Aims:The study aimed to identify the risk factors for catheter-associated urinary tract infection among hospitalized patients. We also tried to explore its potential effect on patient outcomes if possible.Background: Catheter-associated urinary tract infection accounts for a large proportion of healthcare-associated infections and remains a considerable threat to patient safety worldwide.Design: A systematic review and meta-analysis of observational studies. Data sources:We conducted an electronic search in PubMed, EMBASE, Web of Science, and the Cochrane Review methods: Two reviewers searched the articles and extracted the data independently. The quality of the studies was assessed with the Newcastle-Ottawa Scale. RevMan 5.3 was used to perform the meta-analysis.Results: Ten studies involving a total of 8785 participants with or without catheterassociated urinary tract infection were included. The average incidence of catheterassociated urinary tract infection was 13.79 per 1000 catheter days, with a prevalence rate of 9.33%. The meta-analysis demonstrated that patients at high risk for catheter-associated urinary tract infection were female, had a prolonged duration of catheterization, had diabetes, had previous catheterization, and had longer hospital and ICU stays. Additionally, catheter-associated urinary tract infection was also accompanied by an increase in mortality. Conclusions:Healthcare staff should focus on the identified risk factors for catheter-associated urinary tract infection. Further research is needed to investigate the microbial isolates and focus on the intervention strategies of catheter-associated urinary tract infection, so as to reduce its incidence and related mortality. K E Y W O R D Scatheter-associated urinary tract infection, hospitalized, meta-analysis, nursing, observational studies, risk factors, systematic review

This study aimed to explore the clinical characteristic and outcomes of inpatients with diabetic foot ulceration (DFU) in 2019 (prelockdown) and 2020 (postlockdown) due to the COVID-19 pandemic, at an emergency medical service unit. Prediction models for mortality and amputation were developed to describe the risk factors using a machine learning-based approach. Hospitalized DFU patients (N = 23) were recruited after the lockdown in 2020 and matched with corresponding inpatients (N = 23) before lockdown in 2019. Six widely used machine learning models were built and internally validated using 3-fold cross-validation to predict the risk of amputation and death in DFU inpatients under the COVID-19 pandemic. Previous DF ulcers, prehospital delay, and mortality were significantly higher in 2020 compared to 2019. Diabetic foot patients in 2020 had higher hs-CRP levels (P = .037) but lower hemoglobin levels (P = .017). The extreme gradient boosting (XGBoost) performed best in all models for predicting amputation and mortality with the highest area under the curve (0.86 and 0.94), accuracy (0.80 and 0.90), sensitivity (0.67 and 1.00), and negative predictive value (0.86 and 1.00). A long delay in admission and a higher risk of mortality was observed in patients with DFU who attended the emergency center during the COVID-19 post lockdown. The XGBoost model can provide evidence-based risk information for patients with DFU regarding their amputation and mortality. The prediction models would benefit DFU patients during the COVID-19 pandemic.

Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients.Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision-making. We aimed to develop an accurate and explainable prediction model to estimate the risk of in-hospital amputation in patients with DFU. A total of 618 hospitalised patients with DFU were included in this study. The patients were divided into non-amputation, minor amputation or major amputation group.Light Gradient Boosting Machine (LightGBM) and 5-fold cross-validation tools were used to construct a multi-class classification model to predict the three outcomes of interest. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the predictions of the model. Our area under the receiver-operating-characteristic curve (AUC) demonstrated a 0.90, 0.85 and 0.86 predictive ability for non-amputation, minor amputation and major amputation outcomes, respectively. Taken together, our data demonstrated that the developed explainable machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors.Puguang Xie and Yuyao Li contributed equally to this work.

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SummaryAs a perennial forage crop broadly distributed in eastern Eurasia, sheepgrass (Leymus chinensis (Trin.) Tzvel) is highly tolerant to low-temperature stress. Previous report indicates that sheepgrass is able to endure as low as À47.5°C,allowing it to survive through the cold winter season. However, due to the lack of sufficient studies, the underlying mechanism towards the extraordinary low-temperature tolerance is unclear. Although the transcription profiling has provided insight into the transcriptome response to cold stress, more detailed studies are required to dissect the molecular mechanism regarding the excellent abiotic stress tolerance. In this work, we report a novel transcript factor LcFIN1 (L. chinensis freezinginduced 1) from sheepgrass. LcFIN1 showed no homology with other known genes and was rapidly and highly induced by cold stress, suggesting that LcFIN1 participates in the early response to cold stress. Consistently, ectopic expression of LcFIN1 significantly increased cold stress tolerance in the transgenic plants, as indicated by the higher survival rate, fresh weight and other stress-related indexes after a freezing treatment. Transcriptome analysis showed that numerous stress-related genes were differentially expressed in LcFIN1-overexpressing plants, suggesting that LcFIN1 may enhance plant abiotic stress tolerance by transcriptional regulation. Electrophoretic mobility shift assays and CHIP-qPCR showed that LcCBF1 can bind to the CRT/DRE cis-element located in the promoter region of LcFIN1, suggesting that LcFIN1 is directly regulated by LcCBF1. Taken together, our results suggest that LcFIN1 positively regulates plant adaptation response to cold stress and is a promising candidate gene to improve crop cold tolerance.

Primary hepatic carcinoma is 1 of the most common malignant tumors globally, of which hepatocellular carcinoma (HCC) accounts for 85% to 90%. Due to the high degree of deterioration and low early detection rate of HCC, most patients are diagnosed when they are already in the middle and advanced stages, and the prognosis are always poor. RNA sequencing data from the cancer genome atlas was used to explore differences in lncRNA expression profiles. LncRNA was extracted by gdcRNAtools in R package. Multivariate cox analysis was performed on the screened lncRNAs. The relationship between the lncRNA model and prognosis as well as clinical characteristics of patients with HCC was analyzed. Finally, a predictive nomogram in the the cancer genome atlas cohort was established and verified internally Based on the RNA sequencing survival analysis, a 9- lncRNAs prognosis model, including TMCC1-AS1, AC008892.1, AL031985.3, L34079.2, U95743.1, KDM4A-AS1, SACS-AS1, AC005534.1, LINC01116 was established. The 9-lncRNA prognosis model was a reliable tool for predicting prognosis of HCC, and the nomogram of this prognosis model could help clinicians to choose personalized treatment for HCC patients This model was significant to complement clinic characteristics of HCC and to promote personalized management of patients, it also provided a new idea for researches on the prognosis of HCC.

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Aims:The aim of this study was to evaluate the association of time in range (TIR) with amputation and all-cause mortality in hospitalised patients with diabetic foot ulcers (DFUs). Materials and Methods:A retrospective analysis was performed on 303 hospitalised patients with DFUs. During hospitalisation, TIR, mean blood glucose (MBG), coefficient of variation (CV), time above range (TAR) and time below range (TBR) of patients were determined from seven-point blood glucose profiles. Participants were grouped based on their clinical outcomes (i.e., amputation and death). Logistic regression was employed to analyse the association of TIR with amputation and allcause mortality of inpatients with DFUs.Results: Among the 303 enrolled patients, 50 (16.5%) had undergone amputation whereas seven (2.3%) were deceased. Blood glucose was determined in 41,012 samples obtained from all participants. Patients who underwent amputation had significantly lower TIR and higher MBG, CV, level 2 TAR and level 1 TBR whereas deceased patients had significantly lower TIR and higher MBG and level 2 TAR. Both amputation and all-cause mortality rate declined with an increase in TIR quartiles.Logistic regression showed association of TIR with amputation (p = 0.034) and allcause mortality (p = 0.013) after controlling for 15 confounders. This association was similarly significant in all-cause mortality after further adjustment for CV (p = 0.022) and level 1 TBR (p = 0.021), respectively.Conclusions: TIR is inversely associated with amputation and all-cause mortality of hospitalised patients with DFUs. Further prospective studies are warranted to establish a causal relationship between TIR and clinical outcomes in patients with DFUs.