Search

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development. [ABSTRACT FROM AUTHOR]

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F -score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance. [ABSTRACT FROM AUTHOR]

Introduction: Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi‐morbidity/treatment complexity. Some headway has been made to identify genetic and non‐genetic risk factors for T2DM. However, longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi‐morbidity. Methods: This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre‐ and post‐T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n = 237 vs matched non‐T2DM controls n = 44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. Results: For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis, a greater indication of ischaemic heart disease proportions was observed. Post‐T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. Conclusion: These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores. [ABSTRACT FROM AUTHOR]

Chinese medicine (CM) was extensively used to treat COVID-19 in China. We aimed to evaluate the real-world effectiveness of add-on semi-individualized CM during the outbreak. A retrospective cohort of 1788 adult confirmed COVID-19 patients were recruited from 2235 consecutive linked records retrieved from five hospitals in Wuhan during 15 January to 13 March 2020. The mortality of add-on semi-individualized CM users and non-users was compared by inverse probability weighted hazard ratio (HR) and by propensity score matching. Change of biomarkers was compared between groups, and the frequency of CMs used was analyzed. Subgroup analysis was performed to stratify disease severity and dose of CM exposure. The crude mortality was 3.8% in the semi-individualized CM user group and 17.0% among the non-users. Add-on CM was associated with a mortality reduction of 58% (HR = 0.42, 95% CI: 0.23 to 0.77, p = 0.005) among all COVID-19 cases and 66% (HR = 0.34, 95% CI: 0.15 to 0.76, p = 0.009) among severe/critical COVID-19 cases demonstrating dose-dependent response, after inversely weighted with propensity score. The result was robust in various stratified, weighted, matched, adjusted and sensitivity analyses. Severe/critical patients that received add-on CM had a trend of stabilized D-dimer level after 3–7 days of admission when compared to baseline. Immunomodulating and anti-asthmatic CMs were most used. Add-on semi-individualized CM was associated with significantly reduced mortality, especially among severe/critical cases. Chinese medicine could be considered as an add-on regimen for trial use. [ABSTRACT FROM AUTHOR]

Presents information on a study which investigated the treatment of samples of high-phosphorus cast iron using a cw carbon dioxide laser beam. Methods for laser heat treatment; Experimental details; Results and discussion; Conclusions.

Presents a study that produced ceramics in the aluminum-oxygen-tungsten-oxygen system by laser irradiation. Examination of the products by scanning electron microscopy; Formation of a cell structure in the specimens; Comparison of the properties of the samples synthesized by laser and by conventional sintering methods.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. The effect of Chinese medicine (CM) on mortality during acute exacerbation of COPD is unclear. We evaluated the real-world effectiveness of add-on personalized CM in hospitalized COPD patients with acute exacerbation. This is a retrospective cohort study with new-user design. All electronic medical records of hospitalized adult COPD patients (n = 4781) between July 2011 and November 2019 were extracted. Personalized CM exposure was defined as receiving CM that were prescribed, and not in a fixed form and dose at baseline. A 1:1 matching control cohort was generated from the same source and matched by propensity score. Primary endpoint was mortality. Multivariable Cox regression models were used to estimate the hazard ratio (HR) adjusting the same set of covariates (most prevalent with significant inter-group difference) used in propensity score calculation. Secondary endpoints included the change in hematology and biochemistry, and the association between the use of difference CMs and treatment effect. The prescription pattern was also assessed and the putative targets of the CMs on COPD was analyzed with network pharmacology approach. 4325 (90.5%) patients were included in the analysis. The mean total hospital stay was 16.7 ± 11.8 days. In the matched cohort, the absolute risk reduction by add-on personalized CM was 5.2% (3.9% vs 9.1%). The adjusted HR of mortality was 0.13 (95% CI: 0.03 to 0.60, p = 0.008). The result remained robust in the sensitivity analyses. The change in hematology and biochemistry were comparable between groups. Among the top 10 most used CMs, Poria (Fu-ling), Citri Reticulatae Pericarpium (Chen-pi) and Glycyrrhizae Radix Et Rhizoma (Gan-cao) were associated with significant hazard reduction in mortality. The putative targets of the CM used in this cohort on COPD were related to Jak-STAT, Toll-like receptor, and TNF signaling pathway which shares similar mechanism with a range of immunological disorders and infectious diseases. Our results suggest that add-on personalized Chinese medicine was associated with significant mortality reduction in hospitalized COPD patients with acute exacerbation in real-world setting with minimal adverse effect on liver and renal function. Further randomized trials are warranted. [ABSTRACT FROM AUTHOR]

Abstract: Single-phase β-FeSi2 thin films were prepared on Si(100) and Si(111) wafers by using femtosecond laser deposition with a FeSi2 alloy target for the first time. X-ray diffraction (XRD), field scanning electron microscopy (FSEM), scanning probe microscopy (SPM), electron backscattered diffraction pattern (EBSD), and Fourier-transform Raman infrared spectroscopy (FTRIS) were used to characterize the structure, composition, and properties of the β-FeSi2/Si films. The orientation of β-FeSi2 grains was found to depend on the orientation of the Si substrates, and photoluminescence at wavelength of 1.53 μm was observed from the single-phase β-FeSi2/Si thin film at room temperature (20 °C). [Copyright &y& Elsevier]

Abstract: Single-phase β-FeSi2 thin films have been grown on quartz substrates using femtosecond laser deposition (800nm, 50fs, 1KHz) under gas pressure of 3.0×10−4 Pa. X-ray diffraction (XRD) and field-emission scanning electron microscopy (SEM) were used to determine the structural properties and surface images of the films. Typical XRD patterns of the film showed that no other diffraction peak except β-FeSi2 was found. The SEM results indicated that the films were composed of well-distributed grains, in the range 50–150nm in diameter. In addition, normal incidence spectral transmittance and reflectance data suggested that the β-FeSi2 film has a direct energy gap of about 0.85eV. The thickness of the layer and the refractive index of the film were determined by performed calculation in the wavelength range 1.9–2.7μm. Furthermore, the Raman spectra of the films were also discussed. [Copyright &y& Elsevier]

Abstract: Laser sintering has been applied for preparing β-FeSi2 based thermoelectric alloy for the first time. Effects of laser sintering time on alloying, phase transformation and microstructure of FeSi2 were investigated by X-ray diffraction (XRD) analysis and scanning electron microscope (SEM). Effects of annealing temperature and time on phase transformation were also studied through Seebeck coefficients. The results show that for 90s laser-sintered samples, it takes only 15h to obtain β phase under Ar atmosphere followed by an annealing at 1073K. These samples exhibit homogeneous microstructure with average grain size of less than 5μm. A maximum Seebeck coefficient at room temperature could reach 115μV/K. It indicates that laser sintering could be an alternate faster preparation method to generate high quality β-FeSi2 thermoelectric material with little contamination due to its advantages of rapid heating rate, high cooling rate and rapid solidification. [Copyright &y& Elsevier]

The nonlinear absorption (NLA) properties of copper phthalocyanine (CuPc)-doped polymethylmethacrylate (PMMA) thin film in the femtosecond regime were investigated both experimentally and theoretically. The open-aperture (OA) Z-scan measurements of the film were carried out by femtosecond laser pulse. A transition from saturable absorption (SA) to reverse saturable absorption (RSA) was observed as the excitation intensity is increased. The rate equation analysis based on an developed efficient energy level model was performed and the intensity dependence of level populations was obtained, which reveals the source of NLA. The results show that the transition from SA to RSA is ascribed to the fifth-order effect of excited-state absorption (ESA) induced by two-photon absorption (TPA) process. Furthermore, it is found that the CuPc-doped PMMA thin film possesses a large fifth-order coefficient (beta((5))) of 0.24 x 10(-21)cm(3)/W(2). It indicates that the CuPc-doped PMMA thin film could be a promising candidate for optical limiting material.