Search

The digitization of important documents and their segregation can be a beneficial and time-saving activity as individuals will have greater access to important documents and will be able to use them in regular tasks as well as endeavours. In recent years, research into the application of deep networks in robot systems has increased as a direct consequence of the advancements made in classification algorithms over the past few decades. Robotic vision automation for the segregation of sensitive and non-sensitive documents is required for many security concerns. The methodology of this article is initially focused on the identification of a good computer vision-based technique for the classification of sensitive documents from non-sensitive documents. The authors first identified the standard parameters in terms of reliability, loss, precision, and recall by employing deep learning techniques, such as neural networks with convolutions and transfer learning (TL) algorithms. The extraction of features based on pre-trained deep learning models was referenced in numerous publications. Similarly, we applied most of the feature extraction techniques to identify feature extraction from the images. Then, these features were classified by machine and ensemble learning models. However, the pre-trained models-based feature extraction along with machine learning classification resulted better in comparison to the deep learning and TL procedures. Further, the better-identified techniques were applied as the brain behind the vision of a robotic structure to automate the segregation of sensitive documents from non-sensitive documents. This proposed robotic structure could be applied when we have to find some specific and classified document from the haystack.

Background: Lymphadenopathy may be an incidental finding and/or primary or secondary manifestation of underlying diseases which may be neoplastic or non-neoplastic. The aim of the current study was to evaluate the spectrum of lymph node lesions according to the Sydney system in our setting and to assess the risk of malignancy in each category. Methods: The present study was carried out in the Department of Pathology, Nalanda Medical College and Hospital, Patna (Bihar) retrospectively during a one-year period from November 2020 to October 2021. 332 FNACs were performed on patients and the ROM (Risk of Malignancy) was assessed for each diagnostic category. Results: In the present study, 11 cases were categorized as L1, inadequate/non-diagnostic; 291 cases as L2,benign; 06 cases as L3, AUS/ALUS; 03 as L4, Suspicious for malignancy, and finally 21 cases were categorized as L5, Malignant. The ROM was 1.28% in L2, 50% in L3, and 100% in L4,L5. ROM for L1 category could not be assessed. Conclusion: The universal implementation of the Sydney system of reporting lymph node cytopathology can improve the diagnostic accuracy which will not only instill confidence in the pathologists but also help the clinicians to adopt better management strategies.

Rishabh Lohray,1 Kritin K Verma,2 Leo L Wang,3 Dylan Haynes,3 Daniel J Lewis3 1Baylor College of Medicine, Houston, TX, USA; 2Texas Tech University Health Sciences Center, School of Medicine, Lubbock, TX, USA; 3Department of Dermatology, University of Pennsylvania, Philadelphia, PA, USACorrespondence: Daniel J Lewis, Penn Dermatology Oncology Center, Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Boulevard, Suite 1-330S, Philadelphia, PA, 19104, USA, Tel +1 215-360-0909, Fax +1 215-662-4132, Email Daniel.Lewis@pennmedicine.upenn.eduIntroduction: Avelumab is a programmed cell death-ligand 1 (PD-L1) inhibitor approved by the Food and Drug Administration for advanced Merkel cell carcinoma (MCC). Studies conducted in real-world settings have shed light on its effectiveness and safety in clinical settings.Areas Covered: Real-world studies on avelumab for MCC from North and South America, Europe, and Asia have been presented in this review. Most studies are on patients over age 70 and have a male-predominant sex ratio. Overall response rates range from 29.1% to 72.1%, (disease control rate: 60.0– 72.7%; complete response rate: 15.8%– 37.2%; partial rate: 18.2– 42.1%; stable disease: 7.1– 30.9%; progressive disease: 7.1– 40.0%) and median progression free survival ranges from 8.1 to 24.1 months depending on the population studied. Immunosuppressed patients appear to benefit from avelumab as well, with response rates equivalent to the general population. Patients receiving avelumab as a first-line agent tend to have better outcomes than those using it as a second-line therapy. Fatigue, infusion-related reactions, and dyspnea were some of the most common adverse events identified in real-world studies. Autoimmune hepatitis and thyroiditis were also observed.Conclusion: The use of avelumab as a safe and effective treatment option for advanced MCC is supported by real-world data, although additional study is required to assess long-term efficacy and safety outcomes.Keywords: Merkel cell carcinoma, avelumab, real-world studies, immune-checkpoint inhibitors, tumor response, adverse events