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AimsTo explore the possibility of implementing Choosing Wisely on ocular patients in China by investigating the prevalence of abnormalities in routine preoperative blood tests (RPBTs) and its turnaround time (TAT).MethodsData from 102 542 ocular patients between January 2016 and December 2018, at Zhongshan Ophthalmic Center, were pooled from the laboratory information system. The test results were divided into normal and abnormal, including critical values. Ocular diseases were stratified into 11 subtypes based on the primary diagnosis. The TAT of 243 350 blood tests from January 2017 to December 2018 was categorised into transportation time and intralaboratory time.ResultsRPBT was grouped into complete blood count (CBC), blood biochemistry (BBC), blood coagulation (BCG) and blood-borne pathogens (BBP), completed for 97.22%, 87.66%, 94.41% and 95.35% of the recruited patients (male, 52 549 (51.25%); median(IQR) age, 54 (29–67) years), respectively. Stratified by the test items, 9.19% (95% CI 9.07% to 9.31%) were abnormal results, and 0.020% (95% CI 0.019% to 0.022%) were critical; most abnormalities were on the CBC, while glucose was the most common critical item. Classified by the patients’ primary diagnosis, 76.97% (95% CI 76.71% to 77.23%) had at least one abnormal result, and 0.28% (95% CI 0.25% to 0.32%) were critical; abnormal findings were reported in 45.29% (95% CI 44.98% to 45.60%), 54.97% (95% CI 54.65% to 55.30%), 30.29% (95% CI 30.00% to 30.58%) and 11.32% (95% CI 11.12% to 11.52%) for the CBC, BBC, BCG and BBP tests, respectively. The median transportation time and intralaboratory TAT of the samples were 12 min and 78 min respectively.ConclusionBlood abnormalities are common in ocular patients. With acceptable timelines, RPBT is still indispensable in China for patient safety.

Metabolomics based on high-resolution mass spectrometry has become a powerful technique in biomedical research. The development of various analytical tools and online libraries has promoted the identification of biomarkers. However, how to make mass spectrometry collect more data information is an important but underestimated research topic. Herein, we combined full-scan and data-dependent acquisition (DDA) modes to develop a new targeted DDA based on the inclusion list of differential and preidentified ions (dpDDA). In this workflow, the MS1datasets for statistical analysis and metabolite preidentification were first obtained using full-scan, and then, the MS/MS datasets for metabolite identification were obtained using targeted DDA of quality control samples based on the inclusion list. Compared with the current methods (DDA, data-independent acquisition, targeted DDA with time-staggered precursor ion list, and iterative exclusion DDA), dpDDA showed better stability, higher characteristic ion coverage, higher differential metabolites’ MS/MS coverage, and higher quality MS/MS spectra. Moreover, the same trend was verified in the analysis of large-scale clinical samples. More surprisingly, dpDDA can distinguish patients with different severities of coronary heart disease (CHD) based on the Canadian Cardiovascular Society angina classification, which we cannot distinguish through conventional metabolomics data collection. Finally, dpDDA was employed to differentiate CHD from healthy control, and targeted metabolomics confirmed that dpDDA could identify a more complete metabolic pathway network. At the same time, four unreported potential CHD biomarkers were identified, and the area under the receiver operating characteristic curve was greater than 0.85. These results showed that dpDDA would expand the discovery of biomarkers based on metabolomics, more comprehensively explore the key metabolites and their association with diseases, and promote the development of precision medicine.

Recent evidence suggests that small nucleolar RNAs (snoRNAs) are involved in the progression of various cancers, but their precise roles in hepatocellular carcinoma (HCC) remain largely unclear. Here, we report that SNORD17 promotes the progression of HCC through a positive feedback loop with p53. HCC-related microarray datasets from the Gene Expression Omnibus (GEO) database and clinical HCC samples were used to identify clinically relevant snoRNAs in HCC. SNORD17 was found upregulated in HCC tissues compared with normal liver tissues, and the higher expression of SNORD17 predicted poor outcomes in patients with HCC, especially in those with wild-type p53. SNORD17 promoted the growth and tumorigenicity of HCC cells in vitro and in vivo by inhibiting p53-mediated cell cycle arrest and apoptosis. Mechanistically, SNORD17 anchored nucleophosmin 1 (NPM1) and MYB binding protein 1a (MYBBP1A) in the nucleolus by binding them simultaneously. Loss of SNORD17 promoted the translocation of NPM1 and MYBBP1A into the nucleoplasm, leading to NPM1/MDM2-mediated stability and MYBBP1A/p300-mediated activation of p53. Interestingly, p300-mediated acetylation of p53 inhibited SNORD17 expression by binding to the promoter of SNORD17 in turn, forming a positive feedback loop between SNORD17 and p53. Administration of SNORD17 antisense oligonucleotides (ASOs) significantly suppressed the growth of xenograft tumors in mice. In summary, this study suggests that SNORD17 drives cancer progression by constitutively inhibiting p53 signaling in HCC and may represent a potential therapeutic target for HCC.

The identification of molecules within complex mixtures is a major bottleneck in natural products (NPs) research. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) has emerged as the main tool for the high-throughput characterization of NPs. The large amount of data sets by LC–MS/MS presents a challenge for data processing and interpretation, and the LC–MS/MS molecular network (MN) is one of the most prominent tools for analyzing large MS/MS data sets, widely used for rapid classification, identification, and structural speculation of unknown compounds. However, the existence of a large number of redundant nodes leads to false-positive results. To solve this problem, we proposed the in-depth analysis of MN. In this study, in-depth analysis of MN of five NPs representing the common structures of saponin, steroid, flavonoid, alkaloid, and phenolic acid revealed the presence of redundant nodes (including other adducts, isotope, and in-source fragmentation) in addition to the normal nodes, which can lead to false-positive identification results. Additionally, the reasons for different redundant nodes are discussed and experimentally verified, and it was found that the impact of redundant nodes can be mitigated by optimizing the experimental conditions and employing Feature-Based Molecular Networking. Furthermore, Ion Identity Molecular Networking can rapidly discover and screen redundant nodes, simplifying the in-depth analysis of MN and improving the network connectivity of structurally related molecules. Finally, a combination formulation of 7 NPs is used as an example to provide a guide for in-depth analysis of MN for comprehensive characterization of complex systems. This study highlights the importance of an in-depth analysis of MN for better understanding and utilization of MS/MS data in complex systems to reduce the false-positive rate of identification by screening and filtering redundant nodes.

Ultrasonic testing (UT) is increasingly combined with machine learning (ML) techniques for intelligently identifying damage. Extracting significant features from UT data is essential for efficient defect characterization. Moreover, the hidden physics behind ML is unexplained, reducing the generalization capability and versatility of ML methods in UT. In this paper, a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efficiency of UT. Firstly, multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space. Subsequently, a feature selection method based on model interpretable strategy (FS-MIS) is innovatively developed by integrating Shapley additive explanation (SHAP), filter method, embedded method and wrapper method. The most effective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively. The proposed framework is validated by identifying and locating side-drilled holes (SDHs) with 0.5λcentral distance and different depths. An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments. The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival (ToAs) of the scattered waves emitted by adjacent SDHs. The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67% with an average error of 0.25%, significantly improving the time resolution of UT signals. On this basis, the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets. The imaging resolution is enhanced to 0.5λby implementing the total focusing method (TFM). The relative errors of hole depths and central distance are no more than 0.51% and 3.57%, respectively. Finally, the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.