Development of chitosan/alginate/silver nanoparticles hydrogel scaffold for soft tissue engineering applications

A biodegradable scaffold in tissue engineering serves as a temporary skeleton to accommodate and stimulate new tissue growth. Alginate (Alg) and chitosan (Chi) are both popular materials applied as biomaterials or bioimplants. However, Alg derived from brown algae is highly compliant and easily decomposed in fluid, whilst Chi derived from shrimp shells has weak strength. In rectify these problems, the development of Chi and Alg based biodegradable scaffolds incorporated with silver nanoparticles (AgNPs) with enhanced mechanical properties and biosafe function is proposed. Different ratios of chitosan/alginate (Chi/Alg) were prepared and the effect of different ratio (1:1, 1:2 and 2:1) to the mechanical, biological properties with and without AgNPs and keratinocyte cell growth were investigated. The preliminary result of FTIR, UV-Vis, XRD, FESEM and EDS proved the production of silver nanoparticles. Meanwhile, FTIR, swelling/degradation, DMA, TGA, DSC, FESEM and MTT assay was conducted to study the properties of Chi/Alg based scaffold. FTIR analysis shows the crosslinking of Chi/Alg based scaffold. Swelling/degradation and DMA shows Chi/Alg and chitosan/alginate/silver nanoparticles (Chi/Alg/AgNPs) has adequate swelling and compressive modulus that exceed the epidermis’ Young modulus, thus able to provide mechanical support upon application. Meanwhile, the thermal analysis revealed that the onset decomposition temperature of scaffold were at around 70 ºC which is due to the loss of water present in the scaffold thus thermally safe for soft tissue application. Based on FESEM result, there are different in surface structure of Chi/Alg based scaffold. Finally, with the incorporation of 0.3 % PVP synthesised AgNPs in Chi/Alg based scaffold, cells are able to live up to 14 days. As a result, Chi incorporation in the Alg and AgNPs improved physical, mechanical properties of hydrogel itself and provide biosafe environment during the study

The Use of Fuzzy Linear Regression Modeling to Predict High-risk Symptoms of Lung Cancer in Malaysia

Lung cancer is the most prevalent cancer in the world, accounting for 12.2% of all newly diagnosed cases in 2020 and has the highest mortality rate due to its late diagnosis and poor symptom detection. Currently, there are 4,319 lung cancer deaths in Malaysia, representing 2.57 percent of all mortality in 2020. The late diagnosis of lung cancer is common, which makes survival more difficult. In Malaysia, however, most cases are detected when the tumors have become too large, or cancer has spread to other body areas that cannot be removed surgically. This is a frequent situation due to the lack of public awareness among Malaysians regarding cancer-related symptoms. Malaysians must be acknowledged the highrisk symptoms of lung cancer to enhance the survival rate and reduce the mortality rate. This study aims to use a fuzzy linear regression model with heights of triangular fuzzy by Tanaka (1982), H-value ranging from 0.0 to 1.0, to predict high-risk symptoms of lung cancer in Malaysia. The secondary data is analyzed using the fuzzy linear regression model by collecting data from patients with lung cancer at Al-Sultan Abdullah Hospital (UiTM Hospital), Selangor. The results found that haemoptysis and chest pain has been proven to be the highest risk, among other symptoms obtained from the data analysis. It has been discovered that the H-value of 0.0 has the least measurement error, with mean square error (MSE) and root mean square error (RMSE) values of 1.455 and 1.206, respectively