Purpose: Approximately 50%-74% of patients with metastatic human epidermal growth factor receptor 2 (HER2)–positive breast cancer do not respond to trastuzumab, with 75% of treated patients experiencing disease progression within a year. The combination of pyrotinib and capecitabine has showed efficacy in these patients. This study evaluates the efficacy and safety of pyrotinib combined with metronomic vinorelbine for trastuzumab-pretreated HER2-positive advanced breast cancer patients. Materials and Methods: In this phase 2 trial, patients aged 18-75 years with HER2-positive advanced breast cancer who had previously failed trastuzumab treatment were enrolled to receive pyrotinib 400 mg daily in combination with vinorelbine 40mg thrice weekly. The primary endpoint was progression-free survival (PFS), while secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. Results: From October 21, 2019, to January 21, 2022, 36 patients were enrolled and received at least one dose of study treatment. At the cutoff date, 20 experienced disease progression or death. With a median follow-up duration of 35 months, the median PFS was 13.5 months (95% confidence interval [CI], 8.3 to 18.5). With all patients evaluated, an ORR of 38.9% (95% CI, 23.1 to 56.5) and a DCR of 83.3% (95% CI, 67.2 to 93.6) were achieved. The median OS was not reached. Grade 3 adverse events (AEs) were observed in 17 patients, with diarrhea being the most common (27.8%), followed by vomiting (8.3%) and stomachache (5.6%). There were no grade 4/5 AEs. Conclusion Pyrotinib combined with metronomic vinorelbine showed promising efficacy and an acceptable safety profile in HER2-positive advanced breast cancer patients after trastuzumab failure.

Purpose: Approximately 50%-74% of patients with metastatic human epidermal growth factor receptor 2 (HER2)–positive breast cancer do not respond to trastuzumab, with 75% of treated patients experiencing disease progression within a year. The combination of pyrotinib and capecitabine has showed efficacy in these patients. This study evaluates the efficacy and safety of pyrotinib combined with metronomic vinorelbine for trastuzumab-pretreated HER2-positive advanced breast cancer patients. Materials and Methods: In this phase 2 trial, patients aged 18-75 years with HER2-positive advanced breast cancer who had previously failed trastuzumab treatment were enrolled to receive pyrotinib 400 mg daily in combination with vinorelbine 40mg thrice weekly. The primary endpoint was progression-free survival (PFS), while secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. Results: From October 21, 2019, to January 21, 2022, 36 patients were enrolled and received at least one dose of study treatment. At the cutoff date, 20 experienced disease progression or death. With a median follow-up duration of 35 months, the median PFS was 13.5 months (95% confidence interval [CI], 8.3 to 18.5). With all patients evaluated, an ORR of 38.9% (95% CI, 23.1 to 56.5) and a DCR of 83.3% (95% CI, 67.2 to 93.6) were achieved. The median OS was not reached. Grade 3 adverse events (AEs) were observed in 17 patients, with diarrhea being the most common (27.8%), followed by vomiting (8.3%) and stomachache (5.6%). There were no grade 4/5 AEs. Conclusion Pyrotinib combined with metronomic vinorelbine showed promising efficacy and an acceptable safety profile in HER2-positive advanced breast cancer patients after trastuzumab failure.

Purpose: Approximately 50%-74% of patients with metastatic human epidermal growth factor receptor 2 (HER2)–positive breast cancer do not respond to trastuzumab, with 75% of treated patients experiencing disease progression within a year. The combination of pyrotinib and capecitabine has showed efficacy in these patients. This study evaluates the efficacy and safety of pyrotinib combined with metronomic vinorelbine for trastuzumab-pretreated HER2-positive advanced breast cancer patients. Materials and Methods: In this phase 2 trial, patients aged 18-75 years with HER2-positive advanced breast cancer who had previously failed trastuzumab treatment were enrolled to receive pyrotinib 400 mg daily in combination with vinorelbine 40mg thrice weekly. The primary endpoint was progression-free survival (PFS), while secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. Results: From October 21, 2019, to January 21, 2022, 36 patients were enrolled and received at least one dose of study treatment. At the cutoff date, 20 experienced disease progression or death. With a median follow-up duration of 35 months, the median PFS was 13.5 months (95% confidence interval [CI], 8.3 to 18.5). With all patients evaluated, an ORR of 38.9% (95% CI, 23.1 to 56.5) and a DCR of 83.3% (95% CI, 67.2 to 93.6) were achieved. The median OS was not reached. Grade 3 adverse events (AEs) were observed in 17 patients, with diarrhea being the most common (27.8%), followed by vomiting (8.3%) and stomachache (5.6%). There were no grade 4/5 AEs. Conclusion Pyrotinib combined with metronomic vinorelbine showed promising efficacy and an acceptable safety profile in HER2-positive advanced breast cancer patients after trastuzumab failure.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

AIM:To evaluate the characteristics of ocular biometric parameters and the distribution of corneal astigmatism(CA)in patients with high myopia before cataract surgery.METHODS:A prospective cross-sectional study was conducted, and 695 cataract patients(695 eyes)with high myopia [defined as an axial length(AL)≥26.00 mm] scheduled to undergo cataract surgery at our hospital from January 2022 to December 2024 were consecutively enrolled, another 695 cataract patients(695 eyes)with normal ALs(22.00 mm ≤AL≤25.00 mm)who underwent cataract surgery at our hospital during the same period were included in the control group. For patients with both eyes eligible, the right eye was used for analysis. Before cataract surgery, IOL Master 700 was used to measure the ocular biometric parameters of both eyes for each patient in the two groups. The medical records and ocular biometric data in the two groups were recorded and collected.RESULTS:There were no statistically significant differences between the two groups in genger, age, corneal diameter, and central corneal thickness(all P>0.05). In the high myopia group, the mean AL was 29.20±2.61 mm, and 252 eyes(34.1%)had AL ≥30.00 mm(extremely high myopia). The mean anterior chamber depth(ACD), lens thickness, vitreous chamber depth(VCD), CA, AL/corneal radius of curvature and VCD/AL in the high myopia group were 3.45±0.40, 4.41±0.47, 21.34±2.60 mm, 1.18±0.78 D, 3.79±0.38, and 0.73±0.03, respectively, which were all greater than those in the control group(all P<0.01). In the high myopia group, 350 eyes(50.4%)had CA ≥1.00 D, 192 eyes(27.6%)had CA ≥1.50 D, and 94 eyes(13.5%)had CA ≥2.00 D, which were all higher than those in the control group(32.8%, 15.1%, and 6.6%, respectively; all P<0.001). In the high myopia group, 87 eyes(12.5%)had flat corneas, 424 eyes(61.0%)had moderate CA, and 40 eyes(5.8%)had high CA. These proportions were all higher than those in the control group(6.0%, 46.9%, and 2.9%, respectively; all P<0.001). In the high myopia group, ACD and ACD/AL were negatively correlated with AL(r=-0.162 and -0.661, respectively; all P<0.001), while both ACD and ACD/AL in the control group were positively correlated with AL(r=0.338 and 0.105, respectively; both P<0.01). In the high myopia group, CA increased with age when the patient's age was ≥50 years(r=0.197, P<0.001), which was consistent with the control group.CONCLUSION: The standardized ocular biometric data of cataract patients with high myopia before cataract surgery are helpful for ophthalmologists to accurately calculate the intraocular lens(IOLs)power and select the appropriate IOL type. The majority of high myopia patients need simultaneous correction of CA during cataract surgery.

Objective:To explore the clinical features, genetic traits and prognosis status of acute myeloid leukemia (AML) patients with TP53 mutation.Methods:A retrospective case series study was performed. Clinical data of 42 AML patients with TP53 mutation abundance of at least 10% who were admitted to the First Hospital of Jilin University from April 2018 to August 2023 were collected. Chromosomal karyotypes were detected using fluorescence in situ hybridization (FISH), and next-generation sequencing (NGS) was performed on bone marrow samples from these patients. The Kaplan-Meier method was utilized to analyze the recurrence-free survival (RFS) of 22 patients who achieved complete remission (CR) after induction therapy, as well as the overall survival (OS) of 39 patients who underwent treatment. The Cox proportional hazards model was employed to analyze the factors influencing RFS and OS. Thirty-one patients who underwent induction therapy for ≥ 2 courses were divided into the non-refractory group (22 cases) and the refractory group (9 cases) based on whether CR was achieved after induction therapy, and the clinical and genetic characteristics and prognosis of the two groups were compared.Results:Among the 42 AML patients with TP53 mutation, there were 26 males and 16 females, with a median age [ M( Q1, Q3)] of 57.0 (44.0, 63.5) years. At the initial stage, the median white blood cell count in peripheral blood was 4.06×10 9/L (2.59×10 9/L, 27.36×10 9/L), the median proportion of bone marrow primitive cells was 45.25% (29.00%, 80.63%), the proportion of primitive and immature cells in bone marrow cell immune analysis was 28.70% (12.71%, 61.48%), and the median TP53 mutation abundance was 55.48% (38.72%, 73.31%). Among the 42 patients, 23 cases (54.76%) had complex chromosomal karyotypes, with the most frequent abnormal chromosomes being chromosome 5 (47.62%, 20 cases), chromosome 7 (45.24%, 19 cases) and chromosome 17 (26.19%, 10 cases). The mutant genes with high mutation frequency were DNMT3A (19.05%, 8 cases), N/KRAS (19.05%, 8 cases), ASXL1 (16.67%, 7 cases) and NPM1 (16.67%, 7 cases). Among the 39 patients with detailed TP53 mutation data, 36 had missense mutations and 3 had frameshift mutations. In the non-refractory and refractory groups, the N/KRAS mutation rates were 13.6% (3/22) and 55.6% (5/9), respectively ( P = 0.027). The median RFS time of 22 CR patients after induction therapy was 109 d (95% CI: 57-483 d). The results of multivariate Cox regression analysis showed that complex chromosomal karyotype was an independent risk factor for RFS ( HR = 11.819, 95% CI: 1.345-103.880, P = 0.028). The median RFS time of patients without and with complex chromosomal karyotypes was 842 d (95% CI: 0-1 716 d) and 148 d (95% CI: 88-208 d), respectively, and the difference in RFS between the two groups was statistically significant ( P = 0.001). The median OS time of 39 patients receiving treatment was 151 d (95% CI: 75-227 d). The results of multivariate Cox regression analysis showed that NPM1 gene mutation was an independent protective factor for OS ( HR = 0.289, 95% CI: 0.075-1.114, P = 0.071). The median OS time of patients with and without NPM1 gene mutation was 1 562 d (95% CI: 610- 1 710 d) and 136 d (95% CI: 99-173 d), respectively, and the difference in OS between the two groups was statistically significant ( P = 0.020). Conclusions:TP53-mutant AML patients often have poor chromosomal karyotypes and genetic abnormalities, while refractory AML patients often have N/KRAS mutation. The complex chromosomal karyotype is a risk factor for RFS, while NPM1 gene mutation is a protective factor for OS.

ObjectiveIn order to improve the recognition accuracy of named entities in medical record texts and realize the effective mining and utilization of medical record knowledge， a Bert-Radical-Lexicon（BRL） neural network model is constructed to recognize medical record entities with respect to the characteristics of medical record texts. MethodWe selected 408 medical records related to hypertension from the the Complete Library of Famous Medical Records of Chinese Dynasties and constructed a dataset consisting of 1 672 medical records by manually labeling. Then， we randomly divided the dataset into three subsets， including the training set（1 004 cases）， the testing set （334 cases） and the validation set（334 cases）. Based on this dataset， we built a BRL model that fused various text features of medical records， as well as its variants BRL-B， BRL-L and BRL-R， and a baseline model Base for experiments. During the model training phase， we trained the above models using the training set to reduce the risk of overfitting. We continuously monitored the performance of each model on the validation set during training and saved the model with the best performance. Finally， we evaluated the performance of these models on the testing set. ResultCompared with other models， the BRL model had the best performance in the medical records named entity recognition task， with an overall recognition precision of 90.09%， a recall of 90.61%， and the harmonic mean of the precision and recall（F1） of 90.35% for eight types of entities， including disease， symptom， tongue manifestation， pulse condition， syndrome， method of treatment， prescription and traditional Chinese medicine（TCM）. Compared with the Base model， the BRL model improved the overall F1 value of entity recognition by 5.22%， and the F1 value of pulse condition entity increased by 6.92%， which was the largest increase. ConclusionBy incorporating a variety of medical record text features in the embedding layer， the BRL neural network model has stronger named entity recognition ability， and thus extracts more accurate and reliable TCM clinical information.

Objective To explore the differences in tremor characteristics between patients with primary Parkinson's disease(IPD),essential tremor(ET),and Parkinson's disease developed from essential tremor(ET-PD).Method Thirty IPD patients,30 ETs,and 20 ET-PD patients were included,and the frequency,contraction pattern,and presence of harmonics of static and postural tremors were compared among the three groups.Results There were statistically significant differences(P<0.01)among the IPD group,ET group,and ET-PD group in terms of age of onset,disease course,and the unified Parkinson's disease rating scale(UPDRS)score.There were statistically significant differences(P<0.05)in the average frequency of stationary tremors,average frequency of postural tremors,rate of alternating tremors in stationary state,rate of alternating tremors in postural state,and rate of harmonic occurrence in stationary state among the IPD group,ET group,and ET-PD group,as well as in the rate of harmonic occurrence in postural state(P<0.05).Conclusion The IPD group,ET group,and ET-PD group each have significant differences in tremor characteristics.The ET-PD group has both characteristics and uniqueness,and tremor analysis can help identify this disease.

For two young female patients with extramedullary recurrence of reproductive system after allogeneic hematopoietic stem cell transplantation for acute myeloid leukemia. And the characteristics of extramedullary relapse of reproductive system are summarized for exploring possible effective treatments.

ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.