Diagnostic ability of deep learning in detection of pancreatic tumour

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.

Toxicity Assessment of Wild Mushrooms from the Western Ghats, India: An in Vitro and Sub-Acute in Vivo Study

Background: Poisoning by different kinds of toxic mushrooms is unfortunately becoming an increasingly important medical problem, evident from the growing number of reports worldwide since the 1950s. Mycetism being a health concern, deserves scientific attention. In this perspective, the present study aims to assess the potential effects of ingesting the selected wild mushrooms from regions of the Western Ghats, India.

Methods: The preliminary cytotoxicity of the selected mushrooms was studied in vitro on the intestinal NCM460 and the Chang's liver cell lines on the basis of cell viability. Further, the hepatotoxicity was assessed by measuring biologically relevant endpoints such as membrane integrity, mitochondrial stress and oxidative status. A 28 day sub-acute toxicity study was carried out by orally administering the mushroom extracts to mice at 250 and 500 mg/kg body weight. The hematological and serum analysis as well as histological examinations were carried out to evaluate their in vivo toxicity. GC-MS analysis of the mushrooms facilitated the identification of their volatile chemical profile.

Result: The in vitro intestinal cytotoxicity exhibited by these wild mushrooms in comparison to the edible mushroom indicated their potential gastrointestinal toxicity. The pathological findings in small intestine on exposure to Chlorophyllum molybdites and Agaricus endoxanthus also validates the speculations about their intestinal toxicity. The toxic insult to the hepatocytes due to Amanita angustilamellata, Entoloma crassum, and Clarkeinda trachodes was predictive of the observed in vivo hepatotoxicity which was also accompanied by renal toxicity at the higher dose of 500 mg/kg bwt.

Conclusion: The potential toxicity exhibited by these representative mushrooms from the wild warrants caution about their consumption. The present work could also have broader implications for global mycetism.

Pre-treatment of extracellular water soluble pigmented secondary metabolites of marine imperfect fungus protects HDF cells from UVB induced oxidative stress

The melanin precursor of fungal origin was found to be an excellent UVB inhibiting agent as experimented in HDF cells and in small animals.

Synthesis of silver nanoparticle and its application

In this work, silver nanoparticles have been synthesized by wet chemical technique, green synthesis and microbial methods. Silver nitrate (10(-3)M) was used with aqueous extract to produce silver nanoparticles. From the results it was observed that the yield of nanoparticles was high in green synthesis. The size of the silver nanoparticles was determined from Scanning Electron Microscope analysis (SEM). Fourier Transform Infrared spectroscopy (FTIR) was carried out to determine the presence of biomolecules in them. Its cytotoxic effect was studied in cancerous cell line and normal cell line. MTT assay was done to test its optimal concentration and efficacy which gives valuable information for the use of silver nanoparticles for future cancer therapy.