Malignant tumours represent a significant public health issue posing a grave threat to human life and health, garnering considerable attention in the field of biomedical science in recent years. Early screening and diagnosis of tumours provide patients with valuable treatment time, constituting a crucial measure in tumour prevention and management. Surface-enhanced Raman spectroscopy (SERS), with its advantages of ultra-high sensitivity, high precision, and multiplexing capabilities, has been widely applied in the detection of tumour markers. This paper examines SERS-based biosensors for three distinct tumour markers: prostate-specific antigen (PSA) for prostate cancer screening, alpha-fetoprotein (AFP) for primary liver cancer detection, and chromogranin A (CgA) for neuroendocrine tumour diagnosis. Compared to SERS technology, alternative early-stage tumour marker detection methods—such as chemiluminescent immunoassays, enzyme-linked immunosorbent assays, and real-time quantitative PCR—face limitations in widespread clinical adoption due to their higher costs, longer analysis times, and greater operational complexity. To address current clinical application challenges, future advancements in SERS-based biosensor detection of tumour markers will primarily be achieved through innovative improvements to the biosensor substrate.

In an era of increasing life expectancy and rising age-related diseases, assessing the intertwined roles of telomeres and telomerase as molecular drivers of cellular senescence promises transformative advances in extending human healthspan and developing anti-aging solutions. This literature review synthesizes key advancements in understanding telomere and telomerase's role in aging, spanning from foundational mitotic clock hypotheses to contemporary models of cellular and systemic decline. It examines telomerase-mediated telomere elongation as a countermeasure to replicative shortening, highlighting its impact on proliferative capacity across stem and somatic cells. Additionally, the review explores the perspectives on telomerase functions in preventing cellular senescence that extend beyond mere length maintenance, and instead participate in a dynamic model which also considers the state of telomere structures. Drawing from seminal works and empirical evidence over the past thirty years, this synthesis bridges historical discoveries with modern implications, elucidating how telomere and telomerase participate in shaping cell senescence, aging, and tissue regeneration

In recent times, breast cancer has become a global healthcare challenge, whereby women of all ages have been diagnosed with the dangerous disease. Further, breast cancer is understood and seen as complex due to its relatively high mortality rates. This study's objective was to analyze and reveal the long-term patterns in breast cancer occurrences and the prevailing mortality rates, as well as survival rates across the US. A quantitative research design was applied while also using data from SEER website and using both descriptive and regression methods. IBM SPSS version 26 software was specifically used to complete the statistical analysis. The results indicated that there was a gradual increase in breast cancer occurrences recently. Besides, this was followed by a major decline in mortality rates with a slower improvement in the 5-year survival rates. These findings further indicate that early diagnosis and treatment greatly improve population-level survival outcomes among breast cancer patients. The study implies the need for biostatistics research and analysis in understanding survival rates, hence aiding in informed public healthcare decisions.

The current stage of the development of the Human-Machine Interaction (HMI) systems, as rigid motion control systems, is replaced by an anthropomorphic sense of touch, forcing robot manipulators to have skin-like sensory abilities. But nowadays, flexible electronics are facing a fundamental dilemma of sensitivity versus stretchability. The paper will review the process of the ultrasensitive strain sensor design involving a bio-inspired micro-crack structure. It investigates the mechanotransduction of biological systems, first, to derive the theoretical basis of structural gating. Second, the paper investigates the application of modulus mismatch to stress concentration in arthropod-inspired designs. Third, the research explains the Disconnect-Connect model and the effects of quantum tunneling that make gating crack-based sensors so much higher than conventional limits to sensors. Lastly, the paper compares this technology with surface electromyography and finds that it has a better signal-to-noise ratio and latency in closed-loop control. The review states that structural bio-mimicry is an imperative pathway to pursuing the aim of high-fidelity HMI.

With the ageing of the population and the increase in the number of people with limb disfunction, intelligent prosthetic and wearable human-computer interaction systems put forward higher requirements for highly reliable motor intention acquisition technology. As an important bioelectric signal reflecting the state of muscle activity, myoelectric signals have unique advantages in prosthetic control and rehabilitation assistance. This article focusses on the collection and sensing technology of myoelectric signals, and systematically sorts out the structural characteristics, signal performance and application scenarios of different schemes such as surface electro myoelectric sensors, intrusive myoelectric sensors, flexible wet electrodes and flexible dry electrodes. Through the comparative analysis of existing research results, the advantages and limitations of various electro myoelectric sensors in terms of signal quality, spatial selectivity, wearing comfort and system integration are pointed out, and the key challenges such as signal stability, crosstalk suppression and long-term wearing reliability are summarized. This article aims to provide a reference basis for the selection and optimization design of electro myoelectric sensors in intelligent prosthetics and wearable systems.

Anxiety disorders comprise a diverse set of psychiatric conditions. They do not arise from a single pathological pathway but from interactions among neurobiological susceptibility, environmental influences, and regulatory processes. Growing evidence indicates that traditional neurotransmitter-centered models cannot fully explain individual differences in disease risk, disease duration, or treatment response. Epigenetic regulation has thus emerged as a key mechanism linking environmental exposure to persistent alterations in gene expression without changing the DNA sequence. Through epigenetic modification, neurotransmitter receptor expression and function may be dysregulated within anxiety-related neural circuits. This review summarizes current findings on epigenetic modulation of neurotransmitter receptors in anxiety disorders, with particular emphasis on multigenic regulation, receptor-level mechanisms, and the unresolved barriers to clinical translation

In fields such as the early diagnosis of diseases, food safety monitoring, and environmental pollutant screening, the requirements for the sensitivity, specificity, and efficiency of detection technologies are extremely stringent. Traditional detection techniques such as HPLC and ELISA are cumbersome, time-consuming, and costly, and cannot meet the needs of on-site rapid testing and trace target screening. Under this backdrop, the CRISPR-Cas system, with its high targeting specificity and programmability, has brought new directions to the development of biosensors, particularly promoting technological innovations in recognition mechanisms and signal amplification for the detection of non-nucleic acid targets. This study focuses on CRISPR-Cas system-based biosensors, analyzing their structural composition, comparing the detection approaches for nucleic acid and non-nucleic acid targets, and conducting a performance comparison with traditional technologies. Studies have shown that this sensor combines high sensitivity and specificity with low cost and rapid detection, is capable of detecting both nucleic acid and non-nucleic acid targets, and thus has broad application prospects. However, it still has some limitations, such as the complexity of the indirect recognition process and the constraints on signal amplification. The integration of signal amplification, AI, nanotechnology, and microfluidics in the future is expected to break through bottlenecks and achieve industrial applications.

Due to its perfect performance and characteristics like high integration, high sensitivity and high throughput detection ability, microfluidics technology attracts much attention from researchers in related areas, such as DMF and LOC. The driving and controlling technology play vital roles for development of microfluidics chips and system integration. The driving technology by various physics fields and innovative DMF controlling technology are introduced in detail to show the progress of this technology. The microfluidics technology has shown broad application in many fields, such as pharmaceutical engineering, chemical analysis, nanometerscale fabrication. However, the current technology still faces the problems of high driving voltage, a small number of electrode arrays, and low efficiency of system control. It restricts the development of microfluidics technology. As an emerging field, microfluidic technology is developing rapidly and gradually permeating various industries. With continuous technological advancements and increasing market demand, the future prospects of the microfluidics industry are broad. Through policy support and technological innovation, microfluidic technology will play an increasingly important role in areas such as healthcare and environmental monitoring.

Breast cancer is one of the most common cancers, and early detection is crucial for improving patient survival. However, conventional imaging and serum-based diagnostic methods still suffer from limited sensitivity or limited applicability in early-stage detection. In recent years, biomarker-based detection approaches have been widely applied in breast cancer diagnosis and have improved detection accuracy to some extent. Nevertheless, single-biomarker detection is often insufficient to reflect the high heterogeneity of breast cancer, which may lead to inaccurate diagnostic results. In this article, the diagnostic accuracy of single-biomarker detection and multi-biomarker detection is compared. The results show that multi-biomarker detection can significantly increase detection accuracy by providing more comprehensive molecular information. By analyzing multiple biomarkers simultaneously, diagnostic reliability can be improved and the risk of false results can be reduced. In order to achieve highly sensitive and low-cost detection, electrochemical biosensing technology is introduced for multi-biomarker detection. Due to its high sensitivity, fast response, simple operation, and low cost, electrochemical biosensing technology shows great potential for efficient and accurate breast cancer detection.

Analyzing the underlying mechanisms of the brain is an ongoing challenge with extensive application prospects in the field of brain science. With the advancements achieved in computer science, researchers gained a deeper understanding of the function of brain signals via reinforcement learning and deep learning models on supercomputers. This paper focuses on deep learning methods designed for brain signal analysis, respectively discussing the applications of convolutional neural networks(CNNs), recurrent neural networks(RNNs), self-attention models and their mixtures in electroencephalogram(EEG), functional magnetic resonance imaging(fMRI), magnetoencephalogram(MEG), and functional near-infrared spectroscopy(fNIRS). The CNN models have widespread applications, especially in EEG, fMRI while RNN models have unique advantage at dealing with complex temporal data. Self-attention based large transformer models are the hotspots nowadays, general pretrained transformer models can effectively and efficiently handle most cases of brain signal analyzing tasks with enormous computing resources and achieve excellent results.