Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

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Background & objectives:Acute respiratory distress syndrome (ARDS) is a common disorder in critically ill patients and is associated with high mortality. There is a paucity of literature on this condition from developing countries. This prospective observational study was designed to find out the aetiology, outcomes and predictors of mortality in ARDS.Methods:Sixty four consecutive patients who satisfied American-European Consensus Conference (AECC) definition of ARDS from medical Intensive Care Unit (ICU) of a tertiary care centre in New Delhi, India, were enrolled in the study. Demographic, biochemical and ventilatory variables were recorded for each patient. Baseline measurements of serum interleukin (IL)-1β, IL-6, tumour necrosis factor-alpha (TNF-α), procalcitonin (PCT) and high sensitivity C-reactive protein (hsCRP) were performed.Results:Common causes of ARDS included pneumonia [44/64 (68.7%)], malaria [9/64 (14.1%)] and sepsis [8/64 (12.5%]. Eight of the 64 (12.5%) patients had ARDS due to viral pneumonia. The 28-day mortality was 36/64 (56.2%). Independent predictors of mortality included non-pulmonary organ failure, [Hazard ratio (HR) 7.65; 95% CI 0.98-59.7, P=0.05], Simplified Acute Physiology Score (SAPS-II) [HR 2.36; 95% CI 1.14-4.85, P=0.02] and peak pressure (Ppeak) [HR 1.13; 95% CI 1.00-1.30, P = 0.04] at admission.Interpretation & conclusions:Bacterial and viral pneumonia, malaria and tuberculosis resulted in ARDS in a considerable number of patients. Independent predictors of mortality included non-pulmonary organ failure, SAPS II score and Ppeak at baseline. Elevated levels of biomarkers such as TNF-α, PCT and hsCRP at admission might help in identifying patients at a higher risk of mortality.

Objective: To evaluate the efficacy of single dose intravenous adenosine in differentiating atrioventricular nodal re-entrant tachycardia (AVNRT) from concealed pathway mediated atrioventricular re-entrant tachycardia (AVRT) using surface ECG at the bedside. Method: 12 mg of adenosine was administered to 97 consecutive patients who had documented narrow QRS tachycardia without manifest pre-excitation. The test was labelled positive for AVNRT if surface ECG recordings showed signs of dual atrioventricular (AV) node physiology-namely, PR jump or AV nodal echo. The diagnostic value of this test was evaluated by electrophysiological study as the yardstick.Results: The adenosine test was positive for AVNRT in 48 patients (adenosine induced PR jump in 48, AV nodal echo in 3) and negative in 49 patients. On electrophysiological study, 62 patients had AVNRT and 35 had concealed pathway mediated AVRT. Thus, the test had a sensitivity of 74% and specificity of 94%. The positive predictive value was 96% and the negative predictive value was 67%. Conclusion: Single dose (12 mg) intravenous adenosine administered during sinus rhythm can identify dual AV node physiology on surface ECG recording at the bedside. A positive adenosine test identified by a PR jump can differentiate AVNRT from AVRT with a high specificity and positive predictive accuracy.

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Double outlet atrium is a rare cardiac anomaly wherein one of the atriums, most frequently the right atrium, opens into both the ventricles. Although seen more commonly in the setting of atrioventricular septal defect, this arrangement can also be found when one of the atrioventricular connections is atretic due to absence of the atrioventricular connection and the other atrioventricular valve straddles the muscular ventricular septum. It is the specific anatomy and connections of the atrioventricular junction that clarifies the situation and distinguishes between these two types of double outlet atrium. In this report, we present a case of double outlet right atrium co-existing with the absence of left atrioventricular connection. We then discuss the morphologic aspects of this interesting anomaly.

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