BACKGROUND:Precision therapy based on multifunctional nanomaterials is a novel therapeutic model for malignancies that can integrate multiple imaging and therapeutic models into one nanoscale platform to achieve visual combination treatment. OBJECTIVE:To prepare novel nanoparticles loaded with Cu2(OH)PO4 nanoparticles(CuNPs)and nuciferine(NF)(h-PCuNF),and to explore their ability to mediate combined photothermal therapy/photodynamic therapy/chemodynamic therapy/chemotherapy for malignancy. METHODS:The h-PCuNF nanoparticles were synthesized through a double-emulsion procedure,through which the CuNPs and NF were loaded into the shell of hollow poly(lactic-co-glycolic)acid nanocarriers.The morphology,structure,particle size,and zeta potential of the h-PCuNF nanoparticles were characterized.In deionized water,the magnetic resonance imaging and photothermal conversion performances of the h-PCuNF nanoparticles,as well as their capability to implement reactive oxygen species production by mediating photocatalysis and Fenton-like reactions,were evaluated.In liver malignant tumor cell line HepG2 cells,the effectiveness of the photothermal therapy/photodynamic therapy/chemodynamic therapy/chemotherapy combination therapy mediated by the nanoparticles was detected by employing fluorescence imaging and MTT assay. RESULTS AND CONCLUSION:(1)The h-PCuNF nanoparticles possessed a hollow spherical structure in which the CuNPs(drug loading rate and encapsulation rate were 26.3%and 63.2%,respectively)and NF(drug loading rate and encapsulation rate were 11.0%and 52.6%,respectively)were loaded into the shell.The average particle size of the h-PCuNF nanoparticles was(309.2±10.0)nm,while the zeta potential was determined to be(-12.5±0.9)mV.In physiological environments,the nanoparticles possess favorable suspension stability.(2)In deionized water,the h-PCuNF nanoparticles could markedly enhance T1-weighted magnetic resonance imaging images.The h-PCuNF nanoparticles showed remarkable photothermal conversion and photocatalytic reactive oxygen species generation capabilities under near infrared laser irradiation.In addition,the h-PCuNF nanoparticles could consume glutathione and mediate Fenton-like reactions to produce·OH.(3)The h-PCuNF nanoparticles could be taken up by HepG2 tumor cells and were mainly distributed in the cytoplasm.The synergistic therapeutic effect was demonstrated after the nanoparticles were activated by near infrared laser irradiation,because CuNPs mediated photothermal therapy/photodynamic therapy/chemodynamic therapy and NF mediated chemotherapy could synergistically eliminate the tumor cells.

AIM: To investigate the potential inhibitory effect of pterostilbene on the endothelial-to-mesenchymal transition(EndMT)induced by high glucose conditions in human retinal microvascular endothelial cells(HRMECs).METHODS: The optimal concentration of pterostilbene for treating HRMECs was determined using the CCK-8 assay, with 12.5 and 25 μmol/L concentrations selected for subsequent experiments. Four experimental groups were established: control group, high glucose group, high glucose combined with 12.5 μmol/L pterostilbene treatment group, and high glucose combined with 25 μmol/L pterostilbene treatment group. The expression levels of HDAC7 and EndMT-associated markers were detected via Western blot analysis. Cell migration ability was assessed using Transwell migration assays and scratch wound healing tests, while vasculogenic capability was evaluated through tube formation assays.RESULTS: The CCK-8 assay revealed that pterostilbene at a concentration of 22.07 μmol/L inhibited 50% of cell viability in HRMECs. Western blot analysis demonstrated that compared with the control group, the expression levels of HDAC7, ZEB1, Vimentin, and Snail were significantly upregulated in HRMECs cultured in high glucose(all P<0.01), while the expressions of VE-cadherin and CD31 were significantly reduced(all P<0.01). Compared to the high glucose group, the treatment with 12.5 and 25 μmol/L pterostilbene significantly reduced the expression of HDAC7, ZEB1, Vimentin, and Snail under high glucose conditions(all P<0.01). Notably, 25 μmol/L pterostilbene enhanced the expression of VE-cadherin and CD31(all P<0.01). Scratch wound healing tests revealed that HRMECs treated with high glucose exhibited a significantly increased cell migration rate compared to the control group(P<0.05), while the application of 25 μmol/L pterostilbene significantly suppressed HRMECs migration under high glucose conditions(P<0.01). Transwell migration assays demonstrated that the cell migration rate in the high glucose group was significantly higher than that in the control group(P<0.01), with cell migration rate markedly reduced following treatment with both of 12.5 and 25 μmol/L pterostilbene(all P<0.01). The tube formation assay revealed that the ability of HRMECs to form tubular structures was significantly enhanced under high glucose conditions(P<0.01), and both 12.5 and 25 μmol/L of pterostilbene effectively inhibited this effect(all P<0.01).CONCLUSION: Pterostilbene can inhibit HDAC7 expression, suppress EndMT-mediated migration of HRMECs, and impair tube formation under high-glucose conditions.

AIM: To investigate the potential inhibitory effect of pterostilbene on the endothelial-to-mesenchymal transition(EndMT)induced by high glucose conditions in human retinal microvascular endothelial cells(HRMECs).METHODS: The optimal concentration of pterostilbene for treating HRMECs was determined using the CCK-8 assay, with 12.5 and 25 μmol/L concentrations selected for subsequent experiments. Four experimental groups were established: control group, high glucose group, high glucose combined with 12.5 μmol/L pterostilbene treatment group, and high glucose combined with 25 μmol/L pterostilbene treatment group. The expression levels of HDAC7 and EndMT-associated markers were detected via Western blot analysis. Cell migration ability was assessed using Transwell migration assays and scratch wound healing tests, while vasculogenic capability was evaluated through tube formation assays.RESULTS: The CCK-8 assay revealed that pterostilbene at a concentration of 22.07 μmol/L inhibited 50% of cell viability in HRMECs. Western blot analysis demonstrated that compared with the control group, the expression levels of HDAC7, ZEB1, Vimentin, and Snail were significantly upregulated in HRMECs cultured in high glucose(all P<0.01), while the expressions of VE-cadherin and CD31 were significantly reduced(all P<0.01). Compared to the high glucose group, the treatment with 12.5 and 25 μmol/L pterostilbene significantly reduced the expression of HDAC7, ZEB1, Vimentin, and Snail under high glucose conditions(all P<0.01). Notably, 25 μmol/L pterostilbene enhanced the expression of VE-cadherin and CD31(all P<0.01). Scratch wound healing tests revealed that HRMECs treated with high glucose exhibited a significantly increased cell migration rate compared to the control group(P<0.05), while the application of 25 μmol/L pterostilbene significantly suppressed HRMECs migration under high glucose conditions(P<0.01). Transwell migration assays demonstrated that the cell migration rate in the high glucose group was significantly higher than that in the control group(P<0.01), with cell migration rate markedly reduced following treatment with both of 12.5 and 25 μmol/L pterostilbene(all P<0.01). The tube formation assay revealed that the ability of HRMECs to form tubular structures was significantly enhanced under high glucose conditions(P<0.01), and both 12.5 and 25 μmol/L of pterostilbene effectively inhibited this effect(all P<0.01).CONCLUSION: Pterostilbene can inhibit HDAC7 expression, suppress EndMT-mediated migration of HRMECs, and impair tube formation under high-glucose conditions.

ObjectiveTo investigate the value of different noninvasive diagnostic models in the diagnosis of esophageal and gastric varices since there is a high risk of esophageal and gastric varices in patients with compensated hepatitis B cirrhosis and significant portal hypertension， and to provide a basis for the early diagnosis of esophageal and gastric varices. MethodsA total of 108 patients with significant portal hypertension due to compensated hepatitis B cirrhosis who attended Guangdong Provincial Hospital of Traditional Chinese Medicine from November 2017 to November 2023 were enrolled， and according to the presence or absence of esophageal and gastric varices under gastroscopy， they were divided into esophageal and gastric varices group （GOV group） and non-esophageal and gastric varices group （NGOV group）. Related data were collected， including age， sex， imaging findings， and laboratory markers. The chi-square test was used for comparison of categorical data between groups； the least significant difference t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups. The receiver operating characteristic （ROC） curve was plotted to evaluate the diagnostic value of five scoring models， i.e.， fibrosis-4 （FIB-4）， LOK index， LPRI， aspartate aminotransferase-to-platelet ratio index （APRI）， and aspartate aminotransferase/alanine aminotransferase ratio （AAR）. The binary logistic regression method was used to establish a combined model， and the area under the ROC curve （AUC） was compared between the combined model and each scoring model used alone. The Delong test was used to compare the AUC value between any two noninvasive diagnostic models. ResultsThere were 55 patients in the GOV group and 53 patients in the NGOV group. Compared with the NGOV group， the GOV group had a significantly higher age （52.64±1.44 years vs 47.96±1.68 years， t=0.453， P<0.05） and significantly lower levels of alanine aminotransferase ［42.00 （24.00 — 17.00） U/L vs 82.00 （46.00 — 271.00） U/L， Z=-3.065， P<0.05］， aspartate aminotransferase ［44.00 （32.00 — 96.00） U/L vs 62.00 （42.50 — 154.50） U/L，Z=-2.351， P<0.05］， and platelet count ［100.00 （69.00 — 120.00）×10⁹/L vs 119.00 （108.50 — 140.50）×10⁹/L， Z=-3.667， P<0.05］. The ROC curve analysis showed that FIB-4， LOK index， LPRI， and AAR used alone had an accuracy of 0.667， 0.681， 0.730， and 0.639， respectively， in the diagnosis of esophageal and gastric varices （all P<0.05）， and the positive diagnostic rates of GOV were 69.97%， 65.28%， 67.33%， and 58.86%， respectively， with no significant differences in AUC values （all P>0.05）， while APRI used alone had no diagnostic value （P>0.05）. A combined model （LAF） was established based on the binary logistic regression analysis and had an AUC of 0.805 and a positive diagnostic rate of GOV of 75.80%， with a significantly higher AUC than FIB-4， LOK index， LPRI， and AAR used alone （Z=-2.773，-2.479，-2.206， and-2.672， all P<0.05）. ConclusionFIB-4， LOK index， LPRI， and AAR have a similar diagnostic value for esophageal and gastric varices in patients with compensated hepatitis B cirrhosis and significant portal hypertension， and APRI alone has no diagnostic value. The combined model LAF had the best diagnostic efficacy， which provides a certain reference for clinical promotion and application.

Objective: To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features, providing novel insights into the early screening of lung cancer. Methods: This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1, 2019 to December 31, 2024, as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period. The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument, and the facial diagnosis features were extracted from it by deep learning technology. Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics, and the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables. Based on the screened feature variables, four machine learning methods: random forest, logistic regression, support vector machine (SVM), and gradient boosting decision tree (GBDT) were used to establish lung cancer classification models independently. Meanwhile, the model performance was evaluated by indicators such as sensitivity, specificity, F1 score, precision, accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the area under the precision-recall curve (AP). Results: A total of 1 275 patients with pulmonary nodules and 1 623 patients with lung cancer were included in this study. After propensity score matching (PSM) to adjust for gender and age, 535 patients were finally included in the pulmonary nodule group and the lung cancer group, respectively. There were significant differences in multiple color space metrics (such as R, G, B, V, L, a, b, Cr, H, Y, and Cb) and texture metrics [such as gray-levcl co-occurrence matrix (GLCM)-contrast (CON) and GLCM-inverse different moment (IDM)] between the two groups of individuals with pulmonary nodules and lung cancer (P < 0.05). To construct a classification model, LASSO regression was used to select 63 key features from the initial 136 facial features. Based on this feature set, the SVM model demonstrated the best performance after 10-fold stratified cross-validation. The model achieved an average AUC of 0.8729 and average accuracy of 0.799 0 on the internal test set. Further validation on an independent test set confirmed the model’s robust performance (AUC = 0.823 3, accuracy = 0.729 0), indicating its good generalization ability. Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components (including color-B-0, wholecolor-Cr, and lipcolor-Cr) were the core factors in the model’s classification decisions, while texture indicators [GLCM-angular second moment (ASM)_2, GLCM-IDM_1, GLCM-CON_1, GLCM-entropy (ENT)_2] played an important auxiliary role. Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas. The various models constructed based on facial image features all demonstrate good performance, indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction, providing a non-invasive and feasible new approach for early lung cancer screening.

Objective To explore the association of working hours and shift work with occupational stress, fatigue accumulation, and depressive symptoms among primary community medical workers. Methods A total of 516 medical workers from five community medical service centers in Pudong New Area, Shanghai City, were selected as the research subjects using the convenience sampling method. The Core Scale of Occupational Stress Measurement, the Workers' Fatigue Accumulation Self-diagnosis Questionnaire, and the Patient Health Questionnaire were used to assess research subjects' occupational stress, fatigue accumulation, and depressive symptoms, respectively. Results Long working hours (>40 hours/week) were reported by 50.4% of workers among the research subjects, while shift works were reported by 16.9% of the workers. The detection rates of occupational stress, fatigue accumulation, and depressive symptoms were 26.6%, 41.7%, and 30.8%, respectively. Multivariate logistic regression analysis result revealed that, after adjusting for confounders such as age, sex, and education level, longer working hours were associated with higher risks of occupational stress, fatigue accumulation, and depressive symptoms (all P<0.05). Shift workers in community medical centers had higher risks of occupational stress, fatigue accumulation, and depressive symptoms compared with non-shift workers (all P<0.05). Conclusion Long working hours and shift work could increase the risks of occupational stress, fatigue accumulation, and depressive symptoms among community medical workers.

Objective:To explore the definition and connotation of the thriving in the elderly, so as to provide reference for clinical practice.Methods:This study systematically searched databases such as China National Knowledge Infrastructure, WanFangdata, CINAHL, PsycINFO, PubMed, and Web of Science using keywords such as "aged" and "thriving". The search period was from database establishment to June 10, 2023. Walker's eight-step analysis method was used to conduct concept analysis on the thriving in the elderly.Results:A total of 14 articles were included. The defining attributes of the thriving in the elderly included good self-perception, utilizing existing resources to adjust self-expectation, and achieving self-growth and development. The antecedents were a positive attitude, high-quality caregivers and care services, a good physical and humanistic environment. The consequence was that elderly people had great adaptability, subjective experience, and health level.Conclusions:The thriving in the elderly is a subjective well-being achieved by the interaction among an individual, the humanistic and physical environment, which is affected by various factors such as individuals and environment. It is a new concept that has emerged in the international field of geriatric nursing in recent years, focusing on the positive aspects of aging and providing new directions for research in geriatric nursing.

Medical and pharmaceutical research laboratories encompass a wide range of study areas.They utilize diverse materials ranging from animals and microorganisms to nanoparticles and other substances.However,as laboratory waste increases,more biosafety risks are created.In this context,we outlined the safety risks associated with gene amplification,gene recombination,research involving pathogenic microorganisms,nanotechnology,animal experiments,genetically modified animals,and experimental waste.Additionally,we here in propose preventive measures to mitigate laboratory biosafety risks.These measures primarily involve the development of strict legal frameworks,improvement of hardware infrastructure,strengthening of safety awareness,and enhancement of education and training programs.

Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is the main pathogen that causes COVID-19,which is fast-mutating and highly transmissible.The infection has led to a global epidemic.As the main preventive and control measure,vaccination plays a critical role in fighting a-gainst COVID-19.Although a large number of epitope-based and mucosal vaccines have been stud-ied,few peptide epitope vaccines targeting the mucosa and their functional evaluation have been re-ported.In this study,we used SARS-CoV-2 structural protein M peptide epitope predicted by the IEDB database as an antigenic target to design the MS-3S gene containing 3 050 and 1 229 signal peptides and DCpep optimized for insertion into MS2 phage coat proteins.The expression plasmid pSIP:MS-3S was constructed by cloning the PCR fragments seamlessly and was transformed into Lactiplantibacillus plantarum 18 to obtain the recombinant bacterium LP18:MS-3S.Expression conditions such as induction time,inducer concentration,rotational speed and initial pH were opti-mized.The intranasal immunization experiments were performed to examine the vaccine efficacy.The results showed that the 916 bp-long target gene MS-3S modified and optimized was amplified and used to successfully construct the recombinant bacterial strain LP18:MS-3S.The optimal con-ditions for recombinant protein expression were obtained and verified by Western blot,flow cy-tometry,immunofluorescence and other detection methods.The optimal expression conditions were determined as follows:induction time was 4 h with 100 pg/L of SppIP as the optimal induction concentration.Antibody-specific for the epitope was verified by ELISA experiments in serum,alve-olar lavage fluid and fecal dilutions of mice.In summary,a recombinant bacterial strain expressing the epitope antigen of the SARS-CoV-2 M protein peptide was constructed.The obtained protein can induce the body to produce humoral and mucosal immunity,which lays the foundation for the development of a vaccine candidate for the mucosal immunity of COVID-19.

Objective:To construct preeclampsia (PE) prediction models using information from the hospital electronic medical information and clinical laboratory data through logistic regression (LR) and machine learning algorithms, and to compare their predictive performance.Methods:The study was conducted based on the information from Rouji Pregnancy Test Database and the perinatal data of women who visited the Third Affiliated Hospital of Guangzhou Medical University from January 1, 2012, to December 31, 2019. Drawing upon clinical treatment guidelines and related literature, 28 clinical indicators from 2 736 pregnant women at 24 to 28 weeks of gestation were selected after a thorough integration and used for the construction of the PE prediction model dataset. Patients diagnosed with PE comprised the PE group ( n=245), while another 255 cases from the rest who did not have PE were selected, with undersampling method, as the control group. The Random Forest algorithm (RF), eXtreme Gradient Boosting (XGB) algorithm, and LR model were each employed to develop predictive models for PE. Following the construction of the models, external validation of PE prediction accuracy was carried out using data acquired from an independent prospective cohort study on PE that was conducted from June 2019 to December 2022, in which 38 PE cases and 80 controls were chosen. The performance of predictive models were evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic. Results:Indicators included in the construction of the three predictive models suggested that uric acid, creatinine, maternal age, early pregnancy body mass index, urea, triglycerides, red blood cell count, eosinophil count, total cholesterol, neutrophil count, urine protein, alanine aminotransferase, and urine occult blood were influential in PE prediction models. The AUCs for RF, XGB, and LR models in the training and test sets were 0.851 (95% CI:0.730-0.891), 0.955 (95% CI:0.865-0.987), 0.884 (95% CI:0.767-0.923) vs. 0.845 (95% CI:0.723-0.868), 0.907 (95% CI:0.791-0.919), 0.851 (95% CI:0.755-0.893), respectively. In the test set, the accuracy, sensitivity, and specificity for RF, XGB, and LR models were 0.803, 0.607, 0.958, 0.864, 0.790, 0.927, and 0.832, 0.661, 0.971, respectively. In the external validation of the RF, XGB and LR predictive models, the accuracy were 0.822, 0.814, and 0.763; the sensitivity were 0.737, 0.789, and 0.605, and the specificity were 0.863, 0.825, and 0.838, respectively. Among them, XGB model showed the highest Youden's index (0.614). Conclusion:Compared to traditional methods of model construction, machine learning algorithms can establish more effective PE prediction models using real clinical data.