To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

Background Diesel engines are widely used in transportation, agriculture, construction, industry, and other fields. Diesel exhaust, classified as a Group 1 carcinogen, emits particles (DEP) that can penetrate deep into the respiratory tract, posing significant health risks. DEP pollution is particularly severe in confined environments, necessitating effective control measures. Objective Under laboratory simulation conditions, to explore the spatiotemporal distribution characteristics of the mass and number concentrations of DEP as it diffuses indoors and to reveal the effects of ventilation and additional airflow on indoor DEP pollution levels. Methods A diesel engine was placed in a laboratory (length 3.39 m × width 2.85 m × height 2.4 m) with its exhaust emitted from east to west. An air purifier was installed 1 m south of the engine. Eight measurement points (1 m horizontal distance from the exhaust outlet, height: 1 m/1.5 m) were setup to monitor DEP concentrations using portable laser particle sizers. The effects of engine power (4.05 kW vs. 5.15 kW), ventilation (maximum airflow: 600 m³·h⁻¹), additional airflow intensity (low and high), and direction (forward/reverse) on DEP pollution were analyzed. DEP levels of 5 diesel vehicle models were also compared. Results The mass and number concentrations of DEP indoors increased immediately after the diesel engine started. The peak mass concentration time at the eastern measurement point (−1, 0) m opposite to the exhaust direction (17.70 min) was significantly longer than that at the western (1, 0) m (16.20 min), southern (0, -1) m (14.45 min), and northern (0, 1) m (12.70 min) points (P<0.05), with no significant differences between the other points (western, southern, and northern) (P>0.05). The northern point (0, 1) m exhibited the highest DEP mass and number concentration peaks (174.62 μg·m⁻³, 1319.85 p·cm⁻³), significantly exceeding those at the southern (0, −1) m (129.89 μg·m⁻³, 1175.24 p·cm⁻³), eastern (−1, 0) m (140.12 μg·m⁻³, 1120.53 p·cm⁻³), and western (1, 0) m (147.60 μg·m⁻³, 818.62 p·cm⁻³) points (P<0.05), and no significant differences were observed among other points (P>0.05). There were no significant differences in peak DEP mass and number concentrations by measurement heights (P>0.05). Diesel engine power, ventilation, airflow intensity, and airflow direction had no significant effect on the time of peak DEP concentration (P>0.05). However, higher engine power (5.15 kW) produced greater DEP peaks [(186.66±19.29) μg·m⁻³, (1382.79±122.56) p·cm⁻³] than the 4.05 kW engine power [(168.41±12.65) μg·m⁻³, (1284.45±71.30) p·cm⁻³] (P<0.05). The peak mass concentration [(342.04±35.03) μg·m⁻³] and number concentration [(1886.87±57.91) p·cm⁻³] of DEP in the non-ventilated group were significantly higher than those in the ventilated group [(186.66±19.29) μg·m⁻³, (1382.79±122.56) p·cm⁻³] (P<0.001). Forward airflow elevated DEP mass concentrations compared to reverse airflow (287.71 μg·m⁻³ vs. 243.74 μg·m⁻³, t=−2.592, P=0.009), but no effects on DEP number concentration (P>0.05). Significant differences in mass concentration were observed among selected 5 vehicle models (Z=38.109, P<0.001). Conclusion The operation of diesel engines will emit a large amount of particulate matter, causing pollution in enclosed spaces. Diesel engines with greater power have higher levels of DEP pollution emissions. Based on the spatiotemporal distribution characteristics, it is recommended to set the operation position in the reverse direction of exhaust emissions and close to air purifiers, while avoiding the use of additional air flow equipment.

Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.

OBJECTIVES: To develop a risk prediction model for left ventricular adverse remodeling (LVAR) based on cardiac magnetic resonance (CMR) parameters in patients undergoing percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 329 acute STEMI patients undergoing primary PCI at 8 medical centers from January, 2018 to December, 2021 were prospectively enrolled. The parameters of CMR, performed at 7±2 days and 6 months post-PCI, were analyzed using CVI42 software. LVAR was defined as an increase >20% in left ventricular end-diastolic volume or >15% in left ventricular end-systolic volume at 6 months compared to baseline. The patients were randomized into training (n=230) and validation (n=99) sets in a 7∶3 ratio. In the training set, potential predictors were selected using LASSO regression, followed by univariate and multivariate logistic regression to construct a nomogram. Model performance was evaluated using receiver-operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. RESULTS: LVAR occurred in 100 patients (30.40%), who had a higher incidence of major adverse cardiovascular events than those without LVAR (58.00% vs 16.16%, P<0.001). Left ventricular global longitudinal strain (LVGLS; OR=0.76, 95% CI: 0.61-0.95, P=0.015) and left atrial active strain (LAAS; OR=0.78, 95% CI: 0.67-0.92, P=0.003) were protective factors for LVAR, while infarct size (IS; OR=1.05, 95% CI: 1.01-1.10, P=0.017) and microvascular obstruction (MVO; OR=1.26, 95% CI: 1.01-1.59, P=0.048) were risk factors for LVAR. The nomogram had an AUC of 0.90 (95% CI: 0.86-0.94) in the training set and an AUC of 0.88 (95% CI: 0.81-0.94) in the validation set. CONCLUSIONS LVGLS, LAAS, IS, and MVO are independent predictors of LVAR in STEMI patients following PCI. The constructed nomogram has a strong predictive ability to provide assistance for management and early intervention of LVAR.
Humans ; Percutaneous Coronary Intervention ; Prospective Studies ; ST Elevation Myocardial Infarction/diagnostic imaging* ; Ventricular Remodeling ; Magnetic Resonance Imaging ; Male ; Female ; Middle Aged ; Risk Factors ; Aged ; Risk Assessment

OBJECTIVES: To investigate the inhibitory effect of pirfenidone (PFD) on growth of bladder cancer xenograft and its regulatory effect on Treg cells in tumor-bearing mice. METHODS: Thirty-two C57BL/6 mice bearing ectopic bladder tumors were randomized into control and PFD groups (n=16). In PFD group, PFD was administered orally at the daily dose of 500 mg/kg, and tumor growth and survival of the mice were monitored. After treatment for 21 days, the tumors and vital organs were harvested for analysis. Immunohistochemistry was used to assess CD3, CD4, CD8, and FOXP3 expressions in the tumors. Flow cytometry and RT-qPCR were used to analyze the percentage of CD4⁺CD25⁺FOXP3⁺ Treg cells and IL-2, IL-10, and IL-35 expressions in the tumors and spleens; organ damage of the mice was examined with HE staining. RESULTS: Compared with the control group, the PFD-treated mice exhibited significantly lower tumor growth rate with smaller tumor volumes at day 21, along with improved survival at day 28. Immunohistochemistry revealed no significant differences in the infiltration of CD3⁺ and CD8⁺ cells between the two groups, but the percentages of CD4⁺ and FOXP3⁺ cells were significantly lower in the tumors of PFD-treated mice. Flow cytometric analysis confirmed a decrease in CD4⁺CD25⁺FOXP3⁺ Treg cells in the tumors from PFD-treated mice, which also had reduced expression levels of IL-2, IL-10 and IL-35 mRNAs in the tumors. No significant differences were found in Treg cell populations or cytokine expressions in the spleen tissues between the two groups. HE staining showed obvious organ damage in neither of the groups. CONCLUSIONS PFD inhibits bladder cancer growth and enhances survival of tumor-bearing mice possibly by suppressing Treg cells in the tumor microenvironment.
Animals ; Urinary Bladder Neoplasms/drug therapy* ; Mice ; T-Lymphocytes, Regulatory/metabolism* ; Mice, Inbred C57BL ; Interleukins/metabolism* ; Interleukin-10/metabolism* ; Cell Line, Tumor ; Interleukin-2/metabolism* ; Xenograft Model Antitumor Assays ; Female

As the need for antibody production rises, there is an urgent need to lower the costs and enhance the efficiency of the separation process. Currently, the chromatographic media used for antibody separation and purification often focus on individual properties of antibodies, such as affinity, hydrophobicity, and charge, leading to issues like low purification efficiency or inadequate adsorption capacity. To address this, an electrostatically coupled polypeptide affinity medium (FD7-3, 5-diaminobenzoic acid n-sepharose, FD7-DA-Sepharose) was developed for rapid purification of antibodies from cell culture supernatant. This medium utilized 3, 5-diaminobenzoic acid as a spacer to attach the heptapeptide-affinity ligand (FYEILHD, FD7) to agarose microspheres. Antibodies could be adsorbed through charge interactions with the carboxyl functional group of the FD7-DA-Sepharose spacer, while FD7 enhanced electrostatic coupling and affinity adsorption through synergistic effects, significantly increasing the adsorption capacity while maintaining the affinity and specificity. The influences of pH and ionic strength on adsorption capacity were investigated with human immunoglobulin as a model protein. The static adsorption capacity (Q_m) of FD7-DA-Sepharose in the solution of pH 6.0 reached 67.73 mg/mL, representing a 52.68% increase compared with that (44.36 mg/mL) of the commercial Protein A affinity medium. Furthermore, the elution conditions for FD7-DA- Sepharose were mild (20 mmol/L PB, 0.5 mol/L NaCl, pH 6.0), in contrast to the harsh acidic elution (pH 2.7-3.6) typically associated with Protein A, which can damage antibody integrity. The FD7-DA-Sepharose medium was then employed to purify antibodies from cell culture supernatant, achieving the yield of 94.8% and the purity of 98.4%. The secondary structure of the purified antibody was determined by circular dichroism spectroscopy. The results demonstrated that FD7-DA-Sepharose enabled efficient purification of antibodies from cell culture supernatant, which provided a cost-effective solution (approximately one-third the price of commercial Protein A affinity medium) with gentle elution conditions that preserve the natural conformation of antibodies. This approach paves a novel, economical, and efficient way for the separation and purification of antibodies from cell culture supernatant.
Chromatography, Affinity/methods* ; Static Electricity ; Humans ; Sepharose/analogs & derivatives* ; Peptides/chemistry* ; Adsorption ; Antibodies/isolation & purification*

Objective:To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms.Methods:The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms—random forest, decision tree, and XGBoost—were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results:A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95% CI 0.813-0.995), 0.956 (95% CI 0.796-0.992), and 0.973 (95% CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95% CI 0.557-0.964), 0.706 (95% CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion:Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.

Objective To evaluate the predictive value of global longitudinal strain(GLS)measured by cardiac magnetic resonance(CMR)feature-tracking technique for left ventricular remodeling(LVR)after percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 403 patients undergoing PCI for acute STEMI were prospectively recruited from multiple centers in China.CMR examinations were performed one week(7±2 days)and 6 months after myocardial infarction to obtain GLS,global radial strain(GRS),global circumferential strain(GCS),ejection fraction(LVEF)and infarct size(IS).The primary endpoint was LVR,defined as an increase of left ventricle end-diastolic volume by≥20%or an increase of left ventricle end-systolic volume by≥15%from the baseline determined by CMR at 6 months.Logistic regression analysis was performed to evaluate the predictive value of CMR parameters for LVR.Results LVR occurred in 101 of the patients at 6 months after myocardial infarction.Compared with those without LVR(n=302),the patients in LVR group exhibited significantly higher GLS and GCS(P<0.001)and lower GRS and LVEF(P<0.001).Logistic regression analysis indicated that both GLS(OR=1.387,95%CI:1.223-1.573;P<0.001)and LVEF(OR=0.951,95%CI:0.914-0.990;P=0.015)were independent predictors of LVR.ROC curve analysis showed that at the optimal cutoff value of-10.6%,GLS had a sensitivity of 74.3%and a specificity of 71.9%for predicting LVR.The AUC of GLS was similar to that of LVEF for predicting LVR(P=0.146),but was significantly greater than those of other parameters such as GCS,GRS and IS(P<0.05);the AUC of LVEF did not differ significantly from those of the other parameters(P>0.05).Conclusion In patients receiving PCI for STEMI,GLS measured by CMR is a significant predictor of LVR occurrence with better performance than GRS,GCS,IS and LVEF.

Objective To provide evidence-based references for the prevention of surgical site infection(SSI)by sum-marizing the best evidence for the prevention of SSI in adult inpatients.Methods The'6S'evidence resource pyramid model was used to systematically search the related evidence in domestic and foreign databases,guideline websites,and academic socie-ty websites from the inception of the database to September 30,2023.Four researchers evaluated the quality of the included guidelines,and two researchers independently evaluated the quality of other types of literature and rated the level of evidence.Results A total of 12 articles were included,including 6 clinical decision making and 6 clinical guidelines.Thirty best items of the evidence were summarized from 7 aspects:diagnosis,clinical symptoms,influencing factors,patient prevention strategies,preventive strategies for medical staff,intraoperative and postoperative treatment,and consultation and education.Conclusion Clinical staff should develop a standardized management plan for infection prevention based on corresponding evidence to reduce the incidence of SSI instead of taking a single measurement.Moreover,they need to formulate a standardized work process for preventing SSI based on the clinical practice and patients'preference.

Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.