Catalyst-free, volatile organic solvent (VOC)-free synthesis of biobased cross-linked polymers is an important sustainable feature in polyesterification. To date, these polyesters have been extensively studied for their fundamental sustainability across various uses. The ultimate potential sustainability for these materials, however, is constrained to static structural parts due to their intractable rigid three-dimensional (3D) network. Here, we reveal intrinsic dynamic exchangeable bonds within this type of cross-linked semicrystalline network, poly(1,8-octanediol-
Motivation: The metabolic heterogeneities in human are high, it is crucial to improve the slice-encoding coverage in phosphocreatine and glycogen mapping. Goal(s): To develop a 3D-CEST sequence for simultaneous mapping of phosphocreatine and glycogen within the acceptable time. Approach: The optimal sequence using stack-of-star readouts was applied. The patch-based low-rank reconstruction was introduced to accelerate the scan. The concentrations were quantified with ex-vivo and in-vivo experiments. Results: The coverage in slice-encoding dimension was improved to 140 mm. The scan time was reduced from 41.8 to 11.2 minutes. The concentrations of PCr and glycogen were 36.8 ± 14.4 mM and 80.4 ± 12.5 mM, respectively. Impact: This study demonstrates the feasibility of a 3D-CEST imaging method that simultaneously quantifies phosphocreatine and glycogen in skeletal muscle at 5T. It can be accomplished within 11.2 minutes using patch-based low-rank reconstruction. It shows great potential in evaluateing metabolic heterogeneities.
Background The World Health Organization recommends regular hand hygiene monitoring and feedback to improve hand hygiene behaviors and health care–associated infection rates. Intelligent technologies for hand hygiene are increasingly being developed as alternative or supplemental monitoring approaches. However, there is insufficient evidence regarding the effect of this type of intervention, with conflicting results in the literature. Objective We conduct a systematic review and meta-analysis to evaluate the effects of using intelligent technology for hand hygiene in hospitals. Methods We searched 7 databases from inception to December 31, 2022. Two reviewers independently and blindly selected studies, extracted data, and assessed the risk of bias. A meta-analysis was performed using the RevMan 5.3 and STATA 15.1 software. Sensitivity and subgroup analyses were also conducted. Overall certainty of evidence was appraised using the Grading of Recommendations Assessment, Development, and Evaluation approach. The systematic review protocol was registered. Results The 36 studies comprised 2 randomized controlled trials and 34 quasi-experimental studies. The included intelligent technologies involved 5 functions: performance reminders，electronic counting and remote monitoring，data processing，feedback，and education. Compared with usual care, the intelligent technology intervention for hand hygiene improved health care workers’ hand hygiene compliance (risk ratio 1.56, 95% CI 1.47-1.66; P<.001), reduced health care–associated infection rates (risk ratio 0.25, 95% CI 0.19-0.33; P<.001), and was not associated with multidrug-resistant organism detection rates (risk ratio 0.53, 95% CI 0.27-1.04; P=.07). Three covariates, including publication year, study design, and intervention, were not factors of hand hygiene compliance or hospital-acquired infection rates analyzed by meta-regression. Sensitivity analysis showed stable results except for the pooled outcome of multidrug-resistant organism detection rates. The caliber of 3 pieces of evidence suggested a dearth of high-caliber research. Conclusions Intelligent technologies for hand hygiene play an integral role in hospital. However, low quality of evidence and important heterogeneity were observed. Larger clinical trials are required to evaluate the impact of intelligent technology on multidrug-resistant organism detection rates and other clinical outcomes.
Abstract MicroRNAs (miRNAs) are key regulators in various physiological and pathological processes via post‐transcriptional regulation of gene expression. The honey bee ( Apis mellifera ) is a key model for highly social species, and its complex social behaviour can be interpreted theoretically as changes in gene regulation, in which miRNAs are thought to be involved. We used the SOLiD sequencing system to identify the repertoire of miRNAs in the honey bee by sequencing a mixed small RNA library from different developmental stages. We obtained a total of 36 796 459 raw sequences; of which 5 491 100 short sequences were fragments of mRNA and other noncoding RNAs (ncRNA), and 1 759 346 reads mapped to the known miRNAs. We predicted 267 novel honey bee miRNAs representing 380 182 short reads, including eight miRNAs of other insects in 14 107 583 genome‐mapped sequences. We verified 50 of them using stem‐loop reverse‐transcription PCR (RT‐PCR), in which 35 yielded PCR products. Cross‐species analyses showed 81 novel miRNAs with homologues in other insects, suggesting that they were authentic miRNAs and have similar functions. The results of this study provide a basis for studies of the miRNA‐modulating networks in development and some intriguing phenomena such as caste differentiation in A. mellifera .
BACKGROUND The World Health Organization recommends regular hand hygiene monitoring and feedback to improve hand hygiene behaviors and health care–associated infection rates. Intelligent technologies for hand hygiene are increasingly being developed as alternative or supplemental monitoring approaches. However, there is insufficient evidence regarding the effect of this type of intervention, with conflicting results in the literature. OBJECTIVE We conduct a systematic review and meta-analysis to evaluate the effects of using intelligent technology for hand hygiene in hospitals. METHODS We searched 7 databases from inception to December 31, 2022. Two reviewers independently and blindly selected studies, extracted data, and assessed the risk of bias. A meta-analysis was performed using the RevMan 5.3 and STATA 15.1 software. Sensitivity and subgroup analyses were also conducted. Overall certainty of evidence was appraised using the Grading of Recommendations Assessment, Development, and Evaluation approach. The systematic review protocol was registered. RESULTS The 36 studies comprised 2 randomized controlled trials and 34 quasi-experimental studies. The included intelligent technologies involved 5 functions: performance reminders，electronic counting and remote monitoring，data processing，feedback，and education. Compared with usual care, the intelligent technology intervention for hand hygiene improved health care workers’ hand hygiene compliance (risk ratio 1.56, 95% CI 1.47-1.66; <i>P</i><.001), reduced health care–associated infection rates (risk ratio 0.25, 95% CI 0.19-0.33; <i>P</i><.001), and was not associated with multidrug-resistant organism detection rates (risk ratio 0.53, 95% CI 0.27-1.04; <i>P</i>=.07). Three covariates, including publication year, study design, and intervention, were not factors of hand hygiene compliance or hospital-acquired infection rates analyzed by meta-regression. Sensitivity analysis showed stable results except for the pooled outcome of multidrug-resistant organism detection rates. The caliber of 3 pieces of evidence suggested a dearth of high-caliber research. CONCLUSIONS Intelligent technologies for hand hygiene play an integral role in hospital. However, low quality of evidence and important heterogeneity were observed. Larger clinical trials are required to evaluate the impact of intelligent technology on multidrug-resistant organism detection rates and other clinical outcomes.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='translate(0 -400)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -600)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -800)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -1000)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -1200)'/%3e%3cpath d='M0 0h1400v800H0z' style='fill:%23010066'/%3e%3cpath d='M576 100c-166.146 0-301 134.406-301 300s134.854 300 301 300c60.027 0 115.955-17.564 162.927-47.783-27.353 9.44-56.71 14.602-87.271 14.602-147.327 0-266.897-119.172-266.897-266.01 0-146.837 119.57-266.01 266.897-266.01 32.558 0 63.746 5.815 92.602 16.468C696.217 118.91 638.305 100 576 100z' style='fill:%23fc0'/%3e%3cpath d='m914.286 471.429-99.538-53.25 29.43 108.982-66.575-91.165-20.77 110.96-20.428-111.024-66.857 90.96L699.314 418l-99.701 52.943 74.065-85.192-112.8 4.441 103.694-44.62-103.555-44.94L673.8 305.42 600 220l99.538 53.25-29.43-108.982 66.575 91.165 20.77-110.96 20.428 111.023 66.857-90.959L814.97 273.43l99.702-52.944-74.065 85.193 112.8-4.441-103.694 44.62 103.555 44.94-112.785-4.79z' style='fill:%23fc0' transform='matrix(1.2738 0 0 1.2423 -89.443 -29.478)'/%3e%3c/svg%3e)