Off-target effects and clinical outcome in metastatic colorectal cancer patients receiving regorafenib: The TRIBUTE analysis

Regorafenib is an orally administered multikinase inhibitor indicated for the treatment of heavily pretreated metastatic colorectal cancer patients with good performance status, albeit less than 50% treated patients achieve disease stabilisation or better at the first radiological evaluation. In addition to that a particularly broad spectrum of toxicities (experienced as G3 or more NCI CTCAE graded by 50% of patients treated) have led to reconsider its widespread use in the majority of patients. We retrospectively collected data about the magnitude of off-target effects experienced during the first 8-weeks of regorafenib monotherapy and analysed their correlation with overall survival, progression free survival and disease control rate. Our findings suggest that skin rash (Exp (B): 0.52, p = 0.0133) or hypothyroidism (Exp (B): 0.11, p = 0.0349) were significantly correlated with improved overall survival at multivariate regression analysis. It was also demonstrated a statistically significant role of diarrhea as predictor of improved survival but its independent prognostic role was lost at multivariate analysis (Exp (B): 0.63, p = 0.162). This is the first analysis showing a potential correlation between the onset of these forms of side effects and regorafenib efficacy, however sample size limitations and the retrospective nature of our analysis prevent us from drawing definitive conclusion

Wavelet-Based Assessment of the Muscle-Activation Frequency Range by EMG Analysis

The assessment of muscle-recruitment timing from electromyography (EMG) signal is relevant in different fields, including clinical gait analysis and robotic systems to interpret user's motion intention. However, available methods typically provide only information in time domain without evaluating muscle-activation frequency content. This study aims to propose a novel adaptative algorithm for detecting muscle activation in time-frequency domain based on continuous wavelet transform (CWT) analysis. Precisely, the novel contribution of the proposed algorithm consists of evaluating the frequency range of every muscle activations detected in time domain. Performances are evaluated on a test bench of 720 simulated and 105 real surface EMG signals, stratified for signal-to-noise ratio (SNR), and then validated against different reference algorithms. Outcomes indicate that the proposed approach can provide an accurate prediction of muscle onset and offset timing in both simulated (mean absolute error, MAE \approx 10 ms) and real datasets (MAE < 30 ms), minimally affected by the SNR variability and compatible with the timing of EMG-driven assistive devices. Concomitantly, the maximum frequency of the activations is computed, ranging from around 100 Hz up to almost 500 Hz. This suggests a large within-muscle between-muscle variability of the frequency range. In conclusion, the current study introduces a novel reliable wavelet-based algorithm to detect both time and frequency content of muscle activation, suitable in different conditions of signal quality