Ultralong-Time Recovery and Low-Voltage Electroporation for Biological Cell Monitoring Enabled by a Microsized Multipulse Framework.
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ABSTRACT:
SUBMITTER: Lee D
PROVIDER: S-EPMC8717367 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPatients undergoing bronchoscopy in spontaneous breathing are prone to hypoxaemia and hypercapnia. Sedation, airway obstruction, and lung diseases impair respiration and gas exchange. The restitution of normal respiration takes place in the recovery room. Nonetheless, there is no evidence on the necessary observation time. We systematically reviewed current guidelines on bronchoscopy regarding sedation, monitoring and recovery.MethodsThis review was registered at the PROSPERO database (CRD42020197476). MEDLINE and awmf.org were double-searched for official guidelines, recommendation or consensus statements on bronchoscopy from 2010 to 2020. The PICO-process focussed on adults (Patients), bronchoscopy with maintained spontaneous breathing (Interventions), and recommendations regarding the intra- and postprocedural monitoring and sedation (O). The guideline quality was graded. A catalogue of 54 questions was answered. Strength of recommendation and evidence levels were recorded for each recommendation.ResultsSix guidelines on general bronchoscopy and three expert statements on special bronchoscopic procedures were identified. Four guidelines were evidence-based. Most guidelines recommend sedation to improve the patient's tolerance. Midazolam combined with an opioid is preferred. The standard monitoring consists of non-invasive blood pressure, and pulse oximetry, furthermore electrocardiogram in cardiac patients. Only one guideline discusses hypercapnia and capnometry, but without consensus. Two guidelines discuss a recovery time of two hours, but a recommendation was not given because of lack of evidence.ConclusionEvidence for most issues is low to moderate. Lung-diseased patients are not represented by current guidelines. Capnometry and recovery time lack evidence. More primary research in these fields is needed so that future guidelines may address these issues, too.
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Project description:Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.
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