The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort,” and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees’ regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL−1 g−1 day), as illustrated by a minimal bias (mean relative error (%) = + 3%), good precision (MAE = 8.7 μg mL−1 g−1 day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions.
Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.
A series of autophagy-targeted antimetastatic clioquinol (CLQ) platinum(IV) conjugates were designed and prepared by incorporating an autophagy activator CLQ into the platinum(IV) system. Complex 5 with the cisplatin core bearing dual CLQ ligands with potent antitumor properties was screened out as a candidate. More importantly, it displayed potent antimetastatic properties both in vitro and in vivo as expected. Mechanism investigation manifested that complex 5 induced serious DNA damage to increase γ-H2AX and P53 expression and caused mitochondria-mediated apoptosis through the Bcl-2/Bax/caspase3 pathway. Then, it promoted prodeath autophagy by suppressing PI3K/AKT/mTOR signaling and activating the HIF-1α/ Beclin1 pathway. The T-cell immunity was elevated by restraining the PD-L1 expression and subsequently increasing CD3 + and CD8 + T cells. Ultimately, metastasis of tumor cells was suppressed by the synergistic effects of DNA damage, autophagy promotion, and immune activation aroused by CLQ platinum(IV) complexes. Key proteins VEGFA, MMP-9, and CD34 tightly associated with angiogenesis and metastasis were downregulated.
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