Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Microplastics(MPs)are emerging contaminants in aquatic environments characterized by their polar structure,small particle size(Typically less than 5 mm),large surface area,good stability,and resistance to biodegradation.They pose adverse effects on the normal physiological activities of aquatic organisms and can accumulate in biota,including humans.Therefore,there is an urgent need for rapid and accurate quantitative analysis of MPs in water environments.In this study,Raman spectroscopy combined with partial least squares(PLS)was employed for rapid and accurate quantitative analysis of polyethylene(PE)and polystyrene(PS)MPs in real water samples.Initially,33 simulated water samples containing different concentrations of MPs were prepared,and their Raman spectra were collected.Six spectral preprocessing methods(Normalization,multiplicative scatter correction,standard normal variate transformation,first derivative,second derivative,and wavelet transform)were investigated for their impact on the predictive performance of PLS calibration models.Subsequently,three variable selection methods including synergy interval partial least squares(SiPLS),variable importance in projection(VIP)and mutual information(MI)were employed to optimize the input variables of the PLS calibration model.The predictive capability of the PLS calibration model was evaluated and validated using leave-one-out cross-validation.Under the optimal conditions of spectral preprocessing,variable selection,input variables and latent variables,the wavelet transform-partial least squares(WT-PLS)calibration model based on distilled water was established,and the contents of PE and PS in real water samples were predicted with prediction correlation coefficients(R2p)of 0.9540 and 0.8472 for PE and PS,respectively,and prediction errors(Errorp)of 0.0690 and 0.1126,respectively.Furthermore,a mixed sample MI-PLS calibration model was developed,demonstrating the best predictive performance in real water samples(With R2p values of 0.9776 and 0.9755 for PE and PS,respectively,and Errorp values of 0.0360 and 0.0392,respectively).This method provided a novel approach and new methodology for quantitative analysis of MPs and other organic pollutants in real water samples.

Objective To propose an evaluation and screening indicator for the reliability of emergency ventilators.Methods Firstly,a regression model was used to clean the data and remove noise to ensure the accuracy of regression analysis.Then,four groups of highly correlated data combinations,including inspiratory tidal volume-minute expiratory volume,peak airway pressure-minute expiratory volume,peak airway pressure-inspiratory tidal volume and positive end-expiratory pressure(PEEP)-mean airway pressure,were determined with the methods of curve fitting and transfer function,and the difference between the theoretical output and the actual output of the data combinations was regarded as an indicator to judge whether the ventilator functioned well or not;finally,the indicator proposed was applied to single and multiple ventilators,and the feasibility of the indicator was determined by the proportion of the ventilators functioning well.Results The evaluation results with a single ventilator showed the four groups of data combinations had the actual output fitted well with the theoretical output,and all the differences between the theoretical output and the actual output were lower than the threshold;the results with multiple ventilators indicated there were 71.49％ventilators functioning well,which was very close to the actual result that 72％ventilators behaved well.Conclusion The evaluation and screening indicator for emergency ventilators has high feasibility,and theoretical support is provided for reliability assessment and selection of series of emergency treatment equipment.[Chinese Medical Equipment Journal,2024,45(7):8-16]

Aim To explore the feasibility of the cloud exposure system for in vitro exposure experiments on inhalation toxicology.Methods Calu-3 cells cultured at the air-liquid interface(ALI)were exposed to three concentrations of lipopolysaccha-ride(LPS):high,medium,and low(800,400,200 mg·L-1)by the cloud exposure system,and phosphate buffer solu-tion(PBS)was used as a negative control group for one expo-sure,while the high concentration of LPS was used to expose Calu-3 cells for five times.Calu-3 cells were exposed to phos-phate buffer solution(PBS)once as negative control group and to high concentration of LPS solution for five times,and the ac-tivity of Calu-3 cells,the release of lactate dehydrogenase(LDH),TEER,mucin MUC5AC,and the expression of inflam-matory factors IL-6,IL-8 and TNF-α were detected 3 h and 24 h after the end of the exposure,respectively.Results Compared with the PBS-negative control group,after exposure to the Calu-3 cell model at the air-liquid interface with three concentrations of LPS,high,medium,and low,there were no significant changes in the activity and LDH release,but the cellular electrical resist-ance value was reduced,and the barrier function of the cells was impaired;with the increase of the exposure concentration,the LPS promoted the expression of the cellular mucin MUC5AC,which led to a decrease in the expression of cellular IL-6,IL-8,and a decrease in the expression of TNF-α.Expression of IL-6 and IL-8 decreased and TNF-α expression increased;as the fre-quency of exposure increased,LPS inhibited the expression of mucin and increased the expression of IL-6;an increase in the frequency of exposure along with a prolongation of post-exposure assay time resulted in an increase in the expression of cellular IL-8 and TNF-a.Conclusions The ALI cloud exposure ap-proach can effectively reflect the cellular response to positive subjects,and this in vitro exposure can be used in subsequent exposure experiments to evaluate the inhalation toxicity of com-pounds.

Objective: To construct a prognostic model for unsuccessful removal of nasogastric tube (NGT) was the aim of our study. Methods: This study examined patients with swallowing disorders receiving NGT feeding due to stroke or traumatic brain injury in a regional hospital. Clinical data was collected, such as age, sex, body mass index (BMI), level of activities of daily living (ADLs) dependence. Additionally, gather information regarding the enhancement in Functional Oral Intake Scale (FOIS) levels and the increase in food types according to the International Dysphagia Diet Standardization Initiative (IDDSI) after one month of swallowing training. A stepwise logistic regression analysis model was employed to predict NGT removal failure using these parameters. Results: Out of 203 patients, 53 patients (26.1%) had experienced a failed removal of NGT after six months of follow-up. The strongest predictors for failed removal were age over 60 years, underweight BMI, total dependence in ADLs, and ischemic stroke. The admission prediction model categorized patients into high, moderate, and low-risk groups for removal failure. The failure rate of NGT removal was high not only in the high-risk group but also in the moderate-risk groups when there was no improvement in FOIS levels and IDDSI food types. Conclusion Our predictive model categorizes patients with brain insults into risk groups for swallowing disorders, enabling advanced interventions such as percutaneous endoscopic gastrostomy for high-risk patients struggling with NGT removal, while follow-up assessments using FOIS and IDDSI aid in guiding rehabilitation decisions for those at moderate risk.

Background/Aims: Ineffective esophageal motility (IEM) is common in patients with gastroesophageal reflux disease (GERD) and can be associated with poor esophageal contraction reserve on multiple rapid swallows. Alterations in the esophageal microbiome have been reported in GERD, but the relationship to presence or absence of contraction reserve in IEM patients has not been evaluated. We aim to investigate whether contraction reserve influences esophageal microbiome alterations in patients with GERD and IEM. Methods: We prospectively enrolled GERD patients with normal endoscopy and evaluated esophageal motility and contraction reserve with multiple rapid swallows during high-resolution manometry. The esophageal mucosa was biopsied for DNA extraction and 16S ribosomal RNA gene V3-V4 (Illumina)/full-length (Pacbio) amplicon sequencing analysis. Results: Among the 56 recruited patients, 20 had normal motility (NM), 19 had IEM with contraction reserve (IEM-R), and 17 had IEM without contraction reserve (IEM-NR). Esophageal microbiome analysis showed a significant decrease in microbial richness in patients with IEM-NR when compared to NM. The beta diversity revealed different microbiome profiles between patients with NM or IEM-R and IEM-NR (P = 0.037). Several esophageal bacterial taxa were characteristic in patients with IEM-NR, including reduced Prevotella spp.and Veillonella dispar, and enriched Fusobacterium nucleatum. In a microbiome-based random forest model for predicting IEM-NR, an area under the receiver operating characteristic curve of 0.81 was yielded. Conclusions In symptomatic GERD patients with normal endoscopic findings, the esophageal microbiome differs based on contraction reserve among IEM. Absent contraction reserve appears to alter the physiology and microbiota of the esophagus.

BACKGROUND: Patients with atrial fibrillation (AF) and prior stroke history have a high risk of cardiovascular events despite anticoagulation therapy. It is unclear whether catheter ablation (CA) has further benefits in these patients. METHODS: AF patients with a previous history of stroke or systemic embolism (SE) from the prospective Chinese Atrial Fibrillation Registry study between August 2011 and December 2020 were included in the analysis. Patients were matched in a 1:1 ratio to CA or medical treatment (MT) based on propensity score. The primary outcome was a composite of all-cause death or ischemic stroke (IS)/SE. RESULTS: During a total of 4.1 ± 2.3 years of follow-up, the primary outcome occurred in 111 patients in the CA group (3.3 per 100 person-years) and in 229 patients in the MT group (5.7 per 100 person-years). The CA group had a lower risk of the primary outcome compared to the MT group [hazard ratio (HR) = 0.59, 95% CI: 0.47-0.74, P < 0.001]. There was a significant decreasing risk of all-cause mortality (HR = 0.43, 95% CI: 0.31-0.61, P < 0.001), IS/SE (HR = 0.73, 95% CI: 0.54-0.97, P = 0.033), cardiovascular mortality (HR = 0.32, 95% CI: 0.19-0.54, P < 0.001) and AF recurrence (HR = 0.33, 95% CI: 0.30-0.37, P < 0.001) in the CA group compared to that in the MT group. Sensitivity analysis generated consistent results when adjusting for time-dependent usage of anticoagulants. CONCLUSIONS In AF patients with a prior stroke history, CA was associated with a lower combined risk of all-cause death or IS/SE. Further clinical trials are warranted to confirm the benefits of CA in these patients.

Objective To propose a neural network-based ventilator fault prediction method with a self-developed underlying data preprocessing method for ventilator IoT data.Methods Firstly the ventilator IoT data were sorted,categorized and cleaned.Secondly nondimensionalization and encoding of all the data were implemented via feature engineering methods based on data distribution.Thirdly data dimensionality reduction was carried out with a self-encoder.Finally,an artificial neural network was used for training with the abnormal data as the training label and predicting the abnormal data faults as the training objective,and the prediction performance of the neural network model was evaluated by calculating the accuracy,precision,recall rate,negative prediction value and specificity.Results The neural network model behaved well in learning with the accuracy being 99.68％,the precision being 99.66％,the recall rate being 99.99％,the negative prediction value being 99.95％and the specificity being 96.52％.Conclusion The proposed ventilator fault prediction model based on underlying data preprocessing and neural network can be used for the prediction of specific faults,and references are provided for IoT data-based medical equipment operation and maintenance management.[Chinese Medical Equipment Journal,2023,44(9):8-13]