The hazardous effects of formalin and alcoholic fixative in mice: A public health perspective study

Comparative analysis of fixation techniques for signal detection in avian embryos

The choice of fixation method significantly impacts tissue morphology and visualization of gene expression and proteins after in situ hybridization chain reaction (HCR) or immunohistochemistry (IHC), respectively. In this study, we compared the effects of paraformaldehyde (PFA) and trichloroacetic acid (TCA) fixation techniques prior to HCR and IHC on chicken embryos. Our findings underscore the importance of optimizing fixation methods for accurate visualization and subsequent interpretation of HCR and IHC results, with implications for probe and antibody validation and tissue-specific protein localization studies. We found that TCA fixation resulted in larger and more circular nuclei and neural tubes compared to PFA fixation. Additionally, TCA fixation altered the subcellular fluorescence signal intensity of various proteins, including transcription factors, cytoskeletal proteins, and cadherins. Notably, TCA fixation revealed protein signals in tissues that may be inaccessible with PFA fixation. In contrast, TCA fixation proved ineffective for mRNA visualization. These results highlight the need for optimization of fixation protocols depending on the target and model system, emphasizing the importance of methodological considerations in biological analyses.

Effectiveness of fixation methods for wholemount immunohistochemistry across cellular compartments in chick embryos

The choice of fixation method significantly impacts tissue morphology and protein visualization after immunohistochemistry (IHC). In this study, we compared the effects of paraformaldehyde (PFA) and trichloroacetic acid (TCA) fixation prior to IHC on chicken embryos. Our findings underscore the importance of validating fixation methods for accurate interpretation of IHC results, with implications for antibody validation and tissue-specific protein localization studies. We found that TCA fixation resulted in larger and more circular nuclei compared to PFA fixation. Additionally, TCA fixation altered the appearance of subcellular localization and fluorescence intensity of various proteins, including transcription factors and cytoskeletal proteins. Notably, TCA fixation revealed protein localization domains that may be inaccessible with PFA fixation. These results highlight the need for optimization of fixation protocols depending on the target epitope and model system, emphasizing the importance of methodological considerations in biological analyses.

Computer vision based deep learning approach for toxic and harmful substances detection in fruits

Formaldehyde (CH₂O) is one of the significant chemicals mixed with different perishable fruits in Bangladesh. The fruits are artificially preserved for extended periods by dishonest vendors using this dangerous chemical. Such substances are complicated to detect in appearance. Hence, a reliable and robust detection technique is required. To overcome this challenge and address the issue, we introduce comprehensive deep learning-based techniques for detecting toxic substances. Four different types of fruits, both in fresh and chemically mixed conditions, are used in this experiment. We have applied diverse data augmentation techniques to enlarge the dataset. The performance of four different pre-trained deep learning models was then assessed, and a brand-new model named "DurbeenNet," created especially for this task, was presented. The primary objective was to gauge the efficacy of our proposed model compared to well-established deep learning architectures. Our assessment centered on the models' accuracy in detecting toxic substances. According to our research, GoogleNet detected toxic substances with an accuracy rate of 85.53 %, VGG-16 with an accuracy rate of 87.44 %, DenseNet with an impressive accuracy rate of 90.37 %, and ResNet50 with an accuracy rate of 91.66 %. Notably, the proposed model, DurbeenNet, outshone all other models, boasting an impressive accuracy rate of 96.71 % in detecting toxic substances among the sample fruits.