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Surface Coating of Gypsum-Based Molds for Maxillofacial Prosthetic Silicone Elastomeric Material: The Surface Topography

Khalaf S, Ariffin Z, Husein A, Reza F

J Prosthodont, 2015 Jul;24(5):419-23.
PMID: 25219956 DOI: 10.1111/jopr.12213

PURPOSE: This study aimed to compare the surface roughness of maxillofacial silicone elastomers fabricated in noncoated and coated gypsum materials. This study was also conducted to characterize the silicone elastomer specimens after surfaces were modified.
MATERIALS AND METHODS: A gypsum mold was coated with clear acrylic spray. The coated mold was then used to produce modified silicone experimental specimens (n = 35). The surface roughness of the modified silicone elastomers was compared with that of the control specimens, which were prepared by conventional flasking methods (n = 35). An atomic force microscope (AFM) was used for surface roughness measurement of silicone elastomer (unmodified and modified), and a scanning electron microscope (SEM) was used to evaluate the topographic conditions of coated and noncoated gypsum and silicone elastomer specimens (unmodified and modified) groups. After the gypsum molds were characterized, the fabricated silicone elastomers molded on noncoated and coated gypsum materials were evaluated further. Energy-dispersive X-ray spectroscopy (EDX) analysis of gypsum materials (noncoated and coated) and silicone elastomer specimens (unmodified and modified) was performed to evaluate the elemental changes after coating was conducted. Independent t test was used to analyze the differences in the surface roughness of unmodified and modified silicone at a significance level of p < 0.05.
RESULTS: Roughness was significantly reduced in the silicone elastomers processed against coated gypsum materials (p < 0.001). The AFM and SEM analysis results showed evident differences in surface smoothness. EDX data further revealed the presence of the desired chemical components on the surface layer of unmodified and modified silicone elastomers.
CONCLUSIONS: Silicone elastomers with lower surface roughness of maxillofacial prostheses can be obtained simply by coating a gypsum mold.
Surface Coating of Gypsum-Based Molds for Maxillofacial Prosthetic Silicone Elastomeric Material: Evaluating Different Microbial Adhesion

Khalaf S, Ariffin Z, Husein A, Reza F

J Prosthodont, 2017 Dec;26(8):664-669.
PMID: 28177575 DOI: 10.1111/jopr.12460

PURPOSE: To compare the adhesion of three microorganisms on modified and unmodified silicone elastomer surfaces with different surface roughnesses and porosities.
MATERIALS AND METHODS: Candida albicans, Streptococcus mutans, and Staphylococcus aureus were incubated with modified and unmodified silicone groups (N = 35) for 30 days at 37°C. The counts of viable microorganisms in the accumulating biofilm layer were determined and converted to cfu/cm2 unit surface area. A scanning electron microscope (SEM) was used to evaluate the microbial adhesion. Statistical analysis was performed using t-test, one-way ANOVA, and post hoc tests as indicated.
RESULTS: Significant differences in microbial adhesion were observed between modified and unmodified silicone elastomers after the cells were incubated for 30 days (p < 0.001). SEM showed evident differences in microbial adhesion on modified silicone elastomer compared with unmodified silicone elastomer.
CONCLUSIONS: Surface modification of silicone elastomer yielding a smoother and less porous surface showed lower adhesion of different microorganisms than observed on unmodified surfaces.
Fulltext The role of Caesarean section in childhood asthma

Al Yassen AQ, Al-Asadi JN, Khalaf SK

Malays Fam Physician, 2019;14(3):10-17.
PMID: 32175036

Objective: As indicated by previous studies, children born via Caesarean section may have an increased risk of developing asthma compared with those born via vaginal delivery. The aim of this study is to assess the association between a Caesarean section and the risk of childhood asthma. Methods: This was a case-control study carried out in Basrah, Iraq including 952 children aged 3-12 years. Four hundred and seven asthmatic cases and a control group of 545 age-matched non-asthmatic children were enrolled. Binary logistic regression was used to assess the relationship between asthma and birth via Caesarean section.

Results: The mean age of the children was 6.7±2.5 years. Two-hundred eighty-three children (29.7%) were delivered via Caesarean section. The binary logistic regression analysis showed that delivery via Caesarean section was found to be an independent significant risk factor for asthma (OR=3.37; 95% CI=1.76-6.46; p<0.001). In addition, many other risk factors were found to be significant predictors of asthma, including bottlefeeding (OR=27.29; 95% CI=13.54-54.99; p<0.001) and low birth weight (OR=16.7; 95% CI=6.97-37.49; p<0.001).

Conclusion: Caesarean section is significantly associated with an increased risk of childhood asthma.
Fulltext Breast cancer diagnosis using the fast learning network algorithm

Albadr MAA, Ayob M, Tiun S, Al-Dhief FT, Arram A, Khalaf S

Front Oncol, 2023;13:1150840.
PMID: 37434975 DOI: 10.3389/fonc.2023.1150840

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.