No abstract

The protein arginine methyltransferase 5 (PRMT5) methylates a variety of proteins involved in splicing, multiple signal transduction pathways, epigenetic control of gene expression, and mechanisms leading to protein expression required for cellular proliferation. Dysregulation of PRMT5 is associated with clinical features of several cancers including lymphomas, lung cancer, and breast cancer. Here, we describe the characterization of JNJ-64619178, a novel, selective, and potent PRMT5 inhibitor, currently in clinical trials for patients with advanced solid tumors, non-Hodgkin's lymphoma, and lower risk myelodysplastic syndrome.JNJ-64619178 demonstrated a prolonged inhibition of PRMT5 and potent anti-proliferative activity in subsets of cancer cell lines derived from various histologies, including lung, breast, pancreatic, and hematological malignancies. In primary acute myeloid leukemia samples, the presence of splicing factor mutations correlated with a higher ex vivo sensitivity to JNJ-64619178. Furthermore, the potent and unique mechanism of inhibition of JNJ-64619178, combined with highly optimized pharmacological properties, led to efficient tumor growth inhibition and regression in several xenograft models in vivo, with once-daily or intermittent oral dosing schedules. An increase in splicing burden was observed upon JNJ-64619178 treatment. Overall, these observations support the continued clinical evaluation of JNJ-64619178 in patients with aberrant PRMT5 activity driven tumors.

Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients’ pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule detection methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure in conjunction with a region proposal network. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the false positive rate and combat the sample imbalance problem associated with nodule detection. We adopt the squeeze-and-excitation structure to learn effective image features and utilize inter-dependency information of different feature maps. We have validated our method based on publicly available CT scans with manually labelled ground-truth obtained from LIDC/IDRI dataset and its subset LUNA16 with thinner slices. Ablation studies and experimental results have demonstrated that our method could outperform state-of-the-art nodule detection methods by a large margin.

Estimation of absolute temperature distributions is crucial for many thermal processes in the nonlinear distributed parameter systems, such as predicting the curing temperature distribution of the chip, the temperature distribution of the catalytic rod, and so on. In this work, a spatiotemporal model based on the Karhunen-Loève (KL) decomposition, the multilayer perceptron (MLP), and the long short-term memory (LSTM) network, named KL-MLP-LSTM, is developed for estimating temperature distributions with a three-step procedure. Firstly, the infinite-dimensional model is transformed into a finitedimensional model, where the KL decomposition method is used for dimension reduction and spatial basis functions extraction. Secondly, a novel MLP-LSTM hybrid time series model is constructed to deal with the two inherently coupled nonlinearities. Finally, the spatiotemporal temperature distribution model can be reconstructed through spatiotemporal synthesis. The effectiveness of the proposed model is validated by the data from a snap curing oven thermal process. Satisfactory agreement between the results of the current model and the other well-established model shows that the KL-MLP-LSTM model is reliable for estimating the temperature distributions during the thermal process. INDEX TERMS Spatiotemporal modeling, nonlinear distributed thermal processes, Karhunen-Loève decomposition, multilayer perceptron, long short-term memory.