AIM:To investigate the effect of fibroblast growth factor 21(FGF21)on high glucose-induced oxidative stress in retinal pigment epithelial(RPE)cells and to clarify the underlying molecular mechanisms.METHODS:Single-cell sequencing data from the GEO database were analyzed to determine the expression profile of the FGF21 receptor FGFR1 in RPE cells. Human ARPE-19 cells were cultured and randomly assigned to control, high glucose(30 mmol/L), and high glucose+FGF21 analog treatment groups, with additional siFGFR1 and PI3K inhibitor groups. Cell viability in different treatment groups was assessed using CCK-8 assay, intracellular reactive oxygen species(ROS)levels were quantified using DCFH-DA fluorescent probing combined with immunofluorescence staining and flow cytometry. Transcriptome sequencing was performed on cells from the high glucose group and high glucose+FGF21 group to analyze the enrichment level of the PI3K/Akt signaling pathway. Western blotting was performed to detect phosphorylation levels of PI3K/Akt pathway components.RESULTS:Single-cell sequencing revealed specific expression of FGFR1 in RPE cells of retinal tissues from diabetic model mice. Under In vitro experiments, high glucose(30 mmol/L)exposure reduced ARPE-19 cell viability by 49.7% and increased ROS levels by approximately 2-fold. Whereas treatment with the FGF21 analog(60 ng/mL)restored cell viability and attenuated high glucose-induced ROS accumulation. Mechanistic studies demonstrated that FGFR1 knockdown inhibited the antioxidative stress of FGF21. Further validation of the molecular mechanism revealed that high glucose significantly suppressed the PI3K/Akt pathway activation(the levels of p-Akt and p-PI3K were decreased by 33.9% and 36.6%, respectively), while FGF21 effectively reversed this inhibitory effect and restored the expression of p-Akt and p-PI3K. Treatment with the PI3K inhibitor LY294002 inhibited the cytoprotective effect of FGF21 and significantly increased the ROS-positive cells, these findings confirm that PI3K/Akt signaling is indispensable downstream mechanism for FGF21 to exert its effects.CONCLUSION:FGF21 alleviates high glucose-induced oxidative stress and cellular injury in RPE cells by activating the PI3K/Akt signaling pathway through its receptor FGFR1.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Bronchiectasis(BE) is the third major chronic airway disease, and its incidence rate shows a continuously increasing trend. Bronchiectasis is a highly heterogeneous chronic airway disease. Due to structural alterations, airflow limitation, and mucus hypersecretion, clinical treatment faces many challenges. Particularly, problems including Pseudomonas aeruginosa-dominant drug-resistant bacterial colonization, recurrent infections, airway mucus hypersecretion, and impaired lung function are the most urgent, requiring long-term and personalized treatment and management integrating traditional Chinese and western medicine to prevent the recurrence and continuous progression of the disease. In recent years, both traditional Chinese medicine and western medicine have made certain progress in pathogenesis theories, clinical studies, and basic research regarding the therapeutic challenges of bronchiectasis. Therefore, this paper summarized relevant research from the past 10 years and explored future directions and potential advantages of integrated traditional Chinese and western medicine treatment, providing references for optimizing the clinical management strategies for bronchiectasis.
Bronchiectasis/drug therapy* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Animals

OBJECTIVE: To investigate the accuracy and safety of pedicle screw placement using 3D-printed auxiliary guides in scoliosis correction surgery for adolescents. METHODS: A retrospective analysis was conducted on the clinical data of 51 patients who underwent posterior scoliosis correction surgery from January 2020 to March 2023. Among them, there were 35 cases of adolescent idiopathic scoliosis and 16 cases of congenital scoliosis. The patients were divided into two groups based on the auxiliary tool used:the 3D-printed auxiliary guide screw placement group (3D printing group) and the free-hand screw placement group (free-hand group, without auxiliary tools). The 3D printing group included 32 patients (12 males and 20 females) with an average age of (12.59±2.60) years;the free-hand group included 19 patients (7 males and 12 females) with an average age of (14.58±3.53) years. The two groups were compared in terms of screw placement accuracy and safety, spinal correction rate, intraoperative blood loss, number of intraoperative fluoroscopies, operation time, hospital stay, and preoperative and last follow-up scores of the Scoliosis Research Society-22 (SRS-22) questionnaire. RESULTS: A total of 707 pedicle screws were placed in the two groups, with 441 screws in the 3D printing group and 266 screws in the free-hand group. All patients in both groups successfully completed the surgery. There was a statistically significant difference in operation time between the two groups (P<0.05). The screw placement accuracy rate of the 3D printing group was 95.46% (421/441), among which the Grade A placement rate was 89.34% (394/441);the screw placement accuracy rate of the free-hand group was 86.47% (230/266), with a Grade A placement rate of 73.31% (195/266). There were statistically significant differences in the accuracy of Grade A, B, and C screw placements between the two groups (P<0.05), while no statistically significant differences were observed in intraoperative blood loss, number of fluoroscopies, correction rate, or hospital stay (P>0.05). In the SRS-22 questionnaire scores, the scores of functional status and activity ability, self-image, mental status, and pain of patients in each group at the last follow-up were significantly improved compared with those before surgery (P<0.05), but there were no statistically significant differences in all scores between the two groups (P>0.05). CONCLUSION In scoliosis correction surgery, compared with traditional free-hand screw placement, the use of 3D-printed auxiliary guides for screw placement significantly improves the accuracy and safety of screw placement and shortens the operation time.
Humans ; Male ; Scoliosis/surgery* ; Female ; Adolescent ; Printing, Three-Dimensional ; Retrospective Studies ; Pedicle Screws ; Child

OBJECTIVE: To investigate the clinical features and prognosis of central nervous system (CNS) invasion in peripheral T-cell lymphoma (PTCL) and construct a risk prediction model for CNS invasion. METHODS: Clinical data of 395 patients with PTCL diagnosed and treated in the First Affiliated Hospital of Zhengzhou University from 1st January 2013 to 31st December 2022 were analyzed retrospectively. RESULTS: The median follow-up time of 395 PTCL patients was 24(1-143) months. There were 13 patients diagnosed CNS invasion, and the incidence was 3.3%. The risk of CNS invasion varied according to pathological subtype. The incidence of CNS invasion in patients with anaplastic large cell lymphoma (ALCL) was significantly higher than in patients with angioimmunoblastic T-cell lymphoma (AITL) (P <0.05). The median overall survival was significantly shorter in patients with CNS invasion than in those without CNS involvement, with a median survival time of 2.4(0.6-127) months after diagnosis of CNS invasion. The results of univariate and multivariate analysis showed that more than 1 extranodal involvement (HR=4.486, 95%CI : 1.166-17.264, P =0.029), ALCL subtype (HR=9.022, 95%CI : 2.289-35.557, P =0.002) and ECOG PS >1 (HR=15.890, 95%CI : 4.409-57.262, P <0.001) were independent risk factors for CNS invasion in PTCL patients. Each of these risk factors was assigned a value of 1 point and a new prediction model was constructed. It could stratify the patients into three distinct groups: low-risk group (0-1 point), intermediate-risk group (2 points) and high-risk group (3 points). The 1-year cumulative incidence of CNS invasion in the high-risk group was as high as 50.0%. Further evaluation of the model showed good discrimination and accuracy, and the consistency index was 0.913 (95%CI : 0.843-0.984). CONCLUSION The new model shows a precise risk assessment for CNS invasion prediction, while its specificity and sensitivity need further data validation.
Humans ; Lymphoma, T-Cell, Peripheral/pathology* ; Prognosis ; Retrospective Studies ; Central Nervous System Neoplasms/pathology* ; Neoplasm Invasiveness ; Male ; Female ; Central Nervous System/pathology* ; Middle Aged ; Adult

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.

OBJECTIVE: Evaluate the clinical application effect of low-field infant MRI. METHODS: Using literature review, expert consultation, and two rounds of Delphi to determine the evaluation index system. Then retrospectively analyze and compare the data of low-field infant MRI and high-field MRI from January 2023 to December 2024. RESULTS: There is a certain gap between low-field infant MRI and high-field MRI in terms of signal-to-noise ratio, image uniformity, software system reliability, scanning time, user interface friendliness and image result consistency. However, there was no difference in terms of spatial resolution and image quality. The noise, hardware system reliability, mean time between failure and the rate of examination completed without sedation are better than that of high-field MRI. CONCLUSION Low-field infant MRI meets needs of clinical diagnostic and has stable performance. It can be used as a routine screening tool for brain diseases near the bed.
Magnetic Resonance Imaging/methods* ; Humans ; Infant ; Retrospective Studies ; Signal-To-Noise Ratio ; Reproducibility of Results ; Brain Diseases/diagnostic imaging* ; Brain/diagnostic imaging* ; Software

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

Objective:To investigate the value of MULTI-parametric MR imaging with flexible design (MTP) combined with microview diffusion weighted imaging (DWI) in predicting pituitary tumor invasion of cavernous sinus.Methods:This was a cross-sectional study. The clinicopathological and imaging data of 59 patients with suspected pituitary tumor who were admitted to the First Affiliated Hospital of Dalian Medical University from December 2021 to August 2024 were prospectively analyzed. According to Knosp classification, patients with pituitary tumor were divided into non-invasive group (28 cases) and invasive group (31 cases). All patients underwent MTP and microview DWI sequence scanning, and the corresponding parameter values [T 1, R 2*, T 2* and apparent diffusion coefficient (ADC)] were measured on the MTP quantitative map and ADC map at the maximum diameter level of the pituitary tumor. Independent sample t test and Mann-Whitney U test were used to compare the clinical data, quantitative parameter values and proliferation index (Ki-67) between the non-invasive group and the invasive group. Quantitative parameter values with statistically significant differences were jointly included in logistic regression to construct the model. Receiver characteristic curve and area under the curve (AUC) were used to analyze single variables and logistic model in predicting the value invasion of pituitary tumor cavernous sinus invasion. Spearman correlation was used to analyze the correlation between the quantitative values and Ki-67 level. Results:There was no significant difference in age and gender between the non-invasive group and the invasive group ( P>0.05). T 1 and T 2* values in invasion group were higher than those in non-invasion group ( Z=4.42, P<0.001; Z=3.43, P<0.001), R 2* values and ADC values in the invasion group were lower than those in the non-invasion group, and the difference was statistically significant (t=4.07, P<0.001; Z=-3.07, P=0.002). The AUC values of T 1, R 2*, T 2*, ADC, logistic model 1(T 1+R 2*+ADC) and logistic model 2(T 1+T 2*+ADC) for predicting pituitary tumor cavernous sinus invasion were 0.835, 0.767, 0.760, 0.733, 0.926 and 0.925, respectively. Correlation analysis showed that the value of pituitary tumor T 1 was positively correlated with Ki-67 ( r=0.431, P=0.002). Conclusion:Quantitative parameters of MTP sequence and microview DWI sequence can quantitatively evaluate pituitary tumor invasion of cavernous sinus, and the combination of the two sequences can significantly improve the prediction efficiency.