Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.

Essential oils (EOs) are known as any aromatic oily organic substances which are naturally synthesized in plants. Exhibiting a broad range of biological activities, EOs have played a key role in numerous industries for ages, including pharmaceutical, textile, and food. However, the volatility and high sensitivity to environmental influences pose challenges to the application of EOs on industrial scale. Microencapsulation via the spray-drying method is one of the promising techniques to overcome these challenges, thanks to the presence of wall materials that properly protect the core EOs from oxidation and evaporation. By optimization of key factors related to the infeed emulsion properties and spray-drying process, the encapsulation efficiency and retention of encapsulated EOs could be significantly improved, thus allowing a wide range of EO applications. This review attempts to discuss on different determining factors of the spray-drying process to develop an effective encapsulation formula for EOs. Furthermore, recent applications of encapsulated EOs in the fields of foods, pharmaceuticals, and textile industries are also thoroughly addressed.

Mangiferin (C₁₉H₁₈O₁₁) is a C-glucoside xanthone that is mainly found in the leaves of mango (Mangifera indica L). The compound has been recognized for several pharmacological values, including antioxidant and antibacterial activities. Therefore, the present study aims to utilize the old leaves of mango as a potential source for mangiferin extraction and purification, and evaluate its antimicrobial and antioxidant properties. In the present study, mangiferin was extracted from a local variety of mango leaves using ultrasonic-assisted extraction methods and ethanol as the extraction solvent. The crude ethanolic extract of mangiferin was then purified by the liquid–liquid fractionation method with dichloromethane and ethyl acetate, then further separated by HPLC. The crude extract, ethyl acetate fractions and purified mangiferin were investigated for anti-microorganism activity against Escherichia coli, Salmonella spp. and Aspergillus flavus by using the paper disc diffusion assay. The results have shown that the extraction efficiency was 14.17%. The obtained mangiferin was 1.97 g of content and 94.2% of purity. The oxidation resistance of purified mangiferin was 1.77 times higher than the crude extract and 1.05 times higher than the standard mangiferin (IC₅₀ = 13.841 μg/mL). However, the purified mangiferin has shown no inhibitory action against the experimental strains of microorganisms. The findings from the present study suggest an effective scheme of extraction and purification to obtain mangiferin from the local variety of mango with high purity and antioxidant potential.

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.

Background: The establishment of a biofilm by most pathogenic bacteria has been known as one of the resistance mechanisms against antibiotics. A biofilm is a structural component where the bacterial community adheres to the biotic or abiotic surfaces by the help of Extracellular Polymeric Substances (EPS) produced by bacterial cells. The biofilm matrix possesses the ability to resist several adverse environmental factors, including the effect of antibiotics. Therefore, the resistance of bacterial biofilm-forming cells could be increased up to 1000 times than the planktonic cells, hence requiring a significantly high concentration of antibiotics for treatment.Methods: Up to the present, several methodologies employing antibiotics as an anti-biofilm, antivirulence or quorum quenching agent have been developed for biofilm inhibition and eradication of a pre-formed mature biofilm.Results: Among the anti-biofilm strategies being tested, the sub-minimal inhibitory concentration of several antibiotics either alone or in combination has been shown to inhibit biofilm formation and down-regulate the production of virulence factors. The combinatorial strategies include (1) combination of multiple antibiotics, (2) combination of antibiotics with non-antibiotic agents and (3) loading of antibiotics onto a carrier.Conclusion: The present review paper describes the role of several antibiotics as biofilm inhibitors and also the alternative strategies adopted for applications in eradicating and inhibiting the formation of biofilm by pathogenic bacteria.

Soil heavy metal contamination is a severe issue. The detrimental impact of contaminated heavy metals on the ecosystem depends on the chemical form of heavy metals. Biochar produced at 400 °C (CB400) and 600 °C (CB600) from corn cob was applied to remediate Pb and Zn in contaminated soil. After a one month amendment with biochar (CB400 and CB600) and apatite (AP) with the ratio of 3%, 5%, 10%, and 3:3% and 5:5% of the weight of biochar and apatite, the untreated and treated soil were extracted using Tessier’s sequence extraction procedure. The five chemical fractions of the Tessier procedure were the exchangeable fraction (F1), carbonate fraction (F2), Fe/Mn oxide fraction (F3), organic matter (F4), and residual fraction (F5). The concentration of heavy metals in the five chemical fractions was analyzed using inductively coupled plasma mass spectroscopy (ICP-MS). The results showed that the total concentration of Pb and Zn in the soil was 3023.70 ± 98.60 mg kg⁻¹ and 2034.33 ± 35.41 mg kg⁻¹, respectively. These figures were 15.12 and 6.78 times higher than the limit standard set by the United States Environmental Protection Agency (U.S. EPA 2010), indicating the high level of contamination of Pb and Zn in the studied soil. The treated soil’s pH, OC, and EC increased significantly compared to the untreated soil (p > 0.05). The chemical fraction of Pb and Zn was in the descending sequence of F2 (67%) > F5 (13%) > F1 (10%) > F3 (9%) > F4 (1%) and F2~F3 (28%) > F5 (27%) > F1 (16%) > F4 (0.4%), respectively. The amendment of BC400, BC600, and apatite significantly reduced the exchangeable fraction of Pb and Zn and increased the other stable fractions including F3, F4, and F5, especially at the rate of 10% of biochar and a combination of 5:5% of biochar and apatite. The effects of CB400 and CB600 on the reduction in the exchangeable fraction of Pb and Zn were almost the same (p > 0.05). The results showed that CB400, CB600, and the mixture of these biochars with apatite applied at 5% or 10% (w/w) could immobilize lead and zinc in soil and reduce the threat to the surrounding environment. Therefore, biochar derived from corn cob and apatite could be promising materials for immobilizing heavy metals in multiple-contaminated soil.