Drug development is a complex and expensive process from new drug discovery to product approval. Most drug screening and testing rely on in vitro 2D cell culture models; however, they generally lack in vivo tissue microarchitecture and physiological functionality. Therefore, many researchers have used engineering methods, such as microfluidic devices, to culture 3D cells in dynamic conditions. In this study, a simple and low-cost microfluidic device was fabricated using Poly Methyl Methacrylate (PMMA), a widely available material, and the total cost of the completed device was USD 17.75. Dynamic and static cell culture examinations were applied to monitor the growth of 3D cells. α-MG-loaded GA liposomes were used as the drug to test cell viability in 3D cancer spheroids. Two cell culture conditions (i.e., static and dynamic) were also used in drug testing to simulate the effect of flow on drug cytotoxicity. Results from all assays showed that with the velocity of 0.005 mL/min, cell viability was significantly impaired to nearly 30% after 72 h in a dynamic culture. This device is expected to improve in vitro testing models, reduce and eliminate unsuitable compounds, and select more accurate combinations for in vivo testing.

No abstract

This paper investigates the use of machine learning algorithms to aid medical professionals in the detection and risk assessment of diabetes. The research employed a dataset gathered from individuals with type 2 diabetes in Ninh Binh, Vietnam. A variety of classification algorithms, including Decision Tree Classifier, Logistic Regression, SVC, Ada Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighbors Classifier, were utilized to identify the most suitable algorithm for the dataset. The results of the present study indicate that the Random Forest Classifier algorithm yielded the most promising results, exhibiting a cross-validation score of 0.998 and an accuracy rate of 100%. To further evaluate the effectiveness of the selected model, it was subjected to a testing phase involving a new dataset comprising 67 patients that had not been previously seen. The performance of the algorithm on this dataset resulted in an accuracy rate of 94%, especially the study’s notable finding is the algorithm’s accurate prediction of the probability of patients developing diabetes, as indicated by the class 1 (diabetes) probabilities. This innovative approach offers a meticulous and quantifiable method for diabetes detection and risk evaluation, showcasing the potential of machine learning algorithms in assisting clinicians with diagnosis and management. By communicating the diabetes score and probability estimates to patients, the comprehension of their disease status can be enhanced. This information empowers patients to make informed decisions and motivates them to adopt healthier lifestyle habits, ultimately playing a crucial role in impeding disease progression. The study underscores the significance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes.

Peripheral neuropathy is a common complication of type 2 diabetes mellitus (T2DM) that results in nerve conduction abnormalities. This study aimed to investigate the parameters of nerve conduction in lower extremities among T2DM patients in Vietnam. A cross-sectional study was conducted on 61 T2DM patients aged 18 years and older, diagnosed according to the American Diabetes Association’s criteria. Data on demographic characteristics, duration of diabetes, hypertension, dyslipidemia, neuropathy symptoms, and biochemical parameters were collected. Nerve conduction parameters were measured in the tibial and peroneal nerves, including peripheral motor potential time, response amplitude M, and motor conduction speed, as well as sensory conduction in the shallow nerve. The study found a high rate of peripheral neuropathy among T2DM patients in Vietnam, with decreased conduction rate, motor response amplitude, and nerve sensation. The incidence of nerve damage was highest in the right peroneal nerve and left peroneal nerve (86.7% for both), followed by the right tibial nerve and left tibial nerve (67.2% and 68.9%, respectively). No significant differences were found in the rate of nerve defects between different age groups, body mass index (BMI) groups, or groups with hypertension or dyslipidemia. However, a statistically significant association was found between the rate of clinical neurological abnormalities and the duration of diabetes (p < 0.05). Patients with poor glucose control and/or decreased renal function also had a higher incidence of nerve defects. The study highlights the high incidence of peripheral neuropathy among T2DM patients in Vietnam and the association between nerve conduction abnormalities and poor glucose control and/or decreased renal function. The findings underscore the importance of early diagnosis and management of neuropathy in T2DM patients to prevent serious complications.