A low cost smart system to analyze different types of edible Bird's nest adulteration based on colorimetric sensor array

This study was performed to develop a low-cost smart system for identification and quantification of adulterated edible bird's nest (EBN). The smart system was constructed with a colorimetric sensor array (CSA), a smartphone and a multi-layered network model. The CSA were used to collect the odor character of EBN and the response signals of CSA were captured by the smartphone systems. The principal component analysis (PCA) and hierarchical cluster analysis (HAC) were used to inquiry the similarity among authentic and adulterated EBNs. The multi-layered network model was constructed to analyze EBN adulteration. In this model, discrimination of authentic EBN and adulterated EBN was realized using back-propagation neural networks (BPNN) algorithm. Then, another BPNN-based model was developed to identify the type of adulterant in the mixed EBN. Finally, adulterated percentage prediction model for each kind of adulterate EBN was built using partial least square (PLS) method. Results showed that recognition rates of the authentic EBN and adulterated EBN was as high as 90%. The correlation coefficient of percentage prediction model for calibration set was 0.886, and 0.869 for prediction set. The low-cost smart system provides a real-time, nondestructive tool to authenticate EBN for customers and retailers.     

Reducing unnecessary biopsies while detecting clinically significant prostate cancer including cribriform growth with the ERSPC Rotterdam risk calculator and 4Kscore

Introduction

The use of risk calculators predicting clinically significant prostate cancer (csCaP) on biopsy reduces unnecessary biopsies and overdiagnosis of indolent disease compared to a Prostate Specific Antigen (PSA) strategy. Updating these tools using more specific outcome measures and contemporary predictors could potentially lead to further reductions. Our objective was to assess clinical impact of the 4 kallikrein (4K) score, the Rotterdam Prostate Cancer Risk Calculator (RPCRC), and the combination of both for predicting csCaP based on the latest International Society of Urological Pathology grading system and cribriform growth pattern.

Aligning Developmental and Processing Accounts of Implicit and Statistical Learning

A long‐standing question in child language research concerns how children achieve mature syntactic knowledge in the face of a complex linguistic environment. A widely accepted view is that this process involves extracting distributional regularities from the environment in a manner that is incidental and happens, for the most part, without the learner's awareness. In this way, the debate speaks to two associated but separate literatures in language acquisition: statistical learning and implicit learning. Both fields have explored this issue in some depth but, at present, neither the results from the infant studies used by the statistical learning literature nor the artificial grammar learning tasks studies from the implicit learning literature can be used to fully explain how children's syntax becomes adult‐like. In this work, we consider an alternative explanation—that children use error‐based learning to become mature syntax users. We discuss this proposal in the light of the behavioral findings from structural priming studies and the computational findings from Chang, Dell, and Bock's (2006) dual‐path model, which incorporates properties from both statistical and implicit learning, and offers an explanation for syntax learning and structural priming using a common error‐based learning mechanism. We then turn our attention to future directions for the field, here suggesting how structural priming might inform the statistical learning and implicit learning literature on the nature of the learning mechanism.     

Bayesian adaptive lasso for additive hazard regression with current status data

Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time-to-event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a frequentist framework. In this article, we consider additive hazards model in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo methods are developed to implement posterior sampling and inference. The empirical performance of the proposed method is demonstrated by simulation studies. An application to a study on the risk factors of heart failure disease for type 2 diabetes patients is presented.     

Can muscle weakness and disability influence the relationship between pain catastrophizing and pain worsening in patients with knee osteoarthritis? A cross-sectional study

ObjectiveTo verify if the relationship between pain catastrophizing and pain worsening would be mediated by muscle weakness and disability in patients with symptomatic knee osteoarthritis.MethodsThis was a cross-sectional study in a hospital out-patient setting. Convenience sampling was used with a total of 50 participants with symptomatic knee osteoarthritis. Pain and the activities of daily livings (ADL) were assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscale. Pain catastrophizing was assessed using the Coping Strategy Questionnaire (CSQ) subscale. Muscle strength of knee extension and 30-s chair stand test (30CST) were also assessed. Path analysis was performed to test the hypothetical model. Goodness of fit of models were assessed by using statistical parameters such as the chi-square value, goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA).ResultsThe chi-square values were not significant (chi-square = 0.283, p = 0.594), and the indices of goodness of fit were high, implying a valid model (GFI = 1.000; AGFI = 0.997; CFI = 1.000; RMSEA = 0.000). Pain was influenced significantly by muscle strength and ADL; muscle strength was influenced significantly by ADL via 30CST; ADL was influenced by pain catastrophizing.ConclusionThe relationship between pain catastrophizing with pain worsening are mediated by muscle weakness and disability.     

新型冠状病毒肺炎疫情下社区医院人员防控知识强化测评模式效果评价

目的 对社区医院人员防控知识强化测评模式的实施效果进行评价。方法 通过第三方数据收集平台,在规定时间内、实名制进行疫情防控知识问卷考核,以各岗位合格分数为标准,确认能否上岗,防控知识反复强化,题库动态更新,使各岗位人员保持高度认知的实战状态。结果 首次测试,医院全体人员新型冠状病毒肺炎防控知识平均分为78.37,100%的人知晓用过的一次性帽子和口罩的处理;只有23.27%的知晓医疗机构预检点或分诊台物品的消毒频次。强化测评模式实施1周后,医院全体人员新型冠状病毒肺炎防控知识平均分上升至93.84。上岗情况中,培训3 d上岗人数由48人上升至78人,培训第10天为149人,上岗率也由30.19%上升至第3天的49.05%,以及培训第10天的93.71%。结论 强制社区医院人员学习新型冠状病毒肺炎防控知识效果明显,整体水平短期内迅速提升,并保持高度认知,该强化测评模式对一线社区医院人员防控能力的提升有实际应用价值。     

Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

BackgroundThe innovation of immune checkpoint blockade (ICB) represents a promising shift in the treatment of advanced hepatocellular carcinoma (HCC). However, response to ICB has varied largely due to the high tumor heterogeneity and complex tumor microenvironment (TME). The competitive endogenous RNA (ceRNA) network also plays an important role in tumor occurrence and progression, but its relation with tumor-infiltrating immune cells (TICs) remains largely unexplored in HCC. The overriding objective of our study was thus to construct a prognosis-related risk model and to further evaluate the relationship between ceRNA networks and TICs.MethodsDifferentially expressed gene (DEG) analysis was performed to identify the differentially expressed RNAs. Lasso and multivariable Cox regression analyses were used to construct risk models, which were assessed by the area under the receiver operating characteristic curve (AUC of ROC) and Kaplan-Meier (K-M) curves. Then, a single-sample gene set enrichment analysis (ssGSEA) algorithm was adopted to dissect the TICs in HCC samples. Nomograms were constructed and calibration curves were used to verify the discrimination and accuracy of the nomograms. Finally, integration analysis was performed to validate the correlation of ceRNA and TICs.ResultsIn the study, 7 differentially expressed RNAs [5 messenger RNA s (mRNAs) and 2 micro RNAs (miRNAs)] were incorporated to construct a ceRNA risk model. The AUC of the 1-, 3-, and 5-year overall survival (OS) were 0.784, 0.685, and 0.691 respectively. Likewise, 7 types TICs were in the TICs signature model and the AUC of the 1-, 3-, and 5-year OS were 0.706, 0.731, and 0.721 respectively. The integration analysis showed that 7 pairs of mRNA-TICs and 1 pair of miRNA-TICs had a close relation (all correlation coefficients >0.2, P<0.001).ConclusionsThrough constructing two risk models based on ceRNA network and TICs, we identified the hub RNAs and key TICs in the progression and prognosis of HCC, and further explored the relationship between ceRNA and TME. Importantly, targeting these hub RNAs may facilitate the remodeling of the TME and be a potential therapeutic alternative to enhancing the response to ICB, thus improving the prognosis of HCC patients.     

Ovarian hyperstimulation syndrome: a clinical retrospective study on 565 inpatients

Abstract

The aim of this study was to analyze clinical manifestations of 565 ovarian hyperstimulation syndrome (OHSS) inpatients in the largest Obstetrics and Gynecology hospital in China from year 2010 to 2017, to get more understanding of epidemiologic features of this disease, and to provide some insight on the diagnosis, treatment, and preventions of OHSS. It is a clinical retrospective study. In the 565 cases that developed OHSS over an eight-year period between 2010 and 2017 were reviewed, we assessed patients’ general characteristics, clinical manifestations, treatment, prognosis, and the relationship between different indicators and the severity of OHSS. Totally 12 kinds of ovulation induction protocols (Protocol 1: CC; Protocol 2: Gn; Protocol 3: hCG; Protocol 4: GnRh-a; Protocol 5: CC & Gn; Protocol 6: CC & hCG; Protocol 7: Gn & hCG; Protocol 8: GnRh-a & Gn; Protocol 9: CC & Gn & hCG; Protocol 10: GnRh-a & CC & Gn; Protocol 11: Letrozole & Gn & hCG; Protocol 12:GnRh-a & Letrozole & Gn) were analyzed and the Odds Ratio (OR) of each protocol were calculated. Five hundred and sixty-five patients were reviewed in our study. In all these patients, the number of hospitalizations, mean age, primary infertility rate, and pregnancy rate did not differ through the last 8?years. From which we may infer that the incidence rate of OHSS may not change over the last 8?years. Older patients tend to develop into more severe stage easily. The pregnancy rate was much lower in mild stage patients, but no difference was found between patients in moderate, severe and critical stage. Oocytes retrieval is strongly associated with severity. PCOS history, irregular menstrual cycle and infertility type do not seem to affect the severity of OHSS. Twelve kinds of ovulation induction protocols were analyzed, OR of different protocols were calculated, what is noteworthy is that patients who used GnRh easily developed more severe OHSS than the patients who received oocytes retrieval. We suggest that we may choose ovulation induction protocols according to the OR table while treating women with high-risk factors.