Objectives: We aimed to implement our Smart Use of Antibiotics Program to ensure the proper use of antimicrobials, improve patient care and outcomes, and reduce the risks of adverse effects and antimicrobial resistance. Design: We compared the time periods before (baseline) and after (intervention) the implementation of an antibiotic protocol by performing surveillance and assessments of all antibiotic use during a 29-month interrupted period. Setting: Level 3–4 neonatal ICU in one referral center. Patients: All 13,540 infants who received antibiotics during their hospital stay from 2015 to 2017. Interventions: Prospective audit of targeted antibiotic stewardship program. Measurements and Main Results: The primary outcome was the change in total antibiotic days of therapy per 1,000 patient-days between the baseline and intervention periods. The secondary outcomes included readmissions for infection, late-onset sepsis (length of stay), necrotizing enterocolitis, or death in infants at 32 weeks of gestation or younger and the prevalence of multidrug-resistant organism colonization. No differences in safety outcomes were observed between the intervention and baseline periods. Following the implementation of our Smart Use of Antibiotics Program, the total quantity of antibiotics in the intervention phase was significantly decreased from 543 days of therapy per 1,000 patient-days to 380 days of therapy/1,000 patient-days compared with that of baseline (p = 0.0001), which occurred in parallel with a reduction in length of stay from 11.4% during the baseline period to 6.5% during the intervention period (p = 0.01). A reduced multidrug-resistant organism rate was also observed following Smart Use of Antibiotics Program implementation (1.4% vs 1.0%; p = 0.02). The overall readmission rate did not differ between the two periods (1.2% vs 1.1%; p = 0.16). Conclusions: Smart Use of Antibiotics Program implementation was effective in reducing antibiotic exposure without affecting quality of care. Antibiotic stewardship programs are attainable through tailoring to special stewardship targets even in a developing country.
Background Aedes albopictus is the primary vector of dengue fever in China. This mosquito species has a wide distribution range in China and can be found in the tropical climate zones of southern provinces through to temperate climate zones of northern provinces. Insecticides are an important control method, especially during outbreaks of dengue fever, but increasing insecticide resistance raises the risk of failure to control vector-borne diseases. Knockdown resistance (kdr) caused by point mutations in the voltage-gated sodium channel (VGSC) gene is a key mechanism that confers resistance to pyrethroids. In this study we explored the characteristics and possible evolutionary trend of kdr mutation in Ae. albopictus based on analysis of the kdr mutations in field populations of mosquitoes in China. Methods A total of 1549 adult Ae. albopictus were collected from 18 sites in China from 2017 to 2019 and 50 individuals from three sites in the 1990s. A fragment of approximately 350 bp from part of the S6 segment in the VGSC gene domain III was amplified and sequenced. Using TCS software version 1.21A, we constructed haplotypes of the VGSC gene network and calculated outgroup probability of the haplotypes. Data of annual average temperatures (AAT) of the collection sites were acquired from the national database. The correlation between AAT of the collection site and the kdr mutation rate was analyzed by Pearson correlation using SPSS software version 21.0. Results The overall frequency of mutant allele F1534 was 45.6%. Nine mutant alleles were detected at codon 1534 in 15 field populations, namely TCC/TCG (S) (38.9%), TTG/CTG/CTC/TTA (L) (3.7%), TGC (C) (2.9%), CGC (R) (0.3%) and TGG (W) (0.1%). Only one mutant allele, ACC (T), was found at codon 1532, with a frequency of 6.4% in ten field populations. Moreover, multiple mutations at alleles I1532 and F1534 in a sample appeared in five populations. The 1534 mutation rate was significantly positively related to AAT (Pearson correlation: r(18) = 0.624, P = 0.0056), while the 1532 mutation rate was significantly negatively related to AAT (Pearson correlation: r(18) = − 0.645, P = 0.0038). Thirteen haplotypes were inferred, in which six mutant haplotypes were formed by one step, and one additional mutation formed the other six haplotypes. In the samples from the 1990s, no mutant allele was detected at codon 1532 of the VGSC gene. However, F1534S/TCC was found in HNHK94 with an unexpected frequency of 100%. Conclusions Kdr mutations are widespread in the field populations of Ae. albopictus in China. Two novel mutant alleles, F1534W/TGG and F1534R/CGC, were detected in this study. The 1534 kdr mutation appeared in the population of Ae. albopictus no later than the 1990s. The F1534 mutation rate was positively correlated with AAT, while the I1532 mutation rate was negatively correlated with AAT. These results indicate that iInsecticide usage should be carefully managed to slow down the spread of highly resistant Ae. albopictus populations, especially in the areas with higher AAT. Graphical abstract
Weaning is stressful for piglets involving nutritional, physiological, and psychological challenges, leading to an increase in the secretion of cortisol, changes in gut microbiome and metabolites, whereas the underlying relationships remain unclear. To elucidate this, 14 Meishan female piglets were divided into the weaning group and the suckling group at the age of 21 days paired by litter and body weight. After 48 h of experiment, weaned piglets had lower body weight, but higher salivary cortisol level than that of their suckling litter mates ( P < 0.05). The composition of the colonic bacterial community and metabolites were different between the two groups, and the first predominant genus of the suckling and weaned piglets colonic microbiome were Bacteroides and Prevotellaceae-NK3B31 group respectively. The suckling piglets had higher proportions of phylum Bacteroidetes and Lentisphaerae , and genus Bacteroides and Lactobacillus in the colonic microbial community, but lower abundance of genus Prevotellaceae-NK3B31 group than that of the weaned piglets ( P < 0.05). Accordingly, there were 15 colonic metabolites differed between the two groups, in which 2 metabolites (phenylacetic acid and phenol) negatively related to the abundant of Lactobacillus genus ( P < 0.05), while 9 metabolites (acetic acid, arabitol, benzoic acid, caprylic acid, cholesterol, dihydrocholesterol, galactinol, glucose phenol, phenylacetic acid, and oxamic acid, glycerol, propionic acid) positively associated with the proportion of Prevotellaceae-NK3B31 group genus ( P < 0.05). Furthermore, the salivary cortisol level negatively associated with the abundance of phylum Lentisphaerae , but positively associated with the phylum Bacteroidetes and the genus Prevotellaceae-NK3B31 group ( P < 0.05) respectively. These results provide us with new insights into the cause of the gut microbiome and stress, and the contributions of gut microbiome in metabolic and physiological regulation in response to weaning stress.
Exosomes are potential and promising natural noninvasive biomarkers for liquid biopsies and can be involved in various biological and pathological processes in early-stage cancer. Thus, there is an urgent demand to develop low-cost, small-size, remarkable-specificity, and ultrasensitive exosome biosensors for early clinical point-of-care (POC) testing. Although various conventional tumor exosome detection methods have been generally proposed, the low detection sensitivity and specificity significantly hinder their use in cancer clinical diagnosis and prognosis. To address the above challenges, an optical microfiber integrated with MoSe 2 -supported gold nanorods is proposed. To tune the strong localized surface plasmon resonance (LSPR) of the nanointerfaces on the optical microfiber to be in accordance with the operating wavelength of the silica optical microfiber in the telecommunication band, gold nanorods with a high aspect ratio of approximately 10:1 are proposed. Due to the interaction between the excited LSPR effect and the evanescent field of the optical microfiber, the sensor can detect clear cell renal cancer exosomes within a wide concentration range from 10 0 particles/mL to 10 8 particles/mL, with an extremely low limit of detection (LOD) of 9.32 particles/mL, which is lower than that of current various state of the art methods. More importantly, the microfiber with high specificity can successfully differentiate pathological plasma and healthy controls, exhibiting very promising clinical applications in renal cancer diagnosis and prognosis. This work opens up a new approach for the in situ detection and quantification of exosomes with ultrahigh sensitivity in early clinical screening and diagnosis.
In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only calculates the mean survival time of patients within a period of time after the start of follow-up and may not accurately portray the change in a patient's life expectancy over time. The life expectancy can be adjusted for the time the patient has already survived and defined as the conditional restricted mean survival time (cRMST). A dynamic RMST model based on the cRMST can be established by incorporating time-dependent covariates and covariates with time-varying effects. We analysed data from a study of primary biliary cirrhosis (PBC) to illustrate the use of the dynamic RMST model. The predictive performance was evaluated using the C-index and the prediction error. The proposed dynamic RMST model, which can explore the dynamic effects of prognostic factors on survival time, has better predictive performance than the RMST model. Three PBC patient examples were used to illustrate how the predicted cRMST changed at different prediction times during follow-up. The use of the dynamic RMST model based on the cRMST allows for optimization of evidence-based decision-making by updating personalized dynamic life expectancy for patients.
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