Impact of Guidelines for Antihypertensive Treatment on Clinical Practice

Many Guidelines and Consensus Statements were published in 1993 and 1994. The pressure to produce these guidelines comes from clinicians faced with rapid change, from a wish to provide an independent authoritative view rather than rely on commercial promotion of particular treatments, and from governments seeking assistance with the allocation of scarce resources.

Evaluation of the impact that guidelines exert on clinical practice is notoriously difficult, but has been attempted using the self-report questionnaire, or the more reliable but expensive analysis of actual practice data, before and after publication of the guidelines.     

Effects of differential privacy techniques: Considerations for end users

We study the effects of differentially private (DP) noise injection techniques in a survey data setting, using the release of cost of early care and education estimates from the National Survey of Early Care and Education as a motivating example. As an example of how DP noise injection affects statistical estimates, our analysis compares the relative performance of DP techniques in the context of releasing estimates of means, medians, and regression coefficients. The results show that for many statistics, basic DP techniques show good performance provided that the privacy budget does not need to be split over too many estimates. Throughout, we show that small decisions, such as the number of bins in a histogram or the scaling of a variable in a regression equation, can have sometimes dramatic effects on the end results. Because of this, it is important to develop DP techniques with an eye towards the most important aspects of the data for end users.     

Ultrasound Bone Fracture Sensing and Data Communication: Experimental Results in a Pig Limb Sample

Approximately 6.3 million fractures occur each year in the United States alone. Accurately monitoring the progression of fracture healing is essential to be able to advise patients when it is safe to return to normal activity. The most common method used to confirm and monitor fracture healing is the acquisition of multiple radiographic images over the many months required for healing. This imaging method uses large expensive equipment and exposes patients to high levels of ionizing radiation. In the study described here, we tested another technology for monitoring fracture healing that could minimize the need for multiple radiographic images. We tested a piezoelectric transducer fixed to the surface of a bone that uses electromechanical impedance spectroscopy to measure simulated fractures and transmits the measurement data to an acoustic receiver located externally on the skin surface.     

基于数据挖掘的中药辅助化疗治疗晚期结直肠癌的用药规律文献研究＊

目的 分析中药复方辅助化疗治疗晚期结直肠癌的用药规律。方法 借助中国知网、万方数据、重庆维普以及中国生物医学文献数据库数据平台，搜索晚期结直肠化疗并中药经方治疗等相关文献，进行质量评价，剔除低质量文献，罗列组方建立Excel数据库，进行高频药物及其性味归经的描述性统计，基于SPSS Statistics 26.0关联规则及因子分析，SPSS Modeler 18.0聚类分析，Cytoscape软件直观网络图，进行数据挖掘整理与分析。结果 研究纳入相关文献148篇，处方155首，药物191味，用药总频次1823次，核心中药27味（使用频率 ≥ 10%），以白术所用最广。性味以甘温为首，归经以脾经为最。以置信度95%、支持度15%、提升度 > 1进行关联规则分析得到常用三药组合17组。聚类分析以欧式平方距离24为标准得到潜在聚类方剂3个。结论 中药复方用药以清补健脾，甘温扶正；平解邪毒，调气畅腑；肝脾同调，肺胃同治为规律，正合晚期结直肠癌化疗后脾虚毒恋、痰瘀互结之证。研究归纳出中药辅助化疗治疗晚期结直肠癌的用药组方规律，予以临床一定的理论依据及借鉴意义。     

Trends in Use and Outcomes of Same-Day Discharge Following Elective Percutaneous Coronary Intervention

ObjectivesThe aims of this study were to describe trends and hospital variation in same-day discharge following elective percutaneous coronary intervention (PCI) and to evaluate the association between trends in same-day discharge and patient outcomes.BackgroundInsights on contemporary use of same-day discharge following elective PCI are limited.MethodsIn a sequential cross-sectional analysis of 819,091 patients undergoing elective PCI at 1,716 hospitals in the National Cardiovascular Data Registry CathPCI Registry from July 1, 2009, to December 31, 2017, overall and hospital-level trends in same-day discharge were assessed. Among the 212,369 patients who linked to Centers for Medicare and Medicaid Services data, the association between same-day discharge and 30-day mortality and rehospitalization was assessed.ResultsA total of 114,461 patients (14.0%) were discharged the same day as PCI. The proportion of patients with same-day discharge increased from 4.5% in the third quarter of 2009 to 28.6% in the fourth quarter of 2017. From 2009 to 2017, the rate of same-day discharge increased from 4.3% to 19.5% for femoral-access PCI and from 9.9% to 39.7% for radial-access PCI. Hospital-level variation in the use of same-day discharge persisted throughout (median odds ratio adjusted for year and radial access: 4.15). Risk-adjusted 30-day mortality did not change over time, while risk-adjusted rehospitalization decreased over time and more quickly for same-day discharge (P for interaction <0.001).ConclusionsIn the past decade, a large increase in the use of same-day discharge following elective PCI was not associated with worse 30-day mortality or rehospitalization. Hospital-level variation in same-day discharge may represent an opportunity to reduce costs without compromising patient outcomes.     

加州理工大学图书馆数据监管需求评估项目的启示和思考

介绍数据监管的内涵，阐述加州州立理工大学图书馆数据监管需求评估项目，分析项目成果，指出该项目对国内图书馆的启示，即开展科研数据服务营销推广、数据监管培训服务、通过合作实现高质量的数据监管服务等。     

Predicting protein-binding RNA nucleotides using the feature-based removal of data redundancy and the interaction propensity of nucleotide triplets

Several learning approaches have been used to predict RNA-binding amino acids in a protein sequence, but there has been little attempt to predict protein-binding nucleotides in an RNA sequence. One of the reasons is that the differences between nucleotides in their interaction propensity are much smaller than those between amino acids. Another reason is that RNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding RNA nucleotides is much harder than predicting RNA-binding amino acids. We developed a new method that removes data redundancy in a training set of sequences based on their features. The new method constructs a larger and more informative training set than the standard redundancy removal method based on sequence similarity, and the constructed dataset is guaranteed to be redundancy-free. We computed the interaction propensity (IP) of nucleotide triplets by applying a new definition of IP to an extensive dataset of protein-RNA complexes, and developed a support vector machine (SVM) model to predict protein binding sites in RNA sequences. In a 5-fold cross-validation with 812 RNA sequences, the SVM model predicted protein-binding nucleotides with an accuracy of 86.4%, an F-measure of 84.8%, and a Matthews correlation coefficient of 0.66. With an independent dataset of 56 RNA sequences that were not used in training, the resulting accuracy was 68.1% with an F-measure of 71.7% and a Matthews correlation coefficient of 0.35. To the best of our knowledge, this is the first attempt to predict protein-binding RNA nucleotides in a given RNA sequence from the sequence data alone. The SVM model and datasets are freely available for academics at http://bclab.inha.ac.kr/primer.     

Analyzing the attitude of Indian citizens towards COVID-19 vaccine – A text analytics study

Background and aimsThe government of India recently planned to start the process of the mass vaccination program to end the COVID-19 crises. However, the process of vaccination was not made mandatory, and there are a lot of aspects that arise skepticism in the minds of common people regarding COVID-19 vaccines. This study using machine learning techniques analyzes the major concerns Indian citizens voice out about COVID-19 vaccines in social media.MethodsFor this study, we have used social media posts as data. Using Python, we have scrapped the social media posts of Indian citizens discussing about the COVID- 19 vaccine. In Study 1, we performed a sentimental analysis to determine how the general perception of Indian citizens regarding the COVID-19 vaccine changes over different months of COVID-19 crises. In Study 2, we have performed topic modeling to understand the major issues that concern the general public regarding the COVID- 19 vaccine.ResultsOur results have indicated that 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine.ConclusionWith the positive sentiments regarding vaccine is just over 35%, the Indian government needs to focus especially on addressing the fear of vaccines before implementing the process of mass vaccination.     

Lost in Translation (LiT)

Translational medicine is a roller coaster with occasional brilliant successes and a large majority of failures. Lost in Translation 1 (‘LiT1’), beginning in the 1950s, was a golden era built upon earlier advances in experimental physiology, biochemistry and pharmacology, with a dash of serendipity, that led to the discovery of many new drugs for serious illnesses. LiT2 saw the large-scale industrialization of drug discovery using high-throughput screens and assays based on affinity for the target molecule. The links between drug development and university sciences and medicine weakened, but there were still some brilliant successes. In LiT3, the coverage of translational medicine expanded from molecular biology to drug budgets, with much greater emphasis on safety and official regulation. Compared with R&D expenditure, the number of breakthrough discoveries in LiT3 was disappointing, but monoclonal antibodies for immunity and inflammation brought in a new golden era and kinase inhibitors such as imatinib were breakthroughs in cancer. The pharmaceutical industry is trying to revive the LiT1 approach by using phenotypic assays and closer links with academia. LiT4 faces a data explosion generated by the genome project, GWAS, ENCODE and the ‘omics’ that is in danger of leaving LiT4 in a computerized cloud. Industrial laboratories are filled with masses of automated machinery while the scientists sit in a separate room viewing the results on their computers. Big Data will need Big Thinking in LiT4 but with so many unmet medical needs and so many new opportunities being revealed there are high hopes that the roller coaster will ride high again.     

Extracting general practice data for timely vaccine coverage estimates: The England experience

In England, primary care providers use standardised coding systems to record health events such as vaccination as well as patient characteristics. This information can be automatically extracted to estimate coverage for vaccine programmes delivered through primary care, in the general population as well as in specific geographical, ethnic, age or clinical groups. This system provides timely vaccine coverage estimates as well as the flexibility to extract tailored data in order to directly inform a continuously evolving national vaccine programme. It is however limited by the quality and completeness of clinical coding in primary care. A centralised, individual-level register would however improve data quality, completeness and reliability and remains the gold standard.