Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN

The utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital Stethoscope data. The model analysis is observed with max-pooling and without max-pool operations using multi-feature channels on respiratory digital stethoscope data. In addition, COVID-19 sound data and enriched data, which are recently acquired data to enhance model performance using a combination of L2 regularization to overcome the risk of overfitting because of less respiratory sound data, are included in the work. The suggested DCNN with Max-Pooling on the improved dataset demonstrates cutting-edge performance employing a multi-feature channels spectrogram. The model has been developed with different convolutional filter sizes (1 × 12 , 1 × 24 , 1 × 36 , 1 × 48 , and 1 × 60 ) that helped to test the proposed neural network. According to the experimental findings, the suggested DCNN architecture with a max-pooling function performs better to identify respiratory disease symptoms than DCNN without max-pooling. In order to demonstrate the model's effectiveness in categorization, it is trained and tested with the DCNN model that extract several modalities of respiratory sound data.

Modulating effect of Leptadenia reticulata (Retz) Wight & arn against chromate (VI)-induced immunosuppression and oxidative stress on mouse splenic lymphocytes and bone marrow derived macrophages

ETHNOPHARMACOLOGICAL RELEVANCE

Leptadenia reticulata (Retz) Wight & arn is mentioned in the ancient ayurvedic literature as an immune booster and rejuvenator.

AIMS OF THE STUDY

To investigate, the effects of different forms of the extract of Leptadenia reticulata [Aqueous extract (JAE), Padavashesha kashaya (JPK) and Tarpana kashaya (JTK)] to alleviate the experimental immunosuppression induced by the immunotoxicant chromate (VI) in vitro.

MATERIALS AND METHODS

Standard cell proliferation and cytotoxicity assays like MTT assay, trypan blue dye exclusion test, neutral red dye uptake test, NBT reduction test, determination of percentage cell survival and estimation of markers of oxidative stress were performed in the study. The study was conducted on primary cultures of mouse splenic lymphocytes and bone marrow derived macrophages.

RESULTS AND CONCLUSIONS

Treatment with all the three forms of the extract used in the study offered protection against chromate (VI)-induced immunosuppression and the overall protective effect was found to be superior in the case of the aqueous extract of Leptadenia reticulata (JAE). These results confirm that Leptadenia reticulata acts as a modulator and alleviates the immunosuppressive conditions induced by chromate (VI).