Urine strips are widely applied for rapid analysis of various indexes of urine for clinical examinations. The tests mainly rely on the application of a urine analyzer, which suffers several drawbacks and cannot meet the requirements of point-of-care testing (POCT). The integration of a smartphone with a biosensor has recently attracted great attention. We herein propose a human vision-based smartphone algorithm for colorimetric analysis of various urine indexes. A CIEDE2000 formula in CIELab color space is applied for the evaluation of color difference, which may greatly improve the analytical performances of urine strips. The proposed algorithm also possesses merits such as good accuracy, quantitative analysis, and limited calculation task, which is suitable for the application with smartphone platform. Experimental results demonstrate that the proposed method shows excellent reliability compared with the urine analyzer and some other algorithms. In addition, human real samples are successfully analyzed with excellent accuracy. Therefore, this work provides a convenient colorimetric tool for POCT urine analysis.

Plant essential oils contain many secondary metabolites, some of which can effectively inhibit the growth of pathogenic microorganisms, so it is a very promising antibacterial agent. In this study, a qualitative and quantitative method based on high performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) was developed for the simultaneous determination of three bioactive substances, cinnamaldehyde (CNM), thymol (THY), and eugenol (EUG), in the essential oils of plants. Necessary tests for linearity, limit of quantification, recovery, carryover contamination and precision of the method were carried out. Then, the antibacterial activity of 3 bioactive compounds against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was evaluated by minimal inhibitory concentration and the synergistic antimicrobial effect. The results indicated that CNM, THY and EUG had good antibacterial activity. According to the results of fractional inhibitory concentration index (FICI), it is considered that CNM + THY and CNM + THY + EUG has obvious synergistic inhibitory effect on E. coli, and CNM + THY and CNM + EUG has obvious synergistic inhibitory effect on S. aureus. Finally, we analyzed the effect of the bioactive compounds on trace elements in bacteria and found significant changes in magnesium, calcium, copper and iron.

Background: Inhibition of amyloid β protein fragment (Aβ) aggregation is considered to be one of the most effective strategies for the treatment of Alzheimer’s disease. (-)-Epigallocatechin-3-gallate (EGCG) has been found to be effective in this regard; however, owing to its low bioavailability, nanodelivery is recommended for practical applications. Compared to chemical reduction methods, biosynthesis avoids possible biotoxicity and cumbersome preparation processes. Materials and Methods: The interaction between EGCG and Aβ 42 was simulated by molecular docking, and green tea-conjugated gold nanoparticles (GT-Au NPs) and EGCG-Au NPs were synthesized using EGCG-enriched green tea and EGCG solutions, respectively. Surface active molecules of the particles were identified and analyzed using various liquid chromatography-tandem triple quadrupole mass spectrometry methods. ThT fluorescence assay, circular dichroism, and TEM were used to investigate the effect of synthesized particles on the inhibition of Aβ 42 aggregation. Results: EGCG as well as apigenin, quercetin, baicalin, and glutathione were identified as capping ligands stabilized on the surface of GT-Au NPs. They more or less inhibited Aβ 42 aggregation or promoted fibril disaggregation, with EGCG being the most effective, which bound to Aβ 42 through hydrogen bonding, hydrophobic interactions, etc. resulting in 39.86% and 88.50% inhibition of aggregation and disaggregation effects, respectively. EGCG-Au NPs were not as effective as free EGCG, whereas multiple thiols and polyphenols in green tea accelerated and optimized heavy metal detoxification. The synthesized GT-Au NPs conferred the efficacy of diverse ligands to the particles, with inhibition of aggregation and disaggregation effects of 54.69% and 88.75%, respectively, while increasing the yield, enhancing water solubility, and decreasing cost. Conclusion: Biosynthesis of nanoparticles using green tea is a promising simple and economical drug-carrying approach to confer multiple pharmacophore molecules to Au NPs. This could be used to design new drug candidates to treat Alzheimer’s disease.

Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of multiple myocardial infarctions using a bidirectional long short-term memory (BiLSTM) neural network classification was proposed. This algorithm was based on morphological feature extraction, which can greatly improve the diagnostic efficiency of doctors for different kinds of myocardial infarction diseases. The algorithm includes noise reduction and beat segmentation of electrocardiogram (ECG) signals from the Physikalisch-Technische Bundesanstalt (PTB) database. According to the medical diagnosis guide, the distance feature of the whole waveform and the amplitude feature of the branch lead waveform are extracted. According to the extracted features, the long short-term memory network (LSTM) and the BiLSTM neural networks are built to classify and recognize heartbeats. The experimental results show that the accuracy of the morphological feature + BiLSTM algorithm in MI detection is 99.4%. At the same time, among the six common myocardial infarction diseases, the location and recognition rate of the culprit vessel is high. The sensitivity, specificity, PPV, NPV, and F1 score parameters all reach more than 98.4%, and the kappa coefficient also reaches 0.983, while the overall accuracy reaches 98.6%. The accuracy of this algorithm is improved by at least 1% compared with that of other existing algorithms. Thus, this study exhibits a very important clinical application value.