Background: Currently, the CAP protocol mandates evaluation of ≥12 Lymph nodes as a quality indicator for the adequacy of pathologic examination of colorectal cancer resection specimens. Aim: To identify factors that may influence the lymph node yield in colorectal cancer specimens and to compare with the relevant publications. Methods: The retrospective study of seventy patients with loco-regional colorectal adenocarcinomas treated by standard surgical resection from April 2015- April 2017 was included. All cases with inadequate lymph nodes had been re-grossed by another pathologist. Variables like age, gender, primary site, type of surgery, specimen length, tumour size, grade and stage, neoadjuvant therapy and tumour site perforation were evaluated for their impact on the average total number of nodes examined. Results: Out of seventy, eleven [15.71%] patients had inadequate mean nodal yield [MNY]. Of these eleven patients, MNY was greater in males [6.6] than in females [6.4]. MNY was lesser in patients with age >50years [5.71] than patients ≤ 50 years [8]. The yield increased exponentially with increasing tumour stage and tumour size. Yield was higher in tumours with perforation. Specimens longer than 20cm had a higher yield [7.29] than in shorter specimens [5.25]. The yield was lesser when tumour is located more distally [APR:4.5 and AR &sigmoid colectomy:7.7]. Seven patients had taken neoadjuvant therapy [63.6%] of whom, six had moderately differentiated adenocarcinoma & one had no tumour. Conclusion: Factors like neoadjuvant therapy, age & gender of the patient, type of surgery, length of the specimen, tumour size, grade, stage, site & perforation, affect the MNY in colorectal cancers.
The first step in COVID-19 pathogenesis is the viral spike protein priming by Trans Membrane Peptide Receptor Serine S2 (TMPRSS2). TMPRSS2 promotes viral entry, cell to cell transmission, evasion of host immune response, and Angiotensin-Converting Enzyme 2 (ACE2) downregulation. Androgen through Androgen Receptor (AR) increases TMPRSS2 gene expression. Blocking AR may prevent viral entry and other TMPRSS2 mediated actions. ACE2 acts as an entry point for COVID-19 and as the counter regulator in Renin-Angiotensin-Aldosterone System (RAAS). RAAS maintains homeostasis of blood pressure, salt and water, inflammation, and immune response – through its two arms called “killer” and “protective pathways.” The balance between these two pathways determines life or death in disease states. ACE2 converts Angiotensin II to Angiotensin (1-7), which through Mas receptors mediates antiinflammatory, immune-modulatory, and anti-fibrotic actions. Angiotensin II also acts on Angiotensin type 2 Receptor (AT2R) to produce similar actions, called a "protective pathway." Further, Angiotensin II acts through its primary Angiotensin type 1 Receptor (AT1R), causing inflammatory, cytokine storm, and profibrotic response – called "Killer pathway." In COVID, down-regulated ACE2 leads to unabated Angiotensin II/AT1R – "Killer pathway" – actions producing a vicious cycle of "hyper-inflammatory state," resulting in ALI, ARDS, and death. AT1R activation further stimulates the secretion of aldosterone, which through Mineralocorticoid Receptor (MR), augments AT1R mediated 'killer pathway”. None of the COVID guideline drugs modulate this pathogenic mechanism. We examine the first time in history the scientific rationale for combined AR/AT1R/MR blockade for COVID-19 treatment and prevention.
Unclassified renal cell carcinoma (URCC), an aggressive form of renal cell carcinoma (RCC), represents 0.7– 5 .7% of renal tumours. Glomerular sparing in renal neoplasms (GS) is defined as a unique growth pattern in which tumour cells overrun intact glomeruli. An elderly woman presented with symptoms of left flank pain for three months along with fullness in the abdomen. On clinical and radiological examination, a renal mass was revealed and operated upon. A diagnosis of URCC with a glomerular sparing pattern was made on histopathological examination. The pathological details of this rare neoplasm are presented in this article.
This paper construes the toils in facial age estimation in images. The fact that manual age estimation is indeed hard rising out the urge for digital age estimation. To make estimation precise many works have been carried out by considering a lot of constraints. In this paper, facial age estimation is done more accurately. SFTA method is used for feature extraction and meticulous results are obtained for all age groups. Histogram equalization is done using the Otsu algorithm and three layered Deep Neural Network is used to classify the age group. In a Deep neural network, softmax normalization is done in the final layer to preserve the outlier values. By extracting 45 feature values concerning color and gradient, key point descriptor, orientation, shape and texture better estimation are obtained.
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