Implications and Types of Artefacts in Oral Histopathology Tissue Processing

There is a scarcity of information regarding the occurrence and appearance of artefacts in the literature studies observed till date. This study aims to provide a more a comprehensive approach on identifying the different types of artefacts and also attempts to provide with description regarding their relation to the parent slide and the remedies in order to prevent misinterpretation. This study aimed to prepare a comprehensive report of the commonly occurring artefacts in archival collection of pathology laboratory

MicroRNA-142 reduces monoamine oxidase A expression and activity in neuronal cells by downregulating SIRT1

Aberrant expression of microRNAs (miRs) has been implicated in the pathogenesis of several neurodegenerative disorders. In HIV-associated neurocognitive disorders (HAND), miR-142 was found to be upregulated in neurons and myeloid cells in the brain. We investigated the downstream effects of chronic miR-142 upregulation in neuronal cells by comparing gene expression in stable clones of the human neuroblastoma cell line BE(2)M17 expressing miR-142 to controls. Microarray analysis revealed that miR-142 expression led to a reduction in monoamine oxidase (MAO) A mRNA, which was validated by qRT-PCR. In addition to the mRNA, the MAOA protein level and enzyme activity were also reduced. Examination of primary human neurons revealed that miR-142 expression indeed resulted in a downregulation of MAOA protein level. Although MAOA is not a direct target of miR-142, SIRT1, a key transcriptional upregulator of MAOA is, thus miR-142 downregulation of MAOA expression is indirect. MiR-142 induced decrease in MAOA expression and activity may contribute to the changes in dopaminergic neurotransmission reported in HAND

Combined fluorescent in situ hybridization for detection of microRNAs and immunofluorescent labeling for cell-type markers

Identification of the cell type of origin for normal or aberrant gene expression is critical for many studies, and poses a significant problem for some regulatory RNAs such as microRNAs. MicroRNAs are small non-coding RNAs that regulate cellular function by targeting specific mRNAs and reducing the level of their protein product. Aberrant expression of miRNAs in cell-types where they are not normally expressed occurs in several disease conditions. Therefore, it is important to determine not only the expression level of microRNAs, but also where they are expressed. Here we describe a detailed method for fluorescent in situ hybridization (FISH) combined with immunofluorescent labeling for cell-type markers in formalin fixed paraffin embedded (FFPE) sections along with modifications required to adapt the protocol for primary neurons grown in culture. We have combined the specificity and stability of locked nucleic acid (LNA) probes with tyramide signal amplification. To prevent loss of small RNA species, we performed post-fixation with ethylcarbodiimide (EDC). Additionally by omitting protease digestion and using only high temperature with sodium citrate buffer for FFPE sections, we were able to perform immunolabeling for proteins concurrently with in situ hybridization without compromising efficacy of either procedure

Combined fluorescent in situ hybridization for detection of microRNAs and immunofluorescent labeling for cell-type markers

Identification of the cell type of origin for normal or aberrant gene expression is critical for many studies, and poses a significant problem for some regulatory RNAs such as microRNAs. MicroRNAs are small non-coding RNAs that regulate cellular function by targeting specific mRNAs and reducing the level of their protein product. Aberrant expression of miRNAs in cell-types where they are not normally expressed occurs in several disease conditions. Therefore, it is important to determine not only the expression level of microRNAs, but also where they are expressed. Here we describe a detailed method for fluorescent in situ hybridization (FISH) combined with immunofluorescent labeling for cell-type markers in formalin fixed paraffin embedded (FFPE) sections along with modifications required to adapt the protocol for primary neurons grown in culture. We have combined the specificity and stability of locked nucleic acid (LNA) probes with tyramide signal amplification. To prevent loss of small RNA species, we performed post-fixation with ethylcarbodiimide (EDC). Additionally by omitting protease digestion and using only high temperature with sodium citrate buffer for FFPE sections, we were able to perform immunolabeling for proteins concurrently with in situ hybridization without compromising efficacy of either procedure

Strain Pattern Analysis of Mylonites From Sitampundi-Kanjamalai Shear Zone, Thiruchengode, South India

This study aims to investigate the petrography and strain pattern of mylonites from parts of N-S trending Sitampundi-Kanjamalai Shear Zone (SKSZ) around Thiruchengode. The petrographic study indicates the presence of recrystallized quartz, K-feldspar, plagioclase, biotite and some hornblende. The kinematic analysis of Mylonites was done with the help of shear sense indicators such as recrystallized type quartz (quartz ribbon) around the cluster of feldspar, S-C fabric shows dextral shear sense and some sinisterly shear sense in some parts of SASZ which can be considered as a product of partitioning of both strain and vorticity between domains. These all indicates the simple shear extension along E-W direction and the mylonitic foliation shows the pure shear compression along N-S direction. Further the study of bulk strain analysis by Flinn plot method using L and T section of mylonite shows k<1 which lies in the field of flattening zone of finite strain. The kinematic vorticity number is calculated by Rxz/β method which gives the value of 0.36 indicating the general shear. The rigid grain graph shows that the pure shear component is more ­­­­dominant than the simple shear component. The analysis leads to the conclusion that the mylonite has experienced a high temperature shearing of above 700°cat deep crustal level

Application of Least Square Denoising to Improve ADMM Based Hyperspectral Image Classification

AbstractHyperspectral images contain a huge amount of spatial and spectral information so that, almost any type of Earth feature can be discriminated from any other feature. But, for this classification to be possible, it is to be ensured that there is as less noise as possible in the captured data. Unfortunately, noise is unavoidable in nature and most hyperspectral images need denoising before they can be processed for classification work. In this paper, we are presenting a new approach for denoising hyperspectral images based on Least Square Regularization. Then, the hyperspectral data is classified using Basis Pursuit classifier, a constrained L1 minimization problem. To improve the time requirement for classification, Alternating Direction Method of Multipliers (ADMM) solver is used instead of CVX (convex optimization) solver. The method proposed is compared with other existing denoising methods such as Legendre-Fenchel (LF), Wavelet thresholding and Total Variation (TV). It is observed that the proposed Least Square (LS) denoising method improves classification accuracy much better than other existing denoising techniques. Even with fewer training sets, the proposed denoising technique yields better classification accuracy, thus proving least square denoising to be a powerful denoising technique

Upregulation of cathepsin D in the caudate nucleus of primates with experimental parkinsonism

<p>Abstract</p> <p>Background</p> <p>In Parkinson's disease there is progressive loss of dopamine containing neurons in the substantia nigra pars compacta. The neuronal damage is not limited to the substantia nigra but progresses to other regions of brain, leading to loss of motor control as well as cognitive abnormalities. The purpose of this study was to examine causes of progressive damage in the caudate nucleus, which plays a major role in motor coordination and cognition, in experimental Parkinson's disease.</p> <p>Results</p> <p>Using chronic 1-methyl-4phenyl-1,2,3,6-tetrahydropyridine treatment of rhesus monkeys to model Parkinson's disease, we found a upregulation of Cathepsin D, a lysosomal aspartic protease, in the caudate nucleus of treated monkeys. Immunofluorescence analysis of caudate nucleus brain tissue showed that the number of lysosomes increased concurrently with the increase in Cathepsin D in neurons. <it>In vitro </it>overexpression of Cathepsin D in a human neuroblastoma cell line led to a significant increase in the number of the lysosomes. Such expression also resulted in extralysosomal Cathepsin D and was accompanied by significant neuronal death associated with caspase activation. We examined apoptotic markers and found a strong correlation of Cathepsin D overexpression to apoptosis.</p> <p>Conclusions</p> <p>Following damage to the substantia nigra resulting in experimental Parkinson's disease, we have identified pathological changes in the caudate nucleus, a likely site of changes leading to the progression of disease. Cathepsin D, implicated in pathogenic mechanisms in other disorders, was increased, and our <it>in vitro </it>studies revealed its overexpression leads to cellular damage and death. This work provides important clues to the progression of Parkinson's, and provides a new target for strategies to ameliorate the progression of this disease.</p

Clinicopathological study of viral skin lesions

Abstract Introduction: Many viral infections have prominent skin manifestations. simplex, molluscum contagiosum and human papilloma virus which causes verruca vulgaris, condyloma acuminatum, deep palmoplantar wart and verucca plana. They are of increased significance in immunocompromised patients. the viral lesions are diagnosed clinically and serologically, some require biopsy confirmation. identify the specific histological changes in individual viral lesions. To differentiate the lesions that can clinically mimi as bullous lesion or soft tissue mass. SMVMCH for a period of 3 years from 2011 to 2013 were taken for this study. studied, 31 patients were diagnosed to have skin manifestations of various viral infections. The most commonly encountered entity was Verruca molluscum contagiosum (4), herpes (2) and verucca plana the diagnosis were, intranuclear inclusions in herpes; cytoplasmic viral inclusion bodies in MC, deep palmoplantar wart; koilocytes in Verruca and condyloma .These were also associated with o produce warty, bullous and mass like lesions which can mimic non infectious conditions. Histopathological evaluation serves as a valuable tool for identification of virus induced skin changes and aids in approp lesions. In our study most common infection was HPV

Explainable AI Framework for COVID-19 Prediction in Different Provinces of India

In 2020, covid-19 virus had reached more than 200 countries. Till December 20th 2021, 221 nations in the world had collectively reported 275M confirmed cases of covid-19 & total death toll of 5.37M. Many countries which include United States, India, Brazil, United Kingdom, Russia etc were badly affected by covid-19 pandemic due to the large population. The total confirmed cases reported in this country are 51.7M, 34.7M, 22.2M, 11.3M, 10.2M respectively till December 20, 2021. This pandemic can be controlled with the help of precautionary steps by government & civilians of the country. The early prediction of covid-19 cases helps to track the transmission dynamics & alert the government to take the necessary precautions. Recurrent Deep learning algorithms is a data driven model which plays a key role to capture the patterns present in time series data. In many literatures, the Recurrent Neural Network (RNN) based model are proposed for the efficient prediction of COVID-19 cases for different provinces. The study in the literature doesnt involve the interpretation of the model behavior & robustness. In this study, The LSTM model is proposed for the efficient prediction of active cases in each provinces of India. The active cases dataset for each province in India is taken from John Hopkins publicly available dataset for the duration from 10th June, 2020 to 4th August, 2021. The proposed LSTM model is trained on one state i.e., Maharashtra and tested for rest of the provinces in India. The concept of Explainable AI is involved in this study for the better interpretation & understanding of the model behavior. The proposed model is used to forecast the active cases in India from 16th December, 2021 to 5th March, 2022. It is notated that there will be a emergence of third wave on January, 2022 in India.Comment: 12 page

Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India

Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data collected from India Meteorological Department in northeast region over a period of 118 years. We conducted a comparative analysis of these methods to determine their relative effectiveness in predicting rainfall patterns. Using historical rainfall data from multiple weather stations, we trained and validated our models to forecast future rainfall patterns. Our results indicate that both DMD and LSTM are effective in forecasting rainfall, with LSTM outperforming DMD in terms of accuracy, revealing that LSTM has the ability to capture complex nonlinear relationships in the data, making it a powerful tool for rainfall forecasting. Our findings suggest that data-driven methods such as DMD and deep learning approaches like LSTM can significantly improve rainfall forecasting accuracy in the North-East region of India, helping to mitigate the impact of extreme weather events and enhance the region's resilience to climate change.Comment: Paper is under review at ICMC 202