The aim of this study was to explore the clinical value of ultrasonic monitoring in the assessment of pulmonary recruitment and the best positive end-expiratory pressure (PEEP).Between January 2015 and June 2017, 40 patients with acute respiratory distress syndrome in our hospital were randomly divided into 2 groups: ultrasound group (ULS group; n = 20) and oxygenation group (OXY group; n = 20). The PEEP incremental method was used to perform recruitment maneuvers. Ultrasound scoring and the oxygenation method were used to evaluate the pulmonary recruitment endpoint. The best PEEP was chosen by ultrasound scoring and the oxygenation method after achieving the pulmonary recruitment endpoint and sustaining it for 15 minutes.The oxygenation index, PEEP, peak airway pressure (Ppeak), mean airway pressure (Pmean), and dynamic compliance (Cdyn) in the OXY group were significantly lower than those in the ULS group (P < .05) at the pulmonary recruitment endpoint; however, there was no statistical significance in the mean arterial blood pressure (MAP) or heart rate (HR) (P > .05). The best PEEPs in the OXY and ULS groups were 13.1 ± 3.1 and 15.7 ± 4.2 cmH2O, respectively, with a significant difference between the 2 groups (t = 2.227, P = .016). Compared with the basal state, the Cdyn, oxygenation index, Pmean, and Ppeak in both groups significantly increased after pulmonary recruitment (P < .05). Furthermore, the Cdyn and oxygenation index in the ULS group were significantly higher than those in the OXY group after pulmonary recruitment (P < .05). The HR in both groups significantly increased, and the MAP significantly decreased. Two hours after recruitment, the HR and MAP returned to near basal levels without a significant difference between the 2 groups (P > .05).Lung ultrasound can be used to detect the endpoint of lung recruitment and the best PEEP, with good effects on lung compliance and oxygenation improvement.

Herein, we proposed a portable, easy-to-operate, and antifouling microcapsule array chip for target detection. This prepackaged chip was fabricated by innovative and cost-effective 3D ice printing integrating with photopolymerization sealing which could eliminate complicated preparation of wet chemistry and effectively resist outside contaminants. Only a small volume of sample (2 μL for each microcapsule) was consumed to fulfill the assay. All the reagents required for the analysis were stored in ice form within the microcapsule before use, which guaranteed the long-term stability of microcapsule array chips. Nitrite and glucose were chosen as models for proof of concept to achieve an instant quantitative detection by naked eyes without the need of external sophisticated instruments. The simplicity, low cost, and small sample consumption endowed ice-printing microcapsule array chips with potential commercial value in the fields of on-site environmental monitoring, medical diagnostics, and rapid high-throughput point-of-care quantitative assay.

BACKGROUND Intensive care unit (ICU) patients are critically ill and have low immunity. They will undergo various trauma medical procedures during diagnosis and treatment. The use of high-dose hormones and broad-spectrum antibiotics will increase the incidence of nosocomial infection in ICU patients. Therefore, it is necessary to explore the causes of nosocomial infection in ICU and provide basis for the prevention and control of nosocomial infection in ICU. AIM To explore major pathogens of nosocomial infection in ICUs, methods of detection and drug resistance trends. METHODS Risk factors of multidrug-resistant infection were analyzed to provide a basis for clinical rational use of antimicrobial drugs in the ICU. These findings were used to standardize rational use of antimicrobial agents. BD PhoenixTM100 automatic bacterial identification analyzer was used to for cell identification in specimens collected from the ICU between January 2016 and December 2019. Drug sensitivity tests were carried out and drug resistance trends were analyzed using the optical disc diffusion method. Odds ratios and corresponding 95%CI of independent variables were calculated using a logistic regression model. Backward elimination (trend = 0.1) was used as an inclusion criterion for multivariate analysis. All data were analyzed using SPSS version 22.0, and P < 0.05 was considered statistically significant. RESULTS We collected 2070 samples from ICU patients between January 2016 and December 2019. Sample types comprised sputum (1139 strains, 55.02%), blood (521 strains, 25.17%), and drainage fluid (117 strains, 5.65%). A total of 1051 strains of major pathogens, including Acinetobacter baumannii , Escherichia coli ( E. coli ), Pseudomonas aeruginosa ( P. aeruginosa ), Klebsiella pneumoniae ( K. pneumoniae ) and Staphylococcus aureus , were detected, with a detection rate of 35.97% (378/1051). Most of these strains were resistant to antibiotics. Detection rate of E. coli was 21.79% (229/1051), and it was generally sensitive to many antimicrobial drugs. Detection rate of P. aeruginosa was 24.74% (260/1051), and showed low sensitivity to most antibiotics. Detection rate of K. pneumoniae was 9.42% (99/1051), which was generally resistant to multiple antimicrobial drugs and resistant forms. K. pneumoniae was resistant to imipenem for approximate 4 years, and showed a 19.9% (19/99) and 20.20% (20/99) rate of meropenem resistance. Logistic analysis showed that mechanical ventilation and ureteral intubation were risk factors for multidrug-resistant bacterial infections. CONCLUSION ...