Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta-analysis.

AIM: To assess the performance of validated prediction models for acute respiratory distress syndrome (ARDS) by systematic review and meta-analysis. METHODS: Eight databases (Medline, CINAHL, Embase, The Cochrane Library, CNKI, WanFang Data, Sinomed, and VIP) were searched up to March 26, 2023. Studies developed and validated a prediction model for ARDS in adult patients were included. Items on study design, incidence, derivation methods, predictors, discrimination, and calibration were collected. The risk of bias was assessed by the Prediction model Risk of Bias Assessment Tool. Models with a reported area under the curve of the receiver operating characteristic (AUC) metric were analyzed. RESULTS: A total of 25 studies were retrieved, including 48 unique prediction models. Discrimination was reported in all studies, with AUC ranging from 0.701 to 0.95. Emerged AUC value of the logistic regression model was 0.837 (95% CI: 0.814 to 0.859). Besides, the value in the ICU group was 0.856 (95% CI: 0.812 to 0.899), the acute pancreatitis group was 0.863 (95% CI: 0.844 to 0.882), and the postoperation group was 0.835 (95% CI: 0.808 to 0.861). In total, 24 of the included studies had a high risk of bias, which was mostly due to the improper methods in predictor screening (13/24), model calibration assessment (9/24), and dichotomization of continuous predictors (6/24). CONCLUSIONS: This study shows that most prediction models for ARDS are at high risk of bias, and the discrimination ability of the model is excellent. Adherence to standardized guidelines for model development is necessary to derive a prediction model of value to clinicians.

Experiences and management needs of endocrine therapy-related symptoms in patients with breast cancer: a systematic review and qualitative evidence synthesis protocol.

INTRODUCTION: Patients with breast cancer and endocrine therapy-related symptoms often experience pain, self-denial, anxiety, fear of recurrence and despair, which can be extremely physically and psychologically traumatising for the patients. Failure to receive effective support and management reduces adherence to medications, leading to a higher risk of relapse and mortality. Clearly, it is paramount to identify what support these patients may need and how to meet their symptom management needs. This paper outlines a protocol to synthesise qualitative evidence on endocrine therapy symptom experiences, management expectations and preferences of patients with breast cancer. METHODS AND ANALYSIS: The following databases were searched in November 2023 with no date restriction applied: The Cochrane Library, PubMed, Embase, Web of Science, Scopus, CINAHL and OpenGrey. Published studies on qualitative or mixed-method on symptom experiences and management needs during endocrine therapy in patients with breast cancer will be retrieved. We will also search for reference lists and perform a forward citation search. Before inclusion in this review, two reviewers will independently apply the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research to ensure methodological validity. Any disagreements regarding the evaluation of the articles will be resolved through discussion with or by a third reviewer. Data will be extracted using the standardised data extraction tool EndNote20 for unified management, assessment, and review of information. The common sense model of self-regulation will guide data extraction and synthesis. The final synthesised findings will be graded according to the GRADE-CERQual approach to establish confidence. ETHICS AND DISSEMINATION: This systematic review addressed previously published studies without personally identifiable participant information. Ethical approval from the research committee was not required. The findings of this systematic review will be disseminated to various key stakeholders and published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42023406987.

Transcriptome analysis reveals the peptide toxins diversity of Macrothele palpator venom.

Spider venom is a large pharmacological repertoire of different bioactive peptide toxins. However, obtaining crude venom from some spiders is challenging. Thus, studying individual toxins through venom purification is a daunting task. In this study, we constructed the cDNA library and transcriptomic sequencing from the Macrothele palpator venom glands. Subsequently, 718 high-quality expressed sequence tags (ESTs) were identified, and grouped into three categories, including 449 toxin-like (62.53 %), 136 cellular component (18.94 %) and 133 non-matched (18.52 %) based on the gene function annotation. Additionally, 112 non-redundant toxin-like peptides were classified into 13 families (families A-M) based on their sequence homology and cysteine framework. Bioinformatics analysis revealed a high sequence similarity between families A-J and the toxins from Macrothele gigas in the NR database. In contrast, families K-M had a generally low sequence homology with known spider peptide toxins and unpredictable biological functions. Taken together, this study adds many new members to the spider toxin superfamily and provides a basis for identifying various potential biological tools in M. palpator venom.

A Fast Prediction Model for Liquid Metal Transfer Modes during the Wire Arc Additive Manufacturing Process.

The liquid metal transfer mode in wire arc additive manufacturing (WAAM), plays an important role in determining the build quality. In this study, a fast prediction model based on the Young-Laplace equation, momentum equation, and energy conservation, is proposed, to identify the metal transfer modes, including droplet, liquid bridge, and wire stubbing, for a given combination of process parameters. To close the proposed model, high-fidelity numerical simulations are applied, to obtain the necessary inputs required by the former. The proposed model's accuracy and effectiveness are validated by using experimental data and high-fidelity simulation results. It is proved that the model can effectively predict the transition from liquid bridge, to droplet and wire stubbing modes. In addition, its errors in dripping frequency and liquid bridge height range from 6% to 18%. Moreover, the process parameter windows about transitions of liquid transfer modes have been established based on the model, considering wire feed speed, travel speed, heat source power, and material parameters. The proposed model is expected to serve as a powerful tool for the guidance of process parameter optimization, to achieve high-quality builds.

Combination of α-lactalbumin and gum arabic for microencapsulation of L-menthol: The effects on flavor release during storage and rehydration.

L-menthol-containing food products generally show the flavor loss during storage due to their high volatility. The hydrophobicity of L-menthol also causes the inadequate flavor release during rehydration. In this study, the stability of L-menthol was enhanced by microencapsulation and the effect of different powder drying techniques was also investigated. The highest efficiency (76.58-78.66 %) and loading content (18.58-28.35 mg/g) of encapsulations were obtained by using a mass ratio of 2:1(α-LA: GA). Then they were dried by non-thermal spray freeze drying (SFD) technique compared to spray drying (SD) and freeze-drying (FD) process. The SFD particles were shown to be spherical and porous with the highest porosity (86.82 %). α-LA/GA based microparticles with spherical shapes were demonstrated to largely enhance flavor retention during high humidity storage. In addition, the porous structures of SFD powders could cause rapid rehydration in liquid models, and the release behaviors of loaded L-menthol followed the Fickian diffusion. Consequently, the SFD technique shows great potential to produce microparticles by regulating the release behaviors of L-menthol during storage and rehydration.

Full-length 16S rRNA gene sequencing and machine learning reveal the bacterial composition of inhalable particles from two different breeding stages in a piggery.

Bacterial loading aggravates the harm of particulate matter (PM) to public health and ecological systems, especially in operations of concentrated animal production. This study aimed to explore the characteristics and influencing factors of bacterial components of inhalable particles at a piggery. The morphology and elemental composition of coarse particles (PM10, aerodynamic diameter ≤ 10 µm) and fine particles (PM2.5, aerodynamic diameter ≤ 2.5 µm) were analyzed. Full-length 16 S rRNA sequencing technology was used to identify bacterial components according to breeding stage, particle size, and diurnal rhythm. Machine learning (ML) algorithms were used to further explore the relationship between bacteria and the environment. The results showed that the morphology of particles in the piggery differed, and the morphologies of the suspected bacterial components were elliptical deposited particles. Full-length 16 S rRNA indicated that most of the airborne bacteria in the fattening and gestation houses were bacilli. The analysis of beta diversity and difference between samples showed that the relative abundance of some bacteria in PM2.5 was significantly higher than that in PM10 at the same pig house (P < 0.01). There were significant differences in the bacterial composition of inhalable particles between the fattening and gestation houses (P < 0.01). The aggregated boosted tree (ABT) model showed that PM2.5 had a great influence on airborne bacteria among air pollutants. Fast expectation-maximization microbial source tracking (FEAST) showed that feces was a major potential source of airborne bacteria in pig houses (contribution 52.64-80.58 %). These results will provide a scientific basis for exploring the potential risks of airborne bacteria in a piggery to human and animal health.

Characteristics and Traceability Analysis of Microbial Assemblage in Fine Particulate Matter from a Pig House.

Fine particulate matter (PM2.5) can carry numerous substances and penetrate deep into the respiratory tract due to its small particle size; associated harmful microorganisms are suspected to increase health risks for humans and animals. To find out the microbial compositions of PM2.5 in piggeries, their interaction and traceability, we collected PM2.5 samples from a piggery while continuously monitoring the environmental indicators. We also identified pathogenic bacteria and allergens in the samples using high-throughput sequencing technology. We analyzed the microbial differences of PM2.5 samples at different heights and during different times of day and investigated the microbial dynamics among the PM2.5 samples. To better understand the interaction between microorganisms and environmental factors among different microbial communities, we applied the network analysis method to identify the correlation among various variables. Finally, SourceTracker, a commonly used microbial traceability tool, was used to predict the source of airborne microorganisms in the pig house. We identified 14 potential pathogenic bacteria and 5 allergens from PM2.5 in the pig houses, of which Acinetobacter was the dominant bacterium in all samples (relative abundance > 1%), which warrants attention. We found that bacteria and fungi directly affected the the microbial community. The bacterial community mainly played a positive role in the microbial community. Environmental variables mainly indirectly and positively affected microbial abundance. In the SourceTracker analysis using fecal matter and feed as sources and PM2.5 sample as sink, we found that fecal matter made the greatest contribution to both bacterial and fungal components of PM2.5. Our findings provide important insights into the potential risks of pathogens in PM2.5 to human and animal health and their main sources.

Characteristics of PM2.5 and Its Correlation with Feed, Manure and NH3 in a Pig-Fattening House.

Fine particulate matter (PM), including PM2.5 in pig houses, has received increasing attention due to the potential health risks associated with PM. At present, most studies have analyzed PM2.5 in Chinese pig houses utilizing natural ventilation. These results, however, are strongly affected by the internal structure and regional environment, thus limiting their applicability to non-mechanically ventilated pig houses. This experiment was carried out in an environmentally controlled pig house. The animal feeding operation and manure management in the house were typical for Southwest China. To reduce the influence of various environmental factors on PM2.5, the temperature and humidity in the house were maintained in a relatively stable state by using an environmental control system. The concentration of PM2.5 in the pig house was monitored, while the biological contents and chemical composition of PM2.5 were analyzed, and feed, manure, and dust particles were scanned using an electron microscope. Moreover, bacterial and fungal contents and some water-soluble ions in PM2.5 were identified. The results showed that the concentration of PM2.5 in the pig house was strongly affected by pig activity, and a phenomenon of forming secondary particles in the pig house was found, although the transformation intensity was low. The concentration of PM2.5 had negative correlations of 0.27 and 0.18 with ammonia and hydrogen sulfide, respectively. Interestingly, a stronger correlation was observed between ammonia and hydrogen sulfide and ammonia and carbon dioxide concentrations (the concentration of ammonia had stronger positive correlations with hydrogen sulfide and carbon dioxide concentrations at +0.44 and +0.59, respectively). The main potential sources of PM2.5 production were feed and manure. We speculate that manure could contribute to the broken, rough, and angular particles that formed the pig house PM2.5 that easily adhered to other components.

A model to assess the risk of peripherally inserted central venous catheter-related thrombosis in patients with breast cancer: a retrospective cohort study.

PURPOSE: Limited risk assessment tool to stratify the risk of PICC-related thrombosis (PICC-RVT) in breast cancer patients. This study developed a model to assess the risk of PICC-RVT in breast cancer patients. METHODS: We conducted a retrospective cohort study of 1284 breast cancer patients receiving PICC insertion from January 1, 2015, to August 31, 2019, at a cancer specialized hospital in Hunan province, China. The entire population was divided into two groups at a ratio of 3:1 which included a derivation sample (n = 978), and a validation sample (n = 284). PICC-RVT was confirmed by ultrasonography in the presence of clinical symptoms and signs. RESULTS: PICC-RVT occurred in 40 (4.09%) of the derivation sample patients. Multivariable analysis identified 9 variables: chronic obstructive pulmonary disease, prior central venous catheter placement, higher level of platelets, higher level of D-dimer, lower level of activated partial thromboplastin time, menopause, no prior breast surgery, upper extremity lymphedema, and endocrine therapy. Points were assigned to each variable according to regression coefficient. The model had an area under the receiver operating characteristics curve (AUC) of 0.850 (95% CI 0.776 to 0.924), The Hosmer-Lemeshow goodness-of-fit was 5.780 (p = 0.328). At a cutoff value of 3.5, the sensitivity and specificity were 75% and 83%, respectively. CONCLUSION: Several disease-specific factors of breast cancer (e.g., menopause, endocrine therapy, and upper extremity lymphedema) play important roles in the development of PICC-RVT. Patients at higher PICC-RVT risk could be candidates for close post-insertion monitoring and interventions to prevent PICC-RVT.

Prediction of Ammonia Concentration in a Pig House Based on Machine Learning Models and Environmental Parameters.

Accurately predicting the air quality in a piggery and taking control measures in advance are important issues for pig farm production and local environmental management. In this experiment, the NH3 concentration in a semi-automatic piggery was studied. First, the random forest algorithm (RF) and Pearson correlation analysis were combined to analyze the environmental parameters, and nine input schemes for the model feature parameters were identified. Three kinds of deep learning and three kinds of conventional machine learning algorithms were applied to the prediction of NH3 in the piggery. Through comparative experiments, appropriate environmental parameters (CO2, H2O, P, and outdoor temperature) and superior algorithms (LSTM and RNN) were selected. On this basis, the PSO algorithm was used to optimize the hyperparameters of the algorithms, and their prediction performance was also evaluated. The results showed that the R2 values of PSO-LSTM and PSO-RNN were 0.9487 and 0.9458, respectively. These models had good accuracy when predicting NH3 concentration in the piggery 0.5 h, 1 h, 1.5 h, and 2 h in advance. This study can provide a reference for the prediction of air concentrations in pig house environments.

Catheter dwell time and risk of catheter failure in adult patients with peripheral venous catheters.

AIMS AND OBJECTIVES: To explore whether the risk of peripheral venous catheters failure remained constant throughout catheter use in adult patients. BACKGROUND: Peripheral venous catheters, widely used in adult patients, may have a critical threshold dwell time associated with increased risk of catheter failure. DESIGN: Prospective, observational study. We have complied with the STROBE checklist of items. METHODS: This study was conducted from July-October 2018 in Hunan, China. Data on patient factors, catheter factors and catheter failure events were collected. Poisson regression was used to assess the effect of catheter dwell time on catheter failure while adjusting for other variables. RESULTS: A total of 1,477 patients were included in the analysis. There were 854 cases (57.8%) of catheter failure. The median dwell time to catheter failure was 52 hr (interquartile range: 36-73 hr). The incidence rate of catheter failure significantly increased by 1.1%/h in the first 38 hr after catheter insertion. From 39-149 hr, the incidence rate significantly decreased, and at >149 hr, there was no significant change in the incidence rate. Meanwhile, factors such as vascular quality and infused drugs showed having an impact on catheter failure events. CONCLUSIONS: The risk of catheter failure may not remain constant throughout the dwell time. The results suggest that nurses should assess the insertion site frequently in the first 38 hr. RELEVANCE TO CLINICAL PRACTICE: The significant increase in the risk of catheter failure per hour may warrant close and frequent inspection of insertion site during the first 38 hr.