DARNet: Deep Attention Module and Residual Block-Based Lung and Colon Cancer Diagnosis Network.

Accurate and efficient lung and colon cancer classification is vital for early detection and treatment planning. Traditional methods require manual effort and expert analysis, leading researchers to explore deep learning models. However, deep learning-based lung and colon cancer classification models face challenges such as generalization, overfitting, gradient vanishing, and hyperparameter tuning.

HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.

Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training.

Nitrogen functionalized biomass derived mesoporous carbon nanomaterials for electrochemical detection of lead (II) ions.

This study has explored the sustainable solution after designing an economical metal-free biomass-derived nanocarbon for the selective sensing of lead. The nitrogen and sulfur-rich mesoporous nanocarbon is designed through a facile hydrothermal-assisted thermal annealing method. The high-temperature treatment gave nanocarbon unique carbon dot decorated layered morphology, while nitrogen and sulfur precursor thiourea and melamine strengthened the nanomaterial stability, sensitivity, and selectivity toward lead metal ions.

Heterogenization of Ionic Liquid on Multiwalled Carbon Nanotubes for Lead(II) Ion Detection.

The presence of lead(II) ion poses a significant threat to water systems due to their toxicity and potential health hazards. The detection of Pb ions in contaminated water is very crucial. The ionic liquid functionalized multiwalled carbon nanotubes (IL@MWCNT) nanocomposite was fabricated using ionic liquid (IL) 1-methyl-3-(4-sulfobutyl)-imidazolium chloride and multiwalled carbon nanotubes (MWCNTs) for detection of lead(II) ions.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions.

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm.

Parthenium hysterophorus derived nanostructures as an efficient carbocatalyst for the electrochemical sensing of mercury(II) ions.

The sustainable utilization of resources motivate us to create eco-friendly processes for synthesizing novel carbon nanomaterials from waste biomass by minimizing chemical usage and reducing energy demands. By keeping sustainability as a prime focus in the present work, we have made the effective management of Parthenium weeds by converting them into carbon-based nanomaterial through hydrothermal treatment followed by heating in a tube furnace under the nitrogen atmosphere. The XPS studies confirm the natural presence of nitrogen and oxygen-containing functional groups in the biomass-derived carbon.

Nitrogen and sulfur functionalized microporous carbon nanomaterial derived from waste coconut husk for the efficient detection and removal of ofloxacin.

This study uses waste coconut husk to synthesize carbon quantum dots decorated graphene-like structure for sustainable detection and removal of ofloxacin. The XRD spectrum shows the carbon nanomaterial's layered structure with turbostratic carbon stacking on its surface. The FESEM and HRTEM studies claim the successful development of quantum dots decorated 2D layered structure of carbon nanomaterial.

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet).

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space.

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models.

Development of metal free carbon catalyst derived from Parthenium hysterophorus for the electrochemical detection of dopamine.

Parthenium hysterophorus, one of the seven most hazardous weeds is widely known for its allergic, respiratory and skin-related disorders. It is also known to affect biodiversity and ecology. For eradication of the weed, its effective utilization for the successful synthesis of carbon-based nanomaterial is a potent management strategy.

Efficient Evolving Deep Ensemble Medical Image Captioning Network.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate.

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models.

MRBSChain a novel scalable medical records binance smart chain framework enabling a paradigm shift in medical records management.

Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is the new paradigm where patient is an inherent part of the healthcare ecosystem controlling the access and sharing of his/her personal medical care information.

Pachydermoperiostosis with Hearing Loss.

A 21-year-old unmarried man, born of a non-consanguineous marriage, presented to the dermatology department with progressive thickening of the facial skin and eyelids, plus increased folds over his forehead for the last 5 months. He also complained of progressive enlargement of his hands and feet, with intermittent joint pains in his wrists, elbows, and ankles, along with occasional abdominal pain. He had a hearing loss and increased sweating.

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm.

The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognize such occurrences in them, and no procedures for discriminating them from normal people have been created so far.

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease.

Background: Artificial intelligence techniques are widely used in solving medical problems. Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease. But these techniques suffer from overfitting and hyper-parameters tuning problems.

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning.

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging.

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment.

Biostimulation of Anaerobic Digestion Using Iron Oxide Nanoparticles (IONPs) for Increasing Biogas Production from Cattle Manure.

The effect of synthesised IONPs employing a nontoxic leaf extract of as a reducing, capping, and stabilizing agent for increasing biogas and methane output from cattle manure during anaerobic digestion (AD) was investigated in this study. Furthermore, the UV-visible spectra examination of the synthesized nanoparticles revealed a high peak at 432 nm. Using a transmission electron microscope, the average particle size of IONPs observed was 30-80 nm, with irregular, ultra-small, semi-spherical shapes that were slightly aggregated and well-distributed.

Effect of countermeasure bodygear on cardiac-vascular-respiratory coupling during 6-degree head-down tilt: An earth-based microgravity study.

Prolonged exposure to microgravity causes physiological deconditioning in humans. Herein, a novel designed countermeasure gravitational load modulation bodygear has been developed to deal with the ill effects of the microgravity environment. The bodygear is designed to provide the wearer an axial loading from the shoulder to the feet that simulate Earth's gravity.

Colonic tuberculosis masquerading as crohn's disease.

Intestinal tuberculosis (TB) is a diagnostic challenge and can closely mimic Crohn's disease (CD) and colon cancer. These disease entities very closely resemble each other in symptomatology, imaging, appearance, and pathology. We present a case of colonic TB where the initial diagnostic workup was suggestive of CD.

Densely connected convolutional networks-based COVID-19 screening model.

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results.