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.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

The number of patients with reduced blood volume due to haemorrhage, fractures, severe infections, extensive burns and tumours is increasing, and traditional blood products are no longer able to meet the increasing clinical demand. Therefore, plasma substitutes have become particularly important in fluid resuscitation, especially artificial colloidal solutions, which have a sustained volume expansion time and a good volume expansion effect, and can significantly improve the circulatory status of patients. This article aims to review the classification of artificial colloidal plasma substitutes and their research progress in clinical practice, in order provide a more rigorous, professional and standardized reference for medicine.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Osteoarthritis （OA） and stroke are common clinical diseases that reduce patients' quality of life and place a burden on families and society. Ruyi Zhenbaowan， a classic prescription in Tibetan medicine， have the functions of clearing heat， awakening the brain and opening orifices， relaxing tendons and promoting meridian circulation， and eliminating yellow water. Clinically， they are used to treat osteoarthritis， post-stroke sequelae， neuropathic pain， and other related conditions. Modern pharmacological studies have demonstrated their anti-inflammatory， analgesic， and nerve-repairing effects. However， current research remains insufficient regarding the appropriate indications， timing， and efficacy of this medicine in treating relevant diseases. To enhance clinicians' understanding of this medicine and promote its standardized and rational clinical use， a panel of national experts， including clinical specialists， Tibetan medicine practitioners， pharmacologists， and methodologists， formulated this consensus based on clinical experience and evidence-based practice. The Cochrane systematic review framework， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system， and the nominal group method were employed to generate seven graded recommendations and 19 consensus-based suggestions. These recommendations clearly define the key points in the clinical application of Ruyi Zhenbaowan， including therapeutic indications， dosage and administration， treatment duration， and medication safety. The consensus specifically addresses the clinical efficacy， appropriate timing of administration， dosage strategies， treatment cycles， and combination medication strategies for treating osteoarthritis and stroke and provides an overview of safety considerations. The aim is to provide standardized guidance for hospitals and healthcare institutions nationwide to ensure the rational application of Ruyi Zhenbaowan in the treatment of osteoarthritis and stroke， reduce medication-related risks， and further leverage its clinical advantages. This consensus has been approved and issued by the China Association of Chinese Medicine， with the standard number GS/CACM 369-2024.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:To investigate the application value of single-sperm sequencing in resolving the carrier status of preim-plantation genetic testing(PGT)for chromosomal structural rearrangements in Robertsonian translocations.Methods:Haplotypes were constructed by single-sperm isolation combined with single-sperm sequencing for a patient with 45,XY,der(13;14)(q10;q10).Twenty single-sperm samples were isolated by mechanical braking and subjected to whole-genome amplification(WGA),and then the Asian Screening Array(ASA)gene chip was used to detect the 183 708 single nucleotide polymorphisms(SNP)of the WGA products.The single sperm associated with the translocation that could be used as haplotype inference was detected by copy number variation(CNV)sequencing,and the chromosomal haplotypes with normal and Robertsonian translocations were inferred.Three biopsy samples of embryonic trophoblast cells were used as the objects.After whole-genome amplification,high-throughput sequencing was employed to determine the status of the translocation chromosome carried by the embryos.The available blastocysts were selected for transfer,and the amniotic fluid samples were taken at 18 weeks of gestation to confirm whether the fetus carried the pathogenic muta-tion.Results:A total of 6 037 SNP sites were screened by single-sperm sequencing,and 30 sites selected to distinguish normal and translocation haplotypes.Preimplantation haplotype analysis showed that all the three embryos were euploids without Robertsonian translocation chromosome.Genetic testing of amniotic fluid in the second trimester confirmed that the karyotype of the fetus was 46,XN,carrying no Robertsonian translocation chromosome.Conclusion:For male carriers of Robertsonian translocation,single sperm sequencing can be used to screen SNP sites to construct haplotypes for distinguishing normal and Robertsonian translocation em-bryos,and to provide a basis for embryo selection by preimplantation chromosomal structural genetic testing.

Jumonji domain-containing protein D3(JMJD3)is a 2-oxoglutarate-dependent dioxygenase that specif-ically removes transcriptional repression marks di-and tri-methylated groups from lysine 27 on histone 3(H3K27me2/3).The erasure of these marks leads to the activation of some associated genes,thereby influencing various biological processes,such as development,differentiation,and immune response.However,comprehensive descriptions regarding the relationship between JMJD3 and inflammation are lacking.Here,we provide a comprehensive overview of JMJD3,including its structure,functions,and involvement in inflammatory pathways.In addition,we summarize the evidence supporting JMJD3's role in several inflammatory diseases,as well as the potential therapeutic applications of JMJD3 inhibitors.Additionally,we also discuss the challenges and opportunities associated with investigating the functions of JMJD3 and developing targeted inhibitors and propose feasible solutions to provide valuable insights into the functional exploration and discovery of potential drugs targeting JMJD3 for inflammatory diseases.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.