Magnetoactive liquid-solid phase transitional matter

Infrared spectroscopy has emerged as a promising technique for studying the composition of biological samples like lipids that play important roles in cellular functions and are involved in various diseases. For this reason, lipids are a target of interest in many biomedical studies. The objective of the present study is to utilize Fourier-Transform Infrared (FT-IR) spectroscopy to examine the main lipid components of human cells (phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, C18 ceramide, sphingosine-1-phosphate, ceramide-1-phosphate, sphingomyelin, cholesterol, and triolein). FT-IR analysis on the previously mentioned lipid samples was performed in Attenuated Total Reflection (ATR) mode. The obtained spectra clearly evidence the contributions of the different functional groups that are present in the examined samples. Detailed assignments of spectral features were carried out in agreement with the literature. Similarities and differences among the different types of commercial lipid samples are evidenced and discussed, with particular attention to phospholipid and sphingolipid components. A quantitative analysis of phosphatidylinositol and sphingomyelin spectra using a ratiometric approach is reported. Moreover, a reconstruction procedure of FT-IR spectra of complex lipids useful for chemometrics applications is described. These representative examples of the potential use of the results of the present study can certainly contribute to a larger use of FT-IR spectroscopy in lipidomics. 

Probing Biochemical Differences in Lipid Components of Human Cells by Means of ATR-FTIR Spectroscopy

Minimally invasive procedures assisted by soft robots for surgery, diagnostics, and drug delivery have unprecedented benefits over traditional solutions from both patient and surgeon perspectives. However, the translation of such technology into commercialization remains challenging. The lack of perception abilities is one of the obstructive factors paramount for a safe, accurate and efficient robot-assisted intervention. Integrating different types of miniature sensors onto robotic end-effectors is a promising trend to compensate for the perceptual deficiencies in soft robots. For example, haptic feedback with force sensors helps surgeons to control the interaction force at the tool-tissue interface, impedance sensing of tissue electrical properties can be used for tumor detection. The last decade has witnessed significant progress in the development of multimodal sensors built on the advancement in engineering, material science and scalable micromachining technologies. This review article provides a snapshot on common types of integrated sensors for soft medical robots. It covers various sensing mechanisms, examples for practical and clinical applications, standard manufacturing processes, as well as insights on emerging engineering routes for the fabrication of novel and high-performing sensing devices.

Integrated Sensors for Soft Medical Robotics.

Vascular interventional surgery (VIS) robots are capable of performing various operations such as drug delivery, coil filling, balloon stent expansion, percutaneous catheter ablation, and thrombectomy. Some reviews of VIS robots have focused on slave robots and master controllers. However, to the best of authors’ knowledge, there is no review article on the comprehensive description of all technologies of the VIS robots. Therefore, this article comprehensively analyzes the key technologies of the VIS robot. First, according to the type of VIS, the progress of the VIS robots and some typical applications are summarized. Second, the research status and existing problems of four key technologies related to the VIS robots are discussed: surgical instrument delivery system, force perception and haptic feedback technology, surgical navigation with multimodal image fusion, and virtual surgical systems. Finally, we analyze the obstacles to VIS robot development and outline the future directions of active research areas.

The critical technologies of vascular interventional robotic catheterization: A Review

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient–provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019–2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles’ search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019–2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

/pdf/a-review-of-machine-learning-methods-recently-applied-to-3drc2r0x.pdf

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

Interventional surgery has the advantages of small skin incision, little bleed loss, low postoperative infection and short recovery time, and thus has gradually become the preferred surgical approach over traditional open surgeries. Even though great achievements have been made towards clinical applications, limitations still exist, among which the loss of natural tactile perception of surgeons due to their indirect touch sense along the long catheter to the intervening human tissue is the crucial one. In recent years, researchers have dedicated great efforts in developing advanced medical catheters with smart tactile perception ability and made considerable progress. In this regard, we review the most recent development on the state-of-the-art miniature flexible and soft tactile sensors that are able to be integrated in the tip or on the side wall of medical catheters, with focus on the sensing mechanism, design requirement, device configuration and sensing performance of different types of sensors as well as their application demonstration in synthetic anatomical models and in-vivo animal experiment. After reviewing the representative research work, challenges that still exist are summarized and prospects toward future development are put forward.

A brief review on miniature flexible and soft tactile sensors for interventional catheter applications

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

https://www.mdpi.com/2218-273X/12/12/1815/pdf?version=1670325425

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

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Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

A brief review on miniature flexible and soft tactile sensors for interventional catheter applications

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers