a 15.6% higher speed performance when averaged over the entire dataset and compared with various state-of-the art models. This performance was observed to be better, due to which it can be used for various clinical deployments. Moreover, this performance was observed to be consistent across multiple tumor types and spinal cord regions, which makes the model highly scalable for a wide variety of spinal cord tumor classification scenarios.

Novel Benzothiazole Derivatives Synthesis and its Analysis as Diuretic Agents

Benzothiazoles, an anticonvulsant, antiviral, antihypertensive, and cancer-fighting medication of the heterocyclic scaffold family, also acts as antibacterial and antiviral agents. There is much interest in this chemical’s production because of the strong and vital biological action it possesses. Substituted aromatic aldehydes were combined with 2-amino-benzothiazole-6-sulfonic acid amides, or Schiff base derivatives, to create Schiff base derivatives. Recrystallized, characterized, and tested for diuretic efficacy in vivo using online tools, m.p. (melting point), Rf, FTIR (Fourier transform infrared), 1H-NMR (proton nuclear magnetic resonance) data The molecular characteristics of all the substances created were estimated using Lipinski’s rule of 5, OSIRIS (software) molecular property explorer, Molsoft, and Autodock 4.0 docking software. Male Wistar rats were used to make all the compounds traditionally in order to test for diuretic activity. Neither the elemental nor the spectral information for the synthesized compounds disagreed. There were five different methods used to evaluate these compounds: Lipinski rule of five, Molsoft to determine molecular characteristics, PASS (prediction of activity spectra for substances) values to determine the diuretic effect, and OSIRIS software to determine toxicology. In order to investigate the diuretic effects of the selected drugs, docking analysis was used. Acetazolamide was shown to have a diuretic effect that was superior to that of compounds IIIb and IIIe, whereas 2-{(E)-[(3-hydroxyphenyl)methylidene]amino}-1,3-benzothiazole-6-sulfonamide (IIIb) was found to be the most promising potential.