Diagnosis of Malignant Melanoma using a Neural Network

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1], Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this thesis, a novel neural network approach for the automated distinction of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics is presented. The approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. Promising results have been obtained using this method on real skin cancer images

SARS-CoV-2: comparison of IgG levels at 9 months post second dose of vaccination in COVID-survivor and COVID-naïve healthcare workers

Background: Natural (asymptomatic/symptomatic COVID-19 infection) and artificial (vaccination) exposure to the pathogen represent two modes of acquiring active immunity. No definitive guidelines exist regarding whether COVID-survivors (with infection/re-infection/re-re-infection in the three COVID-19 waves) require a modified vaccination schedule. Most countries are offering a third vaccine dose and many are contemplating a fourth dose. Our aim was to gauge the IgG-antibody levels 9m post second vaccination in healthcare workers (HCW) and compare these with IgG-levels 1m post-vaccination in the same cohort for any decline, and to compare the post-vaccination IgG-levels in COVID-survivors and COVID-naïve HCW at 9m.Methods: This prospective observational single-centric cohort study included 63 HCW of either sex, aged 18-70y who completed 9m post-vaccination. The IgG-titre was tested at 9-10m post second vaccination in COVID-survivors and COVID-naïve HCW.Results: At 1m and 9m post-vaccination IgG-levels in COVID-survivors (23.097±4.58 and 15.103±4.367 respectively; p<0.0001) and COVID-naïve HCW (16.277±6.36 and 9.793±6.928 respectively; p=0.0013) had unequal variance (Welsch test; p=0.0022 at 9m). 9/31 COVID-naïve HCW but none of the 32 COVID-survivors tested COVID-positive in the second wave post second vaccination. 11/31 and 3/32 HCW belonging to the former and latter groups developed COVID-19 in the third wave consequently deferring their third/precautionary vaccination.Conclusions: Although HCW with IgG-levels in all brackets developed COVID-19, the severity of symptoms corresponded with the IgG-levels. COVID-19 is here to stay, but in peaceful co-existence in endemic proportions. Considering evidence that immunity acquired by vaccination/natural infection is ephemeral, re-invention of vaccines to match the ever-mutating virus is foreseen.

Neural Network Diagnosis of Malignant Melanoma from Color Images

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, the authors are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images

Diagnosis of malignant melanoma using a neural network

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1], Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this thesis, a novel neural network approach for the automated distinction of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics is presented. The approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. Promising results have been obtained using this method on real skin cancer images --Abstract, page iii

An Updated Model of Price-to-Book

The price-to-book (PB) ratio is a measure of the relative value that the market places on a share of stock. We have estimated an empirical equation of two stages that explain about 62% of the variation in annual PB levels for the S&P 500 companies from the year 2000 to 2009. We explored the market\u27s ability to anticipate changes in performance and found that the market price appears to reflect anticipatory information not present in the model value. This paper both advances understanding of PB \u27s determinants and provides a tool for managers who wish to enhance their firm\u27s PB

Clinico- pathological profile and course of malignant pleural effusion in a tertiary care teaching hospital in western U.P. with special reference to lung cancer

Background: Malignant pleural effusion is a major clinical problem associated with primary and metastatic pleural malignancies. Pleural effusions from an unknown primary are responsible for 7-15% of all malignant pleural effusions. Presence of malignant pleural effusion puts the patient in advanced stage and renders the prognosis as poor. Aim: In this study we intend to find out the incidence of malignant pleural effusion, its aetiology and clinical course in patients attending a tertiary care teaching hospital. Results: A total of 308 patients were included in this study. A majority of the patients were in age group 50- 70 years (median age = 58.8 years; range 32- 85 yrs). Male to female ratio was 2.5:1. The major primary cancers were lung cancer (135), lymphoma (40), breast cancer (36), female genital tract (30) gastrointestinal (21), and others (8). In 38 cases primary remained unknown. The yields of pleural fluid cytology, blind pleural biopsy, CT/USG guided pleural biopsy and thoracoscopy were 60%, 49%, 76% and 91% respectively. Chemical pleurodesis yielded complete response in 80%, incomplete response in another 13% patients. Only 136 (44%) cases could be followed up for minimum of 6 months. A majority of them (95, 69.85%) died. Conclusion: We conclude that malignant pleural effusion is a commonly misdiagnosed medical entity. Lung cancer is the commonest cause. Despite all efforts, in about 15% of the cases, primary remains undiagnosed. Thoracoscopy/pleuroscopy is a cost effective measure for diagnosis. Chemical pleurodesis provides expected results but mortality remains high

Diagnosing Malignant Melanoma using a Neural Network

In recent years, there has been a rising interest in the early detection of skin cancer, particularly malignant melanoma, via automated screening and diagnosis process. In this paper, we present a novel neural network approach for the automated distinction of melanoma from three other benign categories of tumors which exhibit melanoma-like characteristics. Our approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. We have obtained promising results using our method on real skin cancer images

Tuberous sclerosis complex presenting as afebrile encephalopathy: Diverse etiology, unitary approach

Background: Tuberous sclerosis complex (TSC) is a multisystem genetic disorder with a range of physical manifestations that require regular evaluation, surveillance, and management. Central nervous system manifestations are the major cause of morbidity and mortality in TSC patients. Encephalopathy, which may be due to multiple etiologies, maybe seen sometimes in these patients. Clinical Description: We are sharing a series of three cases, presenting to us within a 6-month period with encephalopathy, who were admitted and treated by us. Etiologies were identified in all three cases with variable spectrum from nonconvulsive status epilepticus to autistic regression to valproate-associated hyperammonemia. Management: All three patients admitted with us were treated as per clinical guidelines for respective etiologies. All patients respond well to treatment and were discharged and are under regular follow-up. Conclusion: There are various etiologies for encephalopathy in a child with TSC. Therefore, thorough history, examination, and investigations should be carried out in every child with TSC to find out the likely cause of encephalopathy, and treatment should be initiated according to the underlying cause

Integrative Approach of MAP and Active Antimicrobial Packaging for Prolonged Shelf-Life of Composite Bottle Gourd Milk Cake

The current research explored the integrative effect of antimicrobial edible films and modified atmosphere packaging (MAP) on the quality parameters and shelf-life of bottle gourd burfi, which is a heat-desiccated composite Indian confection. The edible antimicrobial films prepared using a combination of nisin and natamycin (NANIF) were evaluated for their antimicrobial activity as the first line of defense against Bacillus cereus and Aspergillus niger. The product was wrapped in developed films, which was followed by flushing of the altered environment employing MAP in a closed PP box and evaluation during refrigerated storage at 4 &plusmn; 2 &deg;C, comparing the product with the control counterpart. During this period, the physicochemical, sensory, and microbiological status of the product was assessed. Results indicated a significant (p &le; 0.05) variance between the two kinds of samples wherein the antimicrobial film produced excellent results in terms of being less supportive toward microbial growth, thereby extending the life of film-treated samples beyond 35 days compared to the control (21 days). In addition, the product conformed to the legal standards of microbiological count well under the permissible limits laid by the FSSAI. Furthermore, the sensory characteristics of the product did not change much, illustrating the significance of the integrative approach