The Diagnostic Value of Oligoclonal Band Detection in Viral Encephalitis

Objective: This study aims to explore the differences in cerebrospinal fluid oligoclonal band (CSF-OCB) expression among different age groups in viral encephalitis and its reference value for diagnosis. Methods: Forty-two patients with viral encephalitis were divided into two groups: 25 adults and 17 children. The presence of oligoclonal bands in the cerebrospinal fluid (CSF) was detected using polyacrylamide gel electrophoresis, and CSF routine analysis was conducted for comparative analysis. Results: The CSF-OCB positivity rate was higher in the adult group (48%) compared with the pediatric group (11.76%), with a statistically significant difference (P Conclusion: 1) The expression of CSF-OCB positivity in patients with viral encephalitis is age-related, with higher positivity rates observed in adults compared to children. 2) Although CSF oligoclonal band detection is not a specific diagnostic marker for viral encephalitis in adults, it still holds certain reference value.

CVRgram for Demodulation Band Determination in Bearing Fault Diagnosis under Strong Gear Interference

Fault-related resonance frequency band extraction-based demodulation methods are widely used for bearing diagnostics.However,due to the high peaks of strong gear meshing interference,the classical band selection methods have poor performance and cannot work well for bearing fault type detection.As such,the CVRgram-based bearing fault diagnosis method is proposed in this paper.In the proposed method,inspired by the conditional variance(CV)index and root mean square(RMS),a novel index,named the CV/root mean square(CVR),is first proposed.The CVR index has high robustness for the interference of non-Gaussian or Gaussian noise and has the ability to determine the center frequency of the weak bearing fault-related resonance frequency band under strong interference.Secondly,motived by the Kurtogram,the CVRgram algorithm is developed for adaptively determining the optimal filtering parameters.Finally,the CVRgram-based bearing fault diagnosis method under strong gear meshing interference is proposed.The performance of the CVRgram-based method is verified by both the simulation signal and the experiment signal.The comparison analysis with the Kurtogram,Protrugram,and CVgram-based method shows that the proposed technique has a much better ability for bearing fault detection under strong noise interference.

Micro EEG/ECG signal's chopper-stabilization amplifying chip for novel drycontact electrode

Facing the body＇s EEG（electroencephalograph, 0.5–100 Hz, 5–100 μV） and ECG＇s（electrocardiogram,〈 100 Hz, 0.01–5 mV） micro signal detection requirement, this paper develops a pervasive application micro signal detection ASIC chip with the chopping modulation/demodulation method. The chopper-stabilization circuit with the RRL（ripple reduction loop） circuit is to suppress the ripple voltage, which locates at the single-stage amplifier＇s outputting terminal. The single-stage chopping core＇s noise has been suppressed too, and it is beneficial for suppressing noises of post-circuit. The chopping core circuit uses the PFB（positive feedback loop） to increase the inputting resistance, and the NFB（negative feedback loop） to stabilize the 40 dB intermediate frequency gain. The cascaded switch-capacitor sample/hold circuit has been used for deleting spike noises caused by non-ideal MOS switches, and the VGA/BPF（voltage gain amplifier/band pass filter） circuit is used to tune the chopper system＇s gain/bandwidth digitally. Assisted with the designed novel dry-electrode, the real test result of the chopping amplifying circuit gives some critical parameters： 8.1 μW/channel, 0.8 μVrms（@band-widthD100 Hz）, 4216–11220 times digitally tuning gain range, etc. The data capture system uses the NI CO＇s data capturing DAQmx interface,and the captured micro EEG/ECG＇s waves are real-time displayed with the PC-Labview. The proposed chopper system is a unified EEG/ECG signal＇s detection instrument and has a critical real application value.