Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection

Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of an electronic stethoscope, (2) a decision-making subsystem, and (3) a subsystem for displaying and visualizing the results in an understandable form. The electronic stethoscope captures the patient’s phonocardiographic sounds, filters and digitizes them, and then sends the resulting phonocardiographic sounds to the decision-making system. The decision-making system classifies sounds into normal and abnormal using machine learning techniques, and as a result identifies abnormal heart sounds. The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff, but also for patients and recommends further actions to patients. As a result of the study, we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%. More accurately, the proposed stethoscope can identify normal heart sounds with 93.5% accuracy, abnormal heart sounds with 93.25% accuracy. Moreover, speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.     

Comparison and Validation of Different Magnetic Force Microscopy Calibration Schemes

The future of consumer electronics depends on the capability to reliably fabricate nanostructures with given physical properties. Therefore, techniques to characterize materials and devices with nanoscale resolution are crucial. Among these is magnetic force microscopy (MFM), which transduces the magnetic force between the sample and a magnetic oscillating probe into a phase shift, enabling the locally resolved study of magnetic field patterns down to 10 nm. Here, the progress done toward making quantitative MFM a common tool in nanocharacterization laboratories is shown. The reliability and ease of use of the calibration method based on a magnetic reference sample, with a calculable stray field, and a deconvolution algorithm is demonstrated. This is achieved by comparing two calibration approaches combined with numerical modeling as a quantitative link: measuring the probe's effect on the voltage signal when scanning above a nanosized graphene Hall sensor, and recording the MFM phase shift signal when the probe scans across magnetic fields produced by metallic microcoils. Furthermore, in the case of the deconvolution algorithm, it is shown how it can be applied using the open‐source software package Gwyddion. The estimated magnetic dipole approximation for the most common probes currently in the market is also reported.     

Lowbush blueberries inhibit scavenger receptors CD36 and SR-A expression and attenuate foam cell formation in ApoE-deficient mice

Blueberries have recently been reported to reduce atherosclerotic lesion progression in apoE deficient (apoE(-/-)) mice. However, the underlying mechanisms are not fully understood. The objective of this study was to determine whether lowbush blueberries altered scavenger receptor expression and foam cell formation in apoE(-/-) mice. ApoE(-/-) mice were fed AIN-93 diet (CD) or CD formulated to contain 1% freeze-dried lowbush blueberries (BB) for 20 weeks. Gene expression and protein levels of scavenger receptor CD36 and SR-A in aorta and thioglycollate-elicited peritoneal macrophages (PM) were lower in mice fed BB (P < 0.05). In the second experiment, apoE(-/-) mice were fed CD or BB for 5 weeks. PM were collected and cultured. Gene expression and protein levels of CD36 and SR-A were found to be lower in PM of BB fed mice (P < 0.05). In PM from BB fed mice, fewer oxLDL-induced foam cells were formed compared to those from mice fed CD. Gene expression and protein levels of PPARγ were lower in the PM of BB fed mice (P < 0.05). Detectable isomers of hydroxyoctadecadienoic acids (HODEs) and hydroxyeicosatetraenoic acid (HETEs) were also lower in the PM of BB fed mice (P < 0.05 or P < 0.01). In conclusion, BB inhibited expression of the two major scavenger receptors CD36 and SR-A in PM of apoE(-/-) mice, at least in part through down-regulating PPARγ and reducing its endogenous ligands HODEs and HETEs. We proposed that BB mediated reduction of scavenger receptor expression and attenuation of oxLDL-induced foam cell formation in PM of apoE(-/-) mice are important mechanisms of the athero-protective effects of BB.     

Trends in Chemometrics: Food Authentication,Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value‐added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real‐life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high‐dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.     

3-Pyridinylidene Derivatives of Chemically Modified Lupane and Ursane Triterpenes as Promising Anticancer Agents by Targeting Apoptosis

Cancer persists as a global challenge due to the extent to which conventional anticancer therapies pose high risks counterbalanced with their therapeutic benefit. Naturally occurring substances stand as an important safer alternative source for anticancer drug development. In the current study, a series of modified lupane and ursane derivatives was subjected to in vitro screening on the NCI-60 cancer cell line panel. Compounds 6 and 7 have been identified as highly active with GI₅₀ values ranging from 0.03 µM to 5.9 µM (compound 6) and 0.18–1.53 µM (compound 7). Thus, these two compounds were further assessed in detail in order to identify a possible antiproliferative mechanism of action. DAPI (4′,6-diamidino-2-phenylindole) staining revealed that both compounds induced nuclei condensation and overall cell morphological changes consistent with apoptotic cell death. rtPCR analysis showed that both compounds induced upregulation of proapoptotic Bak and Bad genes while downregulating Bcl-XL and Bcl-2 antiapoptotic genes. Molecular docking analysis revealed that both compounds exhibited high scores for Bcl-XL inhibition, while compound 7 showed higher in silico Bcl-XL inhibition potential as compared to the native inhibitor ATB-737, suggesting that compounds may induce apoptotic cell death through targeted antiapoptotic protein inhibition, as well.     

Investigating the Role of Sulfate Groups for the Binding of Gd3+ Ions to Glycosaminoglycans with NMR Relaxometry

Glycosaminoglycans (GAGs) are highly negatively charged macromolecules with a large cation binding capacity, but their interaction potential with exogeneous Gd³⁺ ions is under-investigated. These might be released from chelates used as Gadolinium-based contrast agents (GBCAs) for clinical MR imaging due to transmetallation with endogenous cations like Zn²⁺. Recent studies have quantified how an endogenous GAG sequesters released Gd³⁺ ions and impacts the thermodynamic and kinetic stability of some GBCAs. In this study, we investigate and compare the chelation ability of two important GAGs (heparin and chondroitin sulfate), as well as the homopolysaccharides dextran and dextran sulfate that are used as models for alternative macromolecular chelators. Our combined approach of MRI-based relaxometry and isothermal titration calorimetry shows that the chelation process of Gd³⁺ into GAGs is not just a long-range electrostatic interaction as proposed for the Manning model, but presumably a site-specific binding. Furthermore, our results highlight the crucial role of sulfate groups in this process and indicate that the potential of a specific GAG to engage in this mechanism increases with its degree of sulfation. The transchelation of Gd³⁺ ions from GBCAs to sulfated GAGs should thus be considered as one possible explanation for the observed long-term deposition of Gd³⁺ in vivo and related observations of long-term signal enhancements on T₁-weighted MR images.