scholarly journals Modification and Validation of the Phosphate Removal Model: A Multicenter Study

2021 ◽  
pp. 1-10
Author(s):  
Weichen Zhang ◽  
Qiuna Du ◽  
Jing Xiao ◽  
Zhaori Bi ◽  
Chen Yu ◽  
...  
Keyword(s):  
Polynomial Regression ◽  
External Validation ◽  
Original Model ◽  
Phosphate Concentration ◽  
Phosphate Removal ◽  
Internal Validation ◽  
Modified Model ◽  
New Model ◽  
Significant Difference ◽  
Removal Model

<b><i>Background:</i></b> Our research group has previously reported a noninvasive model that estimates phosphate removal within a 4-h hemodialysis (HD) treatment. The aim of this study was to modify the original model and validate the accuracy of the new model of phosphate removal for HD and hemodiafiltration (HDF) treatment. <b><i>Methods:</i></b> A total of 109 HD patients from 3 HD centers were enrolled. The actual phosphate removal amount was calculated using the area under the dialysate phosphate concentration time curve. Model modification was executed using second-order multivariable polynomial regression analysis to obtain a new parameter for dialyzer phosphate clearance. Bias, precision, and accuracy were measured in the internal and external validation to determine the performance of the modified model. <b><i>Results:</i></b> Mean age of the enrolled patients was 63 ± 12 years, and 67 (61.5%) were male. Phosphate removal was 19.06 ± 8.12 mmol and 17.38 ± 6.75 mmol in 4-h HD and HDF treatments, respectively, with no significant difference. The modified phosphate removal model was expressed as Tpo<sub>4</sub> = 80.3 × <i>C</i><sub>45</sub> − 0.024 × age + 0.07 × weight + β × clearance − 8.14 (β = 6.231 × 10<sup>−3</sup> × clearance − 1.886 × 10<sup>−5</sup> × clearance<sup>2</sup> – 0.467), where <i>C</i><sub>45</sub> was the phosphate concentration in the spent dialysate measured at the 45th minute of HD and clearance was the phosphate clearance of the dialyzer. Internal validation indicated that the new model was superior to the original model with a significantly smaller bias and higher accuracy. External validation showed that <i>R</i><sup>2</sup>, bias, and accuracy were not significantly different than those of internal validation. <b><i>Conclusions:</i></b> A new model was generated to quantify phosphate removal by 4-h HD and HDF with a dialyzer surface area of 1.3–1.8 m<sup>2</sup>. This modified model would contribute to the evaluation of phosphate balance and individualized therapy of hyperphosphatemia.

scholarly journals Deep Learning Model for Screening Sepsis Using Electrocardiography

2021 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ye Rang Lee ◽  
Min-Seung Jung ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
...  
Keyword(s):  
Septic Shock ◽  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Medical Emergency ◽  
Validation Dataset ◽  
Internal Validation ◽  
Significant Difference ◽  
Life Threatening ◽  
Sepsis And Septic Shock

Abstract Background: Sepsis is a life-threatening organ dysfunction and is a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, it is difficult to screen the occurrence of sepsis. In this study, we propose an artificial intelligence based on deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).Methods: This retrospective cohort study included 46,017 patients who admitted to two hospitals. 1,548 and 639 patients underwent sepsis and septic shock. The DLM was developed using 73,727 ECGs of 18,142 patients and internal validation was conducted using 7,774 ECGs of 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs of 20,101 patients from another hospital to verify the applicability of the DLM across centers.Results: During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of an DLM using 12-lead ECG for screening sepsis were 0.901 (95% confidence interval 0.882–0.920) and 0.863 (0.846–0.879), respectively. During internal and external validation, AUC of an DLM for detecting septic shock were 0.906 (95% CI = 0.877–0.936) and 0.899 (95% CI = 0.872–0.925), respectively. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs were 0.845–0.882. A sensitivity map showed that the QRS complex and T wave was associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who admitted with infectious disease, The AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs 0.574, p<0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs 0.725, p=0.018).Conclusions: The DLM demonstrated reasonable performance for screening sepsis using 12-, 6-, and single-lead ECG. The results suggest that sepsis can be screened using not only conventional ECG devices, but also diverse life-type ECG machine employing the DLM, thereby preventing irreversible disease progression and mortality.

scholarly journals Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244957
Author(s):  
Denize Tyska ◽  
Adriano Olnei Mallmann ◽  
Juliano Kobs Vidal ◽  
Carlos Alberto Araújo de Almeida ◽  
Luciane Tourem Gressler ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Contamination Level ◽  
Internal Validation ◽  
Significant Difference

Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.

scholarly journals Deep-learning model for screening sepsis using electrocardiography

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ye Rang Lee ◽  
Min-Seung Jung ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
...  
Keyword(s):  
Septic Shock ◽  
Deep Learning ◽  
Confidence Interval ◽  
Characteristic Curve ◽  
External Validation ◽  
Medical Emergency ◽  
Validation Dataset ◽  
Internal Validation ◽  
Significant Difference ◽  
Sepsis And Septic Shock

Abstract Background Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). Methods This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. Results During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882–0.920) and 0.863 (0.846–0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877–0.936) and 0.899 (95% CI, 0.872–0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845–0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). Conclusions The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

scholarly journals Development of a Questionnaire To Measure Teachers’ Student-Centred Perspectives Based On The Onion Model

2021 ◽  
Author(s):  
Lukas Daniel Leatemia ◽  
Jeroen J. G. van Merrienboer ◽  
Astrid Pratidina Susilo
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
External Validity ◽  
External Validation ◽  
Personal Characteristics ◽  
Internal Validation ◽  
Teaching Perspectives ◽  
Significant Difference ◽  
Confirmatory Factor ◽  
Post Hoc

Abstract Background Teachers with a teacher-centred perspective have difficulties applying student-centred approaches in Problem Based Learning (PBL) because they are inclined to show teacher-centred behaviours. The six aspects explained in Korthagen’s Onion Model (environment, behaviour, competencies, beliefs, identity, and mission) are assumed to contribute to teachers’ perspectives, showing that both the environment and personal characteristics influence behaviours. For teachers to function properly in PBL, those six aspects should reflect a student-centred perspective. Previous instruments to measure teaching perspectives focused on only a few of these relevant aspects. Therefore, we developed the Student-Centred Perspective of Teachers (SCPT) questionnaire with subscales for each aspect in the Onion Model. This study aimed to provide evidence for its internal and external validity. Methods The SCPT was distributed in a survey to 795 teachers from 20 medical schools. For the internal validation, Confirmatory Factor Analysis was performed to analyse theoretical fit model validation, convergent validation, and discriminant validation. For the external validation, teachers’ perspective scores were compared among three groups of amount of PBL training using Analysis of Variance (ANOVA) and post-hoc Least Significant Difference (LSD) tests. The p-value for all tests was set at .05. Results A total of 543 out of 795 teachers (68.3%) participated. Confirmatory Factor Analysis showed the evidence of the SCPT’s internal validation with acceptable fit for the six subscales measured by 19 items and the following Composite Reliability scores: environment (.72), behaviour (.74), competencies (.63), beliefs (.55), identity (.76), and mission (.60). All items’ factors loadings reached a good standard (.5 or greater). Only the environment subscale had the Average Variance Extracted (AVE) score higher than .5 and the Maximum Shared Variance score lower than the AVE score. ANOVA and Post-hoc LSD tests showed that teachers who participated in more PBL training showed significantly higher student-centred perspectives, providing evidence for external validity. Conclusion The SCPT is a reliable and valid instrument to measure teaching perspectives. Identifying aspects that do not represent the adoption of a student-centred perspective may provide valuable input for faculty development in the context of PBL.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

scholarly journals Phosphate Removal Using Polyethylenimine Functionalized Silica-Based Materials

2021 ◽  
Vol 13 (3) ◽  
pp. 1502
Author(s):  
Maria Xanthopoulou ◽  
Dimitrios Giliopoulos ◽  
Nikolaos Tzollas ◽  
Konstantinos S. Triantafyllidis ◽  
Margaritis Kostoglou ◽  
...  
Keyword(s):  
Sorption Capacity ◽  
Removal Rate ◽  
Phosphate Concentration ◽  
Phosphate Removal ◽  
Equilibrium Studies ◽  
Maximum Sorption Capacity ◽  
Algae Blooms ◽  
Maximum Sorption ◽  
Phosphate Anions ◽  
Adsorbent Dose

In water and wastewater, phosphate anions are considered critical contaminants because they cause algae blooms and eutrophication. The present work aims at studying the removal of phosphate anions from aqueous solutions using silica particles functionalized with polyethylenimine. The parameters affecting the adsorption process such as pH, initial concentration, adsorbent dose, and the presence of competitive anions, such as carbonate, nitrate, sulfate and chromate ions, were studied. Equilibrium studies were carried out to determine their sorption capacity and the rate of phosphate ions uptake. The adsorption isotherm data fitted well with the Langmuir and Sips model. The maximum sorption capacity was 41.1 mg/g at pH 5, which decreased slightly at pH 7. The efficiency of phosphate removal adsorption increased at lower pH values and by increasing the adsorbent dose. The maximum phosphate removal was 80% for pH 5 and decreased to 75% for pH 6, to 73% for pH 7 and to 70% for pH 8, for initial phosphate concentration at about 1 mg/L and for a dose of adsorbent 100 mg/L. The removal rate was increased with the increase of the adsorbent dose. For example, for initial phosphate concentration of 4 mg/L the removal rate increased from 40% to 80% by increasing the dose from 0.1 to 2.0 g/L at pH 7. The competitive anions adversely affected phosphate removal. Though they were also found to be removed to a certain extent. Their co-removal provided an adsorbent which might be very useful for treating waters with low-level multiple contaminant occurrence in natural or engineered aquatic systems.

scholarly journals Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level

2021 ◽  
Vol 10 (6) ◽  
pp. 227
Author(s):  
Yago Martín ◽  
Zhenlong Li ◽  
Yue Ge ◽  
Xiao Huang
Keyword(s):  
Large Scale ◽  
Local Population ◽  
External Validation ◽  
Population Estimates ◽  
County Level ◽  
Internal Validation ◽  
Population Movements ◽  
Dynamic Component ◽  
Home Location ◽  
Non Local

The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Internal validation reveals over 80% accuracy when compared with users’ self-reported home location. External validation results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds the potential to introduce the dynamic component often lacking in population estimates. This study could potentially benefit various fields such as demography, tourism, emergency management, and public health and create new opportunities for large-scale mobility analyses.

scholarly journals Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2726
Author(s):  
Uli Fehrenbach ◽  
Siyi Xin ◽  
Alexander Hartenstein ◽  
Timo Alexander Auer ◽  
Franziska Dräger ◽  
...  
Keyword(s):  
Liver Metastases ◽  
External Validation ◽  
High Accuracy ◽  
Hepatic Tumor ◽  
Liver Malignancies ◽  
Internal Validation ◽  
Matthew’S Correlation Coefficient ◽  
Therapy Success ◽  
Neuroendocrine Liver Metastases

Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). Results: Internal validation of the model’s accuracy showed a high overlap for NELM and livers (Matthew’s correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model’s measurements correlated well with MCC’s evaluation of therapeutic response.

scholarly journals Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Early Stage ◽  
Current Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Decision Tree Model ◽  
Tree Model ◽  
Chinese Adult ◽  
Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

2021 ◽  
Author(s):  
Sang-Heon Lim ◽  
Young Jae Kim ◽  
Yeon-Ho Park ◽  
Doojin Kim ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
External Validation ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Similarity Coefficients ◽  
Internal Validation ◽  
Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

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