Significance Synthetic biology and genetic engineering would greatly benefit from engineered genetic elements that are orthogonal to the host in which they operate. Two-component signaling pathways are the prevalent signal processing modality in prokaryotes that is also found in low eukaryotes and plants but absent from vertebrate cells. Here we investigate whether the elements of prokaryotic two-component pathways are operational in mammalian cells. We find that the core biochemical processes are maintained, whereas the capacity to sense chemical ligands is diminished or obscured. We use the pathways for multiinput gene regulation and show that they can serve as a rich source of orthogonal building blocks for gene expression control in mammalian cells. Our findings open new avenues in synthetic circuit design.
Background: Community pharmacists are healthcare professionals who play a major role in providing health care services. Continuing pharmacy education modules are designed to update the pharmacists' knowledge and skill to improve their practice. Objectives: This study was performed to evaluate the influence of continuing pharmacy education on pharmacist knowledge, attitude and practice towards 3 diseases viz diabetes mellitus, hypertension and peptic ulcer disease in selected districts of Kerala and Tamilnadu in South India. Method: A prospective open label study was performed among the community pharmacists in selected; 6 districts of Kerala and 4 districts in Tamilnadu over a period of 18 months from July 2013 to December 2014. The prepared course content for the diseases was provided to community pharmacist. The Knowledge, Attitude and Practice (KAP) were assessed by a questionnaire at baseline and post CPE follow up. The feedback for the lecture modules was taken. Statistical analysis was done by using GraphPad Prism Statistical Software version 6.02 and SPSS version 22 for windows. Results: Among 156 from Kerala and 157 community pharmacists from Tamilnadu, as per the protocol only 60 (38.46%) community pharmacists and 53 (33.76%) completed the study in Kerala and Tamilnadu respectively. Among them 62.83% (n=71) were male, 69.02% (n=78) were B. Pharm degree holders and 30.97% (n= 35) were diploma in pharmacy holders respectively. The baseline score for Knowledge, Attitude and Practice (KAP) were 33.78 (Kerala), and 32.25 (Tamilnadu) respectively. Statistically significant improvement in Knowledge, Attitude and Practice scores were found at the end of the study. Conclusion: The specified continuing pharmacy education program made a significant change (P<0.0001) in pharmacist's knowledge, attitude and practice. The Community pharmacist gave a satisfactory score in feedback for the lectures delivered.
1556 Background: Much information describing a patient’s cancer treatment remains in unstructured text in electronic health records and is not recorded in discrete data fields. Accurate data completeness is essential for quality care improvement and research studies on de-identified patient records. Accessing this high-value content often requires manual and extensive curation review. Methods: AstraZeneca, CancerLinQ, ConcertAI, and Tempus have developed a natural language processing (NLP)-assisted process to improve clinical cohort selection for targeted curation efforts. Hybrid, machine-learning model development included text classification, named entity recognition, relation extraction and false positive removal. A subset of nearly 60,000 lung cancer cases were included from the CancerLinQ database, comprised of multiple source EHR systems. NLP models extracted EGFR status, stage, histology, radiation therapy, surgical resection and oral medications. Based on the results, cases were selected for additional manual curation, where curators confirmed findings of the NLP-processed data. Results: NLP methods improved cohort identification. Successfully returned cases using the NLP method ranged from 75.2% to 96.5% over more general case selection criteria based on limited structured data. For all cohorts combined, 84.2% of the cases sent out for NLP curation were returned with curated content (Table). Each cohort contained a range of NLP-derived elements for curators to further review. In comparison, more general case selection criteria yielded a total of 3,878 cases returned out of 41,186 lung cancer cases sent for curation, for a success rate of only 9.6%. Conclusions: NLP-driven case selection of six distinct, complex lung cohorts resulted in an order of magnitude improvement in eligibility over candidate selection using structured EHR data alone. This study demonstrates NLP-assisted approaches can significantly improve efficiency in curating unstructured health data. [Table: see text]
e14056 Background: Determination of the metastatic status of a patient is important for outcomes research and candidacy for clinical trials. Structured data in EMR may not always capture the metastatic status, and it is useful to extract it automatically from physician notes. Contextual understanding of the notes is important to resolve issues such as a) local vs distal metastasis b) statements involving family history of metastasis or physician instructing the patient to look for certain signs of metastasis c) text indicating suspicion of metastasis or absence of metastasis d) indirect utterances, e.g. cancer has spread to the bone. e) corrections to previous findings. Methods: We used a set of 20138 breast cancer patients from Concerto HealthAI real world oncology dataset that includes data from CancerLinQ Discovery to build & validate the set of NLP algorithms. 5300 sentences from 1500 patients were annotated & algorithms manually validated by data abstractors for 500 patients. The algorithms developed were the following: 1) Classification of a sentence into 3 classes: Distal/Local metastasis, Suspicious & Other 2) Classification of a sentence into 2 classes: Distal or Local 3) Classification of a patient into 2 classes: Distal metastasis or not distal metastasis 4) Multi label classification for detecting sites of metastasis. Sentence level algorithms were built using Deep Learning and patient level aggregation of sentence level prediction was done using ML approaches including temporal features. Pretrained ULMFiT model was fine-tuned with Concerto HealthAI’s corpus for sentence classification tasks. Results: At a sentence level, we obtained an accuracy of 0.85 for the distal/local vs suspicious vs irrelevant model and 0.97 for the distal vs not distal metastasis model. Our patient level metrics are shown in the table. The classes used for sites of metastasis are Brain, Bone, Lung, Liver, Distant Lymph nodes & Unknown sites. Subset accuracy (mean fraction of labels which match ) of 0.93 was obtained on the hold out test set at patient level. Conclusions: Metastatic status & site of metastasis can be reliably extracted automatically from clinical notes using deep learning techniques. This information will be valuable for clinical trial matching, outcomes research and other applications. [Table: see text]
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