Objective To evaluate the short-term efficacy of transcatheter aortic valve replacement (TAVR) using Venus A-Plus valve delivery system in patients with severe aortic stenosis. Methods The clinical data of patients undergoing TAVR in our hospital from August 2018 to March 2022 were collected and they were divided into a Venus A-Plus and a Venus A group according to the type of valve delivery system used. The perioperative data of the two groups were compared. Results A total of 121 patients were included, including 70 patients in the Venus A-Plus group [45 males and 25 females with a mean age of (67.81±6.62) years], and 51 patients in the Venus A group [33 males and 18 females with a mean age of (68.25±7.01) years]. All patients underwent TAVR, and the postoperative hemodynamic features (left ventricular ejection fraction, mean cross-valve pressure difference, peak flow rate) were significantly improved (P<0.05). There was no statistical difference in surgical success rate, all-cause mortality, conversion to thorax opening, valve-in-valve placement, moderate or above perivalvular regurgitation, new left bundle branch block or new right bundle branch block between the two groups (P>0.05). Conclusion TAVR with Venus A-Plus valve delivery system in patients with severe aortic stenosis shows comparable efficacy to the first-generation Venus A system and is satisfactory, safe and reliable.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Background Bufei Huoxue Capsule (BHC) has gradually been used in clinical practice to treat patients with pneumoconiosis in recent years. However, the comprehensive evaluation of its efficacy and safety is lacking. Objective To systematically assess the therapeutic potential of BHC for pneumoconiosis. Methods By searching 9 databases, e.g. China National Knowledge Infrastructure, Wanfang Database, VIP Chinese Science and Technology Journals Database, SinoMed, Yiigle, PubMed, Embase, Cochrane Library, and Web of Science, randomized controlled trials (RCTs) related to the use of BHC for the treatment of pneumoconiosis were identified. The search covered the period from the inception of these databases to August 31, 2025. This systematic review conformed to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020. The included studies underwent quality appraisal by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Subgroup analysis and sensitivity analysis were applied to assess the robustness of results and explore the potential heterogeneity among the studies. Results A total of 19 RCTs were included and all were reported in Chinese. The therapy containing BHC showed a superior impact on forced expiratory volume in one second (FEV₁) (95%CI: 0.38, 0.66; I²=69%, P<0.05), FEV₁% (95%CI: 3.37, 5.72; I²=12%, P<0.05), forced vital capacity (FVC) (95%CI: 0.34, 0.45; I²=0%, P<0.05), FEV₁/FVC% (95%CI: 6.29, 10.35; I²=21%, P<0.05), 6-minute walking distance (6MWD) score (95%CI: 16.32, 27.64; I²=22%, P<0.05), St. George's Respiratory Questionnaire (SGRQ) score (95%CI: −1.47, −1.05; I²=13%, P<0.05), and total effective rate (95%CI: 2.71, 6.78; I²=0%, P<0.05) in patients with pneumoconiosis. In addition, BHC had less adverse reactions reported (95%CI: 0.69, 1.74; I²=0%, P=0.70). Conclusions Combined BHC therapy can significantly improve the lung function and the quality of life in patients with pneumoconiosis, with a good safety profile. However, high-quality RCTs with multicenter, large-sample, double-blind, and standardized protocols still need to be conducted in the future to provide more reliable evidence.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

Objective: To establish a method for determining the molecular weight and molecular weight distribution of Poly(Lactide-co-Glycolide Acid) (PLGA) using Size Exclusion Chromatography-Refractive Index-Multiangle Laser Light Scattering (SEC-RI-MALLS) and Size Exclusion Chromatography-Refractive Index (SEC-RID), and to compare the results obtained from these two methods.
Methods: For SEC-RI-MALLS, tetrahydrofuran was used as the mobile phase, Shodex GPC KF-803L was employed as the chromatographic column with a flow rate of 1 mL·min^-1, column temperature at 30 ℃, and an injection volume of 100 μL. For SEC-RID, tetrahydrofuran was also used as the mobile phase, Agilent PLgel 5 μm MIXD-D was used as the chromatographic column with a flow rate of 1 mL·min^-1, column temperature at 30 ℃, differential detector temperature at 35 ℃, and an injection volume of 20 μL. The molecular weight and molecular weight distribution were calculated using Agilent’s GPC software. The newly established methods were validated methodologically, and the molecular weight and molecular weight distribution of 13 batches of samples were determined.
Results: The precision, accuracy, stability, and repeatability tests for SEC-RI-MALLS showed RSD values of 1.35%, 1.58%, 1.53%, and 1.26%, respectively. The SEC-RID method exhibited good linearity (r=0.999 9), with RSD values for precision, accuracy, stability, and repeatability tests (n=6) of 2.05%, 1.62%, 1.30%, and 2.97%, respectively. The results obtained from SEC-RI-MALLS were lower than those from SEC-RID, and the molecular weight distribution coefficient was smaller, but the results from the paired T-test performed with the value measured by SEC-RID method and the value measured by SEC-RI-MALLS method multiplied a conversion coefficient of 1.5 showed no significant difference between the two methods.
Conclusion: Both methods are stable and reliable, and can be used for the determination of PLGA molecular weight and molecular weight distribution based on the specific situations.

Objective To satisfy the normalized disinfection in the orthopedic ward,an air sterilizer based on low-temperature plasma has been developed to investigate its sterilization results in a dynamic environment of hospitalization where patients,companions and medical workers are involved.Methods This study took an orthopedics ward in the Secondary Affiliated Hospital of Xi'an Jiaotong University,as the research object,where a home-made low-temperature plasma air sterilizer was utilized.A six-stage viable Andersen cascade impactor was used to sample the natural bacteria in the ward before and after machine operation for three hours.The species and quantity of bacteria in the ward were analyzed.Results The ozone concentration in the indoor dynamic environment decreased to below 5 ppbv.After three-hour disinfection,the elimination rate of natural bacteria reached 92.35％.The final colony forming unit decreased to～150 CFU/m3;the extinction rates of Staphylococcus hominis,Bacillus cereus,molds,and Micrococcus luteus were 90.48％,80.90％,87.50％,and 92.82％respectively.Even all Haemophilus massiliensis disappeared after two-hour treatment.Conclusion Intermittent disinfection of the dynamic environment in the ward using low-temperature plasma synergistic catalyst has enabled the indoor ozone concentration to reach the first-level national standard line,effectively suppressing secondary pollution caused by ozone leakage while efficiently killing suspended microorganisms in the air,which is close to the disinfection level Ⅰenvironment specified in Hygienic Standard for Disinfection in Hospitals(GB 15982-2012).The results also show that the plasma catalytic synergistic disinfection and sterilization has the technical advantages of efficient disinfection and human-machine coexistence,which can ensure indoor air quality safety,reduce the workload of nursing staff,and thus is an effective method to assist or even replace the existing physical and chemical means.

Objective To investigate the risk factors for ligamentum flavum hypertrophy(LFH)and its correlation with clinical symptoms in patients with degenerative lumbar spinal stenosis(DLSS).Methods The clinical and imaging data of 79 patients with DLSS were collected.Patients were divided into four groups based on LFH severity.Quantitative parameters,including lumbar lordosis(LL),sacral slope(SS),facet tropism,facet joint effusion,intervertebral height index,dural sac cross-sectional area(CSA),epidural fat area,and fat infiltration rate(FIR)of the paraspinal muscle were measured on imaging.One-way analysis of variance was used to compare the differences in these parameters among groups.Multiple linear regression analysis was performed to identify the risk fac-tors for LFH,and the correlation between LFH severity and clinical manifestations was analyzed.Results The results of one-way analy-sis of variance showed that there were statistically significant differences among the four groups of patients in terms of sex,body mass index(BMI),LL,epidural fat area and FIR of the multifidus(MF).Multiple linear regression analysis identified that BMI,LL,and epidural fat area as independent risk factors for LFH.Correlation analysis indicated a weak positive association between LFH and dis-ease duration(r=-0.231,P=0.041).Conclusion In DLSS patients,LFH is weakly correlated with disease duration,while BMI,LL,and epidural fat area are risk factors for LFH.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Objective To investigate the new-onset conduction block after transcatheter aortic valve replacement (TAVR) and summarize the relevant experience. Methods The perioperative data of TAVR patients in the Second Hospital of Hebei Medical University from January 2016 to February 2023 were collected, and the new-onset incidence of conduction block after TAVR was analyzed retrospectively. Results Finally 352 patients were included, including 225 males and 127 females, with an average age of (67.2±5.1) years, among whom 256 patients were treated with Venus-A valves, 69 patients with Vita-Flow valves, and 27 patients with J-Valve valves. There were 38 (10.8%) patients of new-onset postoperative block. There were 6 (1.7%) patients of new-onset postoperative grade Ⅲ atrioventricular block, including 5 (2.0%) patients of Venus-A and 1 (1.4%) patient of Vita-Flow. Conduction function was restored in 2 patients within 14 days after surgery, and failed to be restored in 4 patients, who then received permanent pacemaker implantation in the Department of Cardiology. There were 27 (7.7%) patients of new left bundle branch block after surgery, including 22 (8.6%) patients of Venus-A, 4 (5.8%) patients of Vita-Flow and 1 (3.7%) patient of J-Valve; and conduction function was restored within 7 days after surgery in 23 patients, and 5 (1.4%) patients developed new right bundle branch blocks after surgery including 4 (1.5%) patients of Venus-A and 1 (1.4%) patient of Vita-Flow. Conclusion New-onset conduction block is a common complication after TAVR, and the new-onset rate of left bundle branch block is the highest, followed by the grade Ⅲ atrioventricular block. Mastering reasonable methods and applying appropriate strategies can effectively reduce the new-onset rate of postoperative conduction block and improve the overall success rate of TAVR surgery.