Advancement in Biopolymer Assisted Cancer Theranostics.

Applications of nanotechnology have increased the importance of research and nanocarriers, which have revolutionized the method of drug delivery to treat several diseases, including cancer, in the past few years. Cancer, one of the world's fatal diseases, has drawn scientists' attention for its multidrug resistance to various chemotherapeutic drugs. To minimize the side effects of chemotherapeutic agents on healthy cells and to develop technological advancement in drug delivery systems, scientists have developed an alternative approach to delivering chemotherapeutic drugs at the targeted site by integrating it inside the nanocarriers like synthetic polymers, nanotubes, micelles, dendrimers, magnetic nanoparticles, quantum dots (QDs), lipid nanoparticles, nano-biopolymeric substances, etc., which has shown promising results in both preclinical and clinical trials of cancer management. Besides that, nanocarriers, especially biopolymeric nanoparticles, have received much attention from researchers due to their cost-effectiveness, biodegradability, treatment efficacy, and ability to target drug delivery by crossing the blood-brain barrier. This review emphasizes the fabrication processes, the therapeutic and theragnostic applications, and the importance of different biopolymeric nanocarriers in targeting cancer both in vitro and in vivo, which conclude with the challenges and opportunities of future exploration using biopolymeric nanocarriers in onco-therapy with improved availability and reduced toxicity.

A novel artificial intelligence-based predictive analytics technique to detect skin cancer.

One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system's training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated using a Python program, and the current research's parameters are assessed against those of earlier studies. The results demonstrate that, compared to other models, the new research model produces better accuracy for predicting skin cancer by segmentation.

Neural correlates in functional brain mapping among breast cancer survivors receiving different chemotherapy regimens: a qEEG/HEG-based investigation.

BACKGROUND: Post-chemotherapy cognitive impairment commonly known as 'chemobrain' or 'chemofog' is a well-established clinical disorder affecting various cognitive domains including attention, visuospatial working memory, executive function, etc. Although several studies have confirmed the chemobrain in recent years, scant experiments have evaluated the potential neurotoxicity of different chemotherapy regimens and agents. In this study, we aimed to evaluate the extent of attention deficits, one of the commonly affected cognitive domains, among breast cancer patients treated with different chemotherapy regimens through neuroimaging techniques. METHODS: Breast cancer patients treated with two commonly prescribed chemotherapy regimens, Adriamycin, Cyclophosphamide and Taxol and Taxotere, Adriamycin and Cyclophosphamide, and healthy volunteers were recruited. Near-infrared hemoencephalography and quantitative electroencephalography assessments were recorded for each participant at rest and during task performance to compare the functional cortical changes associated with each chemotherapy regimen. RESULTS: Although no differences were observed in hemoencephalography results across groups, the quantitative electroencephalography analysis revealed increased power of high alpha/low beta in left fronto-centro-parietal regions involved in dorsal and ventral attention networks in the Adriamycin, Cyclophosphamide and Taxol-treated group compared with the Taxotere, Adriamycin and Cyclophosphamide and control group. The Adriamycin, Cyclophosphamide and Taxol-treated cases had the highest current source density values in dorsal attention network and ventral attention network and ventral attention network-related centers in 10 and 15 Hz associated with the lowest Z-scored Fast Fourier Transform coherence in the mentioned regions. CONCLUSIONS: The negatively affected neurocognitive profile in breast cancer patients treated with the Adriamycin, Cyclophosphamide and Taxol regimen proposes presumably neurotoxic sequelae of this chemotherapy regimen as compared with the Taxotere, Adriamycin and Cyclophosphamide regimen.

Comparative Analysis of the Chemotherapy-Related Cognitive Impairments in Patients with Breast Cancer: A Community-Based Research.

This study aimed to evaluate the effects of two common chemotherapy regimens on breast cancer (BC) survivors' cognition. The participants comprised 35 patients with BC who underwent two chemotherapy regimens, AC-T and TAC, and 24 matched healthy volunteers. The participants were assessed regarding cognitive function through Addenbrooke's Cognitive Examination and Cambridge Brain Science tests. The results represent the AC-T regimen to be more toxic than the TAC in domains of language, concentration, and visuospatial working memory (P-value = 0.036, 0.008, and 0.031, respectively) and should be prescribed with caution in patients with BC suffering from baseline cognitive impairments.

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.