Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs).

In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by the clinical judgment of a specialist or precisely by a chest CT scan. However, the lack of accessibility to the CT machines in many poor medical centers as well as its expensive service, demands more accessible methods for fast and cheap detection of lung inflammation. Here, we have introduced a novel method for tracing the inflammation and lung involvement in patients with pulmonary inflammation, such as COVID-19, by a simple electrolyte detection in their sputum samples. The presence of the electrolyte in the sputum sample results in the fern-like structures after air-drying. These fern patterns are different in the CT positive and negative cases that are detected by an AI application on a smartphone and using a low-cost and portable mini-microscope. Evaluating 160 patient-derived sputum sample images, this method demonstrated an interesting accuracy of 95%, as confirmed by CT-scan results. This finding suggests that the method has the potential to serve as a promising and reliable approach for recognizing lung inflammatory diseases, such as COVID-19.

Prevalence of the UGT1A1*6 (c.211G>A) Polymorphism and Prediction of Irinotecan Toxicity in Iranian Populations of Different Ethnicities.

BACKGROUND: Pharmacogenetic studies on irinotecan treatment in patients with metastatic colorectal cancer have indicated that genetic polymorphisms in UGT1A1*6 can lead to decreased enzyme activity and accumulation of the toxic metabolite SN-38. Here, we compared the prevalence of UGT1A1*6 in an Iranian population of different ethnicities with those of other populations. MATERIALS AND METHODS: A total of 300 healthy people of different ethnic groups including Persian, Azari, Lure, Kurdish, Arab, Baluch and Caspian in the Iranian population were enrolled. Genotyping of the UGT1A1*6 alleles (G/G, A/G, A/A) was performed by polymerase chain reaction-restriction fragment length polymorphism and direct genomic DNA sequencing. RESULT: The most predictive genotype among the Iranian ethnic groups, especially Persian, was the G/G genotype (wild-type genotype). The frequency of the A/G genotype among the Persian, Azari, Lure, Kurdish, Arab, Baluch and Caspian ethnicities were 15.69% (n = 27), 11.11% (n = 8), 5.88% (n = 1), 9.09% (n = 1), 10% (n = 1), 20% (n = 1) and 0% (n = 0), respectively. Only one person with Persian ethnicity was homozygous for the mutation in UGT1A1*6 (0.58%). Additionally, the frequency of the A and G alleles in Iranians was 6.83 and 93.16%, respectively. CONCLUSION: The identification of the UGT1A1*6 alleles is necessary among the different Iranian ethnic groups before irinotecan therapy, suggesting that genotyping would be helpful for clinicians to optimize chemotherapy or identify individuals at risk of adverse drug reactions before clinical trials.