An XAI-Enhanced EfficientNetB0 Framework for Precision Brain Tumor Detection in MRI Imaging.

BACKGROUND: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has shown promise in improving accuracy. However, the lack of transparency in AI decision-making processes presents a challenge for clinical adoption. METHODS: Recent advancements in deep learning, particularly the utilization of CNNs, have facilitated the development of models for medical image analysis. In this study, we employed the EfficientNetB0 architecture and integrated explainable AI techniques to enhance both accuracy and interpretability. Grad-CAM visualization was utilized to highlight significant areas in MRI scans influencing classification decisions. RESULTS: Our model achieved a classification accuracy of 98.72% across four categories of brain tumors (Glioma, Meningioma, No Tumor, Pituitary), with precision and recall exceeding 97% for all categories. The incorporation of explainable AI techniques was validated through visual inspection of Grad-CAM heatmaps, which aligned well with established diagnostic markers in MRI scans. CONCLUSION: The AI-enhanced EfficientNetB0 framework with explainable AI techniques significantly improves brain tumor classification accuracy to 98.72%, offering clear visual insights into the decision-making process. This method enhances diagnostic reliability and trust, demonstrating substantial potential for clinical adoption in medical diagnostics.

Spatial auto-correlation and endemicity pattern analysis of crimes against children in Tamil Nadu from 2017 to 2021.

Background: A child is a nation's supreme asset and future. India homes 444 million children, aged between 0 and 18 years, contributing to 19% of the world's children. Crime against children is detrimental to their mental and physical health and affects their growth and development. The National Crime Record Bureau recently reported that a crime targeting children happens every 4 minutes. There is a paucity of literature regarding the burden of crime against children. To understand the magnitude and spatial distribution of crime against children, a retrospective surveillance study was conducted in the state of Tamil Nadu, India, from 2017 to 2021. Materials and Methods: This is a cross-sectional analytical type of study conducted in KIMSRC, Chengalpattu, Tamil Nadu. The data from the yearly crime review bulletin of Tamil Nadu from 2017 to 2021 were cleaned, transformed, and analyzed using Python v3.8 and subjected to geospatial auto-correlation and hotspot analysis using the Getis-Ord Gi* in ArcGIS Pro v3.1. The endemicity pattern was studied through cluster analysis with Hierarchical Density Based Scanning in Python and visualization in ArcGIS pro v3.1 in the study area. Results: In Tamil Nadu, only one hotspot district in 2017 [Tiruppattur (95% confidence, P < 0.05)] and one hotspot in 2020 [Villupuram (90% confidence, P < 0.1)] were identified, with others being insignificant. The districts which show very high prevalence of crimes against children are Chennai, Ranipet, Chengalpattu, Viluppuram, Tiruvannamalai, Vellore, Tiruppattur, Krishnagiri, Dharmapuri, Salem, Cuddalore, Thanjavur, Tiruchirappalli, Karur, Tiruppur, Coimbatore, Dindigul, Pudukkottai, Sivaganga, Tenkasi, Thoothukkudi, Tirunelveli, and Kanniyakumari. Conclusion: This study identifies key areas within the state of Tamil Nadu which have a high prevalence of crimes against children and also areas that are hotspots for such crimes. Greater resources and measures can now be targeted toward these areas by stakeholders, which can help in the reduction of crimes against children.

Rubella immunity among pregnant women and the burden of congenital rubella syndrome (CRS) in India, 2022.

BACKGROUND: India aims to eliminate rubella and congenital rubella syndrome (CRS) by 2023. We conducted serosurveys among pregnant women to monitor the trend of rubella immunity and estimate the CRS burden in India following a nationwide measles and rubella vaccination campaign. METHODS: We surveyed pregnant women at 13 sentinel sites across India from Aug to Oct 2022 to estimate seroprevalence of rubella IgG antibodies. Using age-specific seroprevalence data from serosurveys conducted during 2017/2019 (prior to and during the vaccination campaign) and 2022 surveys (after the vaccination campaign), we developed force of infection (FOI) models and estimated incidence and burden of CRS. RESULTS: In 2022, rubella seroprevalence was 85.2% (95% CI: 84.0, 86.2). Among 10 sites which participated in both rounds of serosurveys, the seroprevalence was not different between the two periods (pooled prevalence during 2017/2019: 83.5%, 95% CI: 82.1, 84.8; prevalence during 2022: 85.1%, 95% CI: 83.8, 86.3). The estimated annual incidence of CRS during 2017/2019 in India was 218.3 (95% CI: 209.7, 226.5) per 100, 000 livebirths, resulting in 47,120 (95% CI: 45,260, 48,875) cases of CRS every year. After measles-rubella (MR) vaccination campaign, the estimated incidence of CRS declined to 5.3 (95% CI: 0, 21.2) per 100,000 livebirths, resulting in 1141 (95% CI: 0, 4,569) cases of CRS during the post MR-vaccination campaign period. CONCLUSION: The incidence of CRS in India has substantially decreased following the nationwide MR vaccination campaign. About 15% of women in childbearing age in India lack immunity to rubella and hence susceptible to rubella infection. Since there are no routine rubella vaccination opportunities for this age group under the national immunization program, it is imperative to maintain high rates of rubella vaccination among children to prevent rubella virus exposure among women of childbearing age susceptible for rubella.

Effect of phytopigments on discoloration of nanohybrid composite: An in vitro study.

Objective: The purpose of this study is to comparatively evaluate the effect of discoloration of nanohybrid composite by four different phytopigments. Materials and Methods: Fifty disk-shaped samples of nanohybrid (3M Filtek Z350) resin composites were prepared using an acrylic template of dimension 5 mm × 3 mm. They were randomly divided into five groups and immersed in solutions of tomato powder, beetroot powder, java plum powder, and turmeric powder. Distilled water was used as the control group. The samples were placed in respective solutions for 3 h daily and stored in artificial saliva for the rest of the day for 28 days. Color values (L*, a*, b*) were measured by colorimeter using the CIE L*a*b* system at the end of the 7th and 28th days of immersion. Color differences ΔE*ab were statistically analyzed. Results: All the samples showed a change in color of nanohybrid composite resin to varying degrees. The mean ΔE*ab value obtained with beetroot solution was the highest among all the groups at the end of the 7th and 28th days, depicting that beetroot solution showed maximum mean color variation, followed by java plum solution, turmeric solution, and tomato solution. Conclusion: All the phytopigments used in this study have the potential to discolor the nanohybrid composite resin, with beetroot causing the most severe discoloration.

Enhancing the Bioavailability of the Ellagitannin, Geraniin: Formulation, Characterization, and in vivo Evaluation.

Geraniin (GE), an ellagitannin (ET) renowned for its promising health advantages, faces challenges in its practical applications due to its limited bioavailability. This innovative and novel formulation of GE and soy-phosphatidylcholine (GE-PL) complex has the potential to increase oral bioavailability, exhibiting high entrapment efficiency of 100.2 ± 0.8 %, and complexation efficiency of 94.6 ± 1.1 %. The small particle size (1.04 ± 0.11 µm), low polydispersity index (0.26 ± 0.02), and adequate zeta potential (-26.1 ± 0.12 mV), indicate its uniformity and stability. Moreover, the formulation also demonstrates improved lipophilicity, reduced aqueous and buffer solubilities, and better partition coefficient. It has been validated by various analytical techniques, including Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD) studies. Oral bioavailability and pharmacokinetics of free GE and GE-PL complex investigated in rabbits demonstrated enhanced plasma concentration of ellagic acid (EA) compared to free GE. Significantly, GE, whether in its free form or as part of the GE-PL complex, was not found in the circulatory system. However, EA levels were observed at 0.5 h after administration, displaying two distinct peaks at 2 ± 0.03 h (T1max) and 24 ± 0.06 h (T2max). These peaks corresponded to peak plasma concentrations (C1max and C2max) of 588.82 ng/mL and 711.13 ng/mL respectively, signifying substantial 11-fold and 5-fold enhancements when compared to free GE. Additionally, it showed an increased area under the curve (AUC), the elimination half-life (t1/2, el) and the elimination rate constant (Kel). The formulation of the GE-PL complex prolonged the presence of EA in the bloodstream and improved its absorption, ultimately leading to a higher oral bioavailability. In summary, the study highlights the significance of the GE-PL complex in overcoming the bioavailability limitations of GE, paving the way for enhanced therapeutic outcomes and potential applications in drug delivery and healthcare.

Computational therapeutic repurposing of tavaborole targeting arginase-1 for venous leg ulcer.

Venous leg ulcers (VLUs) pose a growing healthcare challenge due to aging, obesity, and sedentary lifestyles. Despite various treatments available, addressing the complex nature of VLUs remains difficult. In this context, this study investigates repurposing boronated drugs to inhibit arginase 1 activity for VLU treatment. The molecular docking study conducted by Schrodinger GLIDE targeted the binuclear manganese cluster of arginase 1 enzyme (2PHO). Further, the ligand-protein complex was subjected to molecular dynamic studies at 500 ns in Gromacs-2019.4. Trajectory analysis was performed using the GROMACS simulation package of protein RMSD, RMSF, RG, SASA, and H-Bond. The docking study revealed intriguing results where the tavaborole showed a better docking score (-3.957 Kcal/mol) compared to the substrate L-arginine (-3.379 Kcal/mol) and standard L-norvaline (-3.141 Kcal/mol). Tavaborole interaction with aspartic acid ultimately suggests that the drug molecule binds to the catalytic site of arginase 1, potentially influencing the enzyme's function. The dynamics study revealed the compounds' stability and compactness of the protein throughout the simulation. The RMSD, RMSF, SASA, RG, inter and intra H-bond, PCA, FEL, and MMBSA studies affirmed the ligand-protein and protein complex flexibility, compactness, binding energy, van der waals energy, and solvation dynamics. These results revealed the stability and the interaction of the ligand with the catalytic site of arginase 1 enzyme, triggering the study towards the VLU treatment.

Prevalence of Avian coccidiosis in India: a review.

Coccidiosis stands as a highly significant and economically impactful parasitic ailment in poultry, attributed to the intracellular parasite belonging to the genus Eimeria. This affliction poses considerable financial challenges to the poultry industry and is prevalent in most tropical and subtropical regions globally. The primary mode of transmission is through the fecal-oral route, predominantly affecting young chicks and chickens within intensive rearing systems. There are nine distinct Eimeria species that affect poultry, manifesting primarily in caecal and intestinal forms. Diagnosis typically relies on examining fecal samples for oocysts and post-mortem lesions. Molecular techniques are employed for both diagnosis and control of poultry coccidiosis. To combat the disease, anticoccidials are consistently incorporated into feed and water, but this practice may contribute to the emergence of resistant strains. Various vaccines, including live or live attenuated options, are currently in use for coccidiosis prevention.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Performance evaluation of different empirical models for reference evapotranspiration estimation over Udhagamandalm, The Nilgiris, India.

Evapotranspiration (ETo) is an important component of the hydrological cycle and reliable estimates of ETo are essential for assessing crop water requirements and irrigation management. Direct measurement of evapotranspiration is both costly and involves complex and intricate procedures. Hence, empirical models are commonly utilized to estimate ETo using accessible meteorological data. Given that empirical methods operate on various assumptions, it is essential to assess their performance to pinpoint the most suitable methods for ETo calculation based on the availability of input data and the specific climatic conditions of a region. This study aims to evaluate different empirical methods of ETo in the tropical highland Udhagamandalam region of Tamil Nadu, India, utilizing sixty years of meteorological data from 1960-2020. In this study, 8 temperature-based and 10 radiation-based empirical models are evaluated against ETo estimates derived from pan evaporation observation and the FAO Penman-Monteith method (FAO-PM), respectively. Statistical error metrics indicate that both temperature and radiation-based models perform better for the Udhagamandalam region. However, radiation-based models performed better than the temperature based models. This is possibly due to the high humidity of the study region throughout the year. The results suggest that simple temperature and radiation-based models using minimum meteorological information are adequate to estimate ETo and thus find potential application in agricultural water practices, hydrological processes, and irrigation management.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework.

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.

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.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

A Randomized, Double-Blind, Placebo-Controlled Trial Evaluating the Effect of Topical Urea for Secondary Prophylaxis of Hand Foot Skin Reaction in Renal Cell Cancer Patients on Sunitinib Therapy.

INTRODUCTION: Hand foot skin reaction (HFSR) is a common dose-limiting adverse effect of multi kinase inhibitors (MKI) whose mechanism is not fully understood, and the prophylaxis is inadequate. OBJECTIVE: In this pilot study, a double-blind, randomized placebo-controlled trial was conducted to evaluate the effect of topical urea in secondary prevention of sunitinib-induced HFSR in renal cell cancer patients. METHODS: Out of 55 screened patients, 14 were randomized to receive topical urea or placebo for four weeks. The association of HFSR with drug levels of sunitinib and its metabolite (n-desethyl sunitinib), genetic polymorphism of VEGFR2 gene, quality of life (QOL) and biochemical markers was also assessed. RESULTS: The results showed that urea-based cream was not superior to placebo (P = .075). There was no change in the QOL in both the groups. Single nucleotide polymorphism was checked for two nucleotides rs1870377 and rs2305948 located in VEGFR2 gene on chromosome 4. SNP (variant T > A) at rs1870377 was associated with appearance of new HFSR as compared to the wild type, although the association was not statistically significant (OR 0.714). There was no statistically significant difference between mean plasma levels of sunitinib and N-desethyl sunitinib in urea arm as compared to placebo arm as compared to placebo. The best fit population pharmacokinetic model for sunitinib was one compartment model with first order absorption and linear elimination. The median (IQR) of population parameters calculated from the population pharmacokinetics model for Ka, V and Cl was 0.22 (0.21-0.24) h-1, 4.4 (4.09-4.47) L, 0.049 (0.042-0.12) L/hr, respectively. CONCLUSION: The study suggested that the urea-based cream was not superior to placebo in decreasing the appearance of new HFSR in renal cancer patients receiving 4:2 regimen of sunitinib.

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%.

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm.

In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit.

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Challenges in management of Candida auris meningitis secondary to infected ventriculoperitoneal shunt - A case report.

Meningitis in patients with ventriculo-peritoneal shunt (VP shunt) caused by various species of Candida have been widely described in literature. However, reports describing Candida auris as a cause of meningitis is limited. In this case report we describe a case of multidrug resistant Candida auris meningitis secondary to VP shunt infection successfully treated with intrathecal amphotericin B deoxycholate and intravenous liposomal amphotericin B. This is the second case report of successful treatment of Candida auris meningitis from India. More literature regarding the use of intrathecal/intraventricular echinocandins including optimal dosing and duration of therapy is needed.

Phytosynthesis of zinc oxide nanoparticles for enhanced antioxidant, antibacterial, and photocatalytic properties: A greener approach to environmental sustainability.

Multifunctional nanoparticles (NPs) production from phytochemicals is a sustainable process and an eco-friendly method, and this technique has a variety of uses. To accomplish this, we developed zinc oxide nanoparticles (ZnONPs) using the medicinal plant Tinospora cordifolia (TC). Instruments such as UV-Vis, XRD, FTIR, FE-SEM with EDX, and high-resolution TEM were applied to characterize the biosynthesized TC-ZnONPs. According to the UV-vis spectra, the synthesized TC-ZnONPs absorb at a wavelength centered at 374 nm, which corresponds to a 3.2 eV band gap. HRTEM was used to observe the morphology of the particle surface and the actual size of the nanostructures. TC-ZnONPs mostly exhibit the shapes of rectangles and triangles with a median size of 21 nm. The XRD data of the synthesized ZnONPs exhibited a number of peaks in the 2θ range, implying their crystalline nature. TC-ZnONPs proved remarkable free radical scavenging capacity on DPPH (2,2-Diphenyl-1-picrylhydrazyl), ABTS (2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid), and NO (Nitric Oxide). TC-ZnONPs exhibited dynamic anti-bacterial activity through the formation of inhibition zones against Pseudomonas aeruginosa (18 ± 1.5 mm), Escherichia coli (18 ± 1.0 mm), Bacillus cereus (19 ± 0.5 mm), and Staphylococcus aureus (13 ± 1.1 mm). Additionally, when exposed to sunlight, TC-ZnONPs show excellent photocatalytic ability towards the degradation of methylene blue (MB) dye. These findings suggest that TC-ZnONPs are potential antioxidant, antibacterial, and photocatalytic agents.