RESUMEN
BACKGROUND: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune encephalitis due to immune production of anti-NMDAR antibodies against the NR1 subunit of the NMDA receptor which is present throughout the central nervous system. This condition had been reported to be prevalent in patients with certain medical conditions; however so far, there have been limited systematic reviews and meta-analyses on the prevalence and factors associated. OBJECTIVE: This study was to determine the prevalence and factors associated with anti-NMDAR encephalitis among affected patients. MATERIAL AND METHODS: The protocol of this study has been registered (2019: CRD42019142002) with the International Prospective Register of Systematic Reviews (PROSPERO). The primary outcome was the incidence or prevalence of anti-NMDAR encephalitis and secondary outcomes were factors associated with anti-NMDAR encephalitis. RESULTS: There were 11 studies and a total of 873 million patients taken from high-risk populations across 11 countries that were included in the primary analysis. The overall pooled prevalence of anti-NMDAR encephalitis among patients with medical conditions was 7.0% (95% CI = 4.4, 9.6). Those with first episode of psychosis or schizophrenia were at a higher risk of developing anti-NMDAR encephalitis with an odds ratio of 5.976 (95% CI = 1.122, 31.825). CONCLUSION: We found that almost one-tenth of patients with medical conditions had anti-NMDAR encephalitis; particularly those with first episode of psychosis or schizophrenia were among the high-risk medical conditions.
Asunto(s)
Encefalitis Antirreceptor N-Metil-D-Aspartato , Encefalitis Antirreceptor N-Metil-D-Aspartato/epidemiología , Humanos , Prevalencia , Receptores de N-Metil-D-Aspartato/inmunologíaRESUMEN
The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.