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.

Imidacloprid, acephate, and carbaryl are common insecticides that are extensively used in planting mango, a well-known fruit in Vietnam, to ease mango hopper issues. The accurate detection of pesticide residues is critical for mango export to meet quality criteria. This study developed a novel SERS platform by using polydimethylsiloxane (PDMS) to simulate the rose petal structure incorporated with a silver coating layer and silver nanoparticles (AgNPs) to detect imidacloprid, acephate, and carbaryl in mango fruits. In this paper, the rose petal PDMS/Ag-AgNPs replica was considered the most efficient substrate for SERS measurement with an EF of 4.7 × 10⁷. The Raman spectra of the three insecticides obtained from the PDMS/Ag-AgNPs substrate were clearly observed with their characteristic peaks of 1105 cm⁻¹ for imidacloprid, 1083 cm⁻¹, and 1579 cm⁻¹ for acephate, and 727 cm⁻¹ and 1378 cm⁻¹ for carbaryl. The application of PDMS/Ag-AgNPs substrate in quantitative analysis of the three pesticides in mango fruit was evaluated. As a result, the limit of detection was 0.02 mg/kg for imidacloprid, 5 × 10⁻⁵ mg/kg for acephate, and 5 × 10⁻³ mg/kg for carbaryl. The SERS result also revealed that the pesticide residues in the mango sample were within an acceptable limit. This suggested the possibility of the rose petal PDMS/Ag-AgNPs replica for rapid quantification of pesticide residues not only in mango fruit but also in many other agricultural products.

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.

Hepatocellular carcinoma is a common type of cancer associated with a high mortality rate. Among several bioactive compounds, Murrayafoline A (MuA) has been proved as a bio substance that exhibits great potentials in treating liver cancer. In order to overcome the high cytotoxicity and low solubility of MuA, a delivery system based on nanocarriers is necessary to deliver MuA towards the desired target. In the present study, 18β-glycyrrhetinic acid (GA), which is known as a ligand for liver targeting, was used to construct the cholesterol-poly (ethylene glycol)-glycyrrhetinic acid (GA-PEG-Chol) conjugate and liposome for MuA administration. The compound was then examined for therapeutic efficacy and safety in HUVEC and HepG2 cells in 2D and 3D cell cultures. Results have shown that MuA-loaded liposomes had IC₅₀ value of 2 µM in HepG2 and had the cytosolic absorption of 8.83 ± 0.97 ng/10⁵ cells, while The IC50 value of MuA-loaded liposomes in HUVEC cell lines was 15 µM and the the cytosolic absorption was recorded as 3.62 ± 0.61 cells. The drug test on the 3D cancer sphere platform of the HepG2 cancer sphere showed that MuA-loaded GA liposomes had the highest efficacy at a concentration of 100 µg/mL. In short, these results suggest that MuA-loaded GA liposomes have the potential for maintenance drug delivery and liver targeting.

A novel method was developed for the sensitive, cheap and fast quantitation of 10 phthalates in non-alcoholic beverages by liquid–liquid extraction (LLE) combined with gas chromatography tandem mass spectrometry (GC-MS/MS). The best results were obtained when n-hexane was used as extraction solvent. A central composite design (CCD) was applied to select the most appreciated operating condition. The method performance was evaluated according to the SANTE/11945/2015 guidelines and was linear in the 0.1 to 200 µg/L range for 10 phthalate compounds, with r² > 0.996 and individual residuals <15%. Repeatability (RSD_r), within-laboratory reproducibility (RSD_wr), and the trueness range were from 2.7 to 9.1%, from 3.4 to 14.3% and from 91.5 to 118.1%, respectively. The limit of detection (LOD) was between 0.5 to 1.0 ng/L and the limit of quantitation (LOQ) was between 1.5 to 3.0 ng/L for all 10 compounds. The developed method was successfully applied to the analysis of non-alcoholic beverages.