Author: Madera, Julio ; Rodríguez-Gonzalez, Ansel Y. ; Martínez-Santiago, Oscar ; Barigye, Stephen J ; Rodríguez-Maya, Noel Enrique ; Rodríguez-Gonzalez, Ansel Y ; Barigye, Stephen J. ; Castillo-Garit, Juan A ; Castillo-Garit, Juan A. ; Phoobane, Paulina ; Duchowicz, Pablo ; Martínez-López, Yoan ; Jauriga, Yanaima

CONTEXTThis study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R² = 0.902), IB-IBK (R² = 0.82), IB-Kstar (R² = 0.834), IB-MLP (R² = 0.843), and DRF-AWV (R² = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability.METHODSThe computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

01 Apr 2021·Chaos: An Interdisciplinary Journal of Nonlinear ScienceQ2 · MATHEMATICS

Erratum: “On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrasts to VAR and DMD” [Chaos 31(1), 013108 (2021)]

Q2 · MATHEMATICS

Article

Author: Bollt, Erik

01 Dec 2019·European RadiologyQ2 · MEDICINE

Comparison of analgesic techniques in MRI-guided in-bore prostate biopsy

Q2 · MEDICINE

Article

Author: Schimmöller, L ; Arsov, C ; Antoch, G ; Ullrich, T ; Quentin, M ; Albers, P ; Hiester, A ; Blondin, D ; Valentin, B

OBJECTIVESTo evaluate different analgesic techniques in MRI-guided in-bore prostate biopsy (IB-GB) regarding the influence on patient procedural experience of pain.METHODSTwo hundred fifty-two consecutive patients who had received an IB-GB either with intrarectal instillation of 2% lidocaine gel (n = 126, group A) or with periprostatic nerve block (PPNB) with 2% mepivacaine (n = 126, group B) were retrospectively included in this study. Pain scores were measured on a visual analog scale, the operating room time (ORT) was recorded for each biopsy and correlations between the parameters were analysed.RESULTSPain scores for IB-GB were slightly lower in group B compared with group A (2.0 ± 1.9; 2.4 ± 1.7; p = 0.02). In group A, significantly more targeted biopsy cores were acquired (group B: 5.2 ± 1.1; group A: 5.6 ± 0.8; p < 0.01). ORT was comparable and not significantly different in both groups. There was only a weak correlation between pain scores and ORT in group B (r_S = 0.22; p = 0.01), but no correlation between pain scores and the number of biopsy cores or the prostate volume.CONCLUSIONSPain levels are generally low for MRI-guided in-bore biopsy using either PPNB or intrarectal instillation of lidocaine gel. A statistically significant, slightly lower pain score was documented for PPNB and might be preferred when the focus is analgesia. On the other hand, due to the minor difference and easier administration, intrarectal gel instillation seems to be a reasonable practice for standard analgesia for MRI-guided in-bore biopsy.KEY POINTS• Pain levels were low for MRI-guided in-bore biopsy using either PPNB or intrarectal instillation of lidocaine gel as analgesic method. • PPNB prior to IB-GB resulted in a slightly lower pain score but required a higher effort. • Intrarectal gel anaesthesia seems to be a reasonable practice for standard analgesia for IB-GB in an outpatient setting.

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Indication	Highest Phase	Country/Location	Organization	Date
Muscular Dystrophy, Duchenne	Preclinical	US	INNOBIOSCIENCE LLC	30 Nov 2022

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