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A steroid-estrogen-degrading strain ye22 with Estone (E1) as substrate was screened out from tobacco industry wastewater treatment plant sludge. It was identified as Acinetobacter junii by 16S rRNA sequencing. The results showed that the growth characteristics and degradation kinetics of the strain were studied. After 9 days fermentation, the degradation rate of E1 could reach 41.88%. The concentration of E1 in tobaccoindustrial wastewater and tobacco concentrate was higher, reaching 1.41 x 10-4 g/L and 9.01 x 10-5 g/L, respectively. The ye22 strain cultured to logarithmic phrase could degrade 32.07% of E1 in tobacco industry wastewater and 27.23% of E1 in tobacco concentrate within 9 days. [ABSTRACT FROM AUTHOR]

Abstract: BACKGROUND: In this paper, two kinds of commonly used vegetable carbon black (VCB, 3000 mesh; nano) at 50 g kg−1 concentration (based on dried gelatin) were added to 48 g kg−1 of fish gelatin (GEL) solutions and their effects on mechanical, barrier and anti‐photooxidation properties of GEL films were investigated. RESULTS: From the SEM images, it was shown that compared with 3000 mesh VCB (1–2 μm), nano VCB (100–200 nm) made the microstructure of GEL film more compact and more gelatin chains were cross‐linked by nano VCB. The addition of nano VCB significantly increased gelatin film strength with the greatest tensile strength of 52.589 MPa and stiffness with the highest Young's modulus of 968.874 MPa, but led to the reduction of film elongation. Also, the VCB presence significantly improved water vapour and oxygen barrier properties of GEL film. Importantly, nano VCB increased GEL film with better UV barrier property due to its stronger UV absorption nature when compared with micron VCB. This property could help in the preservation of oil samples in the photooxidation accelerated test. CONCLUSION: With improved properties, the nano VCB‐reinforced GEL film may have great potential for application in the edible packaging field, especially for the anti‐photooxidation property. © 2017 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Objective: The study is to examine the relationships between perceived initial cancer disclosure communication with doctors, levels of hope, and levels of trust in doctors among cancer patients in China.Methods: A total number of 192 cancer inpatients in a cancer hospital in China were surveyed. Perceived disclosure strategies, levels of hope, levels of trust in their doctors, as well as the demographic information were obtained from the participants.Results: In addition to age, patients who had higher levels of perceived emotional support from doctors, or higher levels of perceived personalized disclosure from doctors, or higher levels of perceived discussion of multiple treatment plans with doctors were more likely to have higher levels of trust in doctors. In addition to perceived health status, perceived emotional support from doctors significantly predicted participants' levels of hope. That is, patients who had higher higher levels of perceived doctors' emotional support were more likely to have higher levels of hope. Key disclosure person was a marginally significant variable, that is, patients who were mainly disclosed by family members might have higher levels of hope compared with patients who were mainly disclosed by doctors.Conclusions: When communicating with a cancer patient, doctors might not ignore the importance of emotional support during cancer diagnosis communication. Doctors might want to involve family and collaborate with family to find out ways of personalized disclosure. During the communication process, doctors could provide their patients with multiple treatment options and discuss the benefits and side effects of each treatment. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Introduction: Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients undergoing maintenance hemodialysis. However, complications such as thrombosis may occur. This study aimed to construct and validate a machine learning-based risk-prediction model for AVF thrombosis, hypothesizing that such a model can effectively predict occurrences, providing a foundation for early clinical intervention., Methods: The retrospective longitudinal study included a total of 270 patients who underwent maintenance hemodialysis (MHD) at the Hemodialysis Center of the Second Affiliated Hospital of Harbin Medical University between March 2021 and December 2022. During this study, baseline data and scale information of patients between March 2020 and December 2021 were collected. We recorded outcome indicators between March 2021 and December 2022 for subsequent analyses. Five machine learning models were developed (artificial neural network, logistic regression, ridge classification, random forest, and adaptive boosting). The sensitivity (recall), specificity, accuracy, and precision of each model were evaluated. The effect size of each variable was analyzed and ranked. Models were assessed using the area under the receiver-operating characteristic (AUROC) curve., Results: Among the 270 included patients, 105 had AVF thrombosis (55 male and 50 female patients; age range, 29-79 years; mean age, 56.72 years; standard deviation [SD], ±13.10 years). Conversely, 165 patients did not have AVF thrombosis (99 male and 66 female patients; age range, 23-79 years; mean age, 53.58 years; SD, ±13.33 years). During the observation period, approximately 52.6% of patients with AVF experienced long-term complications. The most common complications associated with AVF were thrombosis (105; 38.9%), aneurysm formation (27; 10%), and excessively high output flow (10; 3.7%). Fifty-four (20%) patients with AVF required intervention because of complications associated with vascular access. The AUROC curve of the testing set was between 0.858 and 0.903., Conclusion: In this study, we developed five machine learning models to predict the risk of AVF thrombosis, providing a reference for early clinical intervention., (S. Karger AG, Basel.)

Background: Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post-traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post-ICU patients is an understudied problem., Aims: To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency., Study Design: In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer-Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency., Results: The risk factors of mental disorders were Sepsis-related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post-ICU patients was developed based on the regression model., Conclusions: The prediction model provides a reference for clinically screening patients at high risk of developing post-ICU mental disorders, to enable the implementation of timely preventive management measures., Relevance to Clinical Practice: The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables., (© 2023 British Association of Critical Care Nurses.)