Ventilator-associated pneumonia (VAP), a hospital acquired pneumonia that occurs more than 48 h after mechanical ventilation, is a common complication of mechanical ventilation with a high mortality rate. VAP can cause patients to have difficulty weaning off the ventilator and to stay in the hospital longer, which results in a huge financial burden to patients and a huge demand for medical resources. Several strategies, such as drugs including chlorhexidine, β-lactam antibiotics and probiotics, have been used to prevent VAP in clinic. The incidence and the mortality rate of VAP have been decreased with the development of preventative strategies in the past decades, but VAP remains one of the most common causes of nosocomial infections and death in the intensive care unit. Current challenges in the management of VAP involved the lack of a gold standard for diagnosis, the absence of effective preventative strategies, and the rise in antibiotic resistance. Therefore, in order to reduce the incidence of VAP and improve the outcome of patients with mechanical ventilation, it is necessary to clarify the risk factors of VAP for clinical prevention and control of VAP. This paper reviews the international risk factors of VAP occurrence reported in recent years, including patient characteristics, increased mechanical ventilation time and prolonged length of hospital stay, disorders of consciousness, burns, comorbidities, prior antibiotic therapy, invasive operations, gene polymorphisms, and mentions the corresponding preventive measures. Each factor is not only an independent risk factor of VAP, but also has an influence on each other. A better understanding of risk factors for VAP is helpful for predicting the occurrence of VAP, improving the prevention and control of VAP, and reducing the morbidity and mortality rates of patients with VAP.

We carried out this meta‐analysis to explore the influence of paraoxonase 1 activity on the susceptibility of diabetes mellitus (DM), diabetic macroangiopathy and diabetic microangiopathy. Relevant studies were identified from PubMed, Web of Science and CNKI without language limitation, following the inclusion and exclusion criteria. Statistical analyses were implemented with the STATA 12.0 statistical software. Thirty‐six case‐control studies were included in the meta‐analyses, in which 35 for the association between paraoxonase 1 activity and DM risk, 8 for diabetic macroangiopathy and 7 for diabetic microangiopathy. Paraoxonase 1 activity was significantly associated with the susceptibility of DM in pooled population (SMD = −1.37, 95% CI = −1.79 ∼ −0.96, P = .000), and Asians (SMD = −2.00, 95% CI = −2.56 ∼ −1.44, P = .000), but not in non‐Asians (SMD = −0.44, 95% CI = −0.91 ∼ 0.03, P = .069). However, marked heterogeneity was existed (I2 = 98.10%, P = .000) and subgroup analyses failed to investigate the sources of heterogeneity. Then, meta‐regression was performed and found that ethnicity could explain the observed between‐study heterogeneity (P = .002). Meanwhile, significant associations were found between paraoxonase 1 activity and diabetic macroangiopathy (SMD = −1.06, 95% CI = −1.63 ∼ −0.48, P = .000) and diabetic microangiopathy (SMD = −0.72, 95% CI = −1.32 ∼ −0.13, P = .018). In conclusion, paraoxonase 1 activity plays important roles in the risk of DM, diabetic macroangiopathy and microangiopathy with ethnicity differences. Further studies with large sample and well design are needed to confirm these results.

Fingerprint identification errors may be due to the high similarity of fingerprints from different sources, especially when queries are conducted in a large database with the application of the Automatic Fingerprint Identification System (AFIS). In this study, a database of ten-prints of 6.964 million individuals was used; 20 sets of 60 simulated fingermarks of different qualities were used and compared with fingerprints from the database. A total of 245 queries were conducted based on both the quality of each fingermark and the number of minutiae. Four types of results were obtained from these queries on the large database, and were categorized as follows: (A) Neither Same Source nor Close Non-Match appears in the candidate list, (B) Only Same Source appears, (C) Only Close Non-Matches appear, and (D) Both Same Source and Close Non-Matches appear. When the quality of the fingermark was improved, more minutiae could be identified, and the degree of accuracy of the placement as well as orientation was higher. As a result, highly Close Non-Match fingerprints appeared; this made it harder to distinguish these fingerprints from Same Source fingerprints, especially in the large database. We concluded that more highly Close Non-Matches might appear when the database is consistently expanded, and an increasing number of Close Non-Matches might be found with a higher ranking and score than the Same Source; this would make the identification harder for examiners and might increase the possibility of identification errors.