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Intrinsic plasticity, a fundamental process enabling neurons to modify their intrinsic properties, plays a crucial role in shaping neuronal input‐output function and is implicated in various neurological and psychiatric disorders. Despite its importance, the underlying molecular mechanisms of intrinsic plasticity remain poorly understood. In this study, a new ubiquitin ligase adaptor, protein tyrosine phosphatase receptor type N (PTPRN), is identified as a regulator of intrinsic neuronal excitability in the context of temporal lobe epilepsy. PTPRN recruits the NEDD4 Like E3 Ubiquitin Protein Ligase (NEDD4L) to NaV1.2 sodium channels, facilitating NEDD4L‐mediated ubiquitination, and endocytosis of NaV1.2. Knockout of PTPRN in hippocampal granule cells leads to augmented NaV1.2‐mediated sodium currents and higher intrinsic excitability, resulting in increased seizure susceptibility in transgenic mice. Conversely, adeno‐associated virus‐mediated delivery of PTPRN in the dentate gyrus region decreases intrinsic excitability and reduces seizure susceptibility. Moreover, the present findings indicate that PTPRN exerts a selective modulation effect on voltage‐gated sodium channels. Collectively, PTPRN plays a significant role in regulating intrinsic excitability and seizure susceptibility, suggesting a potential strategy for precise modulation of NaV1.2 channels' function. [ABSTRACT FROM AUTHOR]

Nav1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Nav1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF‐Graph model performed best. Similarly, RF‐Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Nav1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Nav1.5 ion channel and key privileged substructures with high affinity were also extracted. [ABSTRACT FROM AUTHOR]

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n  = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P  < 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients. [ABSTRACT FROM AUTHOR]

Intrinsic plasticity, a fundamental process enabling neurons to modify their intrinsic properties, plays a crucial role in shaping neuronal input-output function and is implicated in various neurological and psychiatric disorders. Despite its importance, the underlying molecular mechanisms of intrinsic plasticity remain poorly understood. In this study, a new ubiquitin ligase adaptor, protein tyrosine phosphatase receptor type N (PTPRN), is identified as a regulator of intrinsic neuronal excitability in the context of temporal lobe epilepsy. PTPRN recruits the NEDD4 Like E3 Ubiquitin Protein Ligase (NEDD4L) to Na V 1.2 sodium channels, facilitating NEDD4L-mediated ubiquitination, and endocytosis of Na V 1.2. Knockout of PTPRN in hippocampal granule cells leads to augmented Na V 1.2-mediated sodium currents and higher intrinsic excitability, resulting in increased seizure susceptibility in transgenic mice. Conversely, adeno-associated virus-mediated delivery of PTPRN in the dentate gyrus region decreases intrinsic excitability and reduces seizure susceptibility. Moreover, the present findings indicate that PTPRN exerts a selective modulation effect on voltage-gated sodium channels. Collectively, PTPRN plays a significant role in regulating intrinsic excitability and seizure susceptibility, suggesting a potential strategy for precise modulation of Na V 1.2 channels' function., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

We used an integrated ensemble learning method to build a stable prediction model for severity in COVID-19 patients, which was validated in multicenter cohorts., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.)

Na v 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na v 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na v 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na v 1.5 ion channel and key privileged substructures with high affinity were also extracted., (© 2022 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Although the Na V 1.7 sodium channel is a promising drug target for pain, traditional screening strategies for discovery of Na V 1.7 inhibitors are very painstaking and time-consuming. Herein, we aimed to build machine learning models for screening and design of potent and effective Na V 1.7 sodium channel inhibitors. We customized the imbalanced data set from ChEMBL and BindingDB to train and filter the best classification model. Then, the whole-cell voltage-clamp was employed to validate the inhibitors. We assembled a molecular group optimization method by combining the Grammar Variational Autoencoder, classification model, and simulated annealing. We found that the RF-CDK model (random forest + CDK fingerprint) performs best in the imbalanced data set. Of the three compounds that may have inhibitory effects, nortriptyline has been experimentally verified. In the molecule optimization process, 40 molecules located in the applicability domain of RF-CDK were used as a starting point, among which 34 molecules evolved to molecules with greater molecular scores (MS). The molecule with the highest MS was derived from CHEMBL2325245. The model and method we developed for Na V 1.7 inhibitors are also applicable to other targets.