Effect of inactivated COVID-19 vaccines on seizure frequency in patients with epilepsy: A multicenter, prospective study.

Objectives: Several COVID-19 vaccines list "uncontrolled epilepsy" as a contraindication for vaccination. This consequently restricts vaccination against COVID-19 in patients with epilepsy (PWE). However, there is no strong evidence that COVID-19 vaccination can exacerbate conditions in PWE. This study aims to determine the impact of COVID-19 vaccination on PWE. Methods: PWE were prospectively recruited from 25 epilepsy centers. We recorded the seizure frequency at three time periods (one month before the first vaccination and one month after the first and second vaccinations). A generalized linear mixed-effects model (GLMM) was used for analysis, and the adjusted incidence rate ratio (AIRR) with 95% CI was presented and interpreted accordingly. Results: Overall, 859 PWE were included in the analysis. Thirty-one (3.6%) and 35 (4.1%) patients were found to have increased seizure frequency after the two doses, respectively. Age had an interaction with time. The seizure frequency in adults decreased by 81% after the first dose (AIRR=0.19, 95% CI:0.11-0.34) and 85% after the second dose (AIRR=0.16, 95% CI:0.08-0.30). In juveniles (<18), it was 25% (AIRR=0.75, 95% CI:0.42-1.34) and 51% (AIRR=0.49, 95% CI:0.25-0.95), respectively. Interval between the last seizure before vaccination and the first dose of vaccination (ILSFV) had a significant effect on seizure frequency after vaccination. Seizure frequency in PWE with hereditary epilepsy after vaccination was significantly higher than that in PWE with unknown etiology (AIRR=1.95, 95% CI: 1.17-3.24). Two hundred and seventeen (25.3%) patients experienced non-epileptic but not serious adverse reactions. Discussion: The inactivated COVID-19 vaccine does not significantly increase seizure frequency in PWE. The limitations of vaccination in PWE should focus on aspects other than control status. Juvenile PWE should be of greater concern after vaccination because they have lower safety. Finally, PWE should not reduce the dosage of anti-seizure medication during the peri-vaccination period.

Safety of inactivated SARS-CoV-2 vaccines in myasthenia gravis: A survey-based study.

Background: Vaccination remains the most effective measure to prevent SARS-CoV-2 infection and worse outcomes. However, many myasthenia gravis (MG) patients are hesitant to receive vaccine due to fear of worsening. Methods: MG patients were consecutively enrolled in two MG centers in North China. The "worsening" after vaccination was self-reported by MG patients, and severity was measured with a single simple question. The general characteristics and disease status immediately prior to the first dose were compared between the worsening and non-worsening groups. Independent factors associated with worsening were explored with multivariate regression analysis. Results: One hundred and seven patients were included. Eleven patients (10.3%) reported worsening after vaccination, including eight patients with mild or moderate worsening and three patients with severe worsening. Only one of them (0.9%) needed an escalation of immunosuppressive treatments. There were significant differences between the worsening and non-worsening groups in terms of Myasthenia Gravis Foundation of America classes immediately before the first dose and intervals since the last aggravation. Precipitating factors might contribute to the worsening in some patients. Logistic regression revealed that only interval since the last aggravation ≤6 months was associated with worsening after SARS-CoV-2 vaccination (P = 0.01, OR = 8.62, 95% CI: 1.93-38.46). Conclusion: SARS-CoV-2 vaccines (an overwhelming majority were inactivated vaccines) were found safe in milder Chinese MG patients who finished two doses. Worsening after vaccination was more frequently seen in patients who were presumed as potentially unstable (intervals since last aggravation ≤6 months). However, mild worsening did occur in patients who were presumed to be stable. Precipitating factors should still be sought and treated for better outcome.

Safety of inactivated SARS-CoV-2 vaccines in myasthenia gravis: A survey-based study

Background Vaccination remains the most effective measure to prevent SARS-CoV-2 infection and worse outcomes. However, many myasthenia gravis (MG) patients are hesitant to receive vaccine due to fear of worsening. Methods MG patients were consecutively enrolled in two MG centers in North China. The “worsening” after vaccination was self-reported by MG patients, and severity was measured with a single simple question. The general characteristics and disease status immediately prior to the first dose were compared between the worsening and non-worsening groups. Independent factors associated with worsening were explored with multivariate regression analysis. Results One hundred and seven patients were included. Eleven patients (10.3%) reported worsening after vaccination, including eight patients with mild or moderate worsening and three patients with severe worsening. Only one of them (0.9%) needed an escalation of immunosuppressive treatments. There were significant differences between the worsening and non-worsening groups in terms of Myasthenia Gravis Foundation of America classes immediately before the first dose and intervals since the last aggravation. Precipitating factors might contribute to the worsening in some patients. Logistic regression revealed that only interval since the last aggravation ≤6 months was associated with worsening after SARS-CoV-2 vaccination (P = 0.01, OR = 8.62, 95% CI: 1.93–38.46). Conclusion SARS-CoV-2 vaccines (an overwhelming majority were inactivated vaccines) were found safe in milder Chinese MG patients who finished two doses. Worsening after vaccination was more frequently seen in patients who were presumed as potentially unstable (intervals since last aggravation ≤6 months). However, mild worsening did occur in patients who were presumed to be stable. Precipitating factors should still be sought and treated for better outcome.

Acute Respiratory Distress Syndrome in a Patient With Acute Promyelocytic Leukemia: Overlapping Between Differentiation Syndrome and COVID-19.

Differentiation syndrome (DS) is a relatively common and severe complication in acute promyelocytic leukemia (APL) patients undergoing induction therapy with all-trans retinoic acid (ATRA) and/or arsenic trioxide (ATO). DS is a multisystem disorder with pulmonary involvement. The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is also a systemic disorder with similar pulmonary and other clinical manifestations as DS. Here, we report an APL case with overlapping between DS and COVID-19. After admission to the hospital, the patient was diagnosed with APL and underwent differentiation therapy with ATRA/ATO. In the meantime, COVID-19 was diagnosed with a positive polymerase chain reaction test of SARS-CoV-2 from an oropharyngeal swab. The patient developed acute respiratory distress syndrome, coagulopathy, and acute kidney injury, which fit the clinical pictures of both DS and COVID-19. The patient died at last and this complicate case imposed big challenges for clinicians due to the laboratory and imaging findings of DS disguised in the context of COVID-19. Therefore, comprehensive treatment strategy should be considered to balance the risk and benefit of differentiation therapy in the context of COVID-19.

Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.