Heat map visualization for electrocardiogram data analysis

In vivo and in vitro chemical composition and biological activity of traditional vs. dispensing granule decoctions of Coptidis Rhizoma: A comparative study

Coptidis Rhizoma (CR) holds significant clinical importance. In this study, we conducted a comparative analysis of CR's dispensing granule decoction (DGD) and traditional decoction (TD) to establish a comprehensive evaluation method for the quality of DGD. We selected nine batches of DGD (three from each of manufacturers A, B and C) and 10 batches of decoction pieces for analysis. We determined the content of representative components using high-performance liquid chromatography and assessed the content of blood components in vivo post-administration using ultra-performance liquid chromatography-mass spectrometry. The antibacterial activity was measured using the drug-sensitive tablet method. To evaluate the overall consistency of DGD and TD, we employed the CRITIC method and Grey relational analysis method. Our CRITIC results indicated no significant difference between the CRITIC scores of DGD-B and TD, with DGD-B exhibiting the highest consistency and overall quality. However, DGD-A and DGD-C showed variations in CRITIC scores compared with TD. After equivalent correction, the quality of DGD-A and DGD-C approached that of TD. Furthermore, our Grey relational analysis results supported the findings of the CRITIC method. This study offers a novel approach to evaluate the consistency between DGD and TD, providing insights into improving the quality of DGD.

Genome-Wide Identification, Characterization, and Expression Analysis of the NAC Gene Family in Litchi chinensis

NAC proteins play an essential role in the growth and development of litchi, especially during reproductive development. However, a comprehensive analysis of the litchi NAC gene family is currently absent. Based on information from the litchi genome, we found that the 112 NAC genes of litchi show an uneven distribution on the chromosomes. Phylogenetic and conserved structural domain analyses indicated that different types of variability were exhibited in the family of litchi NACs (LcNACs). Gene covariance analysis showed that the LcNACs showed better similarity in the same genus than with Arabidopsis. We further investigated the differential expression patterns of LcNACs in buds and rudimentary leaves of litchi. qRT-PCR results implied that they were involved in the process. Profiling of LcNAC promoter elements in litchi showed that they were extensively involved in light response, phytohormone regulation, abiotic stress response, and plant growth and development processes. This study provides new insights into the identification, structural characterization, tissue-specific expression analysis, and promoter response elements of LcNACs. It reveals the characteristics of the LcNACs and lays the foundation for the subsequent understanding of its biological functions and molecular regulatory mechanisms.

Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.

Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins

Protein hydrolysates are, in general, mixtures of amino acids and small peptides able to supply the body with the constituent elements of proteins in a directly assimilable form. They are therefore characterised as products with high nutritional value. However, hydrolysed proteins display an unpleasant bitter taste and possible off-flavours which limit the field of their nutrition applications. The successful identification and characterisation of bitter protein hydrolysates and, more precisely, the peptides responsible for this unpleasant taste are essential for nutritional research. Due to the large number of peptides generated during hydrolysis, there is an urgent need to develop methods in order to rapidly characterise the bitterness of protein hydrolysates. In this article, two enzymatic hydrolysis kinetics of micellar milk caseins were performed for 9 h. For both kinetics, the optimal time to obtain a hydrolysate with appreciable organoleptic qualities is 5 h. Then, the influence of the presence or absence of peptides and their intensity over time compared to the different sensory characteristics of hydrolysates was studied using heat maps, random forests and regression trees. A total of 22 peptides formed during the enzymatic proteolysis of micellar caseins and influencing the bitterness the most were identified. These methods represent simple and efficient tools to identify the peptides susceptibly responsible for bitterness intensity and predict the main sensory feature of micellar casein enzymatic hydrolysates.

Self-Adhesion Conductive Sub-micron Fiber Cardiac Patch from Shape Memory Polymers to Promote Electrical Signal Transduction Function

Myocardial infarction (MI) constitutes the first cause of morbidity and mortality in our life, so using highly conductive and elastic materials to produce an engineered cardiac patch is an effective way to improve the myocardium infarction area function. Here, shape memory polymers of the polyurethane/polyaniline/silicon oxide (PU/PANI/SiO₂) electrospinning sub-micron fiber patch were precisely produced in the case of the hydrogen bonding effect and interaction between the carboxyl groups to provide compatibility, phase mixing/miscibility, and stability. The sub-micron fiber patch prepared by our group has some remarkable characteristics, such as sub-micron fibers, 3D porous structure, special thickness to simulate the extracellular matrix (ECM), elastic deformation, good properties in conducting weak electrical signals, stability to maintain the whole structure, and self-adhesion. This sub-micron fiber material has been proven to be effective, easy, and reliable. Through precise design of the material system, structure regulation, and performance optimization, the aim is to produce a sub-micron fiber cardiac patch to simulate the myocardium ECM and improve conductive signal transduction for potential MI therapy.