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.

ObjectiveTo explore the therapeutic effect of Xuebijing injection （XBJ） on severe acute pancreatitis induced acute lung injury （SAP-ALI） by regulating formyl peptide receptors （FPRs）/nucleotide-binding oligomerization domain-like receptor 3 （NLRP3） inflammatory pathway. MethodsSixty rats were randomly divided into a sham group， a SAP-ALI model group， low-， medium-， and high-dose XBJ groups （4， 8， and 12 mL·kg^-1）， and a positive drug （BOC2， 0.2 mg·kg^-1） group. For the sham group， the pancreas of rats was only gently flipped after laparotomy， and then the abdomen was closed， while for the remaining five groups， SAP-ALI rat models were established by retrograde injection of 5% sodium taurocholate （Na-Tc） via the biliopancreatic duct. XBJ and BOC2 were administered via intraperitoneal injection once daily for 3 d prior to modeling and 0.5 h after modeling. Blood was collected from the abdominal aorta 6 h after the completion of modeling， and the expression of interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） in plasma was measured by enzyme-linked immunosorbent assay （ELISA）. The amount of ascites was measured， and the dry-wet weight ratios of pancreatic and lung tissue were determined. Pancreatic and lung tissue was taken for hematoxylin-eosin （HE） staining to observe pathological changes and then scored. The protein expression levels of FPR1， FPR2， and NLRP3 in lung tissue were detected by the immunohistochemical method. Western blot was used to detect the expression of FPR1， FPR2， and NLRP3 in lung tissue. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of FPR1， FPR2， and NLRP3 in lung tissue. ResultsCompared with the sham group， the SAP-ALI model group showed significantly decreased dry-wet weight ratio of lung tissue （P<0.01）， serious pathological changes of lung tissue， a significantly increased pathological score （P<0.01）， and significantly increased protein and mRNA expression levels of FPR1， FPR2， and NLRP3 in lung tissue （P<0.01）. After BOC2 intervention， the above detection indicators were significantly reversed （P<0.01）. After treatment with XBJ， the groups of different XBJ doses achieved results consistent with BOC2 intervention. ConclusionXBJ can effectively improve the inflammatory response of the lungs in SAP-ALI rats and reduce damage. The mechanism may be related to inhibiting the expression of FPRs and NLRP3 in lung tissue， which thereby reduces IL-1β and simultaneously antagonize the release of inflammatory factors IL-6 and TNF-α.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To investigate the relationship between the morphology of bone marrow plasma cells, the identification of M protein, and the detection of minimal residual disease(MRD)by flow cytometry in elderly patients with multiple myeloma(MM).Additionally, the impact on progression-free survival(PFS)is analyzed.Methods:A total of 60 elderly MM patients with bone marrow morphology reports and corresponding flow MRD detection, collected from February 1, 2017, to January 31, 2022, at Beijing Chaoyang Hospital Affiliated to Capital Medical University, were included in this study.We collected data on plasma cells from morphological examination and flow cytometry-based MRD detection. By combining these findings with M-protein identification results from 35 cases, we analyzed the correlations among these three parameters. Patients were stratified into two groups based on median values of: flow cytometry MRD(0.246% of nucleated cells), morphological plasma cell percentage(3.5%), and M-protein quantification(2.5 g/dl).This stratification enabled evaluation of their prognostic value for PFS.Results:A total of 60 elderly MM patients were enrolled, including 41 males with age of 65.0(63.0, 68.5)years and 19 females with age of 67.0(64.0, 72.0)years The morphology of bone marrow in 60 elderly patients with MM revealed 10 cases of primitive plasma cells(16.67%), 48 cases of immature plasma cells(80.00%), and 2 cases with no plasma cells(3.33%).A positive correlation was observed between the proportion of bone marrow plasma cells and the corresponding flow MRD in terms of both the proportion of nuclear cells and the proportion of plasma cells.Specifically, the proportion of the morphological protoplasma group showed a strong correlation with flow MRD(proportion of plasma cells)( r=0.82, P<0.01), while the proportion of the morphological immature plasma group exhibited moderate correlations with flow MRD(proportion of nuclear cells)( r=0.74, P<0.05)and flow MRD(proportion of plasma cells)( r=0.70, P<0.01).No significant correlation was found between the type and quantity of M protein and flow MRD( P>0.05).The PFS time for the flow MRD ≥0.246%(nucleated cells)group was shorter than that of the<0.246% group( P<0.05).There was no significant difference in PFS between the groups with plasma cell ratios ≥3.5% and<3.5%( P=0.15).Additionally, no significant difference in PFS was observed between patients with M protein quantitation ≥2.5 g/dl and those with M protein quantitation<2.5 g/dl( P=0.94). Conclusions:The proportion of bone marrow plasma cells correlates with flow MRD in elderly patients with MM, and a high MRD load indicates a poor prognosis.However, no significant correlation was found between M protein levels and flow MRD or PFS.Clinical attention should focus on the dynamic monitoring of plasma cell morphology and flow MRD.Nevertheless, the morphological detection of plasma cells remains crucial for auxiliary diagnosis due to its intuitiveness, cost-effectiveness, and broad applicability.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

OBJECTIVE: To assess the feasibility of first polar body transfer (PB1T) combined with preimplantation mitochondrial genetic testing for blocking the transmission of a pathogenic mitochondrial DNA 8993T>G mutation. METHODS: A Chinese family affected with Leigh syndrome which had attended the Reproductive Medicine Centre of the First Affiliated Hospital of Anhui Medical University in September 2021 was selected as the study subject. Controlled ovarian hyperstimulation was carried out for the proband after completing the detection of the mitochondrial DNA 8993T>G mutation load among the pedigree members. Mature MII oocytes were inseminated by intracytoplasmic sperm injection (ICSI), cultured in vitro for 5 to 6 days to the blastocyst stage, and trophoblastocytes were obtained by microbiopsy. Mitochondrial DNA testing (PGT-MT) and chromosomal aneuploidy (PGT-A) analyses were carried out after whole-genome amplification, and the embryos with zero mutation load were selected for transfer. Amniotic fluid and umbilical cord blood samples were collected during middle pregnancy and after birth respectively for mitochondrial DNA testing to verify the reliability of embryo screening. As an attempt, PB1 with good morphology of MII oocytes was selected for transfer into the enucleated oocytoplasm from healthy donors, followed by ICSI fertilization, blastocyst culture and PGT of embryos using the same procedure. This study has been approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No. 2021zhyx-B12). RESULTS: An antagonist protocol was used for ovarian stimulation, and a total of 19 oocytes were obtained, of which 14 MII were fertilized by ICSI, and 2 had developed into blastocysts. PGT-MT was carried out on biopsied trophoblastocytes, in which the mitochondrial DNA 8993T>G mutation load was not detected in one embryo, the other was 100% mutated, and the mutation loads of the remaining unfertilized eggs and developmentally arrested embryos ranged from 0% ~ 100%, presenting a clear biased distribution. With fully informed consent, one PGT-MT zero mutation load blastocyst was transferred and clinical pregnancy was achieved. Mitochondrial DNA and chromosomal testing of amniotic fluid cells during middle pregnancy had revealed no abnormalities. The proband had delivered a healthy boy through Caesarean section at 39⁺⁵ weeks of gestation, and no mutation was detected in the cord blood sample. Five well-formed PBs from 14 eggs were selected for PB1 transfer, followed by ICSI and culture, and two of the reconstituted embryos had formed blastocysts, with none of the above mutations detected in the biopsied samples. CONCLUSION The PGT-MT technology can help families affected with mitochondrial diseases to have healthy offspring. PB1 transfer in combination with ICSI and PGT-MT holds the promise of turning waste into treasure and providing an alternative means of fertility for such families.
Humans ; Preimplantation Diagnosis/methods* ; Female ; DNA, Mitochondrial/genetics* ; Genetic Testing/methods* ; Pregnancy ; Mitochondrial Diseases/genetics* ; Polar Bodies ; Adult ; Feasibility Studies ; Sperm Injections, Intracytoplasmic/methods* ; Embryo Transfer/methods* ; Mutation ; Male ; Blastocyst/metabolism* ; Pedigree

Objective:To investigate the relationship between the morphology of bone marrow plasma cells, the identification of M protein, and the detection of minimal residual disease(MRD)by flow cytometry in elderly patients with multiple myeloma(MM).Additionally, the impact on progression-free survival(PFS)is analyzed.Methods:A total of 60 elderly MM patients with bone marrow morphology reports and corresponding flow MRD detection, collected from February 1, 2017, to January 31, 2022, at Beijing Chaoyang Hospital Affiliated to Capital Medical University, were included in this study.We collected data on plasma cells from morphological examination and flow cytometry-based MRD detection. By combining these findings with M-protein identification results from 35 cases, we analyzed the correlations among these three parameters. Patients were stratified into two groups based on median values of: flow cytometry MRD(0.246% of nucleated cells), morphological plasma cell percentage(3.5%), and M-protein quantification(2.5 g/dl).This stratification enabled evaluation of their prognostic value for PFS.Results:A total of 60 elderly MM patients were enrolled, including 41 males with age of 65.0(63.0, 68.5)years and 19 females with age of 67.0(64.0, 72.0)years The morphology of bone marrow in 60 elderly patients with MM revealed 10 cases of primitive plasma cells(16.67%), 48 cases of immature plasma cells(80.00%), and 2 cases with no plasma cells(3.33%).A positive correlation was observed between the proportion of bone marrow plasma cells and the corresponding flow MRD in terms of both the proportion of nuclear cells and the proportion of plasma cells.Specifically, the proportion of the morphological protoplasma group showed a strong correlation with flow MRD(proportion of plasma cells)( r=0.82, P<0.01), while the proportion of the morphological immature plasma group exhibited moderate correlations with flow MRD(proportion of nuclear cells)( r=0.74, P<0.05)and flow MRD(proportion of plasma cells)( r=0.70, P<0.01).No significant correlation was found between the type and quantity of M protein and flow MRD( P>0.05).The PFS time for the flow MRD ≥0.246%(nucleated cells)group was shorter than that of the<0.246% group( P<0.05).There was no significant difference in PFS between the groups with plasma cell ratios ≥3.5% and<3.5%( P=0.15).Additionally, no significant difference in PFS was observed between patients with M protein quantitation ≥2.5 g/dl and those with M protein quantitation<2.5 g/dl( P=0.94). Conclusions:The proportion of bone marrow plasma cells correlates with flow MRD in elderly patients with MM, and a high MRD load indicates a poor prognosis.However, no significant correlation was found between M protein levels and flow MRD or PFS.Clinical attention should focus on the dynamic monitoring of plasma cell morphology and flow MRD.Nevertheless, the morphological detection of plasma cells remains crucial for auxiliary diagnosis due to its intuitiveness, cost-effectiveness, and broad applicability.

Objective To explore current status and potential subtypes of elderly stroke patients' medication literacy among,and to analyze related influencing factors of different subtypes.Methods A total of 285 elderly stroke patients admitted in the First Affiliated Hospital of Zhengzhou University from November 2023 to June 2024 were selected as subjects.General Information questionnaire,medication literacy scale for elderly patients with chronic diseases,and perceived social support scale were conducted.Latent profile analysis(LPA)of elderly stroke patients' medication literacy was conducted,and Logistic regression analysis was used to explore influencing factors of different profiles.Results Score of medication literacy scale for elderly stroke patients was(48.26±12.51)points.Medication literacy among elderly stroke patients can be divided into 3 profiles,namely proactive-high literacy type(51.9％),balanced-medium literacy type(34.0％),and dependent-low literacy type(14.1％).Logistic regression analysis showed that recent medication types,current place of residence,educational level,diabetes,and social support were the influencing factors of elderly stroke patients' medication literacy(P＜0.05).Conclusion The level of medication literacy among elderly stroke patients is medium,which is characterized by 3 categories.Medical staffs targeted intervention should be adopted according to different category characteristics,so as to accurately meet their nursing needs,finally improve the level of elderly stroke patients' medication literacy.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.