Surface-enhanced Raman spectroscopy (SERS) is a promising ultrasensitive analysis technology due to outstanding molecular fingerprint identification. However, the measured molecules generally need to be adsorbed on a SERS substrate, which makes it difficult to detect weakly adsorbed molecules, for example, the volatile organic compound (VOC) molecules. Herein, we developed a kind of a SERS detection method for weak adsorption molecules with Au@ZIF-8 core–shell nanoparticles (NPs). The well-uniformed single- and multicore–shell NPs can be synthesized controllably, and the shell thickness of the ZIF-8 was able to be precisely controlled (from 3 to 50 nm) to adjust the distance and electromagnetic fields between metal nanoparticles. After analyzing the chemical and physical characterization, Au@ZIF-8 core–shell NPs were employed to detect VOC gas by SERS. In contrast with multicore or thicker-shell nanoparticles, Au@ZIF-8 with a shell thickness of 3 nm could efficiently probe various VOC gas molecules, such as toluene, ethylbenzene, and chlorobenzene. Besides, we were capable of observing the process of toluene gas adsorption and desorption using real-time SERS technology. As observed from the experimental results, this core–shell nanostructure has a promising prospect in diverse gas detection and is expected to be applied to the specific identification of intermediates in catalytic reactions.

medRxiv preprint regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram. RESULTS:The predictive factors including the epidemiological history, wedgeshaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression.The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration.Moreover, decision curve and clinical impact curve were also beneficial for COVID-19 pneumonia patients.

Background Early and accurate identification of septic patients at high risk for ICU mortality can help clinicians make optimal clinical decisions and improve the patients’ outcomes. This study aimed to develop and validate (internally and externally) a mortality prediction score for sepsis following admission in the ICU. Methods We extracted data retrospectively regarding adult septic patients from one teaching hospital in Wenzhou, China and a large multi-center critical care database from the USA. Demographic data, vital signs, laboratory values, comorbidities, and clinical outcomes were collected. The primary outcome was ICU mortality. Through multivariable logistic regression, a mortality prediction score for sepsis was developed and validated. Results Four thousand two hundred and thirty six patients in the development cohort and 8359 patients in three validation cohorts. The Prediction of Sepsis Mortality in ICU (POSMI) score included age ≥ 50 years, temperature < 37 °C, Respiratory rate > 35 breaths/min, MAP ≤ 50 mmHg, SpO2 < 90%, albumin ≤ 2 g/dL, bilirubin ≥ 0.8 mg/dL, lactate ≥ 4.2 mmol/L, BUN ≥ 21 mg/dL, mechanical ventilation, hepatic failure and metastatic cancer. In addition, the area under the receiver operating characteristic curve (AUC) for the development cohort was 0.831 (95% CI, 0.813–0.850) while the AUCs ranged from 0.798 to 0.829 in the three validation cohorts. Moreover, the POSMI score had a higher AUC than both the SOFA and APACHE IV scores. Notably, the Hosmer–Lemeshow (H–L) goodness-of-fit test results and calibration curves suggested good calibration in the development and validation cohorts. Additionally, the POSMI score still exhibited excellent discrimination and calibration following sensitivity analysis. With regard to clinical usefulness, the decision curve analysis (DCA) of POSMI showed a higher net benefit than SOFA and APACHE IV in the development cohort. Conclusion POSMI was validated to be an effective tool for predicting mortality in ICU patients with sepsis.

Background To assess whether acute kidney injury (AKI) is independently associated with hospital mortality in ICU patients with sepsis, and estimate the excess AKI-related mortality attributable to AKI. Methods We analyzed adult patients from two distinct retrospective critically ill cohorts: (1) Medical Information Mart for Intensive Care IV (MIMIC IV; n = 15,610) cohort and (2) Wenzhou (n = 1,341) cohort. AKI was defined by Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We applied multivariate logistic and linear regression models to assess the hospital and ICU mortality, hospital length-of-stay (LOS), and ICU LOS. The excess attributable mortality for AKI in ICU patients with sepsis was further evaluated. Results AKI occurred in 5,225 subjects in the MIMIC IV cohort (33.5%) and 494 in the Wenzhou cohort (36.8%). Each stage of AKI was an independent risk factor for hospital mortality in multivariate logistic regression after adjusting for baseline illness severity. The excess attributable mortality for AKI was 58.6% (95% CI [46.8%–70.3%]) in MIMIC IV and 44.6% (95% CI [12.7%–76.4%]) in Wenzhou. Additionally, AKI was independently associated with increased ICU mortality, hospital LOS, and ICU LOS. Conclusion Acute kidney injury is an independent risk factor for hospital and ICU mortality, as well as hospital and ICU LOS in critically ill patients with sepsis. Thus, AKI is associated with excess attributable mortality.