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Background Previous research has found that multi-drug resistant (MDR) bacteria can be transmitted between ICU patients, and the infections caused by MDR bacteria may negatively affect the efficacy of current treatment. As the speed of diagnostic testing to identify MDR bacteria is relatively slow in clinical practice, the research regarding the prediction of MDR bacteria infections has developed. Objective To develop an evidence-based risk prediction model for MDR bacterial infections in ICU patients, and to verify it using the real-world clinical data collected retrospectively. Methods Potential risk factors for MDR bacterial infections in ICU patients were identified by a meta-analysis of studies regarding MDR bacterial infections in ICU patients included in databases of PubMed, EMBase, the Cochrane Library, CNKI, Wanfang, China Science and Technology Journal Database, Ace Base of CMA during January 2012 to June 2020 using Stata/SE 12.0 software, and were used to develop a risk prediction model by transforming effect size to the standardized regression (β) coefficient. Next the model was fully established and externally verified using the clinical data of adult ICU patients (n=3 908) recruited from Shanghai General Hospital from January 2018 to June 2021. ROC analysis was used to describe the predictive accuracy of the prediction model. Results Seventeen potential risk factors of MDR bacterial infections in ICU patients were identified through the meta-analysis of 31 included studies. The MDR bacterial infection risk prediction model incorporating these 17 factors with corresponding β value as coefficient (derived from converting the risk effect size of each factor) was developed: Logit (P) =-2.476 3 +0.086X1〔gender (male) 〕+0.191X2 (history of hospitalization) +0.392X3 (being transferred from another hospital) +1.723X4 (length of ICU stay) +0.315X5 (other infections) +0.385X6 (chronic obstructive pulmonary disease) +0.131X7 (diabetes) +0.536X8 (renal disease) +0.285X9 (renal failure) +0.565X10 (dialysis) +0.148X11 (mechanical ventilation) +0.742X12 (central venous catheter) +0.336X13 (urinary catheter) +3.483X14 (types of used antimicrobial drugs) +0.174X15 (history of antimicrobial use) +0.975X16 (history of carbapenems use) +1.151X17 (history of aminoglycosides use) . External verification of the model revealed that the model had 64.36% sensitivity, 80.39% specificity, and 0.447 4 Youden index, and an AUC of 0.724. Conclusion Our model has been proved to have good performance in predicting the MDR bacterial infection risk in ICU patients, as well as relatively good applicability, scientificity, and practicability. The development regimen may be used as a reference for developing a risk prediction model for other diseases.

As more and more developed countries adopt policies that favour highly educated immigrants, the impact of such policies on developing countries remains unclear. Some researchers have argued that migrants who are more educated tend to bring their immediate family members to host countries, and thus, send less money to source countries in remittances. While a number of papers have documented the relationship between education and remittance, whether that is related to sponsorship decisions remains under-explored. Using individual-level panel data from the New Immigrant Survey, we show that sponsoring family members leads to lower remittances. Furthermore, we show that college educated immigrants from high-income families are less likely to sponsor relatives, presumably because of the relatively higher opportunity cost of migration of their relatives. Together, these two results suggest a positive association between education and remittances, which is, indeed, what we find in the data. Our extended analysis shows that alternative explanations (such as higher income of more educated immigrants, or repaying implicit educational loans) cannot completely explain the positive association between education and remittances. Our results suggest that skill-based immigration policies are likely to result in more remittances. [ABSTRACT FROM AUTHOR]

In this paper, a new Co3O4-Ni nanocomposite-modified glassy carbon electrode (Co3O4-NiNPs/GCE) was successfully constructed and used to detect glucose and hydrogen peroxide (H2O2). The morphologies and structures of the Co3O4 and Co3O4-Ni nanocomposites were characterized via transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The construction process of the modified electrode was characterized via electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) techniques. Co3O4-NiNPs/GCE shows more excellent electrocatalytic activity for the detection of glucose and H2O2 compared with Co3O4/GCE and NiNPs/GCE. The amperometric i-t method was used for the quantitative analysis of glucose and H2O2. The plots of current difference versus concentration of glucose and H2O2 were linear in the range of 0.3-550 μM and 0.5-89 μM, respectively. The corresponding limits of detection (LODs) were 0.086 μM and 0.23 μM for glucose and H2O2, respectively. This recommended sensor was successfully applied for the quantitative analysis of glucose in fruit and H2O2 in water samples.