Given that the PI3K/AKT pathway has manifested its compelling influence on multiple cellular process, we further review the roles of hyperactivation of PI3K/AKT pathway in various human cancers. We state the abnormalities of PI3K/AKT pathway in different cancers, which are closely related with tumorigenesis, proliferation, growth, apoptosis, invasion, metastasis, epithelial-mesenchymal transition, stem-like phenotype, immune microenvironment and drug resistance of cancer cells. In addition, we investigated the current clinical trials of inhibitors against PI3K/AKT pathway in cancers and found that the clinical efficacy of these inhibitors as monotherapy has so far been limited despite of the promising preclinical activity, which means combinations of targeted therapy may achieve better efficacies in cancers. In short, we hope to feature PI3K/ AKT pathway in cancers to the clinic and bring the new promising to patients for targeted therapies.

Background Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas. Methods This single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The Tree-based Pipeline Optimization Tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomics features. We compared the performance of 10 independent TPOT-generated models based on training and testing cohorts using the area under the curve (AUC) and average precision to obtain the final model. An independent cohort of 22 patients was used to validate the best model. Results Ten prediction models were generated by TPOT, and the accuracy obtained with the best pipeline ranged from 0.788 to 0.867 for the training cohort and from 0.60 to 0.84 for the testing cohort. After comparison, the AUC value and average precision of the final model were 0.903 and 0.911 in the testing cohort, respectively. In the validation set, the AUC was 0.85, and the average precision was 0.855 for the best model. Conclusions The autoML classifier using radiomics features of conventional MR images provides high discriminatory accuracy in predicting the H3 K27M mutation status of midline glioma.

No abstract

KEY WORDS: non-small cell lung cancer; anaplastic lymphoma kinase (ALK); Ventana immunohistochemistry (IHC) assay; fluorescence; fluorescence in situ hybridization (FISH); malignant pleural effusion; cell block.

Anorexia nervosa (AN) is a severe psychiatric disorder with high mortality. The underlying neurobiological mechanisms are not well understood, and high-resolution structural magnetic resonance brain imaging studies have given inconsistent results. Here we aimed to psychoradiologically define the most prominent and replicable abnormalities of gray matter volume (GMV) in AN patients, and to examine their relationship to demographics and clinical characteristics, by means of a new coordinate-based meta-analytic technique called seed-based d mapping (SDM). In a pooled analysis of all AN patients we identified decreased GMV in the bilateral median cingulate cortices and posterior cingulate cortices extending to the bilateral precuneus, and the supplementary motor area. In subgroup analysis we found an additional decreased GMV in the right fusiform in adult AN, and a decreased GMV in the left amygdala and left anterior cingulate cortex in AN patients without comorbidity (pure AN). Thus, the most consistent GMV alterations in AN patients are in the default mode network and the sensorimotor network. These psychoradiological findings of the brain abnormalities might underpin the neuropathophysiology in AN.

Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug–disease, drug–protein and protein–disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug–disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug–disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug–disease associations especially for new diseases.

Background: Pulmonary mucormycosis, a relatively rare but severe pulmonary fungal disease with a high mortality rate, is difficult to diagnose in immunocompromised patients. Conventional cytopathology (CCP) examination of respiratory samples can help detect Mucorales, but its diagnostic sensitivity is poor. The aim of this study was to assess the first application of liquid-based cytopathology test (LCT) to detect Mucorales.Methods: A total of 33 pairs of bronchial brushing samples from 27 patients diagnosed as pulmonary mucormycosis by fiberoptic bronchoscopy biopsy were prepared as slides using both CCP and LCT. LCT and CCP used the same cytology brush to obtain samples at the same site during the same time as the fiberoptic bronchoscopy biopsy. All samples were stained with Papanicolaou, GMS and PAS. CCP and LCT slides were evaluated from the rate of positive detection, 8 cytomorphological features and 7 background features.Results: LCT-prepared slides showed a higher positive rate of Mucorales detection than CCP-prepared slides for Papanicolaou’s staining [28/33 (84.85%) vs. 15/33 (45.45%), p = 0.001] and for “special staining” with GMS and PAS [29/33 (87.88%) vs. 18/33 (54.55%), p = 0.003]. Clearer smear background and more distinct stereoscopic cytopathological features were observed in LCT. Messy yarn-like necrosis observed in conventionally prepared 75.76% (25/33) samples was cytomorphological suggestive for the diagnosis of mucormycosis.Conclusion: This retrospective study suggests that LCT may be better than CCP to detect Mucorales in bronchial brushing samples from patients with pulmonary mucormycosis.

The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections.