Hyperthermia therapy is now being used to treat cancer. However, understanding the pattern of temperature increase in biological tissues during hyperthermia treatment is essential. In recent years, many physicians and engineers have studied the use of computational and mathematical models of heat transfer in biological systems. The rapid progress in computing technology has intrigued many researchers. Many medical procedures also use engineering techniques and mathematical modeling to ensure their safety and assess the risks involved. One such model is the modified Pennes bioheat conduction equation. This paper provides an analytical solution to the modified Pennes bioheat conduction equation with a single relaxation time by incorporating in it the (MGT) equation. The suggested model examines heat transport in biological tissues as forming an infinite concentric spherical region during magnetic fluid hyperthermia. To investigate thermal reactions caused by temperature shock, specifically the influence of heat generation through heat treatment on a skin tumor [AEGP9], the Laplace transformation, and numerical inverse transformation methods are used. This model was able to explain the effects of different therapeutic approaches such as cryotherapy sessions, laser therapy, and physical occurrences, transfer, metabolism support, and blood perfusion. Comparison of the numerical results of the suggested model with those in the literature confirmed the validity of the model’s numerical results.

An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the central nervous system characterized by the demyelination of nerves, neural degeneration, and axonal loss. Cognitive impairment, including memory decline, is a significant feature in MS affecting up to 70% of patients. Thereby, it substantially impacts patients’ quality of life. Biochanin A (BCA) is an o-methylated isoflavone with a wide variety of pharmacological activities, including antioxidant, anti-inflammatory, and neuroprotective activities. Thus, this study aimed to investigate the possible protective effects of BCA on memory decline in the cuprizone (CPZ) model of MS. Thirty Swiss albino male mice (SWR/J) were randomly divided into three groups (n = 10): control (normal chow + i.p. 1:9 mixture of DMSO and PBS), CPZ (0.2% w/w of CPZ mixed into chow + i.p. 1:9 mixture of DMSO and PBS), and CPZ + BCA (0.2% w/w of CPZ mixed into chow + i.p. 40 mg/kg of BCA). At the last week of the study (week 5), a series of behavioral tasks were performed. A grip strength test was performed to assess muscle weakness while Y-maze, novel object recognition task (NORT), and novel arm discrimination task (NADT) were performed to assess memory. Additionally, histological examination of the hippocampus and the prefrontal cortex (PFC) were conducted. BCA administration caused a significant increase in the grip strength compared with the CPZ group. Additionally, BCA significantly improved the mice’s spatial memory in the Y-maze and recognition memory in the NORT and the NADT compared with the CPZ group. Moreover, BCA mitigated neuronal damage in the PFC and the hippocampus after five weeks of administration. In conclusion, our data demonstrates the possible protective effect of BCA against memory deterioration in mice fed with CPZ for five weeks.

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the most common cause of dementia. Various animal models are widely used to investigate its underlying mechanisms, including lipopolysaccharide (LPS)-induced neuroinflammation models. Aim: In this study, we aimed to investigate the effect of different doses (0.25, 0.5, and 0.75 mg/kg) of LPS on short- and long-term spatial memory and hippocampal morphology in an experimental AD mouse model. Materials and methods: Twenty-four adult male Swiss mice (SWR/J) weighing 18–25 g were divided into four groups: control, 0.25 mg/kg LPS, 0.50 mg/kg LPS, and 0.75 mg/kg LPS. All groups were treated with LPS or vehicle for 7 days. Behavioral tests were started (Morris water maze for 6 days and Y maze for 1 day) on the last 2 days of injections. After the behavioral procedures, tissues were collected for further histological investigations. Result: All LPS doses induced significant short- and long-term spatial memory impairment in both the Y maze and Morris water maze compared with the control group. Furthermore, histological examination of the hippocampus indicated degenerating neurons in both the 0.50 mg/kg and 0.75 mg/kg LPS groups, while the 0.25 mg/kg LPS group showed less degeneration. Conclusion: our results showed that 0.75 mg/kg LPS had a greater impact on early-stage spatial learning memory and short-term memory than other doses. Our behavioral and histological findings suggest 0.75 mg/kg LPS as a promising dose for LPS-induced AD models.

Recently, artificial intelligence (AI) with deep learning (DL) and machine learning (ML) has been extensively used to automate labor-intensive and time-consuming work and to help in prognosis and diagnosis. AI’s role in biomedical and biological imaging is an emerging field of research and reveals future trends. Cervical cell (CCL) classification is crucial in screening cervical cancer (CC) at an earlier stage. Unlike the traditional classification method, which depends on hand-engineered or crafted features, convolution neural network (CNN) usually categorizes CCLs through learned features. Moreover, the latent correlation of images might be disregarded in CNN feature learning and thereby influence the representative capability of the CNN feature. This study develops an equilibrium optimizer with ensemble learning-based cervical precancerous lesion classification on colposcopy images (EOEL-PCLCCI) technique. The presented EOEL-PCLCCI technique mainly focuses on identifying and classifying cervical cancer on colposcopy images. In the presented EOEL-PCLCCI technique, the DenseNet-264 architecture is used for the feature extractor, and the EO algorithm is applied as a hyperparameter optimizer. An ensemble of weighted voting classifications, namely long short-term memory (LSTM) and gated recurrent unit (GRU), is used for the classification process. A widespread simulation analysis is performed on a benchmark dataset to depict the superior performance of the EOEL-PCLCCI approach, and the results demonstrated the betterment of the EOEL-PCLCCI algorithm over other DL models.