Sebelas Maret University

About: Sebelas Maret University is a education organization based out in Surakarta, Indonesia. It is known for research contribution in the topics: Population & Public health. The organization has 10901 authors who have published 10832 publications receiving 33057 citations. The organization is also known as: Universitas Negeri Surakarta & Universitas Sebelas Maret.

The field-dependent viscoelastic and transient responses of plate-like carbonyl iron particle based magnetorheological greases:

Abstract: The field-dependent viscoelastic and transient behaviours of plate-like-carbonyl-iron-particle-based magnetorheological greases are experimentally investigated in this study. The plate-like carbony...

Characterization of poly(lactic acid)/hydroxyapatite prepared by a solvent-blending technique: Viscoelasticity and in vitro hydrolytic degradation

Abstract: Poly(lactic acid) (PLA) was solvent blended in a chloroform solution using multiple weight fractions of hydroxyapatite (HAp) (5, 10, and 20 wt%). A miniature laboratory mixing extruder equipped wit...

Barriers and strategies to successful tuberculosis treatment in a high-burden tuberculosis setting: a qualitative study from the patient’s perspective

Abstract: Background Previously treated tuberculosis (TB) patients are a widely reported risk factor for multidrug-resistant tuberculosis. Identifying patients' problems during treatment is necessary to control TB, especially in a high-burden setting. We therefore explored barriers to successful TB treatment from the patients' perspective, aiming to identify potential patient-centred care strategies to improve TB treatment outcome in Indonesia. Methods A qualitative study was conducted in a province of Indonesia with high TB prevalence. Participants from various backgrounds (i.e., TB patients, physicians, nurses, pharmacists, TB activist, TB programmers at the district and primary care levels) were subject to in-depth interviews and focus group discussions (FGDs). All interviews and FGDs were transcribed verbatim from audio and visual recordings and the respective transcriptions were used for data analysis. Barriers were constructed by interpreting the codes' pattern and co-occurrence. The information's trustworthiness and credibility were established using information saturation, participant validation and triangulation approaches. Data were inductively analysed using the Atlas.ti 8.4 software and reported following the COREQ 32-items. Results We interviewed 63 of the 66 pre-defined participants and identified 15 barriers. The barriers were classified into three themes, i.e., socio-demography and economy; knowledge and perception and TB treatment. Since the barriers can be interrelated, we determined five main barriers across all barrier themes, i.e., lack of TB knowledge, stigmatisation, long distance to the health facility, adverse drug reaction and loss of household income. Conclusion The main treatment barriers can be considered to strengthen patient-centred care for TB patients in Indonesia. A multi-component approach including TB patients, healthcare providers, broad community and policy makers is required to improve TB treatment success.

Study of extreme learning machine activation functions for magnetorheological fluid modelling in medical devices application

Abstract: Magnetorheological (MR) fluid applications in various medical equipment have been widely studied because its easiness to utilize and fast response. The application can be divided into, at least, two forms, which are haptic and prosthetic devices. In each equipment design process, rheological models are essential to determine the required inputs to produce enough force or yield stress. However, each existing model has its own limitations, such as agreeable performance on limited inputs ranges of magnetic fields and shear rates. A modeling method using extreme learning machine (ELM) as an intelligent model may be able to solve this problem. Therefore, this paper aims to investigate the ELM performance to model MR fluids behavior using various activation functions. Five activation functions are applied, which are hard limit, sigmoid, sine, triangular basis and radial basis function. Then, the investigation is divided into two cases, which are a wide and low operating shear rate based on the medical devices applications. The comparisons with the experimental data show that the models produce considerably low RMSE or less than 3 kPa, especially for hard limit activation function. The paper also demonstrates the model capability to predict dynamic yield stress.

Assessment of Fractional Factorial Design for the Selection and Screening of Appropriate Components of a Self-nanoemulsifying Drug Delivery System Formulation.

Abstract: Purpose: Recently, a self-nanoemulsifying drug delivery system (SNEDDS) has shown great improvement in the enhancement of drug bioavailability. The selection of appropriate compositions in the SNEDDS formulation is the fundamental step towards developing a successful formulation. This study sought to evaluate the effectiveness of fractional factorial design (FFD) in the selection and screening of a SNEDDS composition. Furthermore, the most efficient FFD approach would be applied to the selection of SNEDDS components. Methods: The types of oil, surfactant, co-surfactant, and their concentrations were selected as factors. 26 full factorial design (FD) (64 runs), 26-1 FFD (32 runs), 26-2 FFD (16 runs), and 26-3 FFD (8 runs) were compared to the main effect contributions of each design. Ca-pitavastatin (Ca-PVT) was used as a drug model. Screening parameters, such as transmittance, emulsification time, and drug load, were selected as responses followed by particle size along with zeta potential for optimized formulation. Results: The results indicated that the patterns of 26 full FD and 26-1 for both main effects and interactions were similar. 26-3 FFD lacked adequate precision when used for screening owing to the limitation of design points. In addition, capryol, Tween 80, and transcutol P were selected to be developed in a SNEDDS formulation with a particle size of 69.7± 5.3 nm along with a zeta potential of 33.4± 2.1 mV. Conclusion: Herein, 26-2 FFD was chosen as the most efficient and adequate design for the selection and screening of SNEDDS composition. The optimized formulation fulfilled the requirement of a quality target profile of a nanoemulsion.