UNDERSTANDING EFFECT OF LASER SPEED AND FORMULATION FACTORS ON PRINTABILITY AND CHARACTERISTICS OF SLS IRBESARTAN TABLETS- APPLICATION OF DECISION TREE MODEL 2021ICCBIKG (2021)

2021 ◽  
Author(s):  
Marijana Madžarević ◽  
◽  
Svetlana Ibrić
Keyword(s):  
Decision Tree ◽  
Selective Laser Sintering ◽  
Pharmaceutical Formulations ◽  
Decision Tree Model ◽  
Tree Model ◽  
Lactose Monohydrate ◽  
3D Objects ◽  
Negative Effect ◽  
Formulation Factors

Selective laser sintering (SLS) is a rapid prototyping technique for the production of 3D objects through selectively sintering powder-based layers materials by combinations of energy from the laser beam and the heated chamber of the printer. The aim of the study was to investigate the effect of laser speed and formulation factors on printability and characteristics of SLS irbesartan tablets. Physical mixtures of hydroxypropylmethylcellulose (46-91%), Candurin® Gold Sheen (3%), colloidal silicon dioxide (1%), and irbesartan (5%) were prepared. Afterward, crospovidone (1-5%), Kollidon®VA 64 Fine (20%), and/or lactose monohydrate (20-45%) were added. Sintratec Kit SLS printer (Sintratec AG, Switzerland) was used for printing tablets. The decision tree model was applied to classify printability factors. Characterization of tablets was done in terms of physicochemical, mechanical and biopharmaceutical characteristics. Correlation between formulation factors, laser speed, and printability was obtained using decision tree model with an accuracy of 80%. FTIR results revealed that there was no interaction between irbesartan and applied excipients. DSC indicated that irbesartan was present in an amorphous form in printed tablets. It was observed that laser speed had a negative effect on weight. Tuning the drug release by laser speed was possible although lactose monohydrate reduced its impact because it was required higher energy for the sintering process. Results suggest that decision tree could be useful tool for predicting the printability of pharmaceutical formulations. Tailoring characteristics of SLS irbesartan tablets by laser speed is possible, however, it needs to be governed by the composition of the whole formulation.

scholarly journals Understanding the Effect of Energy Density and Formulation Factors on the Printability and Characteristics of SLS Irbesartan Tablets—Application of the Decision Tree Model

Pharmaceutics ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1969
Author(s):  
Marijana Madžarević ◽  
Đorđe Medarević ◽  
Stefan Pavlović ◽  
Branka Ivković ◽  
Jelena Đuriš ◽  
...  
Keyword(s):  
Energy Density ◽  
Decision Tree ◽  
Selective Laser Sintering ◽  
Hydroxypropyl Methylcellulose ◽  
Three Dimensional ◽  
Morphological Characteristics ◽  
Pharmaceutical Formulations ◽  
Decision Tree Model ◽  
Tree Model ◽  
Formulation Factors

Selective laser sintering (SLS) is a rapid prototyping technique for the production of three-dimensional objects through selectively sintering powder-based layer materials. The aim of the study was to investigate the effect of energy density (ED) and formulation factors on the printability and characteristics of SLS irbesartan tablets. The correlation between formulation factors, ED, and printability was obtained using a decision tree model with an accuracy of 80%. FT-IR results revealed that there was no interaction between irbesartan and the applied excipients. DSC results indicated that irbesartan was present in an amorphous form in printed tablets. ED had a significant influence on tablets’ physical, mechanical, and morphological characteristics. Adding lactose monohydrate enabled faster drug release while reducing the possibility for printing with different laser speeds. However, formulations with crospovidone were printable with a wider range of laser speeds. The adjustment of formulation and process parameters enabled the production of SLS tablets with hydroxypropyl methylcellulose with complete release in less than 30 min. The results suggest that a decision tree could be a useful tool for predicting the printability of pharmaceutical formulations. Tailoring the characteristics of SLS irbesartan tablets by ED is possible; however, it needs to be governed by the composition of the whole formulation.

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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.

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