Background. Solid organ transplant (SOT) recipients are considered to be "vulnerable" to COVID-19 infection due to immunosuppression. To date, there are no studies that compared the disease severity of COVID-19 in SOT recipients with nontransplant patients. Methods. In this case-control study, we compared the outcomes of COVID-19 between SOT recipients and their matched nontransplant controls. The cases were all adult SOT recipients (N = 41) from our academic health center who were diagnosed with COVID-19 between March 10, 2020 and May 15, 2020 using positive reverse transcriptase polymerase chain reaction for SARS-CoV2. The controls (N = 121) were matched on age (±5 y), race, and admission status (hospital or outpatient). The primary outcome was death and secondary outcomes were severe disease, intubation and renal replacement therapy (RRT). Results. Median age of SOT recipients (9 heart, 3 lung, 16 kidney, 8 liver, and 5 dual organ) was 60 y, 80% were male and 67% were Black. Severe disease adjusted risk of death was similar in both the groups (hazard ratio = 0.84 [0.32-2.20]). Severity of COVID-19 and intubation were similar, but the RRT use was higher in SOT (odds ratio = 5.32 [1.26, 22.42]) compared to non-SOT COVID-19 patients. Among SOT recipients, COVID-19-related treatment with hydroxychloroquine (HCQ) was associated with 10-fold higher hazard of death compared to without HCQ (hazard ratio = 10.62 [1.24-91.09]). Conclusions. Although African Americans constituted one-tenth of all SOT in our center, they represented two-thirds of COVID-19 cases. Despite high RRT use in SOT recipients, the severe disease and short-term death were similar in both groups. HCQ for the treatment of COVID-19 among SOT recipients was associated with high mortality and therefore, its role as a treatment modality requires further scrutiny.

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In today’s real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.

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