This study aimed to determine the reinforcing effect of two weight ratios of Gum Arabic (GA) natural biopolymer, i.e., 0.5% and 1.0% in the powdered composition of glass ionomer luting cement. GA powder was oxidized and GA-reinforced GIC in 0.5 and 1.0 wt.% formulations were prepared in rectangular bars using two commercially available GIC luting materials (Medicem and Ketac Cem Radiopaque). The control groups of both materials were prepared as such. The effect of reinforcement was evaluated in terms of microhardness, flexural strength (FS), fracture toughness (FT), and tensile strength (TS). The internal porosity and water contact angle formation on the study samples were also evaluated. Film thickness was measured to gauge the effect of micron-sized GA powder in GA–GIC composite. Paired sample t-tests were conducted to analyze data for statistical significance (p < 0.05). The experimental groups of both materials containing 0.5 wt.% GA–GIC significantly improved FS, FT, and TS compared to their respective control groups. However, the microhardness significantly decreased in experimental groups of both cements compared to their respective control groups. The addition of GA powder did not cause a significant increase in film thickness and the water contact angle of both 0.5 and 1.0 wt.% GA–GIC formulations were less than 90^o. Interestingly, the internal porosity of 0.5 wt.% GA–GIC formulations in both materials were observed less compared to their respective control groups. The significantly higher mechanical properties and low porosity in 0.5 wt.% GA–GIC formulations compared to their respective control group indicate that reinforcing GA powder with 0.5 wt.% in GIC might be promising in enhancing the mechanical properties of GIC luting materials.

Background: This study aimed to investigate the relationship between stiffness of the bicep brachii muscle (BBM) and distal bicep tendon (DBT) and effects of weight lifting (pre- to post-workout changes) among groups with different body mass indexes (BMI). Methods: Participants were divided into four groups according to BMI: A, underweight (< 18.5 kg/m²); B, normal (18.5–24.9 kg/m²); C, overweight (25.0–29.9 kg/m²); and D, obese (> 30.0 kg/m²). All participants were males who were untrained and had sedentary lifestyle without involvement in sports activities for the past 12 months. Ultrasonographic measurements to determine muscle and tendon stiffness was performed on the dominant side (i.e., right side) of the upper extremities in all participants. Results: Twenty-one healthy and untrained males volunteered to participate in this study; 14 were nonsmokers and 7 were smokers. The mean age and BMI were 22.5 ± 1.5 years and 23.8 ± 6.3 kg/m², respectively. Groups A, B, C, and D had four, ten, four, and three participants, respectively. The BBM thickness did not increase with increase in BMI and was not significantly different (P > .05) between groups. The BBM stiffness was significantly different (all P < .05) from pre- to post-workout values in all groups, whereas DBT stiffness did not follow the same trend. Conclusions: Our study revealed that the BBM thickness is independent of BMI. After weight lifting, BBM stiffness in groups A and B increased for BBM compared to those in groups C and D. A similar trend was also recorded for DBT. Weight lifting in concentric and eccentric motions affects the stiffness of the BBM and DBT, thus weight lifting plays a role in adjusting the stiffness of the BBM and DBT. Trial registration The study was approved by ethics committee of the College of Applied Medical Sciences (CAMS 080-3839; March 14, 2018).

This narrative review explores the role of Artificial Intelligence (AI) in advancing the synthesis, optimization, and clinical translation of dental biomaterials. It critically evaluates how AI-driven approaches address existing challenges related to material performance, biocompatibility, and durability, while identifying current research gaps and outlining future perspectives. A comprehensive literature synthesis was conducted through electronic searches in PubMed, Scopus, Web of Science, and Google Scholar. Selected articles encompassed both experimental and computational studies. Case studies were analyzed to illustrate the application of AI techniques, including machine learning (ML) for predicting mechanical properties, deep learning (DL) for microstructural and imaging analysis, and optimization algorithms ( e.g. , genetic algorithms) for materials discovery and formulation enhancement. The findings indicate that ML models can accurately predict mechanical properties ( e.g. , flexural modulus), DL enables automated detection of material degradation, and optimization algorithms accelerate the discovery of advanced biomaterials such as high-entropy ceramics. AI also facilitates the development of personalized, adaptive materials, including pH-responsive and self-healing resin composites. Nonetheless, barriers remain, including data scarcity, limited generalizability, high computational demands, and lack of standardization. AI is poised to redefine the landscape of dental biomaterials by enabling smarter material design, improved performance prediction, and accelerated clinical translation. This review advances the field by integrating emerging computational approaches with practical needs and identifying unresolved challenges that hinder real-world adoption. Ultimately, leveraging AI alongside technologies such as 3D printing and digital twin systems can drive the development of next-generation biomaterials tailored to patient-specific clinical outcomes.

Background: This study is focused to establish a relationship between poor muscle activity face by obese individuals due to the change in stiffness of the intramuscular mass of the lower limb. This issue is also common among athletes and physically active teenagers. Objective: The study is aimed at a subject assessment diagnosis technique named as Strain Elastography (SE)to measure muscle strain. Further, Finite Element Modelling (FEM) technique is used to investigate the strain and/or deformations generated in the Achilles Tendon (AT) models which were categorized according to their Body Mass Index (BMI) through computationally applied loadings. Methods: Total 54 volunteers with an average age of 21.85 ± 1.28 years were categorized into three groups according to their BMI (kg/m2); under BMI < 18.5 (n=14), normal BMI = 18.5-24.9 (n=20) and over BMI/obese > 25.0 (n=20). Additionally, multiple correlational analyses were performed between full range of BMI values and SE outcome. Results: Presence of significant difference (p<0.05) was measured between different categories for BMI, BFMI, FFMI, DLFC, tendon length, tendon thickness and SR. Moreover, multiple correlational analyses and scatter plot strengthen the results. For FEM simulations, the maximum deformation was observed at the proximal end of the tendon in all three groups. Conclusion: It can be concluded that change in tendon stiffness and resulting change in tendon structure was visualized with increased BMI. Moreover, obese individuals are more prone to tendon injury due to the increment in tendon thickness which causes bulging of the AT due to higher loads.

Background: Contaminated air can have a negative impact on patient recovery, leading to longer hospital stays, higher healthcare costs, and even death. Objective: Our study focuses on improving indoor air quality for patient recovery in healthcare facilities. Methods: We conducted computational analysis using the finite element modeling (FEM) technique to investigate the flow of contaminated air exhaled by a patient. Distinct models were examined: a neonatal intensive care unit (NICU) with two-beds and a coronavirus isolation room (CIR). Using ANSYS, we designed models using actual and real specifications of both NICUs and IRs from local hospitals. We determined the optimal dimensions and locations of outlet vents in NICUs and CIRs using simulations with ANSYS software drawing on our designed modeling of air flow. Outlet vent dimensions and locations were modified to achieve optimal air flow for quickly venting out contaminated air from a patient in a room. Results: The results show a substantial improvement in directly venting out the contaminated air from the patient. Conclusions: It can be concluded that the optimal design of outlet vent locations and dimensions using ANSYS simulation results in finding the optimal path for the quick removal of contaminated air flow from the patient in an NICU and CIR.