Background and aims
The relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) and diabetes mellitus is bidirectional. On one hand, diabetes mellitus is associated with an increased risk of severe COVID-19. On the other hand, new onset diabetes and severe metabolic complications of pre-existing diabetes, including diabetic ketoacidosis (DKA) have been observed in patients with COVID-19. In this report, we describe two patient with diabetes mellitus who presented to our hospital with DKA. We also reviewed almost all published cases of DKA that had been precipitated by COVID-19.
Methods
Two patients were admitted with DKA, who were diagnosed to have COVID-19 on the basis of real time reverse transcription-polymerase chain reaction (RT-PCR) assay. Detailed history, anthropometry, laboratory investigations, imaging studies, clinical course and management outcomes were documented.
Results
First patient (30-year-male) had undiagnosed diabetes and no other comorbidities, and COVID-19 precipitated DKA. He also had COVID-19-associated pneumonia. Second patient (60-year-male) had long duration hypertension with no prior history of diabetes and developed cerebrovascular accident (CVA). He was also diagnosed with COVID-19 (RT-PCR assay) and DKA in the hospital. CVA and COVID-19 could have precipitated DKA. Both patients responded well to treatment and were discharged in a stable condition.
Conclusions
These cases show that COVID-19 can precipitate DKA in a significant number of patients. DKA can occur in patients with pre-existing diabetes or newly diagnosed diabetes. As COVID-19 and diabetes are prevalent conditions, high degree of suspicion is required to diagnose DKA timely in order to improve the prognosis of COVID-19-related diabetic ketoacidosis.
Background and aims
Acute onset diabetes and diabetic ketoacidosis (DKA) can be precipitated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in individuals with no history of diabetes. However, data regarding the follow-up of these individuals are scarce.
Methods
Three patients (data of two patients already published) with acute onset diabetes and DKA, precipitated by coronavirus disease 2019 (COVID-19), were followed for 14 weeks to assess the behavior of the diabetes. Detailed history, anthropometry, laboratory investigations, imaging studies, clinical course and outcomes were documented.
Results
Three individuals developed symptoms suggestive of SARS CoV-2 infection. After a few days, they were detected to have COVID-19 pneumonia, based on reverse transcription-polymerase chain reaction (RT-PCR) assay and chest imaging. In the meantime, they also developed acute onset diabetes and DKA, which were precipitated by COVID-19. They responded well to treatment, including intravenous fluids and insulin. After around one week, they were transitioned to multiple shots of subcutaneous insulin. After about 4–6 weeks, their insulin requirement diminished and oral antihyperglycemic drugs were initiated. At the last follow-up (14 months), they had controlled glycemia with oral antihyperglycemic medicines.
Conclusions
COVID-19 can induce acute onset diabetes and DKA in some individuals with no history of diabetes. These features resemble type 1 diabetes. However, after 4–6 weeks, their requirement for exogenous insulin diminishes and respond to oral antihyperglycemic medications. Long term follow up is required to further understand the type of diabetes induced by SARS CoV-2 infection in these individuals.
Two-dimensional (2D) recursive digital filters find applications in image processing as in medical X-ray processing. Nonsymmetric half-plane (NSHP) filters have definitely positive magnitude characteristics as opposed to quarter-plane (QP) filters. In this paper, we provide methods for stabilizing the given 2D NSHP polynomial by the planar least squares inverse (PLSI) method. We have proved in this paper that if the given 2D unstable NSHP polynomial and its PLSI are of the same degree, the PLSI polynomial is always stable, irrespective of whether the coefficients of the given polynomial have relationship among its coefficients or not. Examples are given for 2D first-order and second-order cases to prove our results. The generalization is done for the Nth order polynomial
Abstract-A methodology to quantify the degradation at circuit level due to negative bias temperature instability (NBTI) has been proposed in this work. Using this approach, a variety of analog/mixed-signal circuits are simulated, and their degradation is analyzed. It has been shown that the degradation in circuit performance is mainly dependent on the circuit configuration and its application rather than the absolute value of degradation at the device level. In circuits such as digital-to-analog converters, NBTI can pose a serious reliability concern, as even a small variation in bias currents can cause significant gain errors.Index Terms-Analog/mixed-signal circuits, circuit lifetime, negative bias temperature instability (NBTI), pMOSFET degradation, threshold-voltage shift.
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