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.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Objective To explore the relationship between postoperative defecation dysfunction and quality of life in children with anorectal malformation(ARM)complicated with spinal cord anomalies(SCA)and analyze the impact of different types of SCA on ARM patients in order to provide a reference for the early clinical identification of high-risk children with poor prognosis.Methods A retrospective analysis was conducted on 282 ARM neonates admitted to our department between June 2015 and April 2021.Radiological examinations were applied to evaluate the development of the spinal cord,and Rintala score and the PedsQL 4.0 scale were employed to assess postoperative defecation function and quality of life,respectively.According to their SCA types and other complications,the patients were grouped.The relationship between these factors and defecation function as well as quality of life was then analyzed.Results Among the 282 subjected children,104(36.9%)had SCA.The incidence of SCA varied significantly across different types of ARM(P=0.002),with the highest incidence observed in vaginal fistula patients(100.0%)and the lowest in children without fistula(13.6%).Radiological findings revealed that sacral bone anomalies were common,with absent coccyx(62.7%)and vertebral anomalies(69.8%)being the most prevalent.The SCA group had significantly lower Rintala bowel function score(12.70±3.24)and PedsQL 4.0 quality of life score(81.42±5.03)than the non-SCA group(P<0.001).As the increment of SCA types,both the Rintala score and PedsQL 4.0 score were in a significant downward trend(P<0.001).Among the children with different types of SCA,those with tethered cord syndrome had the statistically lowest Rintala score(8.05±2.35,P<0.05).Meanwhile,their PedsQL 4.0 score(75.90±3.35)was significantly lower than those of other types except syrinx(P<0.05).Multiple linear regression analysis indicated that both SCA and sacral bone anomalies exerted notably negative impacts on the Rintala score and PedsQL 4.0 score(P≤0.001),with SCA having the most pronounced effect.Conclusion SCA is closely associated with postoperative defecation dysfunction and diminished quality of life in ARM children.The greater the type and number of SCAs,the worse the postoperative defecation function and quality of life.Early identification of concomitant SCAs holds significant clinical value for predicting postoperative outcomes in ARM patients.

This study aimed to explore the molecular mechanism of rubioncolin C(RuC) in inhibiting gastric cancer(GC). AGS and MGC803 cell lines were selected as cellular models. After treating the cells with RuC at different concentrations, the effects of RuC on the proliferation ability of GC cells were assessed using the CCK-8 method, real-time cellular analysis(RTCA), and colony formation assays. Transmission electron microscopy was used to observe subcellular structural changes. Immunofluorescence was applied to detect LC3 fluorescent foci. Acridine orange staining was used to evaluate the state of intracellular lysosomes. Western blot was employed to detect the expression of autophagy-related proteins LC3Ⅱ, P62, and lysosomal cathepsin D(CTSD). The SuperPred online tool was used to predict the target proteins that bound to RuC, and molecular docking analysis was conducted to identify the interaction sites between RuC and CTSD. The drug affinity responsive target stability(DARTS) assay was performed to detect the direct binding interaction between RuC and CTSD. The results showed that RuC significantly inhibited the proliferation and colony formation of GC cells at low concentrations, with 24-hour half-maximal inhibitory concentrations(IC_(50)) of 3.422 and 2.697 μmol·L~(-1) for AGS and MGC803 cells, respectively. After 24 hours of treatment with RuC at concentrations of 1, 2, and 3 μmol·L~(-1), the colony formation rates for AGS cells were 61.0%±1.5%, 28.0%±0.5%, and 18.2%±0.5%, respectively, while the rates for MGC803 cells were 56.0%±0.5%, 23.3%±1.0%, and 11.8%±1.0%, all of which were significantly reduced. Transmission electron microscopy revealed that RuC promoted an increase in autophagosome formation in GC cells. Immunofluorescence detection showed that LC3 fluorescent foci of GC cells increased with the increase in RuC dose. RuC up-regulated the expression of autophagy-related proteins LC3Ⅱ and P62 in GC cells. Acridine orange staining indicated that RuC altered the acidic environment of lysosomes. SuperPred online prediction identified CTSD as a potential target protein of RuC. Western blot analysis revealed that RuC induced the up-regulation of the inactive precursor of CTSD in GC cells. CTSD activity assays indicated that RuC reduced the activity of CTSD. Molecular docking simulations found that RuC bound to the substrate-binding region of CTSD, forming hydrogen bonds with the Tyr205 and Asp231 residues. Microscale thermophoresis and DARTS assays further confirmed that RuC directly bound to CTSD. In summary, RuC inhibits lysosomal activity by targeting and down-regulating the expression of CTSD, thereby inducing autophagosome accumulation in GC cells.
Humans ; Stomach Neoplasms/enzymology* ; Cathepsin D/chemistry* ; Cell Line, Tumor ; Molecular Docking Simulation ; Cell Proliferation/drug effects* ; Autophagosomes/metabolism* ; Autophagy/drug effects*

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis

This study primarily aimed to investigate the prevalence of human papillomavirus (HPV) and other common pathogens of sexually transmitted infections (STIs) in spermatozoa of infertile men and their effects on semen parameters. These pathogens included Ureaplasma urealyticum, Ureaplasma parvum, Chlamydia trachomatis, Mycoplasma genitalium , herpes simplex virus 2, Neisseria gonorrhoeae, Enterococcus faecalis, Streptococcus agalactiae, Pseudomonas aeruginosa , and Staphylococcus aureus . A total of 1951 men of infertile couples were recruited between 23 March 2023, and 17 May 2023, at the Department of Reproductive Medicine of The First People's Hospital of Yunnan Province (Kunming, China). Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping. Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs. The overall prevalence of HPV infection was 12.4%. The top five prevalent HPV subtypes were types 56, 52, 43, 16, and 53 among those tested positive for HPV. Other common infections with high prevalence rates were Ureaplasma urealyticum (28.3%), Ureaplasma parvum (20.4%), and Enterococcus faecalis (9.5%). The prevalence rates of HPV coinfection with Ureaplasma urealyticum, Ureaplasma parvum, Chlamydia trachomatis, Mycoplasma genitalium , herpes simplex virus 2, Neisseria gonorrhoeae, Enterococcus faecalis, Streptococcus agalactiae , and Staphylococcus aureus were 24.8%, 25.4%, 10.6%, 6.4%, 2.4%, 7.9%, 5.9%, 0.9%, and 1.3%, respectively. The semen volume and total sperm count were greatly decreased by HPV infection alone. Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability. Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters, emphasizing the necessity of semen screening for STIs.
Humans ; Male ; Infertility, Male/epidemiology* ; Coinfection/microbiology* ; Papillomavirus Infections/virology* ; Adult ; Sexually Transmitted Diseases/complications* ; China/epidemiology* ; Staphylococcus aureus/isolation & purification* ; Chlamydia trachomatis/isolation & purification* ; Prevalence ; Mycoplasma genitalium/isolation & purification* ; Ureaplasma urealyticum/isolation & purification* ; Neisseria gonorrhoeae/isolation & purification* ; Enterococcus faecalis/isolation & purification* ; Streptococcus agalactiae/isolation & purification* ; Herpesvirus 2, Human/genetics* ; Pseudomonas aeruginosa/isolation & purification* ; Semen/virology* ; Sperm Motility ; Spermatozoa/microbiology* ; Human Papillomavirus Viruses

We propose a strategy to reduce unnecessary prostate biopsies in Chinese patients with total prostate-specific antigen (tPSA) >10 ng ml -1 and Prostate Imaging Reporting and Data System (PI-RADS) scores between 1 and 3. Clinical data derived from 517 patients of The First Affiliated Hospital of USTC (Hefei, China) from January 2020 to December 2023 who met the screening criteria for the study were retrospectively collected. Independent predictors were identified via univariate and multivariate logistic regression analysis. The diagnostic capacity of clinical variables was evaluated using the receiver operating characteristic (ROC) curves and area under the curve (AUC). A prostate biopsy strategy was developed via risk stratification. Of the 517 patients, 17/348 (4.9%) with PI-RADS 1-2 were diagnosed with clinically significant prostate cancer (csPCa), and 27/169 (16.0%) patients with PI-RADS 3 were diagnosed with csPCa. The appropriate prostate-specific antigen density (PSAD) cut-off values were 0.45 ng ml -2 for PI-RADS 1-2 patients and 0.3 ng ml -2 for PI-RADS 3 patients. The appropriate prostate volume (PV) cut-off values were 40 ml for PI-RADS 1-2 patients and 50 ml for PI-RADS 3 patients. The prostate biopsy strategy based on PSAD and PV developed in this study can reduce unnecessary prostate biopsies in patients with tPSA >10 ng ml -1 and PI-RADS 1-3. In the study, 66.5% (344/517) patients did not need to undergo prostate biopsy, at the expense of missing only 1.7% (6/344) patients with csPCa.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Prostate-Specific Antigen/blood* ; Aged ; Middle Aged ; Retrospective Studies ; Prostate/diagnostic imaging* ; Unnecessary Procedures/statistics & numerical data* ; Biopsy/statistics & numerical data* ; China ; ROC Curve

Objective:To evaluate the accuracy and output stability of online general natural language processing(NLP)models for staging diagnosis and identifying medium-and high-risk factors in cervical cancer patients based on pathology reports.Methods:Surgical pathological reports of 65 patients with cervical cancer who received postoperative adjuvant radiotherapy at Shanghai General Hospital from January 2022 to December 2023 were retrospectively selected.These reports were input into two online NLP models,Kimi and ChatGPT-4o,and their output staging diagnosis results were recorded.Then,the results were classified into 3 categories and scored as follows:correct(2 points),basically correct(1 point)and incorrect(0 point).Each pathologic report was tested 5 times to assess the stability of the outputs form Kimi and ChatGPT-4o,and the consistency between the NLP models and clinical physicians in cervical cancer staging was compared.Prompt-based questioning was used to evaluate Kimi's ability to extract medium-and high-risk factors from the pathology reports of cervical cancer patients.Results:There was no statistical significant difference in the staging diagnosis results between the two NLP models and the clinical physicians(x2=5.740,P=0.057).Kimi and ChatGPT-4o respectively produced 56 and 47 correct results,6 and 15 basically correct results,and 3 and 3 incorrect results.Their mean scores were 7.08±2.70 and 7.97±2.97,and the difference between them was statistically significant(P=0.040).In the extraction of risk factors from 65 cervical cancer patients,involving a total of 390 factors,Kimi made only three false-positive errors,with all other factors correctly identified.Conclusion:Online general NLP models can stably output the stage of cervical cancer patients with diagnostic accuracy comparable to clinical physicians.With the assistance of prompt-based questioning,these NLP models can accurately extract medium-and high-risk factors from pathology reports of cervical cancer patients,demonstrating promising clinical application potential.