Histopathological study of neoplastic lesions of large intestine in Kashmir Valley, India

Background: Aim of current study was to study the histopathological spectrum of neoplastic lesions of large intestine and histopathological pattern of colorectal carcinoma in young adults.Methods:We took a combined retrospective & prospective study in the department of pathology. The specimens were collected from subjects diagnosed as colorectal carcinomas in histopathology department and clinical details were sought from the medical records. Variables like age, sex, dietary habit, relevant history, tumor size, location, type of lesion, histological pattern of patients were checked.Results:In the series of 446 patients of colorectal neoplasm, maximum number of patients presented in 4th to 6th decade of life (47.30%), while as (15.46%) were between 20-40 years. The youngest patient with an adenocarcinoma was 18 years (male) of age and the oldest one was 80 years (male) of age. Average age of patients was 50.50 years. The male to female ratio was 1.3:1. The study revealed that the carcinoma of ascending colon was much more prevalent, constituting 107 (40.07%) cases. The proportion of well differentiated carcinoma was highest in left side colon and rectum. The incidence of moderately differentiated and poorly differentiated carcinoma was greater on right side colon.Conclusion:Adenocarcinoma is the most common histological variant of colon carcinomas.

Ensuring Earthquake-Proof Development in a Swiftly Developing Region through Neural Network Modeling of Earthquakes Using Nonlinear Spatial Variables

Northern Pakistan, the center of major construction projects due to the commencement of the China Pakistan Economic Corridor, is among the most earthquake-prone regions globally owing to its tectonic settings. The area has experienced several devastating earthquakes in the past, and these earthquakes pose a severe threat to infrastructure and life. Several researchers have previously utilized advanced tools such as Machine Learning (ML) and Deep Learning (DL) algorithms for earthquake predictions. This technological advancement helps with construction innovation, for instance, by designing earthquake-proof buildings. However, previous studies have focused mainly on temporal rather than spatial variables. The present study examines the impact of spatial variables to assess the performance of the different ML and DL algorithms for predicting the magnitude of short-term future earthquakes in North Pakistan. Two ML methods, namely Modular Neural Network (MNN) and Shallow Neural Network (SNN), and two DL methods, namely Recurrent Neural Network (RNN) and Deep Neural Network (DNN) algorithms, were used to meet the research objectives. The performance of the techniques was assessed using statistical measures, including accuracy, information gain analysis, sensitivity, specificity, and positive and negative predictive values. These metrics were used to evaluate the impact of including a new variable, Fault Density (FD), and the standard seismic variables in the predictions. The performance of the proposed models was examined for different patterns of variables and different classes of earthquakes. The accuracy of the models for the training data ranged from 73% to 89%, and the accuracy for the testing data ranged from 64% to 85%. The analysis outcomes demonstrated an improved performance when using an additional variable of FD for the earthquakes of low and high magnitudes, whereas the performance was less for moderate-magnitude earthquakes. DNN, and SNN models, performed relatively better than other models. The results provide valuable insights about the influence of the spatial variable. The outcome of the present study adds to the existing pool of knowledge about earthquake prediction, fostering a safer and more secure regional development plan involving innovative construction

Utility of FNAC in Conjunction with Cell Block for Diagnosing Space-Occupying Lesion (SOL) of Liver with Emphasis on Differentiating Hepatocellular Carcinoma from Metastatic SOL: Analysis of 61 Cases

Objectives: To study the cytological patterns of fine-needle aspiration cytology (FNAC) obtained from space-occupying lesions (SOLs) of the liver with an aim to differentiate primary hepatocellular carcinoma from metastatic deposits and to evaluate the added advantage and efficacy of studying cell blocks in conjunction with smears for enhancing diagnostic accuracy.   Methods: This prospective study took place over two years (September 2007 to 2009) and included 61 patients with cases of liver SOLs that were clinically or radiologically suspicious for malignancy and who were referred for computed tomography or ultrasonography-guided FNAC. Smears were prepared from the aspirated material, and any remainder was used to make the cell block (n = 55). A final diagnosis was made after evaluating the smears and cell block sections.   Results: On cytomorphology, a diagnosis of moderately differentiated hepatocellular carcinoma (HCC) and metastatic carcinoma was made in 10 (18.2%) and 25 (45.5%) cases, respectively, and were confirmed using cell block sections. In cases where it was difficult to differentiate between well-differentiated HCC and regenerative nodules, and between poorly differentiated HCC and poorly differentiated metastatic carcinoma, a final diagnosis was made with the help of cell blocks sections. Cell blocks assisted in reaching a final diagnosis in 16 (29.1%) cases. Cases that were diagnosed using cytomorphology were confirmed by the cell block method. In these 39 (70.9%) cases we were able to render a diagnosis with much more confidence.   Conclusion: In our experience, difficulties in diagnosing SOL liver are attributed to differentiation of the tumor. Cell block preparation gives an additional advantage as architectural details can be studied that help to reach an accurate diagnosis in problematic and challenging cases. Thus, we strongly recommend the use of the cell block technique in conjunction with cytosmears for the purpose of diagnosis

Extramedullary hematopoiesis presenting as a solitary renal mass and mimicking a malignant tumor: A rare case report

Extramedullary hematopoiesis (EMH) is the development of hematopoietic tissue outside the bone marrow and it most often occurs in the liver and spleen. Renal EMH is quite rare, and there are very few case reports concerning the kidney. We describe a case diagnosed with congenital dyserythropoetic anemia presenting with a solitary renal mass with splenomegaly. CECT showed a heterogeneously enhancing mass lesion suggestive of renal neoplasm. Microscopic examination revealed features of extramedullary hematopoiesis. We intend to present this case because of the rarity of EMH in kidney and to emphasize that its possibility should be kept in mind in any case of solitary renal mass, especially in those patients suffering from chronic hematological disorders

Role of commercially available SARS-CoV-2 detection kits in pandemic of COVID-19 on the basis of N and E gene detection

Coronavirus has blowout worldwide from the time when its revelation in Hubei Province, China in December 2019 introducing a genuine general wellbeing emergency. The capacity to recognize an irresistible specialist in a broad pestilence is vital to the achievement of isolate endeavors notwithstanding the delicate and precise screening of expected instances of disease from patients in a clinical setting. Structural proteins the basic key role-playing in SARS-CoV2 identification include a spike, envelope membrane, nucleocapsid, and helper proteins. N-protein ties to the infection single positive-strand RNA that permits the infection to assume control over human cells and transform them into infection industrial facilities inside the capsid and E-protein shows a significant part in infection gathering, film permeability of the host cell, and infection has cell correspondence. Nucleic-Acid base testing presently offers the most touchy and early discovery of COVID-19. Notwithstanding, analytic advancements have explicit impediments and announced a few false negative and false positive cases, particularly during the beginning phases of contamination. Presently, more refined diagnostics are being created to improve the COVID-19 determination. This article presents an outline of diagnostic approaches to address a few inquiries and issues identified with the constraints of flow innovations and future innovative work difficulties to empower ideal, fast, minimal effort, and precise analysis of arising irresistible illnesses We depict purpose of-care diagnostics that are not too far off and urge scholastics to propel their advancements past origination. Creating fitting and-play diagnostics to deal with the SARS-CoV-2 flare-up would be valuable in forestalling forthcoming pandemics.Keywords: Role of commercially available kits; SARS-CoV2; Pandemic of Covid-19; N gene; E gen

Ensuring Earthquake-Proof Development in a Swiftly Developing Region through Neural Network Modeling of Earthquakes Using Nonlinear Spatial Variables

Northern Pakistan, the center of major construction projects due to the commencement of the China Pakistan Economic Corridor, is among the most earthquake-prone regions globally owing to its tectonic settings. The area has experienced several devastating earthquakes in the past, and these earthquakes pose a severe threat to infrastructure and life. Several researchers have previously utilized advanced tools such as Machine Learning (ML) and Deep Learning (DL) algorithms for earthquake predictions. This technological advancement helps with construction innovation, for instance, by designing earthquake-proof buildings. However, previous studies have focused mainly on temporal rather than spatial variables. The present study examines the impact of spatial variables to assess the performance of the different ML and DL algorithms for predicting the magnitude of short-term future earthquakes in North Pakistan. Two ML methods, namely Modular Neural Network (MNN) and Shallow Neural Network (SNN), and two DL methods, namely Recurrent Neural Network (RNN) and Deep Neural Network (DNN) algorithms, were used to meet the research objectives. The performance of the techniques was assessed using statistical measures, including accuracy, information gain analysis, sensitivity, specificity, and positive and negative predictive values. These metrics were used to evaluate the impact of including a new variable, Fault Density (FD), and the standard seismic variables in the predictions. The performance of the proposed models was examined for different patterns of variables and different classes of earthquakes. The accuracy of the models for the training data ranged from 73% to 89%, and the accuracy for the testing data ranged from 64% to 85%. The analysis outcomes demonstrated an improved performance when using an additional variable of FD for the earthquakes of low and high magnitudes, whereas the performance was less for moderate-magnitude earthquakes. DNN, and SNN models, performed relatively better than other models. The results provide valuable insights about the influence of the spatial variable. The outcome of the present study adds to the existing pool of knowledge about earthquake prediction, fostering a safer and more secure regional development plan involving innovative construction