Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

Progress and trends in neurological disorders research based on deep learning.

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Recent progress in metal oxide-based electrode materials for safe and sustainable variants of supercapacitors.

A significant amount of energy can be produced using renewable energy sources; however, storing massive amounts of energy poses a substantial obstacle to energy production. Economic crisis has led to rapid developments in electrochemical (EC) energy storage devices (EESDs), especially rechargeable batteries, fuel cells, and supercapacitors (SCs), which are effective for energy storage systems. Researchers have lately suggested that among the various EESDs, the SC is an effective alternate for energy storage due to the presence of the following characteristics: SCs offer high-power density (PD), improvable energy density (ED), fast charging/discharging, and good cyclic stability. This review highlighted and analyzed the concepts of supercapacitors and types of supercapacitors on the basis of electrode materials, highlighted the several feasible synthesis processes for preparation of metal oxide (MO) nanoparticles, and discussed the morphological effects of MOs on the electrochemical performance of the devices. In this review, we primarily focus on pseudo-capacitors for SCs, which mainly contain MOs and their composite materials, and also highlight their future possibilities as a useful application of MO-based materials in supercapacitors. The novelty of MO's electrode materials is primarily due to the presence of synergistic effects in the hybrid materials, rich redox activity, excellent conductivity, and chemical stability, making them excellent for SC applications.

Recapitulation of Peste des Petits Ruminants (PPR) Prevalence in Small Ruminant Populations of Pakistan from 2004 to 2023: A Systematic Review and Meta-Analysis.

Peste des petits ruminants (PPR) is an extremely transmissible viral disease caused by the PPR virus that impacts domestic small ruminants, namely sheep and goats. This study aimed to employ a methodical approach to evaluate the regional occurrence of PPR in small ruminants in Pakistan and the contributing factors that influence its prevalence. A thorough search was performed in various databases to identify published research articles between January 2004 and August 2023 on PPR in small ruminants in Pakistan. Articles were chosen based on specific inclusion and exclusion criteria. A total of 25 articles were selected from 1275 studies gathered from different databases. The overall pooled prevalence in Pakistan was calculated to be 51% (95% CI: 42-60), with heterogeneity I2 = 100%, τ2 = 0.0495, and p = 0. The data were summarized based on the division into five regions: Punjab, Baluchistan, KPK, Sindh, and GB and AJK. Among these, the pooled prevalence of PPR in Sindh was 61% (95% CI: 46-75), I2 = 100%, τ2 = 0.0485, and p = 0, while in KPK, it was 44% (95% CI: 26-63), I2 = 99%, τ2 = 0.0506, and p < 0.01. However, the prevalence of PPR in Baluchistan and Punjab was almost the same. Raising awareness, proper surveillance, and application of appropriate quarantine measures interprovincially and across borders must be maintained to contain the disease.

Metagenomics Analysis Reveals the Composition and Functional Differences of Fecal Microbiota in Wild, Farm, and Released Chinese Three-Keeled Pond Turtles (Mauremys reevesii).

The intestine of living organisms harbors different microbiota associated with the biological functioning and health of the host and influences the process of ecological adaptation. Here, we studied the intestinal microbiota's composition and functional differences using 16S rRNA and metagenomic analysis in the wild, farm, and released Chinese three-keeled pond turtle (Mauremys reevesii). At the phylum level, Bacteroidota dominated, followed by Firmicutes, Fusobacteriota, and Actinobacteriota in the wild group, but Chloroflexi was more abundant in the farm and released groups. Moreover, Chryseobacterium, Acinetobacter, Comamonas, Sphingobacterium, and Rhodobacter were abundant in the released and farm cohorts, respectively. Cetobacterium, Paraclostridium, Lysobacter, and Leucobacter showed an abundance in the wild group. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database revealed that the relative abundance of most pathways was significantly higher in the wild turtles (carbohydrate metabolism, lipid metabolism, metabolism of cofactors, and vitamins). The comprehensive antibiotic resistance database (CARD) showed that the antibiotic resistance gene (ARG) subtype macB was the most abundant in the farm turtle group, while tetA was higher in the wild turtles, and srpYmcr was higher in the released group. Our findings shed light on the association between the intestinal microbiota of M. reevesii and its habitats and could be useful for tracking habitats to protect and conserve this endangered species.

Unifying aspect-based sentiment analysis BERT and multi-layered graph convolutional networks for comprehensive sentiment dissection.

Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model's interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

An affordable label-free ultrasensitive immunosensor based on gold nanoparticles deposited on glassy carbon electrode for the transferrin receptor detection.

In recent decades, there has been a concerning and consistent rise in the incidence of cancer, posing a significant threat to human health and overall quality of life. The transferrin receptor (TfR) is one of the most crucial protein biomarkers observed to be overexpressed in various cancers. This study reports on the development of a novel voltammetric immunosensor for TfR detection. The electrochemical platform was made up of a glassy carbon electrode (GCE) functionalized with gold nanoparticles (AuNPs), on which anti-TfR was immobilized. The surface characteristics and electrochemical behaviors of the modified electrodes were comprehensively investigated through scanning electron microscopy, XPS, Raman spectroscopy FT-IR, electrochemical cyclic voltammetry and impedance spectroscopy. The developed immunosensor exhibited robust analytical performance with TfR fortified buffer solution, showing a linear range (LR) response from 0.01 to 3000 µg/mL, with a limit of detection (LOD) of 0.01 µg/mL and reproducibility (RSD <4 %). The fabricated sensor demonstrated high reproducibility and selectivity when subjected to testing with various types of interfering proteins. The immunosensor designed for TfR detection demonstrated several advantageous features, such as being cost-effective and requiring a small volume of test sample making it highly suitable for point-of-care applications.

EccDNA in plant-stress and biotechnological solutions in agriculture.

Extrachromosomal circular DNA (eccDNA) is genetic material that exists outside of chromosomes and holds potential for next-generation genetic engineering in plant biology. By improving plant resilience, growth, and productivity, eccDNA offers a promising solution to global challenges in food security and environmental sustainability, making this a transformative era in agricultural biotechnology.

The Revival of Essay-Type Questions in Medical Education: Harnessing Artificial Intelligence and Machine Learning.

OBJECTIVE: To analyse and compare the assessment and grading of human-written and machine-written formative essays. STUDY DESIGN: Quasi-experimental, qualitative cross-sectional study. Place and Duration of the Study: Department of Science of Dental Materials, Hamdard College of Medicine & Dentistry, Hamdard University, Karachi, from February to April 2023. METHODOLOGY: Ten short formative essays of final-year dental students were manually assessed and graded. These essays were then graded using ChatGPT version 3.5. The chatbot responses and prompts were recorded and matched with manually graded essays. Qualitative analysis of the chatbot responses was then performed. RESULTS: Four different prompts were given to the artificial intelligence (AI) driven platform of ChatGPT to grade the summative essays. These were the chatbot's initial responses without grading, the chatbot's response to grading against criteria, the chatbot's response to criteria-wise grading, and the chatbot's response to questions for the difference in grading. Based on the results, four innovative ways of using AI and machine learning (ML) have been proposed for medical educators: Automated grading, content analysis, plagiarism detection, and formative assessment. ChatGPT provided a comprehensive report with feedback on writing skills, as opposed to manual grading of essays. CONCLUSION: The chatbot's responses were fascinating and thought-provoking. AI and ML technologies can potentially supplement human grading in the assessment of essays. Medical educators need to embrace AI and ML technology to enhance the standards and quality of medical education, particularly when assessing long and short essay-type questions. Further empirical research and evaluation are needed to confirm their effectiveness. KEY WORDS: Machine learning, Artificial intelligence, Essays, ChatGPT, Formative assessment.

In Response to "Acute Oral Mucositis During Hypo-Fractionated Radiation in Squamous Cell Carcinoma of Oral Cavity".

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A comprehensive review of wind power integration and energy storage technologies for modern grid frequency regulation.

Integrating wind power with energy storage technologies is crucial for frequency regulation in modern power systems, ensuring the reliable and cost-effective operation of power systems while promoting the widespread adoption of renewable energy sources. Power systems are changing rapidly, with increased renewable energy integration and evolving system architectures. These transformations bring forth challenges like low inertia and unpredictable behavior of generation and load components. As a result, frequency regulation (FR) becomes increasingly important to ensure grid stability. Energy Storage Systems (ESS) with their adaptable capabilities offer valuable solutions to enhance the adaptability and controllability of power systems, especially within wind farms. This research provides an updated analysis of critical frequency stability challenges, examines state-of-the-art control techniques, and investigates the barriers that hinder wind power integration. Moreover, it introduces emerging ESS technologies and explores their potential applications in supporting wind power integration. Furthermore, this paper offers suggestions and future research directions for scientists exploring the utilization of storage technologies in frequency regulation within power systems characterized by significant penetration of wind power.

A Comprehensive Update of Anti-COVID-19 Activity of Heterocyclic Compounds.

The Coronavirus disease 2019 (COVID-19) pandemic is one of the most considerable health problems across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the major causative agent of COVID-19. The severe symptoms of this deadly disease include shortness of breath, fever, cough, loss of smell, and a broad spectrum of other health issues such as diarrhea, pneumonia, bronchitis, septic shock, and multiple organ failure. Currently, there are no medications available for coronavirus patients, except symptom-relieving drugs. Therefore, SARS-CoV-2 requires the development of effective drugs and specific treatments. Heterocycles are important constituents of more than 85% of the physiologically active pharmaceutical drugs on the market now. Several FDA-approved drugs have been reported including molnupiravir, remdesivir, ritonavir, oseltamivir, favipiravir, chloroquine, and hydroxychloroquine for the cure of COVID-19. In this study, we discuss potent anti-SARS-CoV-2 heterocyclic compounds that have been synthesized over the past few years. These compounds included; indole, piperidine, pyrazine, pyrimidine, pyrrole, piperazine, quinazoline, oxazole, quinoline, isoxazole, thiazole, quinoxaline, pyrazole, azafluorene, imidazole, thiadiazole, triazole, coumarin, chromene, and benzodioxole. Both in vitro and in silico studies were performed to determine the potential of these heterocyclic compounds in the fight against various SARS-CoV-2 proteins.

Synergistic effect of Silver-Nanodiamond composite as an efficient antibacterial agent against E. coli and S. aureus.

Bacterial antimicrobial resistance (BAMR) seems to pose the greatest threat to public health, food safety, and agriculture in this century. The development of novel efficient antimicrobial agents to combat bacterial infections has become a global issue. Silver nanoparticles (Ag NPs) appeared as a feasible alternative to antibiotics. However, Ag NPs face cost, toxicity, and aggregation issues which limit their antibacterial activity. This work aims to stabilize Ag NPs with enhanced antimicrobial activity at comparatively lower Ag concentrations to prevent bacterial infections. For this purpose, the Ag core was covered with nanodiamonds (NDs). Ag-NDs composite have been synthesized by microplasma technique. TEM analysis confirmed the presence of both Ag and NDs in the Ag-NDs composite. A particle size (∼19 nm) was reported for Ag-NDs at the highest concentration as compared to Ag NPs (∼3 nm). The conduction band of the diamond acted as an extremely strong reducing agent for Ag NPs. The large surface area of NDs stabilized the Ag NPs. A redshift (∼400 nm-406 nm) in UV-visible spectra of the Ag-NDs composite indicated the formation of bigger-sized Ag NPs after incorporating NDs. XRD and LIBS analysis verified the increase in intensity of Ag-NPs by increasing ND concentration. The presence of functional groups including OH, CH, and Ag/Ag2O was confirmed by FTIR. Bacterial inhibition growth appeared to be a dose-dependent process. The minimum inhibition concentration value of Ag-NDs composite at the highest NDs concentration against E. coli (∼ 0.69 µg/ml) and S. aureus (∼44 µg/ml). This is the first study to report the smallest MIC for E. coli (<1 µg/ml). Ag-ND composites emerged to be more efficient than Ag NPs and preferred to be used against BAMR. The enhanced antibacterial activity of the Ag-NDs composite makes it a potential candidate for antibiotics, food products, and pesticides.

Synergistic effects of ß-NaFeO2 ferrite nanoparticles for photocatalytic degradation, antibacterial, and antioxidant applications.

Here, synthesis and thorough characterization of ß-NaFeO2 nanoparticles utilizing a co-precipitation technique is presented. XRD analysis confirmed a hexagonal-phase structure of ß-NaFeO2. SEM revealed well-dispersed spherical nanoparticles with an average diameter of 45 nm. The FTIR spectrum analysis revealed weak adsorption bands at 1054 cm-1 suggested metal-metal bond stretching (Fe-Na). UV-Visible spectroscopy indicates a 4.4 eV optical band gap. Colloidal stability of ß-NaFeO2 was evidenced via Zeta potential (-28.5 mV) and Dynamic Light Scattering (DLS) measurements. BET analysis reveals a substantial 343.27 m2 g-1 surface area with mesoporous characteristics. Antioxidant analysis indicates efficacy comparable to standard antioxidants, while concentration-dependent antibacterial effects suggest enhanced efficacy against Gram-positive bacteria, particularly Streptococcus. The Photocatalytic activity of ß-NaFeO2 showed significant pollutant degradation (>90% efficiency), with increased degradation rates at higher nanoparticle concentrations, indicating potential for environmental remediation applications.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites.

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.

Radiotherapy to the neck after neck dissection for head and neck squamous cell carcinoma from an unknown primary: A narrative review.

PURPOSE: To conduct a comprehensive narrative review of the evidence for radiotherapy target volumes to the neck, after neck dissection, for head and neck squamous cell carcinoma from an unknown primary (HNSCCUP). Inclusion or exclusion of mucosal irradiation is not the focus of interest for this review article. MATERIALS AND METHODS: Literature (PubMed-Medline, EMBASE database and Cochrane library) was searched using the relevant keywords. The search results were limited to the studies published in year 2000 or after. RESULTS: Eight studies met the inclusion criteria. All studies were retrospective in nature. The incidence of contralateral recurrence rates in the untreated neck when the involved neck only is treated remains very low (0%-10%). Survival has improved over the past two decades, most likely due to improved diagnostic techniques and the increase in incidence of HPV-related disease. CONCLUSION: Given the rarity of disease, level one evidence from randomised controlled trials is lacking. Available data are retrospective but support unilateral post-operative radiotherapy as a treatment option in selected cases.

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model.

Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the Bidirectional Encoder Representations from Transformer (BERT) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.

Metastatic spinal cord compression: the Spinal Instability Neoplastic Score and early surgical intervention.

OBJECTIVES: To evaluate the value of Spinal Instability Neoplastic Score (SINS) in patients with spine metastasis who subsequently developed or did not develop metastatic spinal cord compression (MSCC). METHODS: In this single institutional retrospective descriptive observational study, of 589 patients with MSCC who were referred for radiotherapy, 34 patients (with 41 compression sites) met the inclusion criteria: availability of diagnostic MRI spine pre-development of MSCC (MRI-1) and at the time of MSCC development (MRI-2) (CordGroup).For comparison, NoCordGroup consisted of 152 patients (160 sites) treated with radiotherapy to spinal metastases. SINS was compared between the two groups. RESULTS: In CordGroup, the median interval between MRI-1 and MRI-2 was 11 weeks. The median SINS was 8 (range: 4-14) and 9 (range: 7-14) on MRI-1 and MRI-2, respectively. In NoCordGroup, the median SINS was 6 (range: 4-10). CONCLUSIONS: Our study showed a trend in difference in SINS value between the two groups. This difference should be a subject of future prospective research in this patient population with poor survival.