Advances in biomarker sciences and technology最新文献

Alzheimer's Disease (AD) is a disorder that worsens over time causing loss of memory and decline of cognitive functions. Current methods for diagnosis consist of neuroimaging scans, magnetic resonance imaging (MRI scans), positron emission tomography (PET scans), and identifying biomarkers in cerebrospinal fluid (CSF). New forms of advanced technology such as machine learning are rising to quickly diagnose AD. This work is a comprehensive review of the research that uses machine learning methods to classify AD cases early. It is a study to provide details for MRI scans and biomarkers used for the recognition of AD and evaluates the execution of both applications while using different classifiers. This paper will discuss and compare various machine learning methods that can be implemented for the classification of Alzheimer's disease. The applications of these algorithms (MRI and biomarkers) are also discussed ultimately proposing the best algorithm and application for classification.

A stroke is a medical emergency characterized by the sudden interruption of blood flow to the brain, leading to cell death from oxygen deprivation. As a leading cause of mortality and long-term disability worldwide, strokes present a growing socioeconomic challenge, exacerbated by demographic shifts and an increasing incidence in younger populations. Analyzing post-stroke conditions is essential for predicting recovery trajectories and guiding personalized treatments. The process is crucial for mitigating long-term cognitive deficits and improving the quality of life for stroke survivors and their families. Epigenetic mechanisms are emerging as key influencers, with four significant modulators—miRNAs, lncRNAs, DNA methylation, and histone modifications—being identified as central to modulating neuronal repair and regeneration. However, this is a lack of knowledge as to how major epigenetic signatures affect post-stroke recovery. The paper systematically reviews the capacity of miRNAs to fine-tune neuroprotective responses and the broader regulatory functions of lncRNAs. Additionally, we examine how shifts in DNA methylation patterns and histone modifications correlate with stroke outcomes, presenting a case for precise epigenetic therapies. In this review, we highlight that these modulators offer promising therapeutic targets for enhancing recovery and provide novel insights into stroke pathology and treatment. We also offer a comparative analysis of the four modulators and present the challenges and future perspectives for each one. The review sets forth a trajectory for future research, underscoring the development of novel epigenetic-based therapeutics as a groundbreaking approach to enhance post-stroke neurorehabilitation and improve patient prognoses.

Lung cancer remains a global health crisis, responsible for significant morbidity and mortality. Late-stage diagnosis often limits treatment options and patient survival. Therefore, identifying reliable and sensitive biomarkers for early detection is crucial. Glycosylation, the addition of glycans to protein/RNA/lipid, is a vital cellular process. Normal glycosylation regulates healthy cell function, while alterations, particularly in fucosylation and sialylation, contribute to lung cancer development and progression. These aberrant glycosylation patterns are associated with processes such as immune modulation, cell migration, proliferation, and cell-cell recognition. Fucosylation, a specific type of glycosylation, is frequently altered in lung cancer, with high levels detected in tumors. Understanding the mechanisms behind this altered fucosylation holds immense potential. It can pave the way for the development of novel therapeutic and diagnostic tools for lung cancer. By analyzing specific fucosylation patterns in bodily fluids, it could lead to early-stage diagnosis. This review delves into the mechanisms of fucosylation in lung cancer initiation and metastasis, proposing promising strategies to target the mechanisms, aiming to inhibit tumor growth and disease progression.

Lean 4.0, a fusion of lean manufacturing and digital technologies, is a powerful tool for implementing Industry 4.0. Its widespread use across sectors is driven by its ability to eliminate waste, reduce errors, boost revenue, and enhance customer care and efficiency. In the healthcare sector, this fourth lean revolution is particularly beneficial, as it helps to curb wastage and enhance efficiency, leading to more effective outcomes. Lean 4.0, a globally recognized best practice for industrial organisations, has proven its worth in enhancing performance and competitiveness. Its success has led to its adoption not only in manufacturing but also in non-manufacturing sectors such as Healthcare, finance, telecommunications, construction, and logistics. Its ease of use and effectiveness make it a superior alternative to traditional medical waste management techniques. Relevant papers on Lean 4.0 for Healthcare in the current literature are identified and studied to evaluate the impact of Lean 4.0 principles in Healthcare. This review-based research studies Lean 4.0 technologies and their needs in Healthcare. It discusses sustainable support for the healthcare sphere through Lean 4.0. Paper briefs several modalities of Lean 4.0 for the healthcare domain. Finally, the paper identifies and discusses the significant applications of Lean 4.0 in Healthcare. Lean 4.0 is essential to lowering errors that lead to medical mistakes since this business deals with life-or-death situations. Healthcare institutions need to reduce their overall waste reduction. With Lean 4.0, staff members working in quality management, clinical operations, consulting, and patient experience can progress into leadership roles in the healthcare sector. Lean 4.0 technologies can reduce waste production, including employing reusable medical equipment or cutting down on packaging waste. The possible difficulty of integrating digital technologies with healthcare systems and practices is Lean 4.0's limitations for the healthcare industry. Healthcare institutions can use refillable syringes and other disposable items to reduce waste rather than disposable plastic syringes. In future, healthcare institutions may save costs and lessen their environmental effect by minimising trash by using lean 4.0 technologies.

The progress made in the field of nanotechnology has resulted in the development of nanomedicine agents, which have demonstrated their efficacy as a promising clinical tool in the fight against different types of cancers. Furthermore, Nanomedicine products possess the potential to achieve intricate targeting strategies and multifunctionality. Currently, nanoparticles possess diverse applications across various scientific disciplines and serve as a crucial component in contemporary medical practices. The specimens have undergone analysis for diverse clinical purposes, including serving as drug carriers, delivering tumor genes, and functioning as contrast compounds in imaging. Diverse nanomaterials derived from organic, inorganic, lipid, or glycan compounds, in addition to synthetic polymers, have been employed to advance and enhance novel cancer therapeutics. The present review centers on the utilization of nanoparticles in clinical settings for the purposes of cancer diagnosis and treatment, with a particular emphasis on their function as drug carriers and targeted therapy agents.

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Methodology

Results

Objective

To explore whether differences between male rats on the next day of mating and on the day of mating can be reflected by the urine proteome.

Methods

Urine samples were collected from male Sprague-Dawley rats on the day of mating and the next day of mating. Urine samples were analysed by the label-free quantitative proteomics technique of high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS). Differential proteins of the urine proteome were analysed for protein function and biological pathways.

Results

54 differential proteins were identified by comparing the urine proteome of rats on the next day of mating with that on the day of mating, and nearly two-thirds of the differential proteins were related to spermatogenesis.

Conclusions

The urine proteome has the potential to reflect spermatogenesis without interfering with it.

Introduction

Methods

Results

Conclusion

Background

Methodology

Results

Conclusion

The current study is related to fabrication of point-of-care molecularly imprinted paper-based analytical devices, specifically related to ovarian cancer early stage detection. The task involves creating a paper coated with silver nanoparticles (AgNPs) that can be used as a transducer via surface enhanced Raman spectroscopy (SERS) to detect the threshold level of the biomarker lysophosphatidic acid (LPA) as a template molecule to diagnose ovarian cancer. MIPs are synthetic receptors designed for a targeted molecule which are prepared from polymers in the presence of target such as small molecules, proteins or cells etc. Removal of the template results in cavities which are structurally and electrostatically complementary to the template molecule. The molecularly imprinted polymer (MIP) layer was anchored on vinyl trimethoxy silane modified-paper, which served as a detector by hydrolytically modifying the paper surface with vinyl trimethoxy silane. To detect LPA in serum samples for ovarian cancer, MIP-paper based on SERS was developed. Patients with benign ovarian tumors had an LPA level of 7.73 μM whereas those with ovarian cancer have a level of 16.99 μM. A healthy person's LPA level is 2.92 μM. Consequently, the rise in LPA levels may aid in the early detection of ovarian cancer. The study refers to the use of a point of care device to diagnose ovarian cancer early, which should be simple, effective, and cost-effective. The analytical tool in the study is a sensor with high selectivity and sensitivity, quick assay times, and inexpensive costs. For LPA in serum, the binding constant of modified paper-based MIP ranges from 11.00 μM to 0.08 μM. The amalgamation of nanoparticles on 2D-paper with MIP have lead to develop sensor which can effectively select LPA in untreated real samples like serum.