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.

Objective: To analyze the structural changes in whole blood and apheresis platelet donations in Chinese Society of Blood Transfusion (CSBT) sentinel units from 2021 to 2025, and to provide evidence for donor recruitment and blood service management. Methods: Aggregated data reported by sentinel sites were collected and stratified according to six indicators: age, sex, donation history, donation volume, donation site, and organization mode. Pearson χ test, Cramer′V coefficient, Cochran-Armitage trend test and simple linear regression were adopted for analysis. Results: A total of 16 256 973 whole blood donations and 1 576 546 apheresis platelet donations were included. Among whole-blood donations, the proportion of donors aged 18-22 years decreased from 27.30% to 10.60%. The proportion of male donors increased from 58.81% to 63.13%; the proportion of people aged 30 and above increased from 58.40% to 75.30%; the proportion of fixed locations rose from 28.37% to 36.57%; the proportion of social groups increased from 21.69% to 39.69%. The 400-mL donations decreased from 62.12% to 53.46%, while individually initiated voluntary donations remained above 50%. Apheresis platelet donations were predominantly from males, individuals aged 30-39 years, and repeat donors; 2-treatment-unit apheresis platelet donations increased from 67.19% to 74.93%, and individually initiated donations accounted for 92.97%-96.02%. Statistically significant overall annual compositional differences were observed for all six indicators (all P<0.001), whereas linear trends for some sub-categories were not statistically significant. Conclusion: In the participating CSBT sentinel units, among whole blood donations, the proportions of donors aged 18-22 years, university-organized group donations, and donations at blood mobiles decreased, whereas the proportions of organizational group donations and donations at fixed venues increased. The apheresis platelet donation cohort is relatively mature and is evolving toward a high-efficiency donation model. Individual voluntary donation remains the primary organization mode for both donation types. Strategies such as youth donor recruitment, optimized layout of fixed donation sites, and retention of repeat donors should be strengthened to improve the resilience of blood supply.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

Objective:To investigate lymph node metastasis(LNM)in the prostatic anterior fat pad(PAFP)of PCa patients after robot-assisted radical prostatectomy(RARP),and analyze the clinicopathological features and prognosis of LNM in the PAFP.Methods:We retrospectively analyzed the clinicopathological data on 1 003 cases of PCa treated by RARP in the Department of Urolo-gy of PLA General Hospital from January 2017 to December 2022.All the patients underwent routine removal of the PAFP during RARP and pathological examination,with the results of all the specimens examined and reported by pathologists.Based on the pres-ence and locations of LNM,we grouped the patients for statistical analysis,compared the clinicopathological features between different groups using the Student's t,Mann-Whitney U and Chi-square tests,and conducted survival analyses using the Kaplan-Meier and Log-rank methods and survival curves generated by Rstudio.Results:Lymph nodes were detected in 77(7.7％)of the 1 003 PAFP samples,and LNM in 11(14.3％)of the 77 cases,with a positive rate of 1.1％(11/1 003).Of the 11 positive cases,9 were found in the upgraded pathological N stage,and the other 2 complicated by pelvic LNM.The patients with postoperative pathological stage≥T3 constituted a significantly higher proportion in the PAFP LNM than in the non-PAFP LNM group(81.8％[9/11]vs 36.2％[359/992],P=0.005),and so did the cases with Gleason score ≥8(87.5％[7/8]vs 35.5％[279/786],P=0.009).No statisti-cally significant differences were observed in the clinicopathological features and biochemical recurrence-free survival between the pa-tients with PAFP LNM only and those with pelvic LNM only.Conclusion:The PAFP is a potential route to LNM,and patients with LNM in the PAFP are characterized by poor pathological features.There is no statistically significant difference in biochemical recur-rence-free survival between the patients with PAFP LNM only and those with pelvic LNM only.Routine removal of the PAFP and inde-pendent pathological examination of the specimen during RARP is of great clinical significance.

Objective To investigate the value of fractional-order calculus(FROC)and continuous-time random-walk(CTRW)diffusion models based on readout segmentation of long variable echo-trains(RESOLVE)in identifying benign and malignant lesions in the head and neck.Methods A retrospective analysis was conducted on 61 patients pathologically confirmed head and neck lesions,including 19 benign lesions(BL)and 42 malignant lesions(ML).The ML were further divided into a lymphoma subgroup(LS)with 9 cases(14 lesions)and a non-lymphoma malignant lesion subgroup(MLS)with 33 cases.The parameters of DFROC,βFROC,μFROC,DCTRW,αCTRW and βCTRW were obtained from the two diffusion models;Independent sample t-tests or U tests were used to compare the differences in each parameter between benign and malignant groups and among various subgroups,and the receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficacy of each parameter.Area under the curve(AUC)was compared by DeLong test.Results DFROC,μFROC,DCTRW and αCTRW showed significant differences between benign and malignant,BL and LS,BL and MLS and LS and MLS,with αCTRW showed the highest diagnostic efficacy;βFROC showed differences between BL and LS,BL and MLS,whileβCTRW did not show differences between benign and malignant groups,and among subgroups.Conclusion FROC and CTRW diffusion models based on RESOLVE can distinguish between benign and malignant head and neck lesions with multiple parameters,and provide metrics reflecting tissue heterogeneity.

Objective:To analyze the efficacy of combination of peginterferon α-2b（Peg-IFN α-2b）with nucleos（t）ide analogues（NAs）on antiviral therapy in children with chronic hepatitis B（CHB）and to provide an optimized clinical treatment strategies for CHB children.Methods:A retrospective analysis was conducted on 30 CHB children treated in The First Affiliated Hospital of the Army Medical University（Southwest Hospital）from January 2022 to January 2025 with treatment duration at least 48 weeks. The enrolled children were aged between 2 and 17 years and divided into the NAs combined with Peg-IFN α-2b（NPI）group（n=13）and NAs group（n=17）by their therapy regimens. The characteristics of baseline，week 12，week 24，week 48 and week 96 were compared between groups，as well as the differences in response to biochemical，immune and viral indicators at each observation point. Logistic regression analysis and receiver operating characteristic（ROC）curve analysis were performed to identify factors influencing the HBsAg seroclearance. Results:At baseline of treatment，the proportion of HBeAg positivity in the NPI group and the NAs group was high（76.9% vs 86.6%， χ2=0.679， P=0.628），and the alanine aminotransferase（ALT）and aspartate aminotransferase（AST）in the NPI group were significantly lower than those in the NAs group（ P<0.001）. At 24 weeks，the decrease in HBsAg in the NPI group was also significantly higher than that in the NAs group（ Z=-3.161， P=0.002）. Finally，the cumulative seroclearance rate of HBsAg at 96 weeks in the NPI group was significantly higher than that in the NAs group（46.15% vs 5.88%， χ2=0.679， P=0.025）. Mulitivariate Logistic regression analysis showed that treatment regimen and gender were risk factors affecting the outcome of HBsAg（ P<0.05）. ROC curve analysis showed that the increase in ALT at 12 weeks compared with baseline（AUC=0.857，Cutoff value=3.615 IU/L），the decrease in ALT at 24 weeks（AUC=0.870，Cutoff value=47.85 IU/L），and the decrease in HBsAg at 12 weeks and especially at 24 weeks（AUC=0.885，Cutoff value=0.97log IU/ml）were effective predictors of HBsAg prognosis at 96 weeks. Conclusion:In CHB children，antiviral regimen Peg-IFN α-2b combined with NAs was more effective than NAs alone in improving the HBsAg seroclearance rate of CHB，and the effects in female were better than in male. The decline of HBsAg and the fluctuation of ALT in the early treatment period are valid predictors of HBsAg clearance.