Objective: To examine the relationship between self-management behaviors and time perspective among patients with comorbid diabetes, so as to provide the evidence for improving self-management behaviors among patients with comorbid diabetes. Methods: The patients with comorbid diabetes who were registered in the chronic disease health management system of Minhang District, Shanghai Municipality in 2021, followed up regularly, and lived in Meilong Town were recruited. Demographic information and family history of diabetes were collected through questionnaire surveys. Time perspective and self-management behaviors were assessed using the Zimbardo Time Perspective Inventory and Diabetes Self-Management Behavior Scale, respectively. The relationship between self-management behaviors and time perspective was analyzed using a multivariable ordinal logistic regression model. Results: A total of 907 patients with comorbid diabetes were enrolled, including 472 males (52.04%) and 435 females (47.96%). There were 652 cases aged 65 years and above, accounting for 71.89%. In terms of the types of time perspective, 280 patients were future-oriented (30.87%), 236 were balanced (26.02%), 162 were sensation-seeking (17.86%), 123 were fatalistic (13.56%), and 106 were negative (11.69%). In terms of the self-management behaviors, 46 patients were good (5.07%), 643 were moderate (70.89%), and 218 were poor (24.04%). Multivariable ordinal logistic regression analysis showed that after adjusting for age, gender, educational level, marital status, occupation status, monthly income, and family history of diabetes, the patients with comorbid diabetes who had a future-oriented time perspective had better self-management behaviors (OR=1.874, 95%CI: 1.204-2.915). Conclusion The self-management behaviors among patients with comorbid diabetes are moderate to poor, and patients with a future-oriented time perspective can better engage in self-management behaviors.

Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

OBJECTIVE To establish fingerprint， chemical pattern recognition and multi-component quantification analysis of Sanzi powder， and evaluate its quality. METHODS HPLC method was adopted. The fingerprints of 15 batches of Sanzi powder were established by using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were also conducted. The variable importance in projection （VIP） value greater than 1 was used as the index to screen the differential markers， and the contents of the differential markers were determined by the same HPLC method. RESULTS A total of 21 common peaks in the HPLC fingerprints of 15 batches of Sanzi powder were calibrated， and the similarities of them were 0.994- 0.999； 6 common peaks were identified， including gallic acid （peak 3）， garminoside （peak 10）， corilagin （peak 11）， chebulinic acid （peak 16）， ellagic acid （peak 18）， crocin Ⅰ （peak 19）. According to the results of cluster analysis， YKD2024LH005，No.YKD2023LH062） principal component analysis and orthogonal partial least squares-discriminant analysis， 15 batches of samples could be clustered into two categories： S1， S5， S7， S9， S14 were clustered into one category； S2-S4， S6， S8， S10-S13， S15 were clustered into one category. VIP values of 11 differential components such as corilagin， chebulinic acid and ellagic acid were higher than 1. Among 15 batches of samples， the contents of corilagin， chebulinic acid and ellagic acid ranged 2.667-5.152， 9.506- 13.522， 0.891-1.811 mg/g. CONCLUSIONS Established HPLC fingerprint and multi-component quantification analysis of Sanzi powder are rapid and simple， and can be used for quality evaluation of Sanzi powder by combining with chemical pattern recognition. Eleven components such as corilagin， chebulinic acid and ellagic acid are differential markers affecting the quality of Sanzi powder.

ObjectiveTo explore the therapeutic effect of Xuebijing injection （XBJ） on severe acute pancreatitis induced acute lung injury （SAP-ALI） by regulating formyl peptide receptors （FPRs）/nucleotide-binding oligomerization domain-like receptor 3 （NLRP3） inflammatory pathway. MethodsSixty rats were randomly divided into a sham group， a SAP-ALI model group， low-， medium-， and high-dose XBJ groups （4， 8， and 12 mL·kg^-1）， and a positive drug （BOC2， 0.2 mg·kg^-1） group. For the sham group， the pancreas of rats was only gently flipped after laparotomy， and then the abdomen was closed， while for the remaining five groups， SAP-ALI rat models were established by retrograde injection of 5% sodium taurocholate （Na-Tc） via the biliopancreatic duct. XBJ and BOC2 were administered via intraperitoneal injection once daily for 3 d prior to modeling and 0.5 h after modeling. Blood was collected from the abdominal aorta 6 h after the completion of modeling， and the expression of interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） in plasma was measured by enzyme-linked immunosorbent assay （ELISA）. The amount of ascites was measured， and the dry-wet weight ratios of pancreatic and lung tissue were determined. Pancreatic and lung tissue was taken for hematoxylin-eosin （HE） staining to observe pathological changes and then scored. The protein expression levels of FPR1， FPR2， and NLRP3 in lung tissue were detected by the immunohistochemical method. Western blot was used to detect the expression of FPR1， FPR2， and NLRP3 in lung tissue. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of FPR1， FPR2， and NLRP3 in lung tissue. ResultsCompared with the sham group， the SAP-ALI model group showed significantly decreased dry-wet weight ratio of lung tissue （P<0.01）， serious pathological changes of lung tissue， a significantly increased pathological score （P<0.01）， and significantly increased protein and mRNA expression levels of FPR1， FPR2， and NLRP3 in lung tissue （P<0.01）. After BOC2 intervention， the above detection indicators were significantly reversed （P<0.01）. After treatment with XBJ， the groups of different XBJ doses achieved results consistent with BOC2 intervention. ConclusionXBJ can effectively improve the inflammatory response of the lungs in SAP-ALI rats and reduce damage. The mechanism may be related to inhibiting the expression of FPRs and NLRP3 in lung tissue， which thereby reduces IL-1β and simultaneously antagonize the release of inflammatory factors IL-6 and TNF-α.

Objective: To analyze the spatio-temporal distribution of pulmonary tuberculosis (PTB) among students in Suzhou City, Jiangsu Province from 2015 to 2023, so as to provide the evidence for the prevention and control of PTB in schools. Methods: Data of PTB cases among students in Suzhou City from 2015 to 2023 were collected from Chinese Disease Prevention and Control Information System and Suzhou Report of Investigation and Disposal of Tuberculosis in Schools. The seasonal incidence of PTB among students was analyzed using seasonal index (SI). The spatio-temporal clustering characteristics of PTB among students were analyzed using spatial autocorrelation and retrospective spatio-temporal permutation scanning. Results: Totally 1 374 PTB cases among students were reported in Suzhou City from 2015 to 2023. PTB cases were reported in each month, and the SIs were 100.69%, 124.38%, 108.98%, 135.04%, 106.61% and 106.61% in April, May, July, September, October and November, respectively, indicating the prevalence of PTB among students. Spatial autocorrelation analysis showed there was a positive spatial correlation of PTB among students in 2019 and 2020 (Moran's I=0.053 and 0.089, both P<0.05). From 2015 to 2023, there were high-high clustering sites mainly in Hengtang Street and Shishan Street. Retrospective spatio-temporal permutation scanning showed a primary cluster in Hengtang Street, with aggregation time in 2017, and 6 secondary clusters covering 25 towns (streets). Conclusion From 2015 to 2023, the PTB cases among students in Suzhou City were mainly concentrated in summer and autumn, and were predominantly clustered in Hengtang Street and Shishan Street.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Objective To explore and verify the prognostic value of endothelial cells in glioblastoma. Methods Through bioinformatics analysis of the TCGA and CGGA databases, we screened endothelial cell-related markers in GBM single-cell data according to a series of criteria. Moreover, univariate Cox regression analysis was performed to obtain and screen endothelial cell prognosis-related markers and construct endothelial cell-related prognostic risk score. qPCR experiments was used to verify the differences in the expression of prognostic markers in GBM tissues and peritumoral normal brain tissues. Kaplan-Meier method was used to construct the survival curve to identify the prognostic efficacy of the prognostic risk score. Results A total of 2 115 prognostic genes of glioblastoma (GBM) were screened. Among them, 1 494 was upregulated and 621 was downregulated. Seven groups of cells were obtained after GBM single-cell sequencing analysis, including AC-like tumor cells, endothelial cells, monocytes/macrophages, NB-like tumor cells, neurons, OC-like tumor cells, and OPC-like tumor cells. According to the differential genes of endothelial cells and the corresponding screening criteria, four genes (DUSP6, STC1, VWA1, and TM4SF1) were screened for risk-score construction. The expression of the target gene in GBM tissues and normal brain tissues around the tumor was significantly up-regulated detected by qPCR. The risk score=0.171*DUSP6+0.144*STC1+0.041*VWA1−0.004*TM4SF1. Conclusion The glioblastoma endothelial cells’ risk score determined in this study can preferably predict the prognosis of patients.

Polymyxin is an essential antibiotic for treating multidrug-resistant Gram-negative bacterial infections； however， its significant nephrotoxicity greatly limits its clinical application. To enhance its safety and improve patient outcomes， the study of novel biomarkers for the early prediction of polymyxin-associated acute kidney injury is critically important. Novel biomarkers， such as cystatin C， kidney injury molecule-1， neutrophil gelatinase-associated lipocalin， N-acetyl-β-glucosaminidase， β2- microglobulin， have shown obvious advantages in the early prediction of polymyxin-associated acute kidney injury. Compared to traditional biomarkers， these biomarkers can provide sensitive and specific diagnostic information in the early stages of kidney injury， helping to optimize individualized treatment plans and reduce clinical risks. However， the high cost of detection and complex operation still limit their clinical promotion. Future research should focus on optimizing the detection technology of new biomarkers， simplifying the operation process and reducing costs， while conducting multi-center， large-scale randomized controlled trials to systematically evaluate the sensitivity and specificity of various novel biomarkers， in order to promote their application in the field of prediction of renal injury in clinical practice.