Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: methods, applications and limitations.

BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE: This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS: Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS: Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS: The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION: Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.

NIR-responsive carboxymethyl-cellulose hydrogels containing thioketal-linkages for on-demand drug delivery system.

Near-infrared (NIR) light-responsive hydrogels have emerged as a highly promising strategy for effective anticancer therapy owing to the remotely controlled release of chemotherapeutic molecules with minimal invasive manner. In this study, novel NIR-responsive hydrogels were developed from reactive oxygen species (ROS)-cleavable thioketal cross-linkers which possessed terminal tetrazine groups to undergo a bio-orthogonal inverse electron demand Diels Alder click reaction with norbornene modified carboxymethyl cellulose. The hydrogels were rapidly formed under physiological conditions and generated N2 gas as a by-product, which led to the formation of porous structures within the hydrogel networks. A NIR dye, indocyanine green (ICG) and chemotherapeutic doxorubicin (DOX) were co-encapsulated in the porous network of the hydrogels. Upon NIR-irradiation, the hydrogels showed spatiotemporal release of encapsulated DOX (>96 %) owing to the cleavage of thioketal bonds by interacting with ROS generated from ICG, whereas minimal release of encapsulated DOX (<25 %) was observed in the absence of NIR-light. The in vitro cytotoxicity results revealed that the hydrogels were highly cytocompatible and did not induce any toxic effect on the HEK-293 cells. In contrast, the DOX + ICG-encapsulated hydrogels enhanced the chemotherapeutic effect and effectively inhibited the proliferation of Hela cancer cells when irradiated with NIR-light.

An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

Facile Fabrication of NIR-Responsive Alginate/CMC Hydrogels Derived through IEDDA Click Chemistry for Photothermal-Photodynamic Anti-Tumor Therapy.

Novel chemically cross-linked hydrogels derived from carboxymethyl cellulose (CMC) and alginate (Alg) were prepared through the utilization of the norbornene (Nb)-methyl tetrazine (mTz) click reaction. The hydrogels were designed to generate reactive oxygen species (ROS) from an NIR dye, indocyanine green (ICG), for combined photothermal and photodynamic therapy (PTT/PDT). The cross-linking reaction between Nb and mTz moieties occurred via an inverse electron-demand Diels-Alder chemistry under physiological conditions avoiding the need for a catalyst. The resulting hydrogels exhibited viscoelastic properties (G' ~ 492-270 Pa) and high porosity. The hydrogels were found to be injectable with tunable mechanical characteristics. The ROS production from the ICG-encapsulated hydrogels was confirmed by DPBF assays, indicating a photodynamic effect (with NIR irradiation at 1-2 W for 5-15 min). The temperature of the ICG-loaded hydrogels also increased upon the NIR irradiation to eradicate tumor cells photothermally. In vitro cytocompatibility assessments revealed the non-toxic nature of CMC-Nb and Alg-mTz towards HEK-293 cells. Furthermore, the ICG-loaded hydrogels effectively inhibited the metabolic activity of Hela cells after NIR exposure.

Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.

Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.

Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

NIR-degradable and biocompatible hydrogels derived from hyaluronic acid and coumarin for drug delivery and bio-imaging.

In this work, bioorthogonal and photodegradable hydrogels derived from norbornene (Nb) functionalized hyaluronic acid and a water soluble coumarin-based cross-linker possessing terminal tetrazine (Tz) groups, were developed for NIR-responsive release of doxorubicin (DOX). The inverse electron demand Diels-Alder cross-linking reaction between Nb and Tz functionalities formed the hydrogels at physiological conditions, whereas N2 gas liberated during the reaction created pores in the hydrogels. The gelation time ranges (about 5-20 min) and the viscoelastic behavior (G' ~ 346-1380 Pa) demonstrated that the resulting hydrogels were injectable and possessed tunable mechanical properties. Moreover, hydrogels released the encapsulated DOX upon NIR irradiation, owing to the NIR-responsive cleavage of coumarin-ester, and consequently, induced anti-tumor activity in BT-20 cancer cells. Additionally, the hydrogels could be excited at various wavelengths of the visible spectrum and can emit green to red fluorescence, demonstrating their simultaneous photo-responsive drug release and bio-imaging applications.

Natural Products/Bioactive Compounds as a Source of Anticancer Drugs.

Cancer is one of the major deadly diseases globally. The alarming rise in the mortality rate due to this disease attracks attention towards discovering potent anticancer agents to overcome its mortality rate. The discovery of novel and effective anticancer agents from natural sources has been the main point of interest in pharmaceutical research because of attractive natural therapeutic agents with an immense chemical diversity in species of animals, plants, and microorganisms. More than 60% of contemporary anticancer drugs, in one form or another, have originated from natural sources. Plants and microbial species are chosen based on their composition, ecology, phytochemical, and ethnopharmacological properties. Plants and their derivatives have played a significant role in producing effective anticancer agents. Some plant derivatives include vincristine, vinblastine, irinotecan, topotecan, etoposide, podophyllotoxin, and paclitaxel. Based on their particular activity, a number of other plant-derived bioactive compounds are in the clinical development phase against cancer, such as gimatecan, elomotecan, etc. Additionally, the conjugation of natural compounds with anti-cancerous drugs, or some polymeric carriers particularly targeted to epitopes on the site of interest to tumors, can generate effective targeted treatment therapies. Cognizance from such pharmaceutical research studies would yield alternative drug development strategies through natural sources which could be economical, more reliable, and safe to use.

General Nutritional Profile of Bee Products and Their Potential Antiviral Properties against Mammalian Viruses.

Bee products have been extensively employed in traditional therapeutic practices to treat several diseases and microbial infections. Numerous bioactive components of bee products have exhibited several antibacterial, antifungal, antiviral, anticancer, antiprotozoal, hepatoprotective, and immunomodulatory properties. Apitherapy is a form of alternative medicine that uses the bioactive properties of bee products to prevent and/or treat different diseases. This review aims to provide an elaborated vision of the antiviral activities of bee products with recent advances in research. Since ancient times, bee products have been well known for their several medicinal properties. The antiviral and immunomodulatory effects of bee products and their bioactive components are emerging as a promising alternative therapy against several viral infections. Numerous studies have been performed, but many clinical trials should be conducted to evaluate the potential of apitherapy against pathogenic viruses. In that direction, here, we review and highlight the potential roles of bee products as apitherapeutics in combating numerous viral infections. Available studies validate the effectiveness of bee products in virus inhibition. With such significant antiviral potential, bee products and their bioactive components/extracts can be effectively employed as an alternative strategy to improve human health from individual to communal levels as well.

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.

Reduction-responsive and bioorthogonal carboxymethyl cellulose based soft hydrogels cross-linked via IEDDA click chemistry for cancer therapy application.

In this work, novel biocompatible and reduction-responsive soft hydrogels were formulated from norbornene (Nb)-functionalized carboxymethyl cellulose (CMCNb). To cross-link the CMC-Nb via a highly bioorthogonal inverse electron demand Diels-Alder (IEDDA) reaction, we employed a water-soluble and reduction-responsive diselenide-based cross-linker possessing two terminal tetrazine (Tz) groups with varying molar concentrations (Nb/Tz molar ratios of 10/10, 10/05, and 10/2.5). The N2 microbubbles liberated as a by-product during the IEDDA reaction generated in-situ pores in hydrogel networks. The resulting hydrogels had highly porous structures and relatively soft mechanical properties (storage moduli in the range 74 ⁓160 Pa). The hydrogels showed high swelling ratios (>35 times), tunable gelation times (1-5 min), and excellent doxorubicin (DOX) loading efficiencies (>85 %). The hydrogels exhibited stimuli-responsive and fast release of DOX (99 %, after 12 h) in the presence of 10 mmol of glutathione as compared to the normal PBS solution (38 %). The cytotoxic effects of blank hydrogels were not observed against HEK-239 cells, while the DOX-encapsulated hydrogels exhibited anti-tumor activity in BT-20 cancer cells. The results indicate potential applications of the CMC-based soft hydrogels in injectable drug delivery systems.

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis.

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.

2D Transition Metal Carbides (MXene) for Electrochemical Sensing: A Review.

MXene, a novel class of 2-dimensional transition metal carbides has evolved as a promising material for various applications owing to its outstanding characteristics such as hydrophilicity, high electrical conductivity, surface area, and attractive topological structure. MXenes can form dispersion in common solvents and constitute composite with other nanomaterials, which can be utilized as effective transducers for molecular sensing. MXene-modified support materials, thus provide an intriguing platform for immobilization of target molecules onto their surface. The literature reveals that it has been increasingly utilized in the sensing of diverse types of analytes including glucose, pharmaceuticals, metals and dyes, cancer markers, pesticides, neurotransmitters, small valuable molecules, and so on. In this review, we summarize the recent updates in the MXene modified materials for sensing. For the convenience of our audience, we have distributed the analytes into categories and discussed them comprehensively. Not only we present the synthesis approach, electrochemical properties and surface chemistry of MXenes but also discussed briefly the current challenges and an outlook for future research in the related area.

Iron deficiency anemia in reproductive age women: A survey study of district Bahawalpur, Punjab, Pakistan.

Iron deficiency anemia (IDA) is one of the foremost health issues among women of reproductive age. The study highlights to assess the level of awareness about the causes, symptoms, prevention and treatment of IDA among women of reproductive age in district Bahawalpur, province Punjab, Pakistan. A randomized study was conducted by using a self-designed standardized questionnaire disseminated to the hostels of female residents and homes in the immediate vicinity of Islamia University Bahawalpur. Females aged 18-45 years without any previous history of medical or gynecological problems were enlisted. A total number of 200 women were surveyed for awareness of iron deficiency anemia. Seventy three percent (73%) of women (n=146) were aware of the term IDA with the highest proportion of women falling in the age bracket 20-35 years. Most (66.9%) of the women were aware of the fact that their diet contains iron and its importance in health. It is concluded that, in reproductive age women the IDA can be prevented and treated through proper guidance and awareness through education.

A highly selective and sensitive ultrasonic assisted dispersive liquid phase microextraction based on deep eutectic solvent for determination of cadmium in food and water samples prior to electrothermal atomic absorption spectrometry.

A simple, fast, green, sensitive and selective ultrasonic assisted deep eutectic solvent liquid-phase microextraction technique was used for preconcentration and extraction of cadmium (Cd) in water and food samples by electrothermal atomic absorption spectrometry (ETAAS). In this technique, a synthesized reagent (Z)-N-(3,5-diphenyl-1H-pyrrol-2-yl)-3,5-diphenyl-2H-pyrrol-2-imine (Azo) was used as a complexing agent for Cd. The main factors effecting the pre-concentration and extraction of Cd such as effect of pH, type and composition of deep eutectic solvent (DES), volume of DES, volume of complexing agent, volume of tetrahydrofuran (THF) and ultrasonication time have been examined in detail. At optimum conditions the value of pH and molar ratio of DES were found to be 6.0 and 1:4 (ChCl:Ph), respectively. The detection limit (LOD), limit of quantification (LOQ), relative standard deviation (RSD) and preconcentration factor (PF) were observed as 0.023 ng L-1, 0.161 ng L-1, 3.1% and 100, correspondingly. Validation of the developed technique was observed by extraction of Cd in certified reference materials (CRMs) and observed results were successfully compared with certified values. The developed procedure was practiced to various food, beverage and water samples.

Improved Gas Chromatographic Determination of Guanidino Compounds Using Isovaleroylacetone and Ethyl Chloroformate as Derivatizing Reagents.

An improved GC method in terms of sensitivity and decrease in the analysis time has been developed for the analysis of eight guanidino compounds: guanidine (G), methylguanidine (MG), creatinine (CTN), guanidinoacetic acid (GAA), guanidinobutyric acid (GBA), guanidinopropionic acid (GPA), argenine (Arg), and guanidinosuccinic acid (GSA), using isovaleroylacetone (IVA) and ethyl chloroformate (ECF) as derivatizing reagents. The separation was obtained from column HP-5 (30 m × 0.32 mm i.d.) with film thickness of 0.25 µm within 11 min. The linear calibrations were obtained with 0.5 to 50 µg/mL with coefficient of determination (R(2)) within 0.9969 - 0.9998. Limits of detections (LODs) were within 5 - 140 ng/mL. The derivatization, separation and determination was repeatable (n = 6) with relative standard deviation (RSD) within 1.2 - 3.1%. The guanidino compounds were determined in deproteinized serum of healthy volunteers and uremic patients within below LOD to 8.8 µg/mL and below LOD to 43.99 µg/mL with RSD within 1.4 - 3.6%. The recovery of guanidino compounds calculated by standard addition from serum was within 96.1 - 98.9%, with RSD 1.4 - 3.6%.