In recent years， diabetic nephropathy （DN） has become an increasingly serious health challenge worldwide， and its morbidity and mortality rates are rapidly increasing. The patients suffering from the disease tend to be younger. DN is not only accompanied by a wide range of renal pathological changes， such as renal hypertrophy， inflammatory cell infiltration， expansion of the tethered membrane stroma， and thickening of the basement membrane but is also the main cause of end-stage renal disease and death in patients with diabetes mellitus. Therefore， it is particularly urgent to explore new strategies for the prevention and treatment of DN. The pathogenesis of DN is intricate and complex， with current research focusing on multifactorial interactions between metabolic and hemodynamic factors， such as hypoxia， inflammatory responses， and fibrotic processes. Notably， hypoxia plays a pivotal role in the initiation and progression of DN. Hypoxia-inducible factor （HIF-1α）， as a key regulatory protein commonly found in hypoxic cells， has a profound impact on various physiological and pathological processes， such as cell metabolism， vascular neogenesis， oxidative stress， and apoptosis. With its unique theoretical system and therapeutic approach， traditional Chinese medicine has demonstrated significant advantages in coping with hypoxic diseases and can slow down the progression of DN by regulating the expression level of HIF-1α and its downstream signaling molecules and exerting anti-inflammatory， antioxidant， and antifibrotic effects， which has positive clinical significance for drug development and early prevention and treatment of DN.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

In recent years, cancer treatment methods and means are becoming more and more diversified, and single treatment methods often have limited efficacy, while the synergistic effect of immunity combined with chemotherapy can inhibit tumor growth more effectively. Based on this, we constructed a sodium alginate hydrogel composite system loaded with chemotherapeutic agents and tumor vaccines (named SA-DOX-NA) with a view to the combined use of chemotherapeutic agents and tumor vaccines. Firstly, the tumor vaccine (named NA) degradable under acidic conditions was constructed by in situ polymerization using chicken ovalbumin (OVA), acrylamide (AAM) and 2-(dimethylamino) ethyl methacrylate (DMAEMA) monomer. Then a hydrogel composite system SA-DOX-NA co-loaded with chemotherapeutic drug doxorubicin (DOX) and NA was prepared using sodium alginate as a matrix. The results showed that SA-DOX-NA had good in situ gel-forming ability and formed a mesh structure that could realize the co-loading of DOX and NA as well as the slow drug release. The electron microscopy results showed that SA-DOX-NA had good in situ gel-forming ability with good internal connectivity, and the three-dimensional mesh structure could realize the co-loading of DOX and NA as well as the slow drug release. The antitumor and immunomodulatory results showed that SA-DOX-NA both effectively inhibited the growth of tumor cells and efficiently promoted the proliferation and activation of DC2.4 dendritic cells without additional adjuvant. In summary, SA-DOX-NA exerts the dual efficacy of chemotherapy and immunotherapy for tumor treatment, and has a good application prospect in local tumor treatment.

As a traditional nutritional and healthy cash crop in China, tea has certain significance in promoting human health and preventing and controlling chronic diseases. Studies have shown that the nutritional health effect of tea is due to its rich functional components, mainly including tea polyphenols, tea pigments, tea polysaccharides, theanine, alkaloids and other bioactive substances. At present, researchers from the academic circles have continuously carried out animal and human experiments on the health effects of various functional components of tea, which has accumulated abundant research data and materials. Based on this, this article reviews the literature on the nutritional and health effects of the main functional components of tea, and adopts the method of evidence-based research to screen and extract relevant data for qualitative and quantitative meta-analysis. Subsequently, the nutritional health effects of the five functional components of tea, namely tea polyphenols, tea pigments, tea polysaccharides, theanine, and alkaloids, are summarized and outlined. Studies have shown that tea polyphenols, tea pigments, tea polysaccharides, theanine and alkaloids have different health effects and are expected to play their unique roles in promoting human health and preventing and controlling diseases.

Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

RNA-binding proteins (RBPs) are ubiquitous components within cells, fulfilling essential functions in a myriad of biological processes. These proteins interact with RNA molecules to regulate gene expression at various levels, including transcription, splicing, transport, localization, translation, and degradation. Understanding the intricate network of RBP-RNA interactions is crucial for deciphering the complex regulatory mechanisms that govern cellular function and organismal development. Ultravidet (UV) cross-linking and immunoprecipitation (CLIP) stands out as a powerful approach designed to map the precise locations where RBPs bind to RNA. By using UV light to create covalent bonds between proteins and RNA, followed by immunoprecipitation to isolate the protein-RNA complexes, researchers can identify the direct targets of specific RBPs. The advent of high-throughput sequencing technologies has revolutionized CLIP, enabling the identification of not only the types but also the exact sequences of RNA bound by RBPs on a genome-wide scale. The evolution of CLIP has led to the development of specialized variants, each with unique features that address specific challenges and expand the scope of what can be studied. High-throughput sequencing CLIP (HITS-CLIP) was one of the first advancements, significantly increasing the throughput and resolution of RNA-protein interaction mapping. Photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) introduced the use of photoactivatable ribonucleosides to enhance cross-linking efficiency and specificity, reducing background noise and improving the detection of low-abundance RNA-protein interactions. Individual-nucleotide resolution CLIP (iCLIP) further refined the technique, achieving unprecedented precision by resolving individual nucleotides involved in RBP binding, which is particularly valuable for studying the fine details of RNA structure and function. Despite the remarkable progress, there remains room for improvement in CLIP technology. Researchers continue to seek methods to increase sensitivity, reduce technical variability, and improve the reproducibility of results. Advances in sample preparation, data analysis algorithms, and computational tools are critical for addressing these challenges. Moreover, the application of CLIP to more diverse biological systems, including non-model organisms and clinical samples, requires the development of tailored protocols and the optimization of existing ones. Looking forward, the field of RNA biology is poised to benefit greatly from ongoing innovations in CLIP technology. The exploration of non-canonical RNA-protein interactions, such as those involving long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), promises to reveal new layers of cellular regulation and may lead to the discovery of novel therapeutic targets. Furthermore, integrating CLIP data with other omics approaches, such as proteomics and metabolomics, will provide a more comprehensive understanding of the dynamic interplay between RNA and its binding partners within the cell. In conclusion, the continuous refinement and expansion of CLIP techniques have not only deepened our knowledge of RNA biology but have also opened up new avenues for investigating the molecular underpinnings of health and disease. As the technology matures, it is expected to play an increasingly pivotal role in both basic and applied research, contributing to the advancement of medical science and biotechnology.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

BACKGROUND: Congenital heart disease (CHD) is a leading cause of birth defect-related mortality. However, more recent CHD mortality data for China are lacking. Additionally, limited studies have evaluated sex, rural-urban, and region-specific disparities of CHD mortality in China. METHODS: We designed a population-based study using data from the Dataset of National Mortality Surveillance in China between 2008 and 2021. We calculated age-adjusted CHD mortality using the sixth census data of China in 2010 as the standard population. We assessed the temporal trends in CHD mortality by age, sex, area, and region from 2008 to 2021 using the joinpoint regression model. RESULTS: From 2008 to 2021, 33,534 deaths were attributed to CHD. The period witnessed a two-fold decrease in the age-adjusted CHD mortality from 1.61 to 0.76 per 100,000 persons (average annual percent change [AAPC] = -5.90%). Females tended to have lower age-adjusted CHD mortality than males, but with a similar decline rate from 2008 to 2021 (females: AAPC = -6.15%; males: AAPC = -5.84%). Similar AAPC values were observed among people living in urban (AAPC = -6.64%) and rural (AAPC = -6.12%) areas. Eastern regions experienced a more pronounced decrease in the age-adjusted CHD mortality (AAPC = -7.86%) than central (AAPC = -5.83%) and western regions (AAPC = -3.71%) between 2008 and 2021. Approximately half of the deaths (46.19%) due to CHD occurred during infancy. The CHD mortality rates in 2021 were lower than those in 2008 for people aged 0-39 years, with the largest decrease observed among children aged 1-4 years (AAPC = -8.26%), followed by infants (AAPC = -7.01%). CONCLUSIONS CHD mortality in China has dramatically decreased from 2008 to 2021. The slower decrease in CHD mortality in the central and western regions than in the eastern regions suggested that public health policymakers should pay more attention to health resources and health education for central and western regions.
Humans ; Heart Defects, Congenital/mortality* ; Male ; Female ; China/epidemiology* ; Infant ; Child, Preschool ; Adult ; Child ; Adolescent ; Infant, Newborn ; Middle Aged ; Young Adult ; Aged ; Rural Population

Definitive treatment of lung cancer with radiotherapy is challenging, as respiratory motion and anatomical changes can increase the risk of severe off-target effects during radiotherapy. Online adaptive radiotherapy (ART) is an evolving approach that enables timely modification of a treatment plan during the interfraction of radiotherapy, in response to physiologic or anatomic variations, aiming to improve the dose distribution for precise targeting and delivery in lung cancer patients. The effectiveness of online ART depends on the seamless integration of multiple components: sufficient quality of linear accelerator-integrated imaging guidance, deformable image registration, automatic recontouring, and efficient quality assurance and workflow. This review summarizes the present status of online ART for lung cancer, including key technologies, as well as the challenges and areas of active research in this field.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods*