ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

With the rapid development of the biopharmaceutical field, the efficient and simultaneous extraction of multiple biological components from biological samples has become a critical process for advancing scientific research. The ability to simultaneously extract various molecular components such as metabolites, DNA, RNA, and proteins is pivotal for multi-omics studies, which aim to comprehensively understand the molecular mechanisms of biological systems. Traditional methods often extract these components separately, leading to challenges such as sample loss, time consumption, contamination, and inconsistencies across different data types. In contrast, simultaneous extraction techniques address these issues by maintaining the consistency of each biological component’s physiological state, improving data reliability and facilitating integration across omic platforms. This review systematically summarizes recent advances in simultaneous extraction technologies, focusing on methods such as methanol/chloroform extraction, TRIzol reagent extraction, and modified Folch extraction, which have shown significant promise in improving the efficiency and integrity of biological sample preparation. These methods offer various advantages, such as reduced sample volume requirements, decreased contamination risk, and enhanced extraction consistency, which are crucial for studies involving small sample sizes or precious clinical specimens. Among these, methanol/chloroform extraction stands out for its simplicity, low cost, and ability to extract a wide range of biological molecules. However, it does face limitations, such as its inefficiency in extracting lipids and potential RNA contamination. On the other hand, the TRIzol reagent method has become a widely adopted technique due to its ability to simultaneously isolate RNA, proteins, and metabolites from the same sample. Despite its effectiveness, the TRIzol method has limitations in RNA quality, especially when handling complex samples or those with high protein content. Modified Folch extraction, which combines liquid-liquid extraction with commercial kits, offers a highly efficient way to extract polar metabolites, lipids, RNA, DNA, and proteins from small tissue samples. This method has proven advantageous in terms of extraction yield, especially for challenging or rare samples, although it requires precise handling to avoid cross-contamination between phases. The integration of automated platforms, microfluidics, and high-throughput systems is another exciting avenue for improving simultaneous extraction. Automation facilitates large-scale, reproducible sample processing with minimal human error, while microfluidics provides high precision in sample handling and enables real-time monitoring of extraction efficiency. These innovations not only enhance the speed and reproducibility of sample preparation but also open new possibilities for single-cell analysis, where sample volumes are often limited, and extraction efficiency is critical. In addition to the technical aspects, the review also highlights the importance of optimizing extraction protocols for specific sample types, such as clinical tissues, plants, and microorganisms. For example, the challenge of extracting multiple components from cancer tissues, where sample degradation and contamination risks are high, can be mitigated by carefully selecting extraction reagents and minimizing sample handling steps. Similarly, in plant studies, where metabolite diversity is vast, the simultaneous extraction methods must be optimized to account for the unique composition of plant tissues, which often include complex secondary metabolites and cell wall components. Looking forward, the development of more efficient and standardized simultaneous extraction methods will be crucial for advancing multi-omics research. There is a growing need for protocols that can be tailored to specific research needs, ensuring both reproducibility and flexibility in diverse applications. Additionally, combining these extraction methods with high-resolution analytical techniques such as mass spectrometry and next-generation sequencing will further enhance the potential of multi-omics studies to provide comprehensive insights into biological systems. As these technologies continue to evolve, their application in personalized medicine, environmental research, and agriculture holds great promise for addressing critical scientific challenges. In conclusion, while simultaneous extraction technologies have made significant strides, several challenges remain in optimizing extraction efficiency, ensuring reproducibility, and reducing costs. Future research should focus on refining extraction protocols, developing innovative extraction reagents, and expanding the scope of these methods to cater to a broader range of biological samples. Ultimately, the continued integration of these advanced techniques will revolutionize the way biological samples are prepared, analyzed, and understood in the context of multi-omics research.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

With the rapid development of the biopharmaceutical field, the efficient and simultaneous extraction of multiple biological components from biological samples has become a critical process for advancing scientific research. The ability to simultaneously extract various molecular components such as metabolites, DNA, RNA, and proteins is pivotal for multi-omics studies, which aim to comprehensively understand the molecular mechanisms of biological systems. Traditional methods often extract these components separately, leading to challenges such as sample loss, time consumption, contamination, and inconsistencies across different data types. In contrast, simultaneous extraction techniques address these issues by maintaining the consistency of each biological component’s physiological state, improving data reliability and facilitating integration across omic platforms. This review systematically summarizes recent advances in simultaneous extraction technologies, focusing on methods such as methanol/chloroform extraction, TRIzol reagent extraction, and modified Folch extraction, which have shown significant promise in improving the efficiency and integrity of biological sample preparation. These methods offer various advantages, such as reduced sample volume requirements, decreased contamination risk, and enhanced extraction consistency, which are crucial for studies involving small sample sizes or precious clinical specimens. Among these, methanol/chloroform extraction stands out for its simplicity, low cost, and ability to extract a wide range of biological molecules. However, it does face limitations, such as its inefficiency in extracting lipids and potential RNA contamination. On the other hand, the TRIzol reagent method has become a widely adopted technique due to its ability to simultaneously isolate RNA, proteins, and metabolites from the same sample. Despite its effectiveness, the TRIzol method has limitations in RNA quality, especially when handling complex samples or those with high protein content. Modified Folch extraction, which combines liquid-liquid extraction with commercial kits, offers a highly efficient way to extract polar metabolites, lipids, RNA, DNA, and proteins from small tissue samples. This method has proven advantageous in terms of extraction yield, especially for challenging or rare samples, although it requires precise handling to avoid cross-contamination between phases. The integration of automated platforms, microfluidics, and high-throughput systems is another exciting avenue for improving simultaneous extraction. Automation facilitates large-scale, reproducible sample processing with minimal human error, while microfluidics provides high precision in sample handling and enables real-time monitoring of extraction efficiency. These innovations not only enhance the speed and reproducibility of sample preparation but also open new possibilities for single-cell analysis, where sample volumes are often limited, and extraction efficiency is critical. In addition to the technical aspects, the review also highlights the importance of optimizing extraction protocols for specific sample types, such as clinical tissues, plants, and microorganisms. For example, the challenge of extracting multiple components from cancer tissues, where sample degradation and contamination risks are high, can be mitigated by carefully selecting extraction reagents and minimizing sample handling steps. Similarly, in plant studies, where metabolite diversity is vast, the simultaneous extraction methods must be optimized to account for the unique composition of plant tissues, which often include complex secondary metabolites and cell wall components. Looking forward, the development of more efficient and standardized simultaneous extraction methods will be crucial for advancing multi-omics research. There is a growing need for protocols that can be tailored to specific research needs, ensuring both reproducibility and flexibility in diverse applications. Additionally, combining these extraction methods with high-resolution analytical techniques such as mass spectrometry and next-generation sequencing will further enhance the potential of multi-omics studies to provide comprehensive insights into biological systems. As these technologies continue to evolve, their application in personalized medicine, environmental research, and agriculture holds great promise for addressing critical scientific challenges. In conclusion, while simultaneous extraction technologies have made significant strides, several challenges remain in optimizing extraction efficiency, ensuring reproducibility, and reducing costs. Future research should focus on refining extraction protocols, developing innovative extraction reagents, and expanding the scope of these methods to cater to a broader range of biological samples. Ultimately, the continued integration of these advanced techniques will revolutionize the way biological samples are prepared, analyzed, and understood in the context of multi-omics research.

Aging is a natural process in which tissue and organ function declines as organisms age. Aging is the inevitable outcome of the organism and is irreversible. The aging process is accompanied by degenerative changes in the morphological structure of multiple organs of the body, leading to an increase in the incidence of chronic diseases such as hypertension, cardiovascular and cerebrovascular diseases, hyperlipidemia, and diabetes year by year. In recent years, natural phytochemicals have attracted widespread attention from the public due to their advantages such as non-toxicity or low toxicity, low cost, and various biological activities. Epigallocatechin gallate (EGCG) is a dietary polyphenol extracted from green tea, which has a variety of biological functions including anti-oxidation, anti-inflammation, hypoglycemic, hypolipidemic and anti-aging activities. The EGCG-mediated anti-aging mechanism has been investigated in many studies with different aging models. This article reviews the research progress on rodents, nematodes, fruit flies, and cell aging models, focusing on summarizing the lifespan extension and physiological changes of rodents, nematodes and fruit flies after EGCG intervention from multiple angles, and exploring potential mechanism by which EGCG delays aging and extends lifespan. This review provides a theoretical reference for the study of the anti-aging mechanism of phytochemicals.

BACKGROUND: Asthma is a common chronic inflammatory airway disease and intermittent hypoxia is increasingly recognized as a factor that may impact disease progression. The present study investigated whether intermittent hypoxia (IH) could aggravate asthma by promoting hypoxia-inducible factor-1α (HIF-1α)/nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing protein 3 (NLRP3)/interleukin (IL)-1β-dependent pyroptosis and the inflammatory response and further elucidated the underlying molecular mechanisms involved. METHODS: A total of 49 patients diagnosed with severe bronchial asthma and diagnosed by polysomnography were enrolled at Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, between January 2022 and December 2022, and their general data and induced sputum were collected. BEAS-2B cells were treated with IL-13 and subjected to IH. An ovalbumin (OVA)-treated mouse model was also used to assess the effects of chronic intermittent hypoxia (CIH) on asthma. Pyroptosis, the inflammatory response, and related signalling pathways were assessed in vivo and in vitro . RESULTS: In this study, as the apnoea and hypopnea index (AHI) increased, the proportion of patients with uncontrolled asthma increased. The proportions of neutrophils and the levels of IL-6, IL-8, HIF-1α and NLRP3 in induced sputum were related to the AHI. NLRP3-mediated pyroptosis, which could be mediated by the HIF-1α signalling pathway, was activated in IL-13 plus IH-treated BEAS-2B cells and in the lungs of OVA/CIH mice. HIF-1α downregulation significantly reduced lung pyroptosis and ameliorated neutrophil inflammation by modulating the NLRP3/IL-1β pathway both in vitro and in vivo . Similarly, pretreatment with LW6, an inhibitor of HIF-1α, effectively blocked the generation of inflammatory cytokines in neutrophils. In addition, administration of the NLRP3 activator nigericin obviously increased lung neutrophil inflammation. CONCLUSIONS Obstructive sleep apnoea-hypopnea syndrome (OSAHS) is a risk factor for asthma exacerbation. IH aggravates neutrophil inflammation in asthma via NLRP3/IL-1β-dependent pyroptosis mediated by the HIF-1α signalling pathway, which should be considered a potential therapeutic target for the treatment of asthma with OSAHS.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Humans ; Asthma/metabolism* ; Animals ; Pyroptosis/physiology* ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Mice ; Signal Transduction/physiology* ; Male ; Hypoxia/metabolism* ; Female ; Interleukin-1beta/metabolism* ; Adult ; Inflammation/metabolism* ; Middle Aged ; Mice, Inbred C57BL

Neuronal reprogramming is an innovative technique for converting non-neuronal somatic cells into neurons that can be used to replace lost or damaged neurons, providing a potential effective therapeutic strategy for central nervous system (CNS) injuries or diseases. Transcription factors have been used to induce neuronal reprogramming, while their reprogramming efficiency is relatively low, and the introduction of exogenous genes may result in host gene instability or induce gene mutation. Therefore, their future clinical application may be hindered by these safety concerns. Compared with transcription factors, small-molecule compounds have unique advantages in the field of neuronal reprogramming, which can overcome many limitations of traditional transcription factor-induced neuronal reprogramming. Here, we review the recent progress in the research of small-molecule compound-mediated neuronal reprogramming and its application in CNS regeneration and repair.
Humans ; Cellular Reprogramming/drug effects* ; Neurons/cytology* ; Animals ; Transcription Factors ; Small Molecule Libraries/pharmacology* ; Nerve Regeneration

Transcription factor PU.1, as a core member of the ETS family, plays a pivotal role in the multi-lineage differentiation of hematopoietic stem cells, particularly in the regulation of erythroid differentiation. PU.1 orchestrates the process of hematopoietic stem cell differentiation towards erythroid cells by modulating the transcription of lineage-determining factors and interacting with other key transcription factors in a fine-tuned manner. PU.1 plays an irreplaceable role in the development and function of red blood cells, with its abnormal expression closely related to the occurrence and progression of various blood diseases, including leukemia, myelodysplastic syndromes, and various types of anemia. This article comprehensively analyzes the functional roles and molecular mechanisms of PU.1 in various stages of erythroid differentiation, as well as its potential roles in related blood diseases. This review not only deepens our understanding of the mechanism by which PU.1 regulates erythroid differentiation, but also provides theoretical grounds for blood disease therapies based on PU.1.
Humans ; Proto-Oncogene Proteins/genetics* ; Trans-Activators/genetics* ; Cell Differentiation/physiology* ; Hematologic Diseases/physiopathology* ; Erythroid Cells/cytology* ; Animals ; Erythropoiesis/physiology*

In a healthy human, the airway mucus forms a thin, protective liquid layer covering the surface of the respiratory tract. It comprises a complex blend of mucin, multiple antibacterial proteins, metabolic substances, water, and electrolytes. This mucus plays a pivotal role in the lungs' innate immune system by maintaining airway hydration and capturing airborne particles and pathogens. However, heightened mucus secretion in the airway can compromise ciliary clearance, obstruct the respiratory tract, and increase the risk of pathogen colonization and recurrent infections. Consequently, a thorough exploration of the mechanisms driving excessive airway mucus secretion is crucial for establishing a theoretical foundation for the eventual development of targeted drugs designed to reduce mucus production. Across a range of lung diseases, excessive airway mucus secretion manifests with unique characteristics and regulatory mechanisms, all intricately linked to mucin. This article provides a comprehensive overview of the characteristics and regulatory mechanisms associated with excessive airway mucus secretion in several prevalent lung diseases.
Humans ; Mucus/metabolism* ; Mucins/physiology* ; Lung Diseases/metabolism* ; Respiratory Mucosa/metabolism* ; Pulmonary Disease, Chronic Obstructive/physiopathology* ; Asthma/physiopathology* ; Cystic Fibrosis/physiopathology* ; Mucociliary Clearance/physiology*