Frontiers in analytical science最新文献

{"title":"Grand Challenges and Perspectives in Biomedical Analysis and Diagnostics","authors":"Q. Cheng","doi":"10.3389/frans.2021.700386","DOIUrl":"https://doi.org/10.3389/frans.2021.700386","url":null,"abstract":"The importance of analytical sciences and biosensing to medical diagnosis has been well recognized by those involved in the field; the recent global pandemic due to severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2) has further elevated the topic to paramount worldwide prominence and urgency (Lippi et al., 2020). While the pandemic may be contained in the near future due to the heroic efforts of medical staff and biotechnologists around the world, the research interest in analytical sciences toward more efficient medical diagnosis will undoubtedly remain for the foreseeable future. Aside from innovative schemes that offer new angles for detection and quantification, evaluations and reevaluations of the state and efficacy of analytical sensing are also required when applied to medical samples. For a broader conversation of the directions of research, it is important to assess the state-of-the-art and significant trends across the field. There have been exciting technical developments in recent years that push forward the accuracy and sensitivity of techniques, expand the scope of analyses beyond simple biomarkers, and improve the accessibility and applicability of analytical methods. In addition, clinical data of increasing depth and complexity are gathered at an extraordinary pace in recent years due to “Big Data” movement in healthcare. Therefore, one of the most prominent trends in analytical science appears to be the application of artificial intelligence and machine learning models to correlate sensed or imaged markers from patients to diagnosis (Rajkomar et al., 2019). Recent examples include an artificial intelligence system that outperformed doctors by 11% in diagnosing breast cancers (McKinney et al., 2020), and a study of machine learning models that used imaging biomarkers and predictive models for rapid diagnosis of COVID-19 (Wynants et al., 2020). This dense, complex approach toward information accumulation also requires a scale-up in the sophistication of models by which the information is treated so that relevant outcomes and knowledge can be obtained. Clearly, the need for technical advances in medical diagnosis is ever-present, and this is manifested in the current pandemic. Mature technologies such as PCR and immunoassays continue to provide reliable tests for the rapidly spreading disease, while in the meantime we have seen a wave of new approaches rolling out of unconventional sectors that are shaping the course of diagnostic development (mass spectrometry, 3D printing, and CRISPR-Cas12, to name a few). The challenges in this field also suggest a range of opportunities, which we aim to describe in this Article. In the interest of brevity, we will organize the discussion into analysis targets, technological developments, and data processing.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45781789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}