Objective To investigate the current situation of career growth of public hospital managers and analyse its influencing factors,in order to provide a reference basis for promoting the level of career growth of hospital managers.Methods A convenient sampling method was applied to select 276 managers from nine public secondary and tertiary public hospitals in Hubei Province as the respondents,and a General Information Questionnaire,Career Growth Scale,and Person-organisation Fit scale were used to conduct the survey.Descriptive statistics,ANOVA,Pearson correlation analysis,and multiple linear regression were used to complete the analysis of the current status of career growth and influencing factors.Results The overall score of hospital managers'career growth is 3.29±0.79;person-organisation fit scores were positively correlated with hospital managers'career growth scores(P＜0.05).Multiple linear regression results showed that the type of hospital,the gender,whether or not they were both hospi-tal functionaries and clinical operations personnel,and the person-organisation fit were the major factors influencing the career growth of hospital managers the(P＜0.05).Conclusion The career growth of hospital managers is general-ly at an intermediate level and is affected by a variety of factors.Hospitals should pay attention to the individual characteristics and job requirements of managers,and improve career growth by completing the performance evalua-tion system,creating a good and fair organisational atmosphere,and easing career promotion stress.

AIM To explore the effects of Hofmeister series ions on encapsulation efficiencies of cinnamaldehyde,paeonol,and ligustilide in inclusion complex of volatile oils from Wenjing Decoction.METHODS The volatile oils were extracted,after which the β-cyclodextrin inclusion complex was prepared,the thermal stability was evaluated,and encapsulation efficiencies and inclusion complex constants of various volatile components in Na2SO4,NaH2PO4,NaCl,NaI,NaSCN solutions were determined.RESULTS The β-cyclodextrin inclusion complex demonstrated good thermal stability within 10 d.After the addtion of Hofmeister series ions,various volatile components displayed increased encapsulation efficiencies and inclusion complex constants,and concentration-dependent manner was observable in the latter.CONCLUSION Hofmeister series ions can affect the binding affinities of volatile components and β-cyclodextrin in volatile oils from Wenjing Decoction,thus regulate their encapsulation efficiencies.

Objective:To investigate the effect of age factor on glymphatic function in patients with Parkinson′s disease (PD) and its potential correlation with overall cognitive performance based on diffusion tensor imaging analysis along the perivascular space(DTI-ALPS) index.Methods:The study was cross-sectional. Clinical and imaging data of 77 PD patients (PD group) who attended the Provincial Hospital of Shandong First Medical University from October 2021 to June 2024 were retrospectively analyzed. In the same period, 30 healthy volunteers matched by age and gender were collected as the normal control (NC) group. All subjects underwent MRI scanning and DTI-ALPS index was calculated based on diffusion tensor imaging. Cognitive functions of 46 patients in the PD group were assessed using the mini-mental state examination (MMSE) and montreal cognitive assessment (MoCA) scores. Independent samples t-tests were used to compare the differences in DTI-ALPS index between the PD and NC groups. After adjusting for confounders, the relationship between DTI-ALPS and age was explored using partial correlation analyses, multiple linear regression models. A mediation model was further developed to explore the mediating effect of DTI-ALPS index between age and cognitive function scores. Results:The DTI-ALPS indices of PD and NC groups were 1.66±0.20 and 1.44±0.17, respectively, and the differences were statistically significant ( t=5.27, P<0.001). The age of patients in the PD group was negatively correlated with the DTI-ALPS index ( r=-0.54, P<0.001), and age (β=-0.467, P<0.001) was an independent influencer of DTI-ALPS index. The DTI-ALPS index was positively correlated with MMSE scores ( r=0.53, P<0.001) and positively correlated with MoCA scores ( r=0.56, P<0.001). The mediation model showed that the DTI-ALPS index fully mediated between age and MMSE scores and partially mediated between age and MoCA scores, with an effect share of 33.25%. Conclusion:Age is an independent risk factor for impaired glymphatic pathway in PD patients, and it may induce cognitive decline in PD patients by exacerbating glymphatic pathway impairment.

AIM:To observe the effect and mech-anism of cuttlebone extract regulating vascular en-dothelial growth factor(VEGF)/phosphatidylinosi-tol 3-kinase(PI3K)/protein kinase B(AKT)pathway on refractory wounds in rats.METHODS:Cuttle-bone extract(CE)was obtained by water extraction of cuttlebone.Fifty SD rats were randomly divided into negative Control group,Model group,Cuttle-bone extract low-dose(CE(L))group,Cuttlebone extract high-dose(CE(H))group,and cuttlebone ex-tract high-dose+inhibitor(CE(H)+LY294002)group.After the refractory wound model was successfully established,0.02％furacillin solution or cuttlebone extract solution were applied to the wound area of rats in each group,and the treatment was adminis-tered once a day.After 14 days of treatment for re-fractory wounds,the changes in wound healing,angiogenesis,inflammation and expression of relat-ed regulatory proteins were quantitatively ana-lyzed by measuring skin ulcer wound area,patho-logical sections,immunofluorescence staining,Eli-sa,Western blot,RT-qPCR and other methods.RE-SULTS:Compared with Model group,CE(L)and CE(H)groups can increase the number of epithelial cells and collagen,and promote the healing of re-fractory wound in rats.Serum VEGF,skin tissue mi-crovascular density,P-PI3K,P-AKT,VEGF protein ex-pression and mRNA expression levels of PI3K,Akt,VEGF and eNOS were increased(P＜0.05),while se-rum TNF-α and IL-6 levels were decreased(P＜0.05).LY294002 could partially reverse the repair-ing effect of high dose cuttlebone extract on refrac-tory wound(P＜0.05).CONCLUSION:Cuttlebone ex-tract can regulate the VEGF/PI3K/AKT signaling pathway,inhibit the inflammatory response of re-fractory wounds in rats,induce angiogenesis and promote wound healing.

During sudden outbreaks of major infectious diseases,traditional vaccine clinical trials often fail to deliver timely and meaningful outcomes.To address this,innovative trial designs are essential to accelerate or restructure the traditional three-phase clinical trial process while maintaining adherence to scientific principles of drug candidate safety and efficacy.This paper presents various innovative vaccine clinical trial designs and concepts,along with critical considerations for their application,to serve as a methodological reference for related research.Adaptive designs provide flexibility by dynamically adjusting trial parameters—such as dose selection,population stratification,and sample size reestimation—based on interim analysis results.Bayesian designs incorporate historical data and prior information,reducing sample size requirements.Master protocol designs enable the evaluation of multiple treatments or target populations within a unified framework,significantly improving efficiency.Additionally,real-world data(RWD),including electronic health records vaccination records and insurance claims,supports the creation of virtual control groups,addressing ethical concerns while enhancing trial feasibility.A hybrid design combining randomized controlled trials(RCTs)with RWD is also proposed to leverage the strengths of both methodologies.These innovative designs optimize the research process,accelerating vaccine development and regulatory approval.By integrating these approaches,robust evidence-based insights can be generated,advancing precision medicine goals and strengthening public health responses to emerging infectious diseases.

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

Objective:To investigate the therapeutic effect and potential mechanism of Nepetoidin B on rheumatoid arthritis (RA).Methods:DBA/1 mice were divided into four groups using the random number method, namely the control group, model group, methotrexate group, and Nepetoidin B group. The collagen-induced arthritis (CIA) model was prepared. Mice were treated from day 21th to day 60th. Arthritis symptoms were evaluated every three days during treatment. At the end of treatment, pathological changes of joint tissue were observed through HE staining. Serum IL-17, IL-6, MDA, and NO levels were measured using ELISA and biochemical colorimetric assays. The Nrf2/HO1 pathway in joint tissues was detected using western blot. A group of CIA mice was treated with Nepetoidin B, followed by an Nrf2 inhibitor to validate the mechanism. One-way analysis of variance was used to compare between multiple groups with homogeneity of variance, pairwise comparison using LSD- t test. Results:The study found that mice treated with methotrexate and Nepetoidin B exhibited a significant reduction in arthritis scores(CIA+Meth group 5.2±1.3, CIA+NepB group 6.8±1.2 vs. CIA group 11.0±1.7, t=6.69, P=0.004; t=5.00, P=0.009), and joint histopathology compared to the CIA mice(CIA+Meth group 1.5±1.0, CIA+NepB group 2.2±0.8 vs. CIA group 4.0±0.9, t=4.44, P<0.001; t=3.84, P=0.005). Additionally, there was a significant decrease in serum IL-17[CIA+Meth group(257±69)ng/ml, CIA+NepB group (279±103)ng/ml vs. CIA group(414±71)ng/ml, t=3.86, P=0.006; t=2.63, P=0.020], IL-6[CIA+Meth group(32±6)ng/ml, CIA+NepB group (44±5)ng/ml vs. CIA group(56±11)ng/ml, t=4.69, P<0.001; t=2.48, P=0.040) ,MDA [CIA+Meth group(22±4)μmol/L, CIA+NepB group(22±8)μmol/L vs. CIA group(34±11)μmol/L, t=2.77, P=0.038; t=2.29, P=0.049]and NO[ CIA+Meth group(37±12)μmol/L, CIA+NepB group(37±11)μmol/L vs. CIA group(56±12)μmol/L, t=2.71, P=0.040; t=2.90, P=0.035] levels, and a significant elevation in the Nrf2( 0.263±0.021, 0.273±0.022 vs. 0.221±0.034, t=3.18, P=0.044; t=2.70, P=0.049)/HO1 (0.524±0.021, 0.501±0.014 vs. 0.453±0.033, t=3.95, P=0.006; t=3.41, P=0.032) pathway in methotrexate and Nepetoidin B treated group. It was also observed that Nrf2 inhibitors could counteract the treatment effects of Nepetoidin B on arthritis (1.8±0.8 vs. 3.2±0.8, t=3.07, P=0.024). Conclusion:Nepetoidin B has the ability to inhibit oxidative stress by activating the Nrf2/HO1 pathway, which alleviates collagen-induced arthritis in mice.

Objective:To explore the safety and efficacy of intraosseous regional administration (IORA) of vancomycin for preventing infection in primary total knee arthroplasty (TKA).Methods:A total of 124 patients with knee osteoarthritis undergoing TKA between February 2024 and May 2024 at nine hospitals were enrolled. Preoperative infection prophylaxis involved either IORA (0.5 g vancomycin administered via intraosseous regional infusion before incision) or intravenous infusion (1 g vancomycin via peripheral vein). The IORA group included 15 males and 47 females with a median age of 66.5 years (range, 60.0-70.0 years), while the intravenous group included 14 males and 48 females with a median age of 66.0 years (range, 61.8-70.3 years) years. Intraoperative samples were collected including fat and synovium tissues after incision, before prosthesis placement, and after tourniquet release; distal femoral cancellous bone during femoral osteotomy; proximal tibial cancellous bone during tibial osteotomy; proximal intercondylar cancellous bone before prosthesis placement; and peripheral blood from non-infused arms at surgery initiation and after tourniquet release. Vancomycin concentrations were measured using liquid chromatography-tandem mass spectrometry. Vital sign changes were recorded from admission to 5~10 minutes post-IORA (IORA group) or post-incision (intravenous group). Follow-ups were conducted on postoperative day 1 and 3, and at 1 and 3 months, to document complications including IORA-related adverse events, periprosthetic joint infections, surgical site infections, red man syndrome, acute kidney injury, deep vein thrombosis and so on.Results:Vancomycin concentrations in bone, fat, and synovial tissue samples were significantly higher in the IORA group than in the intravenous group ( P<0.05), while vancomycin concentrations in blood samples were significantly lower in the IORA group than in the intravenous group ( P<0.05). Only 7.3%(41/558) of tissue samples in the IORA group had vancomycin concentrations below 2.0 μg/g (the minimum inhibitory concentration of vancomycin against coagulase-negative staphylococcus), compared to 59.3%(331/558) in the intravenous group (χ 2=11.285, P<0.001). In the intravenous group, 16.9%(21/124) of blood samples had vancomycin concentrations exceeding 15.0 mg/L (the threshold associated with a significantly increased risk of nephrotoxicity), while all concentrations in the IORA group were below this threshold, the difference was statistically significant (χ 2=22.943, P<0.001). There were no statistically significant difference ( P>0.05) in vital signs changes before and after vancomycin administration between the two groups. Two patients in the intravenous group experienced incision exudate, while no other related complications occurred in either group. Conclusions:Compared to the traditional intravenous infusion of 1 g vancomycin, intraosseous injection of a low dose (0.5 g) of vancomycin achieves higher local tissue concentrations in the knee joint with a lower incidence of adverse reactions and is safe for infection prophylaxis. Despite guidelines not recommending the routine use of vancomycin for preventing infection after primary TKA, intraosseous injection of 0.5 g vancomycin may be considered intraoperatively for primary TKA in the following scenarios: patients in medical institutions with a high prevalence of methicillin-resistant staphylococcus aureus (MRSA) infections, patients with potential preoperative MRSA colonization, or patients with cephalosporin allergy.

Objective:To investigate the association between anti-rituximab antibodies (ARA) and relapse after rituximab (RTX) therapy in children with frequently relapsing or steroid-dependent nephrotic syndrome (FRNS or SDNS).Methods:A retrospective cohort study was conducted. Clinical and laboratory data were collected from 48 FRNS or SDNS children treated with RTX in the Department of Pediatrics, General Hospital of Eastern Theater Command, between April 2024 and October 2024. Data included RTX dosing frequency, relapse events, peripheral CD20? B-cell counts, and ARA levels. With a 6-month observation period after the last RTX therapy, the children were divided into an ARA-positive group and an ARA-negative group based on ARA test results. Chi-square test, independent sample t-test, or Mann-Whitney U test were used to compare relapse rates and laboratory indicators between the two groups. The predictive value of ARA levels for relapse was evaluated using univariate receiver operating characteristic (ROC) curve analysis. Results:Among the 48 children (36 males, 12 females), the age of disease onset was 3.5 (2.0, 6.0) years, the ages at the first and last RTX treatments were 7.0 (5.0, 12.0) years and 9.5 (7.0, 13.0) years, respectively. The overall ARA positive rate was 29% (14/48). The relapse rate in the ARA-positive group was significantly higher than that in the negative group ( P<0.05). The ARA level was 0.01 (0.01, 5.88) μg/L, and all 12 children with ARA levels >5.88 μg/L relapsed. ROC curve analysis showed that ARA levels predicted relapse after RTX treatment in FRNS or SDNS children with an area under the curve (AUC) of 0.73, sensitivity of 0.50, specificity of 1.00, and an optimal cut-off value of 5.02 μg/L. All children received single-dose RTX therapy, with no significant difference in treatment frequency between the two groups ( P>0.05). At 3 months after the last rituximab therapy, CD20? B cell counts were significantly higher in the ARA-positive group ( P<0.05). During follow-up, 15% (7/48) of the children experienced infusion-related adverse reactions, with no significant difference in incidence between the two groups ( P>0.05). Conclusion:ARA is significantly associated with relapse in FRNS or SDNS children after RTX therapy.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.