Agar Biopolymer Films for Biodegradable Packaging: A Reference Dataset for Exploring the Limits of Mechanical Performance

This article focuses on agar biopolymer films that offer promise for developing biodegradable packaging, an important solution for reducing plastics pollution. At present there is a lack of data on the mechanical performance of agar biopolymer films using a simple plasticizer. This study takes a Design of Experiments approach to analyze how agar-glycerin biopolymer films perform across a range of ingredients concentrations in terms of their strength, elasticity, and ductility. Our results demonstrate that by systematically varying the quantity of agar and glycerin, tensile properties can be achieved that are comparable to agar-based materials with more complex formulations. Not only does our study significantly broaden the amount of data available on the range of mechanical performance that can be achieved with simple agar biopolymer films, but the data can also be used to guide further optimization efforts that start with a basic formulation that performs well on certain property dimensions. We also find that select formulations have similar tensile properties to thermoplastic starch (TPS), acrylonitrile butadiene styrene (ABS), and polypropylene (PP), indicating potential suitability for select packaging applications. We use our experimental dataset to train a neural network regression model that predicts the Young’s modulus, ultimate tensile strength, and elongation at break of agar biopolymer films given their composition. Our findings support the development of further data-driven design and fabrication workflows.     

影响血液透析机使用寿命的因素及对策

通过对报损血液透析机的使用寿命及损坏情况进行分析,阐述了影响血液透析机使用寿命的因素。针对血液透析机消毒液的使用情况、操作人员的使用方式、水处理的维护以及透析治疗环境等几方面的因素,提出了相应的解决方法,达到了延长血液透析机使用寿命的目的。     

女性尿毒症患者血液透析前后心理变化

目的：研究女性尿毒症患者开始血液透析前后的心理变化。方法在血液透析前及血液透析后2月,采用症状自评量表（ SCL-90-R）进行调查。结果所选72例女性尿毒症患者血液透析前躯体化,强迫,抑郁,恐怖,精神病性心理变化高于血液透析后,前后比较具有显著性差异（t值分别为6．207、10．160、3．425、9．596和5．808,均P＜0．01）,血液透析前后人际关系、焦虑、敌对、偏执比较无显著性差异（t值分别为1．069、1．058、1．758和1．944,均P＞0．05）。结论女性尿毒症患者透析前后心理变化不同,血液透析可减轻女性尿毒症患者的心理负担。     

过氧乙酸低温内镜灭菌机灭菌效果检测方法的比较

目的比较不同方法用于过氧乙酸低温内镜灭菌机灭菌效果检测的差异。方法分别采用染菌载体法、菌片法、染菌腔镜法、术后腔镜法以及对灭菌机灭菌仓表面采样,共5种方法检测内镜灭菌机的灭菌有效率。结果采样共58份,阳性结果36份,检测合格率为37.93%;染菌载体法、菌片法、染菌腔镜法、术后腔镜法、灭菌仓表面检测合格率分别为94.44%、12.50%、8.33%、16.67%、14.29%;不同检测方法之间差异有统计学意义(χ2=35.54,P<0.01),染菌载体法的检测合格率明显高于其他方法。结论按照《内镜清洗消毒机消毒效果检验技术规范》进行内镜灭菌机灭菌效果检测不可靠,应采取更加接近实际使用状况的试验方法;此外,灭菌机灭菌效果检测细菌培养时间应延长至7d为宜。     

Applicability Assessment of Different Materials for Standards Ensuring Comparability of Optical and Tactile Coordinate Measurements

Multisensor CMMs are systems with an established position on the market, but their popularity still grows, as they provide access to the advantages offered by tactile and contactless measurement methods. Yet there are still questions of the comparability of results obtained using the optical and tactile operation modes of multisensor system. This phenomenon can be assessed by measuring appropriate gauges, most often reference rings or spheres. Due to the completely different nature of probing processes for tactile and contactless measurements, the material from which reference object is made may significantly affect measurement results. In order to assess the influence of this factor on measurement accuracy, three reference spheres made from different materials were measured on optical multisensor CMMs. Measurements involved tactile measurements as well as optical measurements made using different probing systems: a video probe and white light sensor. Results obtained from performed experiments show large differences depending on the material used for spherical standard production. On the basis of obtained results, it can be stated that the best material for a reference object that can be used for comparability tests of tactile and optical measurements is a composite of alumina with at least one oxidic additive.     

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut a_p and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.     

Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants

PurposeThe aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants.Materials and MethodsOf 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model.ResultsThe XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC values seemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth.ConclusionWe have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.     

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

PurposeFetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal.Materials and MethodsIn total, 17492 fetal cardiotocography results were obtained from Ajou University Hospital and 100 fetal cardiotocography results from Czech Technical University and University Hospital in Brno. Board-certified physicians then reviewed the fetal cardiotocography results and labeled 1456 of them as gold-standard; these results were used to train and validate the model. The remaining results were used to validate the clinical effectiveness of the model with the actual outcome.ResultsIn a test dataset, our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 and area under the precision-recall curve (AUPRC) of 0.73 in an internal validation dataset. An average AUROC of 0.73 and average AUPRC of 0.40 were achieved in the external validation dataset. Fetus abnormality score, as calculated from the continuous fetal cardiotocography results, was significantly associated with actual clinical outcomes [intrauterine growth restriction: odds ratio, 3.626 (p=0.031); Apgar score 1 min: odds ratio, 9.523 (p<0.001), Apgar score 5 min: odds ratio, 11.49 (p=0.001), and fetal distress: odds ratio, 23.09 (p<0.001)].ConclusionThe machine learning model developed in this study showed precision in classifying FHR signals. This suggests that the model can be applied to medical devices as a screening tool for monitoring fetal status.     

Plasma Nitrate and Nitrite Kinetics after Single Intake of Beetroot Juice in Adult Patients on Chronic Hemodialysis and in Healthy Volunteers: A Randomized,Single-Blind,Placebo-Controlled,Crossover Study

Nitric oxide (NO) contributes to maintaining normal cardiovascular and renal function. This bioactive signalling molecule is generally formed enzymatically by NO synthase in the vascular endothelium. NO bioactivity can also be attributed to dietary intake of inorganic nitrate, which is abundant in our diet, especially in green leafy vegetables and beets. Ingested nitrate is reduced to nitrite by oral commensal bacteria and further to NO systemically. Previous studies have shown that dialysis, by means of removing nitrate and nitrite from the body, can reduce NO bioactivity. Hence, dietary intervention approaches aimed to boost the nitrate–nitrite–NO pathway may be of benefit in dialysis patients. The purpose of this study was to examine the kinetics of plasma nitrate and nitrite after a single intake of nitrate-rich concentrated beetroot juice (BJ) in adult hemodialysis (HD) patients and in age-matched healthy volunteers (HV). Eight HD patients and seven HV participated in this single center, randomized, single-blind, placebo-controlled, crossover study. Each participant received a sequential single administration of active BJ (70 mL, 400 mg nitrate) and placebo BJ (70 mL, 0 mg nitrate) in a random order separated by a washout period of seven days. For the kinetic analysis, blood samples were collected at different time-points before and up to 44 h after BJ intake. Compared with placebo, active BJ significantly increased plasma nitrate and nitrite levels both in HD patients and HV. The area under the curve and the maximal concentration of plasma nitrate, but not of nitrite, were significantly higher in HD patients as compared with HV. In both groups, active BJ ingestion did not affect blood pressure or plasma potassium levels. Both BJs were well tolerated in all participants with no adverse events reported. Our data provide useful information in planning dietary nitrate supplementation efficacy studies in patients with reduced NO bioactivity.     

Ferroptosis‐based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

BackgroundThis study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.MethodsWe downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the {"type":"entrez-geo","attrs":{"text":"GSE19750","term_id":"19750"}}GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes.ResultsSeventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.ConclusionWe constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.