Applications In Medical Research Research Articles

Individualized image region detection with total variation

AbstractMedical image data have emerged to be an indispensable component of modern medicine. Different from many general image problems that focus on outcome prediction or image recognition, medical image analysis pays more attention to model interpretation. For instance, given a list of medical images and corresponding labels of patients' health status, it is often of greater importance to identify the image regions that could differentiate the outcome status, compared to simply predicting labels of new images. Moreover, medical image data often demonstrate strong individual heterogeneity. In other words, the image regions associated with an outcome could be different across patients. As a consequence, the traditional one‐model‐fits‐all approach not only omits patient heterogeneity but also possibly leads to misleading or even wrong conclusions. In this article, we introduce a novel statistical framework to detect individualized regions that are associated with a binary outcome, that is, whether a patient has a certain disease or not. Moreover, we propose a total variation‐based penalization for individualized image region detection under a local label‐free scenario. Considering that local labeling is often difficult to obtain for medical image data, our approach may potentially have a wider range of applications in medical research. The effectiveness of our proposed approach is validated by two real histopathology databases: Colon Cancer and Camelyon16.

Unraveling the Significance of Nanog in the Generation of Embryonic Stem-like Cells from Spermatogonia Stem Cells: A Combined In Silico Analysis and In Vitro Experimental Approach.

Embryonic stem-like cells (ES-like cells) are promising for medical research and clinical applications. Traditional methods involve "Yamanaka" transcription (OSKM) to derive these cells from somatic cells in vitro. Recently, a novel approach has emerged, obtaining ES-like cells from spermatogonia stem cells (SSCs) in a time-related process without adding artificial additives to cell cultures, like transcription factors or small molecules such as pten or p53 inhibitors. This study aims to investigate the role of the Nanog in the conversion of SSCs to pluripotent stem cells through both in silico analysis and in vitro experiments. We used bioinformatic methods and microarray data to find significant genes connected to this derivation path, to construct PPI networks, using enrichment analysis, and to construct miRNA-lncRNA networks, as well as in vitro experiments, immunostaining, and Fluidigm qPCR analysis to connect the dots of Nanog significance. We concluded that Nanog is one of the most crucial differentially expressed genes during SSC conversion, collaborating with critical regulators such as Sox2, Dazl, Pou5f1, Dnmt3, and Cdh1. This intricate protein network positions Nanog as a pivotal factor in pathway enrichment for generating ES-like cells, including Wnt signaling, focal adhesion, and PI3K-Akt-mTOR signaling. Nanog expression is presumed to play a vital role in deriving ES-like cells from SSCs in vitro. Finding its pivotal role in this path illuminates future research and clinical applications.

Application of Artificial Intelligence in Tissue Engineering.

Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.

Production and characterization of a blood analogue based on alginate microparticles

This work addresses the need for a universal blood substitute by focusing on developing alginate-based microparticles engineered to replicate the mechanical properties of red blood cells (RBCs). Alginate, known for its biocompatibility and tunable mechanical properties, is used to create microparticles that closely resemble RBCs in size and shape. These alginate microparticles are produced through premix membrane emulsification (PME), ensuring a uniform size distribution. The viscosity ratio and the size of the filter pores influence the final size of the microparticles. Filters with pores of 20 µm were used to produce 5 wt% alginate microparticles with a diameter of 7.81 µm ± 0.77, and filters with pores of 30 µm were used to produce 0.5 wt% alginate microparticles with a diameter of 8.69 µm ± 3.30. The Deformability Index (DI) of the alginate microparticles follows the correlation that microparticles with higher elastic modulus exhibit lower DI values. The DI found for 0.5 wt% alginate microparticles solution is higher than the DI found for 5 wt% alginate microparticles. The DI of RBCs is higher than the ones found for the alginate microparticles, however the value is in the range of the experimental error. Rheological studies reveal that suspensions of 25–30 wt% alginate microparticles exhibit a shear-thinning behavior similar to human blood. The DI of the alginate microparticles shows little influence on the rheology of the suspensions. Furthermore, the Cell-Free Layer (CFL) of alginate microparticles is close to that of RBCs, reinforcing the potential of alginate as a suitable material for blood substitute development. The alginate microparticles produced in this work show a great potential to contribute to creating a blood-mimicking fluid for applications in medical treatments and research.

Green Synthesis of Selenium Nanoparticles: Characterization and Therapeutic Applications in Microbial and Cancer Treatments

Selenium is one of these micronutrients that are essential for animals, plants and microorganisms to remain functional. This review is about the green synthesis of selenium nanoparticles and its application in microbial and cancer therapies. Our hypothesis was that Se NPs produced using plant extracts might offer the biocompatibility and environmental friendliness advantages, and hence be a new prospect for medical applications. To test our hypothesis, we conducted a comprehensive analysis of recent literature, exploring various green synthesis conditions and processes for Se NPs. Various characterisation techniques such as spectroscopy, microscopy and physicochemistry were discussed in order to provide insight into the formation and function of green-synthesised Se NPs. Our findings show that Se NPs produced by green chemistry methods have good properties such as uniform size, shape and stability as detailed examples from recent studies reveal. Furthermore, we discussed the therapeutic and theranostic applications of Se NPs produced in this manner: their potential in antimicrobial and anticancer treatments. Through illustrations of cases where Se NPs inhibit microbial growth and cause apoptosis in cancer cells, the practical significance of our findings was underscored. In summary, our review affirms that using green-mediated synthesis Se NPs improves their biocompatibility and therapeutic efficacy, thus opening up new realms for their application in medical research.

Flexible 3D-Printable Materials for Application in Medical Research: A Comprehensive Comparison of Commercially Available Materials

Flexible 3D-Printable Materials for Application in Medical Research: A Comprehensive Comparison of Commercially Available Materials

A Neural Modelling Tool for Non-Linear Influence Analyses and Perspectives of Applications in Medical Research

Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of certain variables (assumed as causal) in determining the behaviour of a target variable; this also allows us to make predictions for future scenarios of these causal variables. We present a neural tool endowed with an ensemble strategy and its applications to influence analyses in terms of pruning, attribution and future predictions (free code issued). We describe some case studies on climatic applications which show reliable results and the potentialities of our method for medical studies. The discovery of the importance and role (linear, non-linear or threshold) of causal variables and the possibility of applying the relationships found to future scenarios could lead to very interesting applications in medical research and the study and treatment of cancer, which are proposed in this paper.

Vortex sorting of rare particles/cells in microcavities: A review.

Microfluidics or lab-on-a-chip technology has shown great potential for the separation of target particles/cells from heterogeneous solutions. Among current separation methods, vortex sorting of particles/cells in microcavities is a highly effective method for trapping and isolating rare target cells, such as circulating tumor cells, from flowing samples. By utilizing fluid forces and inertial particle effects, this passive method offers advantages such as label-free operation, high throughput, and high concentration. This paper reviews the fundamental research on the mechanisms of focusing, trapping, and holding of particles in this method, designs of novel microcavities, as well as its applications. We also summarize the challenges and prospects of this technique with the hope to promote its applications in medical and biological research.

Large language models streamline automated machine learning for clinical studies

A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study’s training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice.

Emerging Landscape of Supercharged Proteins and Peptides for Drug Delivery.

Although groundbreaking biotechnological techniques such as gene editing have significantly progressed, the effective and targeted transport of therapeutic agents into host cells remains a major obstacle to the development of biotherapeutics. Confronting the unique challenge posed by large macromolecules such as proteins, peptides, and nucleic acids adds complexity to this issue. Recent findings reveal that the supercharging of proteins and peptides not only enables control over critical properties, such as temperature resistance and catalytic activity, but also holds promise as a viable strategy for their use in drug delivery. This review provides a concise summary of the attributes of supercharged proteins and peptides, encompassing both their natural occurrence and engineered variants. Furthermore, it sheds light on the present status and future possibilities of supercharged proteins and peptides as carriers for significant biomolecules in the realms of medical research and therapeutic applications.

Selenium Nanoparticles: A Comprehensive Examination of Synthesis Techniques and Their Diverse Applications in Medical Research and Toxicology Studies.

Selenium is a trace and necessary micronutrient for human, animal, and microbial health. Many researchers have recently been interested in selenium nanoparticles (SeNPs) due to their biocompatibility, bioavailability, and low toxicity. As a result of their greater bioactivity, selenium nanoparticles are widely employed in a variety of biological applications. Physical, chemical, and biological approaches can all be used to synthesize selenium nanoparticles. Since it uses non-toxic solvents and operates at a suitable temperature, the biological technique is a preferable option. This review article addresses the processes implemented in the synthesis of SeNPs and highlights their medicinal uses, such as the treatment of fungi, bacteria, cancer, and wounds. Furthermore, we discuss the most recent findings on the potential of several biological materials for selenium nanoparticle production. The precursor, extract, process, time, temperature, and other synthesis criteria will be elaborated in conjunction with the product's physical properties (size, shape, and stability). The synergies of SeNP synthesis via various methods aid future researchers in precisely synthesizing SeNPs and using them in desired applications.

Likelihood ratio combination of multiple biomarkers viasmoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Time-resolved x-ray spectroscopy of nucleobases and their thionated analogs.

The photoinduced relaxation dynamics of nucleobases and their thionated analogs have been investigated extensively over the past decades motivated by their crucial role in organisms and their application in medical and biochemical research and treatment. Most of these studies focused on the spectroscopy of valence electrons and fragmentation. The advent of ultrashort x-ray laser sources such as free-electron lasers, however, opens new opportunities for studying the ultrafast molecular relaxation dynamics utilizing the site- and element-selectivity of x-rays. In this review, we want to summarize ultrafast experiments on thymine and 2-thiouracil performed at free-electron lasers. We performed time-resolved x-ray absorption spectroscopy at the oxygen K-edge after UV excitation of thymine. In addition, we investigated the excited state dynamics of 2-tUra via x-ray photoelectron spectroscopy at sulfur. For these methods, we show a strong sensitivity to the electronic state or charge distribution, respectively. We also performed time-resolved Auger-Meitner spectroscopy, which shows spectral shifts associated with internuclear distances close to the probed site. We discuss the complementary aspects of time-resolved x-ray spectroscopy techniques compared to optical and UV spectroscopy for the investigation of ultrafast relaxation processes.

Triboelectric Insoles with Normal‐Shear Plantar Stress Perception

AbstractPlantar stress perception is highly desired in sports science and medical research, with the constraint of external power supply to impede the portability and flexibility in daily life. Triboelectric nanogenerator (TENG) opens a new avenue for self‐power sensing; however, a comprehensive sensing of normal and shear plantar stress remains elusive, let alone the further exploration from a mapping perspective. Here, a TENG‐based self‐powered sensing insole is developed for a comprehensive normal‐shear plantar stress mapping among four critical positions. At each position, four TENG units with single‐electrode‐mode triboelectrification are incorporated to decouple the normal and shear stress based on asymmetrical outputs. The insole exhibits excellent static characteristics for plantar stress measurement. Also, it can dynamically capture plantar stress variations to distinguish between different states during a standing long jump, enabling sports performance evaluation and rectification. Moreover, with the assistance of artificial intelligence in signal processing, different abnormal gaits can be precisely identified by the insole, highlighting the potential for disease diagnosis and rehabilitation. To the best of the knowledge, this work is the first demonstration of a self‐powered TENG insole with a comprehensive perception of normal and shear plantar stress perception, holding promise for applications in sports science and medical research.

A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers.

Image-based 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. Conventional intensity-based 2D/3D registration approa- ches suffer from a limited capture range due to the presence of local minima in hand-crafted image similarity functions. In this work, we aim to extend the 2D/3D registration capture range with a fully differentiable deep network framework that learns to approximate a convex-shape similarity function. The network uses a novel Projective Spatial Transformer (ProST) module that has unique differentiability with respect to 3D pose parameters, and is trained using an innovative double backward gradient-driven loss function. We compare the most popular learning-based pose regression methods in the literature and use the well-established CMAES intensity-based registration as a benchmark. We report registration pose error, target registration error (TRE) and success rate (SR) with a threshold of 10mm for mean TRE. For the pelvis anatomy, the median TRE of ProST followed by CMAES is 4.4mm with a SR of 65.6% in simulation, and 2.2mm with a SR of 73.2% in real data. The CMAES SRs without using ProST registration are 28.5% and 36.0% in simulation and real data, respectively. Our results suggest that the proposed ProST network learns a practical similarity function, which vastly extends the capture range of conventional intensity-based 2D/3D registration. We believe that the unique differentiable property of ProST has the potential to benefit related 3D medical imaging research applications. The source code is available at https://github.com/gaocong13/Projective-Spatial-Transformers.

Small Sized Yet Powerful: Nuclear Distribution C Proteins in Plants.

The family of Nuclear Distribution C (NudC) proteins plays a pivotal and evolutionarily conserved role in all eukaryotes. In animal systems, these proteins influence vital cellular processes like cell division, protein folding, nuclear migration and positioning, intracellular transport, and stress response. This review synthesizes past and current research on NudC family members, focusing on their growing importance in plants and intricate contributions to plant growth, development, and stress tolerance. Leveraging information from available genomic databases, we conducted a thorough characterization of NudC family members, utilizing phylogenetic analysis and assessing gene structure, motif organization, and conserved protein domains. Our spotlight on two Arabidopsis NudC genes, BOB1 and NMig1, underscores their indispensable roles in embryogenesis and postembryonic development, stress responses, and tolerance mechanisms. Emphasizing the chaperone activity of plant NudC family members, crucial for mitigating stress effects and enhancing plant resilience, we highlight their potential as valuable targets for enhancing crop performance. Moreover, the structural and functional conservation of NudC proteins across species suggests their potential applications in medical research, particularly in functions related to cell division, microtubule regulation, and associated pathways. Finally, we outline future research avenues centering on the exploration of under investigated functions of NudC proteins in plants.

Principle of Free-electron Laser and Applications in Medical Research and Lithography

In recent years, as a breakthrough light source technology, free-electron laser (FEL) has the unique advantage of producing coherent radiation with high brightness, high energy, good directivity and excellent monochromaticity. With this in mind, this study will systematical discuss the historical development of FEL technology, from Einstein's laser theory to Dr. Maiman's first commercial laser, as well as the birth and evolution of FEL. To be specific, the basic principles and construction of FEL technology will be discussed in detail, including key components such as electron sources, electron accelerators, and oscillators. The application of free electron laser (FEL) technology in medicine and lithography will also be demonstrated in this paper. According to the analysis, the current limitations of the state-of-art facilities as well as the future development trends and improvements for FEL system will be clarified based on the evaluations. Overall, these results shed light on guiding further exploration of FEL techniques.

REAP+: A single preparation for rapid isolation of nuclei, cytoplasm, and mitochondria

REAP+ is an enhanced version of the rapid, efficient, and practical (REAP) method designed for the isolation of nuclear fractions. This improved version, REAP+, enables fast and effective extraction of mitochondria, cytoplasm, and nuclei. The mechanical cell disruption process has been optimized to cerebral tissues, snap-frozen liver, and HT22 cells with remarkable fraction enrichment. REAP+ is well-suited for samples containing minimal protein quantities, such as mouse hippocampal slices. The method was validated by Western blot and marker enzyme activities, such as LDH and G6PDH for the cytoplasmic fraction and succinate dehydrogenase and cytochrome c oxidase for the mitochondrial fraction. One of the outstanding features of this method is its rapid execution, yielding fractions within 15 min, allowing for simultaneous preparation of multiple samples. In essence, REAP+ emerges as a swift, efficient, and practical technique for the concurrent isolation of nuclei, cytoplasm, and mitochondria from various cell types and tissues. The method would be suitable to study the multicompartment translocation of proteins, such as metabolic enzymes and transcription factors migrating from cytosol to the mitochondria and nuclei. Moreover, its compatibility with small samples, such as hippocampal slices, and its potential applicability to human biopsies, highlights the potential application in medical research.

A highly sensitive optical fiber sensor enables rapid triglycerides-specific detection and measurement at different temperatures using convolutional neural networks

For specific recognition and sensitive detection of triglycerides (TGs), an optical fiber sensor (OFS) based on an enhanced core diameter mismatch was proposed. The sensitivity of the sensor is significantly increased due to the repetitive excitation of the higher-order cladding modes. A technique for immobilizing lipase using covalent binding technology was presented and demonstrated by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy. The interference dip of the sensor was shifted due to TGs being hydrolyzed in the presence of lipase. The sensor shows an optimal response within 3 min and exhibits a high sensitivity of 0.9933 nm/(mg/ml) and a limit of detection of 0.0822 mg/ml in the concentration range 0–8 mg/ml at a temperature of 37 °C and a pH of 7.4. The response of the sensor to TGs concentration at different temperatures and pH was investigated. The reproducibility, reusability, and stability of the proposed sensor were tested and verified experimentally. The biosensor is highly specific for TGs and unaffected by many other interfering substances. Further, the measurement of TGs concentration at different temperatures was realized. This method provides a new way to detect TGs rapidly and reliably and has potential applications in medical research and clinical diagnosis.

Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data

Detection of low-frequency variants with high accuracy plays an important role in biomedical research and clinical practice. However, it is challenging to do so with next-generation sequencing (NGS) approaches due to the high error rates of NGS. To accurately distinguish low-level true variants from these errors, many statistical variants calling tools for calling low-frequency variants have been proposed, but a systematic performance comparison of these tools has not yet been performed. Here, we evaluated four raw-reads-based variant callers (SiNVICT, outLyzer, Pisces, and LoFreq) and four UMI-based variant callers (DeepSNVMiner, MAGERI, smCounter2, and UMI-VarCal) considering their capability to call single nucleotide variants (SNVs) with allelic frequency as low as 0.025% in deep sequencing data. We analyzed a total of 54 simulated data with various sequencing depths and variant allele frequencies (VAFs), two reference data, and Horizon Tru-Q sample data. The results showed that the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers regarding detection limit. Sequencing depth had almost no effect on the UMI-based callers but significantly influenced on the raw-reads-based callers. Regardless of the sequencing depth, MAGERI showed the fastest analysis, while smCounter2 consistently took the longest to finish the variant calling process. Overall, DeepSNVMiner and UMI-VarCal performed the best with considerably good sensitivity and precision of 88%, 100%, and 84%, 100%, respectively. In conclusion, the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers in terms of sensitivity and precision. We recommend using DeepSNVMiner and UMI-VarCal for low-frequency variant detection. The results provide important information regarding future directions for reliable low-frequency variant detection and algorithm development, which is critical in genetics-based medical research and clinical applications.