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OBJECTIVES: Existing studies evaluating the accuracy of heparin-binding protein for the diagnosis of sepsis have been inconsistent. We conducted a systematic review and meta-analysis to assess the totality of current evidence regarding the utility of heparin-binding protein to diagnose sepsis in patients with presumed systemic infection. DATA SOURCE: PubMed, Embase, the China National Knowledge infrastructure, and WangFang electronic database were searched from inception to December of 2019. STUDY SELECTION: Two independent reviewers identified eligible studies. Cohort and case-control studies, which measured serum levels of heparin-binding protein among adult patients with suspected sepsis, were eligible for inclusion. DATA EXTRACTION: Two reviewers independently extracted data elements from the selected studies. A bivariate random-effects meta-analysis model was used to synthesize the prognostic accuracy measures. Risk of bias of studies was assessed with Quality Assessment of Diagnostic Accuracy Studies 2 tool. DATA SYNTHESIS: We identified 26 studies with 3,868 patients in the meta-analysis. Heparin-binding protein had a pooled sensitivity of 0.85 (95% CI, 0.79–0.90) and a pooled specificity of 0.91 (95% CI, 0.82–0.96) for the diagnosis of sepsis. There was low heterogeneity between the studies (I 2 = 12%), and no evidence of publication bias was detected. Heparin-binding protein had a higher sensitivity and specificity when compared with procalcitonin (0.75 [95% CI, 0.62–0.85] and 0.85 [95% CI, 0.73–0.92]) as well as C-reactive protein (0.75 [95% CI, 0.65–0.84] and 0.71 [95% CI, 0.63–0.77]). Serial measurements of heparin-binding protein also showed that heparin-binding protein levels rose significantly at least 24 hours before a diagnosis of sepsis. CONCLUSIONS: The diagnostic ability of heparin-binding protein is favorable, demonstrating both high sensitivity and specificity in predicting progression to sepsis in critically ill patients. Future studies could assess the incremental value that heparin-binding protein may add to a multimodal sepsis identification and prognostication algorithm for critically ill patients.

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<p class="Abstract">Abstract&mdash;As computers and networks have been developed vigorously, distance learning could be <a href="http://www.google.com.hk/search?hl=zh-CN&amp;safe=strict&amp;rlz=1R2CULB_zh-CN&amp;&amp;sa=X&amp;ei=sD4PTOjWLJDRcayP0eEM&amp;ved=0CBEQBSgA&amp;q=integrated&amp;spell=1">integrated</a> with computer vision <a href="http://tw.wrs.yahoo.com/_ylt=A3TWBY_yKw9MrXoAWcZr1gt./SIG=13ucfgdk3/EXP=1276149106/**http%3a/tw.search.yahoo.com/search%3fei=UTF-8%26rd=r1%26fr=yfp%26p=techniques%26SpellState=%2b%26fr2=sp-top">techniques</a> for the purpose of better learning effects. In this paper, we developed a distance yoga learning system for people to learn/play through the internet. The main point of the interactive learning system essentially consists in that the gesture performed by player, segmented by computer vision <a href="http://tw.wrs.yahoo.com/_ylt=A3TWBY_yKw9MrXoAWcZr1gt./SIG=13ucfgdk3/EXP=1276149106/**http%3a/tw.search.yahoo.com/search%3fei=UTF-8%26rd=r1%26fr=yfp%26p=techniques%26SpellState=%2b%26fr2=sp-top">techniques</a>, should possess the same silhouette for a given yoga posture. For better accuracy, the learning score is calculated by matching the distance transformation of the player silhouette with stored standard yoga posture. In the experiments, 23 postures were defined and six persons were invited to do each posture three times. About 86% of the difference between computer scores and the scores given by a yoga teacher falls within -2.5~2.5.</p><p class="Abstract"><em>&nbsp;</em></p>