Novel Human Bocavirus in Children with Acute Respiratory Tract Infection

Human bocavirus (HBoV) and HBoV2, two human bocavirus species, were found in 18 and 10 of 235 nasopharyngeal aspirates, respectively, from children hospitalized with acute respiratory tract infection. Our results suggest that, like HBoV, HBoV2 is distributed worldwide and may be associated with respiratory and enteric diseases

FLP Recombinase-Mediated Site-Specific Recombination in Silkworm, Bombyx mori

A comprehensive understanding of gene function and the production of site-specific genetically modified mutants are two major goals of genetic engineering in the post-genomic era. Although site-specific recombination systems have been powerful tools for genome manipulation of many organisms, they have not yet been established for use in the manipulation of the silkworm Bombyx mori genome. In this study, we achieved site-specific excision of a target gene at predefined chromosomal sites in the silkworm using a FLP/FRT site-specific recombination system. We first constructed two stable transgenic target silkworm strains that both contain a single copy of the transgene construct comprising a target gene expression cassette flanked by FRT sites. Using pre-blastoderm microinjection of a FLP recombinase helper expression vector, 32 G3 site-specific recombinant transgenic individuals were isolated from five of 143 broods. The average frequency of FLP recombinase-mediated site-specific excision in the two target strains genome was approximately 3.5%. This study shows that it is feasible to achieve site-specific recombination in silkworms using the FLP/FRT system. We conclude that the FLP/FRT system is a useful tool for genome manipulation in the silkworm. Furthermore, this is the first reported use of the FLP/FRT system for the genetic manipulation of a lepidopteran genome and thus provides a useful reference for the establishment of genome manipulation technologies in other lepidopteran species

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong’s public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s44247-022-00001-0

Frequency and Severity of Hypoglycemia in Type 2 Diabetes Mellitus Patients Treated with a Sulfonylurea-Based Regimen at University-Affiliated Hospitals in Korea: The Naturalistic Evaluation of Hypoglycemic Events in Diabetic Subjects Study

Background: We assessed the frequency and severity of hypoglycemia in type 2 diabetes mellitus patients treated with sulfonylurea monotherapy or sulfonylurea+metformin. Methods: We conducted a retrospective, observational, cross-sectional study in 2011 and 2012 including patients with type 2 diabetes mellitus aged >= 30 years who were treated with >= 6 months of sulfonylurea monotherapy or sulfonylurea+metformin at 20 university-affiliated hospitals in Korea. At enrollment, glycated hemoglobin (HbA1c) was assessed; participants completed self-reported questionnaires describing hypoglycemia incidents over the past 6 months. A review of medical records up to 12 months before enrollment provided data on demographics, disease history, comorbidities, laboratory results, and drug usage. Results: Of 726 enrolled patients, 719 were included (55.6% male); 31.7% and 68.3% were on sulfonylurea monotherapy and sulfonylurea+metformin, respectively. Mean +/- standard deviation age was 65.9 +/- 10.0 years; mean HbA1c level was 7.0%+/- 1.0%; 77.8% of patients had hypertension (89.4% used antihypertensive medication); 60.5% had lipid disorders (72.5% used lipid-lowering medication); and 52.0% had one or more micro- or macrovascular diseases. Among patients with A1c measurement (n=717), 56.4% achieved therapeutic goals (HbA1c <7.0%); 42.4% (305/719) experienced hypoglycemia within 6 months of enrollment; and 38.8%, 12.9%, 12.7%, and 3.9% of patients experienced mild, moderate, severe, and very severe hypoglycemia symptoms, respectively. Several reported hypoglycemia frequency as 1-2 times over the last 6 months. The mean number of very severe hypoglycemia episodes was 3.5 +/- 5.5. Conclusion: Among type 2 diabetes mellitus patients treated with sulfonylurea-based regimens, glycemic levels were relatively well controlled but hypoglycemia remained a prevalent side effect.OAIID:RECH_ACHV_DSTSH_NO:T201915802RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A075942CITE_RATE:0FILENAME:2019-Frequency and Severity of Hypoglycemia-NEEDS-kjfm-Free.pdfDEPT_NM:의학과EMAIL:[email protected]_YN:NFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/9b163bcf-1021-468d-b368-d914cb38e290/linkY