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General General

M3DISEEN: A Novel Machine Learning Approach for Predicting the 3D Printability of Medicines.

In International journal of pharmaceutics ; h5-index 67.0

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) 3-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing, which includes producing filaments by hot melt extrusion (HME), using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (

Elbadawi Moe, Muñiz Castro Brais, Gavins Francesca K H, Jie Ong Jun, Gaisford Simon, Pérez Gilberto, Basit Abdul W, Cabalar Pedro, Goyanes Álvaro


3D printed drug products, additive manufacturing, feature engineering, fused filament fabrication, gastrointestinal drug delivery, material extrusion, personalized pharmaceuticals

General General

Diagnosis of renal Diseases based on Machine Learning Methods Using Ultrasound Images.

In Current medical imaging

BACKGROUND : The incidence rate of renal disease is high which can cause end-stage renal disease. Ultrasound is a commonly used imaging method, including conventional ultrasound, color ultrasound, elastography etc. Machine learning is a potential method which has been widely used in clinical.

OBJECTIVE : To compare the diagnostic performance of different ultrasonic image measurement parameters for kidney diseases, and to compare different machine learning methods with human-reading method.

METHODS : 94 patients with pathologically diagnosed renal diseases and 109 normal controls were included in this study. The patients were examined by conventional ultrasound, color ultrasound and shear wave elasticity respectively. Ultrasonic data were analyzed by Support vector machine (SVM), random forest(RF), K-nearest neighbor (KNN) and artificial neural network (ANN), respectively, and compared with the human-reading method.

RESULTS : Only ultrasound elastography data have diagnostic value for renal diseases. The accuracy of SVM, RF, KNN and ANN methods are 80.98%,80.32%,78.03%and79.67% respectively, while the accuracy of human-reading is 78.33%. In the data of machine learning ultrasound elastography, the elastic hardness parameters of renal cortex are most important.

CONCLUSION : Ultrasound elastography is of highest diagnostic value in machine learning for nephropathy,the diagnostic efficiency of machine learning method is slightly higher than that of human-reading method, and the diagnostic ability of SVM method is higher than other methods.

Li Guanghan, Liu Jian, Wu Jingping, Tian Yan, Ma Liyong, Liu Yuejun, Zhang Bo, Mou Shan, Zheng Min


Renal disease, diagnosis, elastography, machine learning, support vector machine., ultrasound image

Radiology Radiology

Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis.

In European journal of radiology open

Purpose : The coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic. CT although sensitive in detecting changes suffers from poor specificity in discrimination from other causes of ground glass opacities (GGOs). We aimed to develop and validate a CT-based radiomics model to differentiate COVID-19 from other causes of pulmonary GGOs.

Methods : We retrospectively included COVID-19 patients between 24/01/2020 and 31/03/2020 as case group and patients with pulmonary GGOs between 04/02/2012 and 31/03/2020 as a control group. Radiomics features were extracted from contoured GGOs by PyRadiomics. The least absolute shrinkage and selection operator method was used to establish the radiomics model. We assessed the performance using the area under the curve of the receiver operating characteristic curve (AUC).

Results : A total of 301 patients (age mean ± SD: 64 ± 15 years; male: 52.8 %) from three hospitals were enrolled, including 33 COVID-19 patients in the case group and 268 patients with malignancies or pneumonia in the control group. Thirteen radiomics features out of 474 were selected to build the model. This model achieved an AUC of 0.905, accuracy of 89.5 %, sensitivity of 83.3 %, specificity of 90.0 % in the testing set.

Conclusion : We developed a noninvasive radiomics model based on CT imaging for the diagnosis of COVID-19 based on GGO lesions, which could be a promising supplementary tool for improving specificity for COVID-19 in a population confounded by ground glass opacity changes from other etiologies.

Xie Chenyi, Ng Ming-Yen, Ding Jie, Leung Siu Ting, Lo Christine Shing Yen, Wong Ho Yuen Frank, Vardhanabhuti Varut


COVID-19, Computed tomography, Infections, Machine learning, Severe acute respiratory syndrome coronavirus 2

General General

Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer

ArXiv Preprint

Foreseeing the brain evolution as a complex highly inter-connected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regions of interest (ROIs) in health and disease. Interestingly, brain graph evolution models remain almost absent in the literature. Here we design an adversarial brain network normalizer for representing each brain network as a transformation of a fixed centered population-driven connectional template. Such graph normalization with respect to a fixed reference paves the way for reliably identifying the most similar training samples (i.e., brain graphs) to the testing sample at baseline timepoint. The testing evolution trajectory will be then spanned by the selected training graphs and their corresponding evolution trajectories. We base our prediction framework on geometric deep learning which naturally operates on graphs and nicely preserves their topological properties. Specifically, we propose the first graph-based Generative Adversarial Network (gGAN) that not only learns how to normalize brain graphs with respect to a fixed connectional brain template (CBT) (i.e., a brain template that selectively captures the most common features across a brain population) but also learns a high-order representation of the brain graphs also called embeddings. We use these embeddings to compute the similarity between training and testing subjects which allows us to pick the closest training subjects at baseline timepoint to predict the evolution of the testing brain graph over time. A series of benchmarks against several comparison methods showed that our proposed method achieved the lowest brain disease evolution prediction error using a single baseline timepoint. Our gGAN code is available at

Zeynep Gurler, Ahmed Nebli, Islem Rekik


General General

The strategies and techniques of drug discovery from natural products.

In Pharmacology & therapeutics ; h5-index 80.0

Natural products have been the main sources of new drugs. The different strategies have been developed to find the new drugs based on natural products. The traditional and ethic medicines have provided information on the therapeutic effects and resulted in some notable drug discovery of natural products. The special activities of the medicine plants such as the side effects have inspired scientists to develop the novel small molecular. The microorganisms and the endogenous active substances from human or animal also become the important approaches to the drug discovery. The tremendous progress in technology led to the new strategies in drug discovery from natural products. The bioinformation and artificial intelligence have facilitated the research and development of natural products. We will provide a scene of strategies and technologies for drug discovery from natural products in this review.

Zhang Li, Song Junke, Kong Linglei, Yuan Tianyi, Li Wan, Zhang Wen, Hou Biyu, Lu Yang, Du Guanhua


Drug discovery, Natural product, Strategy, Technique

General General

Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.

In Stem cells translational medicine

Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently underway to improve our current situation. Unfortunately, a vaccine option is optimistically at least a year away. It is imperative that for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem-cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS-CoV-2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS-CoV-2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti-SARS-CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.

Esmail Sally, Danter Wayne R


DeepNEU, SARS-CoV-2, antiviral, drug discovery and repurposing, pandemic preparedness, unsupervised learning