AIM: To compare the efficacy of small-incision lenticule extraction(SMILE)and femtosecond assisted laser in situ keratomileusis(FS-LASIK)in the treatment of patients with myopia and astigmatism.METHODS: Retrospective analysis. A total of 100 cases(200 eyes)of patients with myopia and astigmatism treated in our hospital from December 2021 to December 2022 were collected. Among them, 50 cases(100 eyes)were divided into SMILE group and 50 cases(100 eyes)were divided into FS-LASIK group according to the treatment plans. The visual acuity and astigmatism, corneal morphology parameters, subjective visual quality scores, ocular surface indicators, postoperative complications, and quality of life were compared between the two groups before and after surgery.RESULTS: There was no significant difference in uncorrected visual acuity(UCVA), best corrected visual acuity(BCVA), astigmatism, corneal asphericity Q value, corneal surface regularity index(SRI), corneal thickness, and corneal curvature between the two groups before surgery and at 1 d, 1, and 6 mo after surgery(all P>0.05). At 1 and 6 mo after surgery, the subjective visual quality score, the quality of life score, Schirmer I test(SⅠt)and tear film break-up time(BUT)in the SMILE group were better than that in the FS-LASIK group(all P<0.05). The incidence of complications in the SMILE group was lower than that in the FS-LASIK group at 6 mo after surgery(P=0.005).CONCLUSION: Both SMILE and FS-LASIK have good clinical effects in the treatment of myopia with astigmatism, but the SMILE could alleviate ocular surface injury, reduce the risk of complications and improve the quality of lifes for patients.

[Objective] To explore the clinical significance and application value of establishing a database for red blood cell alloantibody detection. [Methods] Patients who were scheduled for blood transfusion in our hospital from January 1, 2020 to May 1, 2024 were selected as the research subjects. A red blood cell alloantibody detection database was established using Microsoft Office Excel software to register the detection data of patients' alloantibodies and antibodies of undetermined specificity (AUS). A retrospective analysis was conducted on the clinical characteristics, antibody distribution, antibody decay and repeat positivity of the patients in the database. The LISS-IAT method was routinely used for antibody screening and identification. [Results] Among the alloantibodies, the Rh blood group system had the highest detection rate, followed by antibodies of the MNS blood group system and the Lewis blood group system. The predominant antibody in the Rh blood group system was anti-E. In the univariate analysis, the positivity of antibody was significantly associated with the patient's gender, age, blood transfusion history, pregnancy history and type of disease (all P<0.001). In the database, 48 patients experienced antibody decay, accounting for 15.24%(48/315), with an average time span of antibody decay ranging from 22 to 1 324 days. Six cases showed repeat positivity after decay, which were related to blood transfusions. The shortest interval between blood transfusions that led to antibody repeat positivity was 3 days, and the longest interval was 427 days. Among 58 cases with AUS, 3 converted into alloantibodies, among which 2 were anti-E and 1 was anti-Lea. [Conclusion] Establishing a red blood cell alloantibody detection database is an effective way to guide ambiguous cross-matching in clinical practice and is also an effective measure for the management of transfusion risks.

To develop a guideline terminology system and promote its standardization, thereby enhancing medical staff's accurate understanding and correct application of guidelines.

Objective To describe the significance of the pretreatment inflammatory indicators in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) after undergoing radical radiotherapy. Methods The data of 246 ESCC patients who underwent radical radiotherapy were retrospectively collected. Receiver operating characteristic (ROC) curves were drawn to determine the optimal cutoff values for platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII). The Kaplan-Meier method was used for survival analysis. We conducted univariate and multivariate analyses by using the Cox proportional risk regression model. Software R (version 4.2.0) was used to create the nomogram of prognostic factors. Results The results of the ROC curve analysis showed that the optimal cutoff values of PLR, NLR, and SII were 146.06, 2.67, and 493.97, respectively. The overall response rates were 77.6% and 64.5% in the low and high NLR groups, respectively (P<0.05). The results of the Kaplan-Meier survival analysis revealed that the prognosis of patients in the low PLR, NLR, and SII group was better than that of patients in the high PLR, NLR, and SII group (all P<0.05). The results of the multivariate Cox regression analysis showed that gender, treatment modalities, T stage, and NLR were independent factors affecting the overall survival (OS). In addition, T stage and NLR were independent factors affecting the progression-free survival (PFS) (all P<0.05). The nomogram models of OS and PFS prediction were established based on multivariate analysis. The C-index values were 0.703 and 0.668. The calibration curves showed excellent consistency between the predicted and observed OS and PFS. Conclusion The pretreatment values of PLR, NLR, and SII are correlated with the prognosis of patients with ESCC who underwent radical radiotherapy. Moreover, NLR is an independent factor affecting the OS and PFS of ESCC patients. The NLR-based nomogram model has a good predictive ability.

Objective Emergency response to public health emergencies constitutes a vital component of the modernization of national governance systems and capacities, directly impacting national security, social stability, and public health. This study aims to analyze the key issues and research hotspots in the field of emergency response to public health emergencies, providing theoretical foundations and practical guidance for formulating scientific and effective emergency strategies and policies. Ultimately, it seeks to enhance the nation’s capability to respond to public health emergencies and safeguard public health. Methods Using core journals indexed in the China National Knowledge Infrastructure (CNKI) database as the data source, 1094 journal articles in the field of emergency response to public health emergencies in China from 2003 to September 2024 were selected as the research sample. Citespace visualization software was employed to organize and analyze the research process in the field of emergency response to public health emergencies. Results The current research in this field predominantly involves small-scale cooperation, and the research process can be divided into four stages. The research in the emerging stage mainly focuses on the themes of emergency mechanism and emergency management, the development stage mainly centers on the themes of emergency capacity improvement and system exploration, the outbreak stage emphasizes the themes of COVID-19 and epidemic prevention and control, and the sustainable development stage highlights collaborative governance and informatization. Conclusion The COVID-19 pandemic has accelerated the development of research in this field. Currently, the field shows a continuous growth, with an increasing volume of publications. The focus of current research has shifted towards collaborative governance and the study of assessment tools, aiming to enhance the efficiency and effectiveness in responding to public health emergencies. Moreover, the field is advancing toward more systematic and comprehensive research. However, there is still a need to strengthen scientific communication and collaboration.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Stroke is the main cause of death and disability among adults in China and is characterized by high incidence， disability， mortality， and recurrence rates. The combination of traditional Chinese and Western medicine has great potential in treating stroke and its sequelae. The classic traditional Chinese medicine prescription Da Chengqitang （DCQT） has a long history and proven efficacy in treating stroke. Clinically， DCQT is often used to treat stroke and its sequelae. However， the number and quality of clinical trials of DCQT in treating stroke need to be improved. Because of the insufficient basic research， the active ingredients and multi-target mechanism of action of DCQT remain unclear. Our research group has previously confirmed that DCQT can effectively reverse neurological damage， reduce iron deposition， and downregulate the levels of pro-inflammatory cytokines in the rat model of hemorrhagic stroke. The treatment mechanism is related to the nuclear factor erythroid 2-related factor 2 （Nrf2）-mediated signaling pathway and p38 mitogen-activated protein kinase （MAPK） signaling-mediated microglia activation. To clarify the pharmacodynamic basis and anti-stroke mechanism of DCQT， this article reviews the research progress in the treatment of stroke with DCQT in terms of clinical trials， pharmacodynamic material basis， safety evaluation， and mechanisms of absorbed components. This article summarizes 45 major phytochemical components of DCQT， 11 of which are currently confirmed absorbed components. Among them， emodin， rhein， chrysophanol， aloe-emodin， synephrine， hesperidin， naringin， magnolol， and honokiol can be used as quality markers （Q-markers） of DCQT. The mechanism of DCQT in treating stroke is complex， involving regulation of inflammatory responses， neuronal damage， oxidative stress， blood-brain barrier， brain-derived neurotrophic factor， and anti-platelet aggregation. This article helps to deeply understand the pharmacodynamic basis and mechanism of DCQT in treating stroke and provides a theoretical basis for the clinical application of DCQT in treating stroke and the development of stroke drugs.

Lactylation, a recently identified post-translational modification of proteins, is induced by lactic acid and can occur at multiple lysine residues in both histone and non-histone proteins. This modification plays a role in disease pathogenesis by affecting transcriptional regulation, mitochondrial metabolism, and immune inflammation. Significant advancements have been made in understanding the mechanisms of lactylation in various ophthalmic diseases, including retinal neovascularization, uveitis, melanoma, and myopia. This paper provides a comprehensive review of the relationship between lactic acid and lactylation, the regulatory mechanisms of lactylation, and the role of lactylation in different ocular diseases. Additionally, it addresses current research limitations and future directions, which is of great significance to elucidate the molecular mechanisms of lactylation in eye diseases and improving the diagnosis and targeted treatment of these conditions.

Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

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.