Head and neck squamous cell carcinomas (HNSCC) include heterogeneous group of tumors, classified according to their anatomical site. It is the sixth most prevalent cancer globally. Among South Asian countries, India accounts for 40% of HNC malignancies with significant morbidity and mortality. In the present study, we have performed exome sequencing and analysis of 51 Head and Neck squamous cell carcinoma samples. Besides known mutations in the oncogenes and tumour suppressors, we have identified novel gene signatures differentiating buccal, alveolar, and tongue cancers. Around 50% of the patients showed mutation in tumour suppressor genes TP53 and TP63. Apart from the known mutations, we report novel mutations in the genes AKT1, SPECC1, and LRP1B, which are linked with tumour progression and patient survival. A highly curated process was developed to identify survival signatures. 36 survival-related genes were identified based on the correlation of functional impact of variants identified using exome-seq with gene expression from transcriptome data (GEPIA database) and survival. An independent LASSO regression analysis was also performed. Survival signatures common to both the methods led to identification of 4 dead and 3 alive gene signatures, the accuracy of which was confirmed by performing a ROC analysis (AUC=0.79 and 0.91, respectively). Also, machine learning-based driver gene prediction tool resulted in the identification of IRAK1 as the driver (p-value = 9.7 e-08) and also as an actionable mutation. Modelling of the IRAK1 mutation showed a decrease in its binding to known IRAK1 inhibitors.
Aim to determine the effectiveness of Endolaryngeal Core Biopsy and Fine Needle aspiration Cytology in endophytic or submucosal laryngeal malignancies. Background: Endophytic and submucosal laryngeal tumours pose as diagnostic as well as management challenges to the head and neck surgeon. Identifying the tumour location and extent of disease is crucial for the surgeon to determine the treatment options and the potential outcomes. Persistent laryngeal edema following radiotherapy also presents with a diagnostic dilemma, as distinguishing between recurrent laryngeal carcinoma and radiotherapy sequels; which include fibrosis, oedema and soft tissue and cartilage necrosis, can be confusing and punch biopsies performed in such cases would more than often yield inadequate or superficial tissue. Using a core biopsy gun for acquiring biopsy specimen is known to have more cellular material, less damage to the surrounding structure depth control, immediate analysis with higher accuracy rates. Technique: Patients with suspected laryngeal malignancy are initially evaluated with flexible endoscopy with Narrow band imaging (NBI) and appropriate imaging. These patients are subjected to Microlaryngoscopic under general anaesthesia. A core biopsy gun is used to obtain samples from the suspicious area. Simultaneously, with the aid of microlaryngeal forceps, an FNAC is also done and sent for rapid processing. Conclusion: Trucut biopsy is a novel diagnostic tool that can be commonly used in early laryngeal malignancies especially in those cases where there is strong suspicion of malignancy and the lesion is found to be submucosal. Clinical significance: we noted that a routine DL scopy and Biopsy in submucoal disease often results in a acquiring a non-representative sample as well as causing inadvertent trauma to the surrounding mucosa. We encourage Laryngeal surgeons to routinely use Core biopsies and FNAC during routine microlaryngeal Examination for better yield, faster diagnosis and faster planning of treatment protocols.
Objectives: Radiological lung changes in COVID-19 infections present a noteworthy avenue to develop chest X-ray (CXR) -based testing models to support existing rapid detection techniques. The purpose of this study is to evaluate the accuracy of artificial intelligence (AI) -based screening model employing deep convolutional neural network for lung involvement. Material and Methods: An AI-based screening model was developed with state-of-the-art neural networks using Indian data sets from COVID-19 positive patients by authors of CAIR, DRDO, in collaboration with the other authors. Our dataset was comprised of 1324 COVID-19, 1108 Normal, and 1344 Pneumonia CXR images. Transfer learning was carried out on Indian dataset using popular deep neural networks, which includes DenseNet, ResNet50, and ResNet18 network architectures to classify CXRs into three categories. The model was retrospectively used to test CXRs from reverse transcriptase-polymerase chain reaction (RT-PCR) proven COVID-19 patients to test positive predictive value and accuracy. Results: A total of 460 RT-PCR positive hospitalized patients CXRs in various stages of disease involvement were retrospectively analyzed. There were 248 males (53.92%) and 212 females (46.08%) in the cohort, with a mean age of 50.1 years (range 12–89 years). The commonly observed alterations included lung consolidations, ground-glass opacities, and reticular–nodular opacities. Bilateral involvement was more common compared to unilateral involvement. Of the 460 CXRs analyzed, the model reported 445 CXRs as COVID -19 with an accuracy of 96.73%. Conclusion: Our model, based on a two-level classification decision fusion and output information computation, makes it a robust, accurate and reproducible tool. Based on the initial promising results, our application can be used for mass screening.
The milder form of infection and higher rates of recovery witnessed among COVID-19 patients in India is indicative of the potential intervention of other “unconventional” biological mechanisms. The recently established similarity between beta-coronavirus strains in animals and humans led us to hypothesize that previous contact with infected dogs or cattle could shield humans from the circulating SARS-CoV-2 virus. We further believe that our hypothesis, if confirmed by further studies, could be used as a potential vaccine strategy.
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