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.

Gender recognition based on the analysis of fingerprint residue can assist investigators in narrowing down the scope of investigation and play an important role in the field of criminal investigation.This study established a quantitative analysis method for lipid substances in fingerprints based on gas chromatography-mass spectrometry(GC-MS).Fatty acids in fingerprints were methylated using sulfuric acid methanol derivatization reagent(7%,V/V),the extraction reagent was dichloromethane-methanol(1∶1,V/V)solution,the reaction temperature was 70℃and the heating time was 45 min.Quantitative analysis of the relative content of 23 kinds of fatty acids and squalene in fingerprints residue by different genders was conducted,and orthogonal partial least squares-discriminant analysis(OPLS-DA)was used to reduce the dimensionality of the quantitative results.A total of 13 kinds of components in the fingerprints were selected to maximize the difference in relative content between male and female fingerprints.Three machine learning models,including binary logistic regression(BLR),support vector machine(SVM)and random forest(RF),were further used as feature variables to classify the gender of fingerprints.The classification performance of each model was compared through five indicators,and it was found that the most suitable model for binary classification of fingerprint gender was SVM model.The results showed that the SVM fingerprint residual gender binary classification model established based on the relative content data of 13 kinds of lipid substances in fingerprints achieved a classification accuracy of 90%and an area under the receiver operating characteristic curve(AUC)value of 0.98.This study provided a new research method for detecting lipid components in fingerprints and a methodological basis for gender recognition of fingerprints.

Objective:To construct a key item checklist for the Mini-HTA report specification,providing scientific guidance for drafting each section of Mini-HTA research reports,enhancing their standardization,scientific rigor,and completeness,thereby improving the efficiency and quality of health decision-making.Methods:Based on preliminary literature review and qualitative systematic review,a pool of problem items for the Mini-HTA report specification was formed.Delphi questionnaires were distributed,and the Delphi technique was employed through two rounds of expert consultation to optimize and select key items.Results:Through two rounds of Delphi expert consultation,the initial Mini-HTA report specification item checklist was screened,integrated,and supplemented.A finalized key item checklist was constructed,comprising 8 first-level items(Title,Abstract,Introduction,Methods,Results,Discussion,Conclusion,and Other Relevant Information)and 48 second-level items.Conclusion:The constructed key item checklist for the Mini-HTA report specification provides scientific guidance for drafting Mini-HTA research reports.It helps enhance the standardization and transparency of the assessment process and the reliability of results,thereby optimizing the efficiency and quality of health decision-making.

Objective To construct a glioma grading evaluation model based on magnetic resonance diffusion tensor imaging(DTI)parameters,systemic immune-inflammation index(SII),and albumin to globulin ratio(AGR),and analyze its evaluation effect.Methods A total of 120 patients with brain glioma treated in our hospital from January 2022 to January 2024 were selected and divided into a high-grade glioma(HGG)group of 53 cases and a low-grade glioma(LGG)group of 67 cases according to the WHO tumor classification.The magnetic resonance DTI parameters,SII,and AGR of patients between the two groups were compared.A binary Logistic regression analysis model was constructed to analyze the risk factors for the occurrence of HGG;the receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to analyze the evaluation value of the glioma grading model.Results Compared with the LGG group,the HGG group had lower fractional anisotropy(FA)value,apparent diffusion coefficient(ADC),and AGR of the tumor body and the peritumoral edema area(P<0.05),and higher SII(P<0.05).Binary Logistic regression analysis showed that decresed ADC value of the tumor body and the peritumoral edema area,decreased AGR,and increased SII were all the risk factors for the occurrence of HGG(P<0.05).The ROC curve analysis showed that the AUC of the combined assessment of FA value and ADC of the tumor body,FA value and ADC of the peritumoral edema area,SII and AGR for glioma grading was higher than that of their individual assessment(P<0.05).Conclusion The occurrence of HGG is related to the ADC of the tumor body and peritumoral edema area,SII,and AGR.The model constructed based on magnetic resonance DTI parameters,SII and AGR has high evaluation value for the grading of glioma.

Objective:To analyze the key factors,hierarchical structure and internal action paths that affect the ability improvement of medical insurance management personnel in public hospitals,and to provide theoretical basis and practical reference for strengthening the construction of medical insurance management talent teams in hospitals.Methods:Through the mutual verification of literature analysis,policy interpretation and interview results,an index system of influencing factors for the ability improvement of medical insurance management personnel in public hospitals was constructed.A hybrid method combining DEMATEL-ISM-MICMAC was adopted to define the relationships,divide the levels and conduct driving force-dependence analysis of the influencing factors.Results:There are a total of 12 key factors influencing the ability improvement of medical insurance management personnel in public hospitals.Through the ISM model,these influencing factors can be classified into four levels:surface factors,intermediate factors,deep factors,and essential factors.With the help of MICMAC analysis,it can be classified into the spontaneous factor group of"low driving force-low dependence",the independent factor group of"high driving force-low dependence"and the dependent factor group of"low driving force-high dependence".Conclusion:Policy interpretation and knowledge reserve are the fundamental driving factors for ability improvement;Professional ethics and responsibilities are the deep-seated supporting factors of the ability system.The regulatory capacity of medical insurance funds is a key outcome and performance manifestation.

Objective:To analyze the key factors,hierarchical structure and internal action paths that affect the ability improvement of medical insurance management personnel in public hospitals,and to provide theoretical basis and practical reference for strengthening the construction of medical insurance management talent teams in hospitals.Methods:Through the mutual verification of literature analysis,policy interpretation and interview results,an index system of influencing factors for the ability improvement of medical insurance management personnel in public hospitals was constructed.A hybrid method combining DEMATEL-ISM-MICMAC was adopted to define the relationships,divide the levels and conduct driving force-dependence analysis of the influencing factors.Results:There are a total of 12 key factors influencing the ability improvement of medical insurance management personnel in public hospitals.Through the ISM model,these influencing factors can be classified into four levels:surface factors,intermediate factors,deep factors,and essential factors.With the help of MICMAC analysis,it can be classified into the spontaneous factor group of"low driving force-low dependence",the independent factor group of"high driving force-low dependence"and the dependent factor group of"low driving force-high dependence".Conclusion:Policy interpretation and knowledge reserve are the fundamental driving factors for ability improvement;Professional ethics and responsibilities are the deep-seated supporting factors of the ability system.The regulatory capacity of medical insurance funds is a key outcome and performance manifestation.

Objective:To compare the characteristics and temperature changes of single and double fiber Nd∶YAG laser in fresh isolated pig liver,and to provide reference for clinical ablation treatment.Methods:Single-needle single-point and double-needle double-point ablations were perf ormed on fresh isolated pig livers using a 5 W power laser,and the morphology,range,and surrounding temperature changes of the ablation lesions caused by the two in vitro liver tissues were observed.Results:The ablation lesions were divided into carbonized area,necrotic area and deformed area from inside to outside.The carbonized area in the center of the ablation lesion in the double-fiber group was larger and the cell necrosis was more thorough.The aspect ratio(LD/TD)of the laser ablation lesion in the single-fiber group was larger than that in the double fiber group(P＜0.001).The transverse diameter(TD)and volume(V)of the ablation lesion in the double-fiber group were larger than those in the single-fiber group(P＜0.001).There was no significant difference in the longitudinal diameter(LD)of the ablation lesion between the double-fiber group and the single-fiber group(P＞0.05).There was no significant difference in the temperature of 20 s,40 s and 60 s at 5 mm and 10 mm beside the ablation center between the two groups(all P＞0.05).Conclusion:Under the condition of 5 W,the temperature changes around the single and double fiber ablation are similar.The single fiber is suitable for small tumor ablation,and the double fiber ablation range is larger,which can be used for one-time full coverage ablation of larger cancer nodules.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.