Respiratory Fluoroquinolones vs. Other Commonly Used Antimicrobials in Mild-to-Moderate Severity Community-Acquired Pneumonia

 ObjectiveRespiratory Fluoroquinolones (RFQs) are widely used in the treatment of community-acquired pneumonia (CAP) in our part of the world. Our aim was to find if there was outcome difference between RFQ-based versus RFQ-exempt regimens. MethodsA retrospective study of RFQs versus other commonly used antimicrobial therapy (OUAT) in the treatment of patients with mid-to-moderate CAP adjusted by pneumonia severity score (PSI). Rates of treatment outcome at end-of- therapy i.e. clinical improvement, length of hospital stay and speed of recovery were evaluated. Patients were included if they had Mild-to-Moderate severity CAP, ≥18 years old, completed ≥ 3 days of antimicrobials.Results320 patients were included, mean age for all groups was 49.63 years (P = 0.204), males 60.3 % (P = 0.219). All had similar PSI score (Pearson X2 test = 13.75, P = 0.185). The first group (24.4%) is composed of RFQs monotherapy. The second group (50.6%) is composed of RFQs plus β-lactams. The third group (25%) is composed of OUAT. Diabetes was the most common comorbidity among all (P = 0.847). There was no significant difference among the three groups in clinical improvement (P = 0.424) and speed of recovery (P = 0.398), however length of hospital stay was significantly shorter for the RFQs monotherapy (P = 0.004)Cumulative curve for probability of discharge did not show significant difference among the three therapy groups (P ≥ 0.20)ConclusionThere were no significant difference among the groups regarding end-of-treatment clinical improvement rates, speed of recovery and probability of hospital discharge. However, they significantly differ in length of hospital stay for RFQs monotherapy (P = 0.004).Â

Application Of Digital Signal Processing And Machine Learning For Electromyography: A Review

Digital signal processing (DSP) and Machine learning (ML) have emerged as promising approaches to automate prediction tasks into electromyography (EMG) muscles conditions. To fill the research gap, This paper reviews the state-of-the-art applications of DSP and ML for EMG signal analysis. DSP techniques to extract information of EMG signal is highly needed. The major disadvantage of the frequency domain approach is it does not represent temporal information. Many time-frequency analysis techniques have been proposed. However, there is a compromise between time and frequency resolution. The techniques that minimize the EMG noise and analyze signal characteristics are discussed together to identify the best performance with the highest percentage of accuracy and efficiency. The most appropriate method depends on the EMG signal patterns, the quality and quantity of the signals and training data developed, and various types of user factors