COVID-19's lessons on structural inequality should have been painful and embarrassing to all of us. These daily experiences of an unacceptable status quo among US children are still with us in a post-COVID America. Addressing the multi-sectoral factors that undermine the nation...s health should remain urgent priorities for all health professionals.

Motivation

A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.

Results

We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies.

Availability and implementation

The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction .

Contact

nazong@ucsd.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Importance

The COVID-19 pandemic is changing how health care providers practice. As some telemedicine and telecommunication support tools have been incorporated into the otolaryngology practice in response to safety and access demands, it is essential to review how these tools and services can help facilitate facial trauma evaluation during a time when clinical resources are limited.

Objective

To review applications of telemedicine for the evaluation of facial trauma to better direct utilization of these methods and technologies during times of limited access to clinical resources such as the COVID-19 pandemic.

Methods

A systematic review was conducted using PubMed, Embase, and Web of Science.

Results

After screening 158 titles and abstracts, we identified 16 eligible studies involving facial trauma evaluation using telemedicine. Telemedicine opportunities for facial trauma evaluation have the potential to be developed in the areas of multidisciplinary remote consultations, facial trauma triage, patient engagement, and postoperative follow-up.

Conclusion

The COVID-19 pandemic is posing obstacles for both providers and patients in the delivery of health care at a time of limited clinical resources. Telemedicine may provide a potential useful tool in the evaluation and triage of facial injuries and patient engagement.

Despite clear indications of need to improve depression treatment, practical tools that efficiently measure psychotherapy are not available. We developed a patient-report measure of psychotherapy for depression that assesses Cognitive Behavioral (CBT), Interpersonal (IPT), and Psychodynamic therapies. 420 patients with depression from a large managed behavioral health care organization completed the measure. The three subscales measuring CBT, IPT, and Psychodynamic Therapy showed good internal consistency, appropriate item-total correlations, and were supported by a 3-factor structure. Our results suggest that a patient questionnaire is a promising approach for assessing psychotherapy in quality improvement interventions.

Viral infections are some of the most common sources of respiratory illness in pediatric and adult populations worldwide. Influenza and coronaviruses are viral pathogens that could lead to severe respiratory illness and death. More recently, respiratory illness from coronaviruses, accounts for more than 1 million deaths in the United States alone. This article will explore the epidemiology, pathogenesis, diagnosis, treatment, and prevention of severe acute respiratory syndrome caused by coronavirus-2, and Middle Eastern respiratory syndrome.

Acute lymphoblastic leukemia affects both children and adults. Rising costs of cancer care and patient burden contribute to the need to study factors influencing outcomes. This study explored the quality of datasets generated from a clinical research institution. The 'fit-for-use' of data prior to examining survival/complications was determined through a systematic approach guided by the Weiskopf et al. 3x3 Data Quality Assessment Framework. Constructs of completeness, correctness, and currency were explored for the data dimensions of patient, variables, and time. There were 11 types of data retrieved. Sufficient data points were found for patient and variable data in each dataset (≥70% of its cells filled with patient level data). Although there was concordance between variables, we found the distribution of lab values and death data to be incorrect. There were missing values for labs ordered and death dates. Our study showed that datasets retrieved can vary, even from the same institution.