OBJECTIVE To compare the efficacy and safety of evolocumab and probucol in patients with ultra-high-risk atherosclerotic cardiovascular disease （ASCVD） following percutaneous coronary intervention （PCI）. METHODS A retrospective analysis was conducted on 156 ultra-high-risk ASCVD patients who underwent PCI in our institution between January 1， 2023 and December 31， 2024. According to the lipid-lowering regimen， the patients were categorized into evolocumab group （ n ＝86） and probucol group （ n ＝70）. Changes in lipid parameters [total cholesterol （TC）， low-density lipoprot ein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， triglycerides， lipoprotein （a）， and lipid goal achievement rate ] ， inflammatory markers [interleukin-6 （IL-6） and C-reactive protein （CRP） ] ， and cardiac function indices （left ventricular ejection fraction， left ventricular end-systolic diameter， left ventricular end-diastolic diameter， and N-terminal pro-B-type natriuretic peptide） were compared between two groups at baseline and after 6 months of treatment. The incidence of adverse clinical events during treatment， including acute myocardial infarction， in-stent restenosis， acute heart failure， cerebral hemorrhage， and stroke， was also evaluated. RESULTS No statistically significant differences were observed between the two groups at baseline （ P ＞0.05）. After 6 months of treatment， both groups demonstrated significant improvements in lipid profiles （except HDL-C） and inflammatory markers compared to those at baseline （ P ＜0.05）. The evolocumab group exhibited greater reductions in TC， LDL-C， IL-6， and CRP， along with a higher lipid target achievement rate， compared with the probucol group （ P ＜0.05）. There were no statistically significant differences in the cardiac function-related indicators before and after treatment between the two groups， nor in the incidence of adverse events during the treatment （ P ＞0.05）. CONCLUSIONS For ultra-high-risk ASCVD patients after PCI， both of the above treatment options are associated with improvements in blood lipid and inflammatory response， with good safety during short-term follow-up. Evolocumab shows superior efficacy in TC， LDL-C and inflammatory markers reduction and lipid target achievement， compared to probucol.

This study summarizes the preoperative and intraoperative nursing experience in 5 cases of bridge-to-transplant heart transplantation with left ventricular assist device(LVAD)explant.Key points of nursing include:preoperative care and assessment of LVAD patients,preoperative discussion of the multidisciplinary team,safe transfer of patients to surgical rooms and other preoperative preparation,cardiomyocardial protection and multidisciplinary team cooperation during bridging transplantation,and intra-operative patient safety management.All 5 patients in this group successfully completed the surgery and were discharged.Pressure sores,wound infections,and other postoperative complications have not occurred.Postoperative cardiac function of 5 patients in this group were classified as New York Heart Association class Ⅰ～Ⅱ.The follow-up period for the 5 patients in this group ranged from 6 months to 6 years.The results of the most recent echocardiography follow-up showed that the left ventricular ejection fraction of all patients was all above 65％,with well prognosis.

Objective:To explore the relationship between autistic trait and pain empathy among college students, as well as the mediating role of personal pain perception.Methods:From October to December 2023, a cross-sectional survey was conducted among 1 195 college students using the autism spectrum quotient, pain sensitivity questionnaire, fear of pain questionnaire, pain catastrophizing scale and empathy for pain scale.SPSS 27.0 software was used for descriptive statistics and correlation analysis.A structural equation model was constructed using Mplus 8.3 to examine the mediating effect of personal pain perception, which was composed of pain sensitivity, fear and catastrophizing cognition.Results:Autistic trait(22.00(18.00, 26.00))was significantly positively correlated with pain sensitivity (55.00(43.00, 70.00)), fair of pain (69.00(60.00, 78.00)), and pain catastrophizing cognition (16.00(8.00, 23.00)) ( r=0.112, 0.154, 0.204, all P<0.001).Autistic trait was also significantly positively correlated with affective distress(62.00(46.00, 80.00), r=0.162, P<0.001) and vicarious pain (14.00(8.00, 20.00), r=0.096, P<0.001) of pain empathy.Pain sensitivity, fear of pain and pain catastrophizing cognition were significantly positively correlated with affective distress( r=0.244, 0.332, 0.375, all P<0.001) and vicarious pain ( r=0.210, 0.232, 0.285, all P<0.001) of pain empathy.The effects of autistic trait on affective distress and vicarious pain dimensions of pain empathy were fully mediated by personal pain perception, with the mediating effects of 0.115( P<0.001, 95% CI=0.073-0.165) and 0.085( P<0.001, 95% CI=0.053-0.124). Conclusions:The autistic trait of college students can predict the affective distress and vicarious pain of pain empathy indirectly through personal pain perception.

Objective:To explore the relationship between autistic trait and pain empathy among college students, as well as the mediating role of personal pain perception.Methods:From October to December 2023, a cross-sectional survey was conducted among 1 195 college students using the autism spectrum quotient, pain sensitivity questionnaire, fear of pain questionnaire, pain catastrophizing scale and empathy for pain scale.SPSS 27.0 software was used for descriptive statistics and correlation analysis.A structural equation model was constructed using Mplus 8.3 to examine the mediating effect of personal pain perception, which was composed of pain sensitivity, fear and catastrophizing cognition.Results:Autistic trait(22.00(18.00, 26.00))was significantly positively correlated with pain sensitivity (55.00(43.00, 70.00)), fair of pain (69.00(60.00, 78.00)), and pain catastrophizing cognition (16.00(8.00, 23.00)) ( r=0.112, 0.154, 0.204, all P<0.001).Autistic trait was also significantly positively correlated with affective distress(62.00(46.00, 80.00), r=0.162, P<0.001) and vicarious pain (14.00(8.00, 20.00), r=0.096, P<0.001) of pain empathy.Pain sensitivity, fear of pain and pain catastrophizing cognition were significantly positively correlated with affective distress( r=0.244, 0.332, 0.375, all P<0.001) and vicarious pain ( r=0.210, 0.232, 0.285, all P<0.001) of pain empathy.The effects of autistic trait on affective distress and vicarious pain dimensions of pain empathy were fully mediated by personal pain perception, with the mediating effects of 0.115( P<0.001, 95% CI=0.073-0.165) and 0.085( P<0.001, 95% CI=0.053-0.124). Conclusions:The autistic trait of college students can predict the affective distress and vicarious pain of pain empathy indirectly through personal pain perception.

Objective:To summarize the clinical and genetic characteristics of 23 children with pathogenic ACAN gene variants, enhance the understanding of this disorder, and explore possible regulatory mechanisms. Methods:A retrospective case series summary.The clinical characteristics and genetic analysis results of 23 children with ACAN gene variants treated in the Department of Endocrinology, Genetics and Metabolism, Children′s Hospital of Soochow University from January 2016 and September 2024 were analyzed retrospectively.Transcriptome sequencing was performed on peripheral blood samples from 3 of affected children and 3 age-matched healthy children as controls.Differentially expressed genes (DEGs) in the peripheral blood transcriptome profiles were identified.Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted to explore the potential signaling pathways involved. Results:Among the 23 cases, there were 13 males and 10 females, aged from 2 years and 8 months to 12 years old, with 11 cases presenting advanced bone age.Thirteen cases were treated with growth hormone (GH), including 6 cases who received concomitant gonadotropin-releasing hormone analogue therapy.The treatment duration ranged from 3 to 70 months, resulting in varying degrees of height improvement in all treated patients.Transcriptomic analysis identified 811 DEGs, with 516 up-regulated and 295 down-regulated.GO and KEGG enrichment analyses revealed that the heterozygous ACAN variants were significantly associated with FcγR-mediated phagocytosis, nuclear factor-κB signaling pathway, the intestinal immune network for IgA production, rheumatoid arthritis, and systemic lupus erythematosus signaling pathways. Conclusions:The predominant clinical manifestations of patients with ACAN gene variants are short stature and advanced bone age.Although GH provocation tests may indicate normal GH levels, GH therapy can be effective in improving height.Immune-related factors may play a role in the pathogenesis of this disorder.

Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.

Normal heart function depends on complex regulation by the brain, and abnormalities in the brain‒heart axis affect various diseases, such as myocardial infarction and anxiety disorders. However, systematic tracking of the brain regions associated with the input and output of the heart is lacking. In this study, we injected retrograde transsynaptic pseudorabies virus (PRV) and anterograde transsynaptic herpes simplex virus (HSV) into the left ventricular wall of mice to identify the whole-brain regions associated with the input to and output from the heart. We successfully detected PRV and HSV expression in at least 170 brain subregions in both male and female mice. Sex differences were discovered mainly in the hypothalamus and medulla, with male mice exhibiting greater correlation and hierarchical clustering than female mice, indicating reduced similarity and increased modularity of virus expression patterns in male mice. Further graph theory and multiple linear regression analysis of different injection timelines revealed that hub regions of PRV had highly similar clusters, with different brain levels, suggesting a top-down, hierarchically transmitted neural control pattern of the heart. Hub regions of HSV had scattered clusters, with brain regions gathered in the cortex and brainstem, suggesting a bottom-up, leapfrog, multipoint neural sensing pattern of the heart. Both patterns contain many hub brain regions that have been previously overlooked in brain‒heart axis studies. These results provide brain targets for future research and will lead to deeper insight into the brain mechanisms involved in specific heart conditions.
Animals ; Male ; Female ; Heart/physiology* ; Mice ; Herpesvirus 1, Suid ; Brain/physiology* ; Mice, Inbred C57BL ; Brain Mapping ; Efferent Pathways/physiology* ; Afferent Pathways/physiology* ; Simplexvirus ; Sex Characteristics

Background Kurtosis reflecting noise's temporal structure is an effective metric for evaluating noise-induced hearing loss (NIHL), and its threshold is still unclear. Objective To explore the energy range of kurtosis and the threshold of NIHL induced by kurtosis in this energy rangeMethods Using cross-sectional design, 4631 noise-exposed workers in manufacturing industry were selected. The hearing loss and noise exposure data of each worker were collected. Logistic regression model was used to establish the dose-response relationship between noise exposure and hearing loss and estimate the range of energy effects. Restricted cubic spline model was utilized to analyze the dose-response relationship curves of kurtosis to noise-induced hearing loss (NIHI) and high-frequency noise-induced hearing loss (HFNIHL) under different 8 h equivalent sound levels (L_{EX,8 h}), as well as to estimate the kurtosis effect threshold. Results The logistic regression model analysis showed that the prevalence of NIHI and HFNIHL increased significantly with increase of L_{EX,8 h} for steady noise; when L_{EX,8 h} of non-steady noise was 80-100 dB(A), the risk of hearing loss increased with an increase in kurtosis (P<0.05). The restricted cubic spline model showed that when L_{EX,8 h} of non-steady noise was 0-80 dB(A) or >100 dB(A), there was no significant relationship between kurtosis and NIHI or HFNIHL. When L_{EX,8 h} was 80-100 dB(A), there was a significant nonlinear dose-response relationship between kurtosis and NIHL or HFNIHL (P <0.001), and kurtosis > 28.08 exerted effect in elevating NIHI or HFNIHL. Conclusion The effect threshold of kurtosis on NIHL is 28.08 when non-steady noise levels are in the range of 80-100 dB(A). The effect threshold of kurtosis needs further verification.

Background Noise-induced hearing loss (NIHL) is a prevalent occupational health problem in workplace settings, with non-steady noise exposure being particularly widespread. Although kurtosis-adjusted equivalent sound level (\begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}) methods based on linear regression are available to assess hearing damage, the complexity of kurtosis effects necessitates introducing new metrics through nonlinear regression to improve predictive accuracy for hearing loss from non-steady noise exposure. Objective To examine the adjustment terms [\begin{document}$ \mathrm{lg}(\beta _{N}/{\beta }_{G}) $\end{document}] and coefficients (λ) of \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} using nonlinear regression and evaluate the method’s effectiveness in assessing occupational hearing loss associated with non-steady noise exposure. Methods A cross-sectional study design was employed, enrolling 1034 manufacturing workers and evaluating noise exposure to estimate hearing loss metrics. Quantile regression analysis quantified the proportional contributions of influencing factors for noise-induced permanent threshold shift at 3, 4, and 6 kHz frequencies (NIPTS₃₄₆). \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} was computed using multiple linear regression and nonlinear least squares optimization. Paired t-tests analyzed predictive efficacy before and after kurtosis adjustment to evaluate its impact on ISO 1999 NIPTS₃₄₆ predictions. Chow tests compared the similarity of logistic regression curves for high-frequency noise-induced hearing loss (HFNIHL) incidence between non-steady noise (\begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}) and steady noise (\begin{document}$ {{L}}_{\text{EX,}\text{8}\text{ h}} $\end{document}), thereby validating the effectiveness of \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}. Results The quantile regression analysis showed that kurtosis was a significant indicator of occupational hearing loss risk from non-steady noise (contribution rate was 27.5%, P<0.05). The linear regression and nonlinear least squares methods obtained six different combinations of coefficients (λ) and adjustment terms [\begin{document}$ \mathrm{lg}(\beta _{N}/{\beta }_{G}) $\end{document}]. Incorporating these \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} adjustments into the ISO 1999 predictive model significantly reduced NIPTS underestimation (from 14.2 dB HL with \begin{document}$ {{L}}_{\text{EX,}\text{8}\text{ h}} $\end{document} to 11.5–5.6 dB HL with \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}, P < 0.001). The model \begin{document}$ {{{L'}}_{\text{EX,}\text{8}\text{ h}\text{,6}}=L}_{\mathrm{E}\mathrm{X},8\;\mathrm{h}}+7.9\mathrm{lg}({{\beta }_{N}}/{10}) $\end{document} achieved the most significant improvement, reducing underestimation by 8.7 dB HL. The logistic curve analysis further demonstrated that all \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} dose-response relationships for non-steady noise aligned more closely with the steady noise group than \begin{document}$ {{L}}_{\text{EX,}\text{8}\text{ h}} $\end{document}, with \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}\text{,6}} $\end{document} showing the smallest divergence (difference: 4.1%). Conclusions \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} demonstrates superior efficacy in assessing the risk of occupational hearing loss induced by non-steady noise exposure. The \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} metric, constructed by calculating adjustment terms and coefficients through nonlinear regression analysis, exhibits greater effectiveness than linear regression methods in evaluating occupational hearing loss associated with non-steady noise exposure.

Background Temporal kurtosis (without frequency weighting, i.e., Z-weighted kurtosis) can evaluate noise-induced hearing loss (NIHL). However, few studies have considered the function of frequency weighting (A- or C-weighted) kurtosis on NIHL. Objective To study the significance of A- and C-weighted kurtosis adjustment for equivalent sound level (L'_{EX,8 h}) in evaluating occupational hearing loss. Methods A cross-sectional survey was used to select 973 noise-exposed workers in seven industries as the subjects. The noise exposure of all workers was assessed by distributions of A-, C-, and Z-weighted kurtosis (e.g., K_A, K_C, and K_Z) and respective adjusted equivalent sound level (e.g., L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z). The significance of A- and C-weighted kurtosis in evaluating NIHL was evaluated by correlations between three types of L'_{EX,8 h} and NIHL, and improvement of noise-induced permanent threshold shift (NIPTS) underestimation predicted by the ISO prediction model (Acoustics—Estimation of noise-induced hearing loss, ISO 1999-2013). Results The median K_A, K_C, and K_Z were 68.33, 28.22, and 19.82, respectively. The binary logistic regression showed that L_{EX, 8 h}-K_A, L_{EX, 8 h}-K_C, and L'_{EX, 8 h}-K_Z were risk factors for NIHL (OR>1, P<0.001). The receiver operating characteristic (ROC) curve showed that when the outcome variable was noise-induced hearing impairment (NIHI), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.625, 0.628, and 0.625, respectively. When the outcome variable was high-frequency noise-induced hearing loss (HFNIHL), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX, 8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.624, 0.623, and 0.622, respectively (P<0.05). The order of underestimation improvement values predicted by L'_{EX,8 h} for NIPTS₁₂₃₄ was: L'_{EX,8 h}-K_A (4.68 dB HL)>L'_{EX,8 h}-K_C (4.38 dB HL)>L'_{EX,8 h}-K_Z (4.28 dB HL) (P<0.001). The order of underestimation improvement values predicted by L'_{EX,8 h}-K for NIPTS₃₄₆ was: L'_{EX,8 h}-K_A (7.20 dB HL)>L'_{EX,8 h}-K_C (6.83 dB HL)>L'_{EX,8 h}-K_Z (6.71 dB HL) (P<0.001). Conclusion The adjustment of A- and C-weighted kurtosis to equivalent sound level L_{EX,8 h} can effectively improve the accuracy of the ISO 1999 prediction model in NIPTS prediction, and compared with the C-weighted, the A-weighted kurtosis can improve the result of the ISO 1999 prediction model in terms of underestimating NIPTS.