Objective To systematically evaluate the efficacy and safety of pyrotinib-based therapy in patients with human epidermal growth factor receptor 2 (HER2)-positive advanced breast cancer based on real-world data, and to explore factors associated with prognosis. Methods A total of 102 patients with HER2-positive advanced breast cancer who received pyrotinib at Zhongshan Hospital, Fudan University, were included retrospectively from January 1, 2019, to January 31, 2024, and prospectively from February 1, 2024, to November 30, 2025. Follow-up was conducted until March 31, 2026. Clinical and follow-up data of the patients were collected, and survival analysis was performed by Kaplan-Meier method. Prognostic factors were analyzed through multivariable Cox regression and logistic regression. Results Among the 102 patients, the median follow-up duration was 31.3 months, the median progression-free survival (PFS) was 12.9 months, the median overall survival (OS) has not been reached (estimated to be 64.8 months), and the objective response rate (ORR) was 35.29%. Multivariate Cox regression analysis revealed that lung metastasis (HR＝2.562，P＝0.006), third-line and subsequent treatment (HR＝3.857，P＝0.010), and pyrotinib monotherapy (HR＝3.682，P＝0.015) were associated with shorter PFS. Eastern Cooperative Oncology Group performance status (ECOG PS)≥2 (HR＝16.948，P＜0.001) and third-line and subsequent treatment (HR＝3.958，P＝0.034) were associated with shorter OS. No associated factors related to ORR were found. Regarding safety, diarrhea was the most common adverse event, occurring in 74.51% of patients. Conclusions In real-world clinical practice, pyrotinib-based therapy demonstrated clinical efficacy and manageable safety in patients with HER2-positive advanced breast cancer. Lung metastasis, pyrotinib monotherapy, ECOG PS≥2, and pyrotinib administration in multiple lines of therapy may affect patient prognosis.

Objective: To compare the transcriptomic profiles between three distinct metabolic dysfunction⁃associat⁃mal murine model that more closely resembles human MASH progression . Methods: Forty 8 ⁃week⁃old male C57BL/6J mice were randomly assigned to either a control group fed normal chow diet ( NCD) or one of three MASH model groups receiving high⁃fat high⁃cholesterol diet (HFHCD) , choline⁃deficient high⁃fat diet (CDHFD) ,from three randomly selected mice per group were collected for mRNA sequencing ( mRNA⁃seq) analysis . Mean⁃bases . Overlap of functional profiles was analyzed by gene set enrichment analysis (GSEA) profiles to compare the mouse transcriptome with that of human patients at different stages of the disease . Additionally , Pearson ′s correla⁃tion analysis was used to explore the correlation between gene expression of murine models and human MASH . Results: Seven commonly up⁃regulated genes (Col1a1 , Smoc2 , Col6a1 , Gpx3 , Col16a1 , Spp1 and Crtap) were de⁃ways involving steatosis , hepatocellular injury and fibrosis were detected in the three MASH models at the pathway level . HFHCD and MCD might share more common traits . In comparing gene expression and pathway profiles be⁃tween different murine models and patients with different stages of MASH , all three murine MASH models showed a closer resemblance to the human progressive stages of MASH . Notably , the transcriptomic features of the CDHFD model were more consistent with those of human MASH . Conclusion There are certain similarities and differences among the transcriptional profiles of the three MASH models . The MASH models are more similar to the advanced stage of MASH in human patients . Compared to the other two models , the CDHFD model ′ s transcriptome profile more closely resembles human MASH .

Antibody (Ab) humanization is critical to reduce immunogenicity and enhance efficacy in the preclinical phase of the development of therapeutic Abs originated from animal models. Computational suggestions have long been desired, but available tools focused on immunogenicity calculation of whole Ab sequences and sequence segments, missing the individual residue sites. This study introduces Site-specific Immunogenicity for Therapeutic Antibody (SITA), a novel computational framework that predicts B-cell immunogenicity score for not only the overall antibody, but also individual residues, based on a comprehensive set of amino acid descriptors characterizing physicochemical and spatial features for antibody structures. A transfer-learning-inspired framework was purposely adopted to overcome the scarcity of Ab-Ab structural complexes. On an independent testing dataset derived from 13 Ab-Ab structural complexes, SITA successfully predicted the epitope sites for Ab-Ab structures with a receiver operating characteristic (ROC)-area unver the ROC curve (AUC) of 0.85 and a precision-recall (PR)-AUC of 0.305 at the residue level. Furthermore, the SITA score can significantly distinguish immunogenicity levels of whole human Abs, therapeutic Abs and non-human-derived Abs. More importantly, analysis of an additional 25 therapeutic Abs revealed that over 70% of them were detected with decreased immunogenicity after modification compared to their parent variants. Among these, nearly 66% Abs successfully identified actual modification sites from the top five sites with the highest SITA scores, suggesting the ability of SITA scores for guide the humanization of antibody. Overall, these findings highlight the potential of SITA in optimizing immunogenicity assessments during the process of therapeutic antibody design.

Adaptive immunity is a critical component of the human immune system, playing an essential role in identifying antigens and orchestrating a tailored immune response. This review delves into the significant strides made in the development of epitope prediction tools, their integration into vaccine design, and their pivotal role in enhancing immunotherapy strategies. The review emphasizes the transformative potential of these tools in refining our understanding and application of immune responses. Adaptive immunity distinguishes itself from innate immunity by its ability to recognize specific antigens and remember past infections, leading to quicker and more effective responses upon subsequent exposures. This facet of immunity involves complex interactions between various cell types, primarily B cells and T cells, which recognize distinct epitopes presented by antigens. Epitopes are small sequences or configurations on antigens that are recognized by the immune receptors on B cells and T cells, acting as the focal points of immune recognition and response. Epitopes can be broadly classified into two types: linear (or sequential) epitopes and conformational (or discontinuous) epitopes. Linear epitopes consist of a sequence of amino acids in a protein that are recognized by B cells and T cells in their primary structure form. Conformational epitopes, on the other hand, are formed by spatially distinct amino acids that come together in the tertiary structure of the protein, often recognized by the immune system only when the protein folds into its native conformation. The role of epitopes in the immune response is critical as they are the primary triggers for the activation of B cells and T cells. When an epitope is recognized, it can stimulate B cells to produce antibodies, mobilize helper T cells to secrete cytokines, or prompt cytotoxic T cells to kill infected cells. These actions form the basis of the adaptive immune response, tailored to eliminate specific pathogens or infected cells effectively. The prediction of B cell and T cell epitopes has evolved with advances in computational biology, leading to the development of several sophisticated tools that utilize a variety of algorithms to predict the likelihood of epitope regions on antigens. Tools employing machine learning methods, such as support vector machines (SVMs), XGBoost, random forest, analyze large datasets of known epitopes to classify new sequences as potential epitopes based on their similarity to known data. Moreover, deep learning has emerged as a powerful method in epitope prediction, leveraging neural networks capable of learning high-dimensional data from vast amounts of immunological inputs to identify patterns that may not be evident to other predictive models. Deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and ESM protein language model have demonstrated superior accuracy in mapping the nonlinear relationships inherent in protein structures and epitope interactions. The application of epitope prediction tools in vaccine design is transformative, enabling the development of epitope-based vaccines that can elicit targeted immune responses against specific parts of the pathogen. These vaccines, by focusing the immune response on highly specific regions of the pathogen, can offer high efficacy and reduced side effects. Similarly, in cancer immunotherapy, epitope prediction tools help identify tumor-specific antigens that can be targeted to develop personalized immunotherapeutic strategies, thereby enhancing the precision of cancer treatments. The future of epitope prediction technology appears promising, with ongoing advancements anticipated to enhance the precision and efficiency of these tools further. The integration of broader immunological data, such as patient-specific immune profiles and pathogen variability, along with advances in AI and machine learning, will likely drive the development of more adaptive, robust, and clinically relevant prediction models. This will not only improve the effectiveness of vaccines and immunotherapies but also contribute to our broader understanding of immune mechanisms, potentially leading to breakthroughs in the treatment and prevention of multiple diseases. In conclusion, the development and refinement of epitope prediction tools stand as a cornerstone in the advancement of immunological research and therapeutic design, highlighting a path toward more precise and personalized medicine. The ongoing integration of computational models with experimental immunology holds the promise of revolutionizing our approach to combating infectious diseases and cancer.

Objective To explore the the correlation between cystatin C(Cys C),beta-2 microglobulin(β2-MG)and ischemic cerebral small vessel disease(CSVD)and its subgroups.Methods Totally 234 patients with CSVD were assigned to the study group,and 92 elderly people with no abnormal findings in head MRI were selected as controls.The CSVD patients were further divided into the subgroups of lacunar infarction(LI),white matter lesion(WML)and LI+WML.Each group was compared risk factors include the blood level of Cys C and β2-MG.Results There were statistically significant differences between CSVD group and control group in cystatin C(Cys C)and β2-MG(P<0.05).Cystatin C(Cys C)and β2-MG there were statistically significant differences between WML group and control group(P<0.05),and also between WML+LI group and control group(P<0.05).Logistic regression analysis and comparison across subgroups showed Cys C and β2-MG to be the common risk factors for WML group and WML+LI group inpatients with ischemic cerebral small vessel disease.Conclusion Cys C and β2-MG are the common risk factors for WML group and WML+LI group inpatients with ischemic cerebral small vessel disease.The risk factors vary across different CSVD subgroups.

Objective To investigate the diagnosis value of contrast-enhanced ultrasound (CEUS) in the differentiation of hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) of the liver with ≤3 cm of maximum diameter. Methods The image characteristics in 48 lesions of HCC with maximum diameter≤3 cm and 48 lesions of FNH with maximum diameter≤3 cm confirmed by pathology were retrospectively analyzed. The phase changes, enhancement patterns and enhancement characteristics of the lesions in the two groups were compared. Results All lesions in the two groups showed high-echo in the arterial phase. The contrast arrival time in HCC group and FNH group was 17(15, 19) s and 15(12, 18.75) s (P＝0.017); the peak time in the two groups was 21(17, 25) s and 22(19, 26) s (P＞0.05). The main enhancement patterns of HCC group and FNH group in arterial phase were homogeneous enhancement and centrifugal enhancement, respectively. All HCC lesions showed homogeneous enhancement, which was significantly higher than FNH (2.08%, P＜0.05); 97.91% of FHN lesions showed centrifugal enhancement, which was higher that of HCC lesions (0, P＜0.05). During the CEUS process, 87.5% of HCC lesions showed “rapid fill-in and rapid wash-out”, which was significantly higher than that of FNH lesions（8.33%，P＜0.05）; 91.67% of FNH lesions showed “rapid fill-in” and “synchronous/slow wash-out” which was significantly higher than that of HCC lesions （12.50%，P＜0.05）. Conclusion CEUS is helpful in the differential diagnosis of FNH and HCC with maximum diameter≤3 cm．

Humans ; Asthma/drug therapy* ; Biological Therapy

Objective: To investigate the features of contrast-enhanced ultrasound (CEUS) in hepatic epithelioid hemangioendothelioma (HEHE) in order to improve the preoperative diagnosis rate. Methods: CEUS images of 32 pathologically-proven cases of hepatic epithelioid hemangioendothelioma from January 2004 to August 2021 were collected. Lesions were analyzed to observe the features of enhancement mode, enhancement intensity, and distinct enhancement phases. Results: Among the 32 cases, one had a solitary lesion, 29 had multiple lesions, and two had diffuse-type lesions. Contrast-enhanced ultrasound revealed a total of 42 lesions in 32 cases. In terms of arterial phase enhancement, 18 lesions had overall enhancement, six lesions had uneven dendritic enhancement, 16 lesions had rim-like enhancement, and two lesions had just slight peripheral spot enhancement around the lesions. Among the three cases, there were multiple lesions that had overall enhancement and ring enhancement. In terms of the enhancement phase, 20 lesions showed "fast progression", 20 lesions showed "same progression", and two lesions showed "slow progression". During the late arterial or early portal venous phases with rapid washout, all lesions manifested as hypoechoic. With peaked enhanced intensity, 11 lesions had a lower enhancement intensity than the surrounding normal liver parenchyma; 11 lesions had the same enhancement degree as the surrounding normal liver parenchyma; and 20 lesions had a higher enhancement degree than the surrounding normal liver parenchyma. All 16 ring-enhancing lesions had marked hyperenhancement. In the typical enhancing lesions, four showed hyperenhancement, five showed low enhancement, and nine showed isoenhancement. In the dendrite-enhancing lesions, there were two isoenhancing and four hypoenhancing. Contrast-enhanced ultrasound delineated the boundaries of all lesions more clearly than two-dimensional ultrasound. Conclusion: Contrast-enhanced ultrasound has certain value in the diagnosis of hepatic epithelioid hemangioendothelioma.
Humans ; Hemangioendothelioma, Epithelioid/pathology* ; Contrast Media ; Retrospective Studies ; Liver Neoplasms/pathology* ; Portal Vein/pathology* ; Ultrasonography