(Re)organisation of the somatosensory system after early brain lesion: A lateralization index fMRI study

OBJECTIVE: To evaluate the relationship between neural (re)organization of the somatosensory cortex and impairment of sensory function (2-point discrimination [2PD]) in individuals with unilateral cerebral palsy. METHODS: We included 21 individuals with unilateral cerebral palsy. 2PD thresholds were evaluated on thumb pads, and activation of the somatosensory cortex was recorded by functional MRI (fMRI) during passive movements of the affected hand. A lateralization index (LI) was calculated for the primary sensory (S1) and secondary sensory (S2) cortices and the correlation between the LI and 2PD thresholds was analysed. RESULTS: We found a significant negative correlation between the 2PD thresholds and the S2 LI (r=-0.5, one-tailed P-value=0.01) and a trend towards a negative correlation with the S1 LI (r=-0.4, one-tailed P-value=0.05). CONCLUSION: High levels of activation in the contralesional hemisphere were associated with high levels of sensory impairment in individuals with unilateral cerebral palsy. The interhemispheric (re)organization of the somatosensory system may not effectively compensate for somatosensory impairment

Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation

Purpose: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (="true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose (R)-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE. Methods: Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients. Results: Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = -1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = -1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = -3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = -3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points. Conclusion: The MARS ML models developed using "true" MeltDose (R)-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.</p

Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation

Purpose: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (="true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose (R)-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE. Methods: Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients. Results: Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = -1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = -1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = -3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = -3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points. Conclusion: The MARS ML models developed using "true" MeltDose (R)-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.Personalised Therapeutic