Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

Cancer remains one of the leading causes of mortality worldwide, making early detection a critical factor in improving patient outcomes and survival rates. Developing advanced biosensors is essential for achieving early detection and accurate cancer diagnosis. This review offers a comprehensive overview of the development and application of carbon dots (CDs) and glassy carbon (GC) biosensors for early cancer detection. It covers the synthesis of CDs and GC, electrode fabrication methods, and electrochemical and optical transduction principles. This review explores various biosensors, including enzymatic and non-enzymatic, and discusses key biomarkers relevant to cancer detection. It also examines characterization techniques for electrochemical and optical biosensors, such as electrochemical impedance spectroscopy, cyclic voltammetry, UV–VIS, and confocal microscopy. The findings highlight the advancements in biosensor performance, emphasizing improvements in sensitivity, selectivity, and stability, as well as underscoring the potential of integrating different transduction methods and characterization approaches to enhance early cancer detection.

This article presents microneedles analyses where the design parameters studied included length and inner and outer diameter ranges. A mathematical model was also used to generalize outer and inner diameter ratios in the obtained ranges. Following this, the range of inner and outer diameters was completed by mechanical simulations, ranging from 30 μm to 134 μm as the inner diameter range and 208 μm to 250 μm as the outer diameter range. With these ranges, a mathematical model was made using fourth-order polynomial regressions with a correlation of 0.9993, ensuring a safety factor of four in which von Misses forces of the microneedle are around 17.931 MPa; the ANSYS software was used to analyze the mechanical behavior of the microneedles. In addition, the microneedle concept was made by 3D printing using a bio-compatible resin of class 1. The features presented by the microneedle designed in this study make it a promising option for implementation in a transdermal drug-delivery device.

Diabetes mellitus is an endocrine disorder that affects glucose metabolism, making the body unable to effectively use the insulin it produces. Transdermal drug delivery (TDD) has attracted strong interest from researchers, as it allows minimally invasive and painless insulin administration, showing advantages over conventional delivery methods. Systems composed of microneedles (MNs) assembled in a transdermal patch provide a unique route of administration, which is innovative with promising results. This paper presents the design of a transdermal patch composed of 25 microneedles manufactured with 3D printing by stereolithography with a class 1 biocompatible resin and a printing angle of 0°. Finite element analysis with ANSYS software is used to obtain the mechanical behavior of the microneedle (MN). The values obtained through the analysis were: a Von Misses stress of 18.057 MPa, a maximum deformation of $2.179\times {10}^{-3}$, and a safety factor of 4. Following this, through a flow simulation, we find that a pressure of 1.084 Pa and a fluid velocity of 4.800 $\frac{\mathrm{m}}{\mathrm{s}}$ were necessary to ensure a volumetric flow magnitude of $4.447\times {10}^{-5}\frac{{\mathrm{cm}}^3}{\mathrm{s}}$. Furthermore, the parameters found in this work are of great importance for the future implementation of a transdermal drug delivery device.

Explore the multifaceted role of anatomical models in medicine, their development processes, and the revolutionary impact of 3D printing technologies in this field. We delve into the diverse applications of anatomical models, outlining the stages involved in their development for different parts of the human body, and highlighting the transformative potential of 3D printing in creating these models. By examining the intersection of traditional anatomical modeling techniques with cutting-edge 3D printing technologies, we aim to provide a comprehensive overview of the current state and future prospects of anatomical modeling in biomedical engineering.