Background Per- and polyfluoroalkyl substances (PFASs) are a class of persistent organic pollutants that pose potential health risks to humans. Infants and young children have higher requirements for food safety due to the underdeveloped detoxification and immune systems. Therefore, developing a comprehensive method for determination of PFASs and their novel alternatives in infant complementary food is of great significance. Objective To develop an analytical method using liquid chromatography high-resolution mass spectrometry technology for determination of 55 PFASs in plant- and animal-derived infant complementary fruit purees. Methods Oasis WAX (200 mg, 6 CC) solid-phase extraction columns were used for sample enrichment and purification. The pH of the acetonitrile extract was adjusted using 0%, 1%, 1.5%, and 2% formic acid aqueous solutions to evaluate its impact on the recovery rate of target compounds. Additionally, the impact of a 2 mL methanol wash during the purification process on the recovery of target compounds was assessed to determine the optimal pretreatment conditions. Three types of chromatographic columns—Agilent Poroshell 120 EC-C₁₈, Thermo InfinityLab Poroshell 120 Aq-C₁₈, Acquity Waters BEH-C₁₈, and changes in mobile phase, were compared for their effects on retention time, peak shape, and response of target compounds. The method was validated in terms of selectivity, linear range, detection limit, and precision. The established method was applied to 49 commercial samples of infant complementary fruit purees. Results Adjusting the sample pH using 1.5% formic acid water and incorporating a 2 mL methanol wash during purification achieved satisfactory recovery rates. The target compounds were chromatographically separated using an Agilent Poroshell 120 EC-C₁₈ column with a gradient elution system. The mobile phase consisted of methanol-water (methanol/water: 2/98, v/v) containing 5 mmol·L⁻¹ ammonium formate as mobile phase A, and methanol as mobile phase B. Good separation was achieved within 15 min, resulting in optimal chromatographic peak shapes. The 55 target compounds exhibited good linearity across the standard curve range, with correlation coefficients (R²) greater than 0.99. The method detection limits ranged from 0.02 to 0.05 µg·L⁻¹. In the plant- and animal-based fruit puree samples, the spiked recovery rates ranged from 60% to 112% and 57% to 119%, respectively, with relative standard deviations (RSD) ≤ 30%. A total of 9 traditional PFASs and 5 novel PFASs were positive in 49 samples of infant complementary fruit purees. Conclusion This method enables comprehensive detection of 55 traditional and emerging PFASs, offering wide coverage, high accuracy, and excellent sensitivity. It provides technical support for characterizing contamination by traditional and emerging PFASs in food matrices.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Merkel cell polyomavirus (MCV) was named thus because it is the causative agent of Merkel cell carcinoma (MCC), with 80% of MCC cases being MCV-positive. MCV has been classified as a 2A carcinogen. It promotes carcinogenesis by integrating T antigens into the cell genome. The anti-MCV seroprevalence in the general population is as high as 90%. Usually, MCV is latent after infection in immunocompetent patients, and the incidence of MCC in immunosuppressive or defective patients, such as those with organ transplants, chronic lymphocytic leukemia, and HIV infection, is remarkably high. Patients with HIV/AIDS are a typical population with acquired immunodeficiency. At present, the research on patients with HIV/AIDS and MCV infection, activation, and pathogenesis is limited. In this paper, the progress of previous research is reviewed and the relationship between HIV infection and MCV activation is systematically investigated to provide a reference for the prevention and treatment of MCC in key populations, such as patients with HIV/AIDS.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background With the progression of industrialization, an increasing number of emerging contaminants are entering aquatic environments, posing significant threats to the safety of drinking water. Therefore, establishing a system for identifying unknown hazardous factors and implementing safety warning mechanisms for drinking water is of paramount importance. Among these efforts, non-target screening plays a critical role, but its effectiveness is largely constrained by the scope of coverage of sample pre-treatment methods. Objective To integrate modern chromatography/mass spectrometry techniques with advanced data mining methods to develop a non-discriminatory sample pre-treatment method for comprehensive enrichment of unknown contaminants in drinking water, laying a technical foundation for the discovery and identification of unknown organic hazardous factors in drinking water. Methods A non-discriminatory pre-treatment method based on supramolecular and solid-phase extraction was developed. The final target compounds including 333 pesticides, 194 pharmaceuticals and personal care products (PPCPs), and 59 per- and polyfluoroalkyl substances (PFASs) were used for optimizing the pre-treatment method, confirming its coverage. The impacts of different eluents on the absolute recovery rates of target compounds were compared to select the conditions with the highest recovery for sample pre-treatment. The effects of different supramolecular solvents and salt concentrations on target compound recovery were also evaluated to determine the most suitable solvent and salt concentration. Results The solid-phase extraction elution solvents, supramolecular extraction solvents, and salt concentrations were optimized based on the target compound recovery rates. The optimal recovery conditions were achieved using 2 mL methanol, 2 mL methanol (containing 1% formic acid), 2 mL ethyl acetate, 2 mL dichloromethane, hexanediol supramolecular solvent, and 426 mg salt. The detection method developed based on these conditions showed a good linear relationship for all target compounds in the range of 0.1-100.0 ng·mL⁻¹, with R² > 0.99. The method’s limit of detection ranged from 0.01 ng⁻¹ to 0.95 ng⁻¹, and 95% of target compounds were recovered in the range of 20%-120%, with relative standard deviation (RSD) less than 30%, indicating good precision. Conclusion The combined pre-treatment method of solid-phase extraction and supramolecular solvent extraction can effectively enrich contaminants in drinking water across low, medium, and high polarities, enabling broad-spectrum enrichment of diverse trace contaminants in drinking water. It provides technical support for broad-spectrum, high-throughput screening and identification of organic pollutants in drinking water, and also serves as a reference for establishing urban drinking water public safety warning systems.

Based on the strategy of metabolomics combined with bioinformatics, this study analyzed the potential allergens and mechanism of pseudo-allergic reactions (PARs) induced by the combined use of Reduning injection and penicillin G injection. All animal experiments and welfare are in accordance with the requirements of the First Affiliated Experimental Animal Ethics and Animal Welfare Committee of Henan University of Chinese Medicine (approval number: YFYDW2020002). Based on UPLC-Q-TOF/MS technology combined with UNIFI software, a total of 21 compounds were identified in Reduning and penicillin G mixed injection. Based on molecular docking technology, 10 potential allergens with strong binding activity to MrgprX2 agonist sites were further screened. Metabolomics analysis using UPLC-Q-TOF/MS technology revealed that 34 differential metabolites such as arachidonic acid, phosphatidylcholine, phosphatidylserine, prostaglandins, and leukotrienes were endogenous differential metabolites of PARs caused by combined use of Reduning injection and penicillin G injection. Through the analysis of the "potential allergen-target-endogenous differential metabolite" interaction network, the chlorogenic acids (such as chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, and isochlorogenic acid A) and β-lactam allergens in the combination of the two may be mainly regulated by PLD1, PLA2G12A and CYP1A1. The three upstream signal target proteins mainly activate the arachidonic acid metabolic pathway, promote the degranulation of mast cells, release downstream endogenous inflammatory mediators, and induce PARs.

Twelve compounds were isolated from the ethyl acetate fraction of the 80% aqueous ethanol extract of the roots and stems of Dalbergia rimosa Roxb. by silica gel, MCI, Sephadex LH-20 column chromatography, and semi-preparative HPLC. Their structures were identified by spectral analysis such as UV, IR, MS, 1D/2D NMR and by comparison with literature information as dalbergiquinol A (1), dalbergiquinol B (2), R-(-)-3′-hydroxy-2,4,5-trimethoxydalbergiquinol (3), neokhriol A (4), mucronulatol (5), (3R)-7,2′,3′-trihydroxy-4′-methoxy-isoflavane (6), isomucronulatol (7), (3S)-violanone (8), 3′-O-methylviolanone (9), eryvarin M (10), (±)-α,3,4,2′,4′-pentahydroxydihydrochalcone (11) and (-)-butin (12). Compound 1 and 2 are new compounds, and compounds 3-12 were isolated from this plant for the first time. Compounds 1, 2, 4, 6, 8, 11, 12 showed good scavenging effect on DPPH free radical.

Objective:Anterior cruciate ligament injury is the most common type of knee joint ligament injury.Anterior cruciate ligament reconstruction has a high failure rate,with bone tunnel abnormalities as the most significant factor in these failures.Digital orthopedic technology can effectively develop implementation plans for the revision,thus increasing the success rate.This study aims to develop a surgical plan for anterior cruciate ligament revision by employing multiplanar reconstruction(MPR)for measuring bone tunnel position and diameter,and simulating bone tunnel creation via 3D printing preoperatively. Methods:A total of 12 patients who underwent anterior cruciate ligament revision at the Third Xiangya Hospital of Central South University between 2014 and 2021 were retrospectively studied.The data included patient demographics,preoperative formulated knee joint 3D printing models,and preoperative knee CT scans.The study measured the bone tunnel's diameter and position to guide the establishment of revision bone tunnels during surgery,reassessed the postoperative bone tunnels,and evaluated knee joint functional scores[including International Knee Documentation Committee Knee Evaluation Form(IKDC)score,Lysholm score,and Tegner exercise level score]. Results:Preoperative measurements revealed suboptimal femoral tunnels positions in 4 patients and tibial tunnels positions in 2 patients.MPR and 3D printing technology were used to guide the establishment of a new bone canal during surgery,and postoperative measurements were satisfactory for all patients.Preoperative measurements demonstrated the interclass correlation coefficient for femoral tunnels and tibial tunnels diameters were 0.843(P<0.05)and 0.889(P<0.001),respectively.Meanwhile,the intraclass correlation coefficient were 0.811(P<0.05)and 0.784(P<0.05),respectively.The intraoperative diameter of femoral and tibial tunnels showed excellent correlation with postoperative CT measurements,with intraclass correlation coefficient values of 0.995(P<0.001)and 0.987(P<0.001),respectively.All bone tunnel positions were within the normal range.At the final follow-up,knee joint function scores in all 12 patients improved significantly compared to pre-surgery(P<0.001),and the reoperation rate was zero. Conclusion:MPR and 3D printing technology can accurately measure the parameters of reconstructed anterior cruciate ligament bone tunnels.Personalized revision plans for patients with reconstruction failure enhances the success rate of revision surgery and improves patient prognosis.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.