SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45 000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2 000 000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200 000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs

Concomitant Orbital Cavernous Hemangioma and Solitary Fibrous Tumor

Cavernous hemangiomas are the most frequent tumors in orbital region. Hemeangiopricytomas and solitary fibrous tumors are uncommon neoplasms found in different location, including the orbit. We present a 50-years-old man with coincide unilateral orbital cavernous hemangioma and solitary fibrous tumor. The initial diagnosis was orbital tumor. Computed tomography (CT) was performed. On CT two intraconal solid mass in right orbit positioned relatively near each other Antro-postriorly were observed. Tumors were excised totally with lateral orbitotomy method and sent for histopathologic examination. To our knowledge, only one case report of splenic hemangiopricytoma adjacent to cavernous hemangioma of colon has been reported in the literature. Our case shows the coincidence of hemangiopricytoma adjacent to cavernous hemangioma is possible and should be considered

Prevalence and correlates of depressive symptoms among Rohingya (forcibly displaced Myanmar nationals or FDMNs) older adults in Bangladesh amid the COVID-19 pandemic

BACKGROUND: Depression is globally a crucial communal psychiatric disorder, which is more common in older adults. The situation is considerably worse among millions of older (forcibly displaced Myanmar nationals or FDMNs) Rohingya adults, and the coronavirus disease-2019 (COVID-19) pandemic may exacerbate the already existing precarious situation. The present study investigated depressive symptoms and their associated factors in older adult Rohingya FDMNs in Cox Bazar, Bangladesh, during the COVID-19 pandemic. METHOD: A total of 416 older adults aged 60 years and above residing in Rohingya camps situated in the South Eastern part of Bangladesh were interviewed using a 15-item Geriatric Depression Scale (GDS-15) in Bengali language. Chi-square test was performed to compare the prevalence of depressive symptoms within different categories of a variable and a binary logistic regression model was performed to determine the factors associated with depressive symptoms. RESULTS: More than 41% of Rohingya older adults had depressive symptoms (DS). Socio-demographic and economic factors such as living alone, dependency on family for living, poor memory, feelings of being left out, difficulty in getting medicine and routine medical care during COVID-19, perception that older adults are at highest risk of COVID-19 and pre-existing non-communicable chronic conditions were found to be significantly associated with developing DS. Higher DS was also evident among older female Rohingya FDMNs. CONCLUSION: DS are highly prevalent in older Rohingya FDMNs during COVID-19. The findings of the present study call for immediate arrangement of mental health care services and highlight policy implications to ensure the well-being of older FDMNs

HormoNet: a deep learning approach for hormone-drug interaction prediction

Abstract Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet