Citations

The COVID-19, due to SARS-CoV-2, has uncovered many real-world issues when it comes to healthcare management and has led to a widespread mortality. Observations thus far from the reports of COVID-19 have indicated that certain risk groups for example, those with pre-existing cardiovascular (CV) disease, hypertension, diabetes, chronic kidney disease and tobacco use are prone to disease development and specifically development of severe disease and possible fatality. It is increasingly evident that many CV conditions occur frequently. These include myopericarditis, acute coronary syndromes, thrombosis, arrhythmias, hypertension and heart failure. Many professional organisations and societies related to cardiology have produced guidelines or recommendations on most of the above-mentioned aspects. Given these rapid developments, the aims of this review manuscript were to summarise and integrate recent publications with newly developed guidelines and with the first-hand experience of frontline physicians and to yield a pragmatic insight and approach to CV complications of COVID-19. We emphasise on a strategic tier-based approach for initial assessment and management of COVID-19, and then delve into focused areas within CV domains, and additionally highlighting the role of point-of-care ultrasound especially lung ultrasound, echocardiography and electrocardiography, in the management of these patients. We hope this paper will serve as a useful tool in the CV management of COVID-19 for clinicians practicing in both developing and developed countries.

PURPOSE The Fast Healthcare Interoperability Resources (FHIR) is emerging as a next-generation standards framework developed by HL7 for exchanging electronic health care data. The modeling capability of FHIR in standardizing cancer data has been gaining increasing attention by the cancer research informatics community. However, few studies have been conducted to examine the capability of FHIR in electronic data capture (EDC) applications for effective cancer clinical trials. The objective of this study was to design, develop, and evaluate an FHIR-based method that enables the automation of the case report forms (CRFs) population for cancer clinical trials using real-world electronic health records (EHRs). MATERIALS AND METHODS We developed an FHIR-based computational pipeline of EDC with a case study for modeling colorectal cancer trials. We first leveraged an existing FHIR-based cancer profile to represent EHR data of patients with colorectal cancer, and then we used the FHIR Questionnaire and QuestionnaireResponse resources to represent the CRFs and their data population. To test the accuracy of and overall quality of the computational pipeline, we used synoptic reports of 287 Mayo Clinic patients with colorectal cancer from 2013 to 2019 with standard measures of precision, recall, and F1 score. RESULTS Using the computational pipeline, a total of 1,037 synoptic reports were successfully converted as the instances of the FHIR-based cancer profile. The average accuracy for converting all data elements (excluding tumor perforation) of the cancer profile was 0.99, using 200 randomly selected records. The average F1 score for populating nine questions of the CRFs in a real-world colorectal cancer trial was 0.95, using 100 randomly selected records. CONCLUSION We demonstrated that it is feasible to populate CRFs with EHR data in an automated manner with satisfactory performance. The outcome of the study provides helpful insight into future directions in implementing FHIR-based EDC applications for modern cancer clinical trials.

Intestinal innate lymphoid cells (ILCs) contribute to the protective immunity and homeostasis of the gut, and the microbiota are critically involved in shaping ILC function. However, the role of the gut microbiota in regulating ILC development and maintenance still remains elusive. Here, we identified opposing effects on ILCs by two Helicobacter species, Helicobacter apodemus and Helicobacter typhlonius , isolated from immunocompromised mice. We demonstrated that the introduction of both Helicobacter species activated ILCs and induced gut inflammation; however, these Helicobacter species negatively regulated RORγt + group 3 ILCs (ILC3s), especially T-bet + ILC3s, and diminished their proliferative capacity. Thus, these findings underscore a previously unknown dichotomous regulation of ILC3s by Helicobacter species, and may serve as a model for further investigations to elucidate the host–microbe interactions that critically sustain the maintenance of intestinal ILC3s.

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

Background: Since healing of anterior cruciate ligament (ACL) grafts occurs by formation of a fibrovascular scar-tissue interface rather than by reformation of the native fibrocartilage transition zone, the purpose of our study was to examine expression of various signaling molecules and transcription factors that are known to be involved in embryologic insertion-site development following ACL reconstruction. We also aimed to characterize a murine model of ACL reconstruction to allow future study of the molecular mechanisms of healing. Methods: Seventy-nine mice underwent reconstruction of the ACL with autograft. Healing was assessed using histology in 12 mice and quantitative real-time polymerase chain reaction (qRT-PCR) gene-expression analysis in 3 mice at 1 week postoperatively (Group-1 mice) and by biomechanical analysis in 7, histological analysis in 7, immunohistochemical analysis in 5, microcomputed tomography analysis in 5, and qRT-PCR analyses in 8 at 2 weeks (Group-2 mice) and 4 weeks (Group-3 mice) postoperatively. Fifteen additional mice did not undergo surgery and were used for biomechanical (7 mice), qRT-PCR (3 mice), and immunohistochemical (5 mice) analyses to obtain baseline data for the native ACL. Results: Histological analysis demonstrated healing by formation of fibrovascular tissue at the tendon-bone interface. Immunohistochemical analysis showed a positive expression of proteins in the Indian hedgehog, Wnt, and parathyroid hormone-related protein (PTHrP) pathways. There was minimal Sox-9 expression. Gene-expression analysis showed an initial increase in markers of tissue repair and turnover, followed by a subsequent decline. Mean failure force and stiffness of the native ACL were 5.60 N and 3.44 N/mm, respectively. Mean failure force and stiffness were 1.29 N and 2.28 N/mm, respectively, in Group 2 and were 1.79 N and 2.59 N/mm, respectively, in Group 3, with 12 of 14 failures in these study groups occurring by tunnel pull-out. Conclusions: The spatial and temporal pattern of expression of signaling molecules that direct embryologic insertion-site formation was not adequate to restore the structure and composition of the native insertion site. Clinical Relevance: Development of a murine model to study ACL reconstruction will allow the use of transgenic animals to investigate the cellular, molecular, and biomechanical aspects of tendon-to-bone healing following ACL reconstruction, ultimately suggesting methods to improve healing in patients.

Metastatic breast cancer continues to be a life-threatening diagnosis that impacts hundreds of thousands of patients around the world. Targeted therapies are usually associated with less toxicity compared with cytotoxic chemotherapies and often induce response or durable disease control in estrogen receptor (ER) and/or HER2+ breast cancers. Drugs that target CDK 4/6 either alone or in combination with endocrine therapy have demonstrated substantial improvements in progression-free survival (PFS) compared with endocrine monotherapy. Most recently, PARP inhibitors have shown longer PFS compared with physician’s choice of chemotherapy in BRCA-associated cancers, leading to the first U.S. Food and Drug Administration (FDA) approval of a targeted therapy with the potential to benefit a subgroup of patients with triple-negative breast cancer (TNBC). Finally, newer drug delivery strategies using antibody drug conjugates have also allowed a “targeted approach” to deliver moderate to extremely potent cytotoxins directly to sites of metastatic disease, with less toxicity.

Shear wave elastography (SWE) is a powerful method for the diagnosis of tissue disorders or degeneration based on tissue elasticity. In SWE application, it was recognized that the wave speed depends not only on the tissue elasticity but also on the structural shape, leading to different theoretical models. For liver, skin and myocardium, the appropriate theoretical model is known to be shear wave, Rayleigh wave and Lamb wave theory, respectively. Therefore, appropriate theoretical model should be adopted for the proper application of SWE. In this study, we verify these theoretical models in gelatin samples of different thicknesses, using experimental and numerical SWE tests. The results indicate that the wave speed was influenced by the ratio of the wavelength and sample thickness and the measurement region. Based on these results, the selection of theoretical model could be divided into three cases, and the appropriate theoretical model can be selected accordingly.

Abstract Purpose: DNA topoisomerase inhibitors are commonly used for treating small-cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) repairs DNA damage caused by this class of drugs and may therefore influence treatment outcome. In this study, we investigated whether common TDP1 single-nucleotide polymorphisms (SNP) are associated with overall survival among SCLC patients. Experimental Design: Two TDP1 SNPs (rs942190 and rs2401863) were analyzed in 890 patients from 10 studies in the International Lung Cancer Consortium (ILCCO). The Kaplan–Meier method and Cox regression analyses were used to evaluate genotype associations with overall mortality at 36 months postdiagnosis, adjusting for age, sex, race, and tumor stage. Results: Patients homozygous for the minor allele (GG) of rs942190 had poorer survival compared with those carrying AA alleles, with a HR of 1.36 [95% confidence interval (CI): 1.08–1.72, P = 0.01), but no association with survival was observed for patients carrying the AG genotype (HR = 1.04, 95% CI, 0.84–1.29, P = 0.72). For rs2401863, patients homozygous for the minor allele (CC) tended to have better survival than patients carrying AA alleles (HR = 0.79; 95% CI, 0.61–1.02, P = 0.07). Results from the Genotype Tissue Expression (GTEx) Project, the Encyclopedia of DNA Elements (ENCODE), and the ePOSSUM web application support the potential function of rs942190. Conclusions: We found the rs942190 GG genotype to be associated with relatively poor survival among SCLC patients. Further investigation is needed to confirm the result and to determine whether this genotype may be a predictive marker for treatment efficacy of DNA topoisomerase inhibitors. Clin Cancer Res; 23(24); 7550–7. ©2017 AACR.

Abstract Introduction: DNA topoisomerase inhibitors (e.g. etoposide, irinotecan, teniposide, topotecan) are commonly used for treating small cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) plays a role in repairing DNA damage caused by DNA topoisomerase inhibitors and it is believed to be responsible for resistance to this class of drugs. The purpose of this study was to determine whether common TDP1 polymorphisms are associated with overall survival among SCLC patients. Method: Two common TDP1 SNPs (rs942190 and rs2401863, r2(European)=0.63 and r2(East Asian)=0.26) were analyzed in 898 patients from 10 studies in the International Lung Cancer Consortium, conducted in the U.S., Canada, Japan, Italy, Spain and the United Kingdom. Genotype data were obtained from either the OncoArray Consortium or other existing genome-wide association studies. Cox proportional hazard regression models with robust standard error were used to evaluate genotype associations for overall survival at 36 months post-diagnosis, adjusting for age, sex, race and tumor stage. Results: Patients homozygous for the minor allele (GG) of TDP1 SNP rs942190 were associated with poorer survival compared to those carrying both major alleles (AA), with a hazard ratio (HR) of 1.36 (95% confidence interval (CI): 1.07-1.72, p-value=0.01), but there was no association with survival for patients with the heterozygous genotype (HR=1.02, 95% CI: 0.83-1.27, p-value=0.83). For rs2401863, patients carrying both minor alleles (CC) tended to have better survival than patients carrying AA alleles, but this was within the limits of chance given no significant association (HR=0.79, 95% CI: 0.61-1.02, p-value=0.07). In addition, multi-tissue expression quantitative trait loci (eQTL) analyses from the Genotype-Tissue Expression Project indicated a potential effect of rs942190 on lung tissue, with higher TDP1 gene expression for the GG than AG or AA genotypes. Conclusion: We found that among SCLC patients the TDP1 rs942190 GG genotype was associated with poor overall survival. Further study could determine if this genotype can serve as a predictive marker for the treatment efficacy of DNA topoisomerase inhibitors in SCLC. Citation Format: Pawadee Lohavanichbutr, Lori C. Sakoda, Christopher I. Amos, Susanne M. Arnold, David C. Christiani, Michael P. Davies, John K. Field, Eric B. Haura, Rayjean J. Hung, Takashi Kohno, Maria Teresa Landi, Geoffrey Liu, Yi Liu, Michael W. Marcus, Grainne M. O'Kane, Matthew B. Schabath, Kouya Shiraishi, Stacey A. Slone, Adonina Tardón, Ping Yang, Kazushi Yoshida, Ruyang Zhang, Xuchen Zong, Noel S. Weiss, Chu Chen. Common TDP1 polymorphisms in relation to survival among small cell lung cancer patients in a multicenter study from the International Lung Cancer Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2248. doi:10.1158/1538-7445.AM2017-2248