Purpose: Dual anti–human epidermal growth factor receptor 2 (HER2) drugs have become the standard regimen for neoadjuvant systemic treatment (NST) to HER2-positive breast cancer patients. However, the efficacy varies greatly among patients with different HER2 protein expression levels. Materials and Methods: A total of 575 HER2-positive breast cancer patients from multiple centers throughout China from 2013 to 2022 were retrospectively analyzed. We compared clinicopathological features in different HER2 immunohistochemistry classes (HER2 2+/in situ hybridization [ISH] + or HER2 3+), and their difference in response to NST and survival with single or dual anti-HER2 drugs. Drug sensitivity assays were used to evaluate different efficacy of anti-HER2 drugs in vitro. Results: Compared to HER2 3+ subgroup, the HER2 2+/ISH+ group had a higher proportion of hormone receptor–positive status (48.7% vs. 76.1%, p < 0.001), more HER2 protein loss after NST, lower pathological complete response (pCR) rate (46.07% vs. 16.24%, p < 0.001), and tended to have worse disease-free survival (DFS). In HER2 2+/ISH+ patients, treated with pertuzumab and trastuzumab in combination had no significant improvement in pCR (19.12% vs. 12.24%, p=0.287) and DFS (p=0.908) than using alone. Drug sensitivity assay showed poor efficacy with dual anti-HER2 drugs in HER2 2+/ISH+ cell lines; however, fam-trastuzumab deruxtecan drugs had a satisfactory effect. Conclusion Owing to the differences in clinicopathological features and treatment efficacy, we considered the HER2 2+/ISH+ group to be a distinct subtype and defined it as the HER2-moderate–positive subgroup. In this subgroup, dual anti-HER2 drugs did not exert significant improvement in pCR and DFS. Therefore, treatment optimization is warranted, with antibody-drug conjugate drugs as potential options.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

OBJECTIVE: To summarize the latest research progress of graphene and its derivatives (GDs) in bone repair. METHODS: The relevant research literature at home and abroad in recent years was extensively accessed. The properties of GDs in bone repair materials, including mechanical properties, electrical conductivity, and antibacterial properties, were systematically summarized, and the unique advantages of GDs in material preparation, functionalization, and application, as well as the contributions and challenges to bone tissue engineering, were discussed. RESULTS: The application of GDs in bone repair materials has broad prospects, and the functionalization and modification technology effectively improve the osteogenic activity and material properties of GDs. GDs can induce osteogenic differentiation of stem cells through specific signaling pathways and promote osteogenic activity through immunomodulatory mechanisms. In addition, the parameters of GDs have significant effects on the cytotoxicity and degradation behavior. CONCLUSION GDs has great potential in the field of bone repair because of its excellent physical and chemical properties and biological properties. However, the cytotoxicity, biodegradability, and functionalization strategies of GDs still need to be further studied in order to achieve a wider application in the field of bone tissue engineering.
Graphite/pharmacology* ; Tissue Engineering/methods* ; Humans ; Osteogenesis/drug effects* ; Biocompatible Materials/pharmacology* ; Bone Regeneration ; Tissue Scaffolds/chemistry* ; Cell Differentiation ; Bone and Bones ; Bone Substitutes/chemistry* ; Animals

OBJECTIVES: To investigate the prognostic significance of PDZ-binding kinase (PBK) in pan-cancer and its potential as a therapeutic target for pancreatic cancer. METHODS: PBK expression levels were investigated in 33 cancer types based on data from TCGA, GEO and CPTAC databases. RT-PCR and Western blotting were employed to examine PBK expression in clinical pancreatic cancer specimens and cell lines. The diagnostic and prognostic value of PBK in pancreatic cancer was evaluated using survival analysis, Cox regression analysis, ROC curve analysis, and clinical correlation studies. Gene enrichment and immune correlation analyses were conducted to explore the potential role of PBK in tumor microenvironment, and its correlation with drug sensitivity was investigated using GDSC and CTRP datasets. In pancreatic cancer BXPC-3 cells, the effects of lentivirus-mediated PBK knockdown on cell proliferation, migration, and invasion were examined using CCK-8, colony formation, and Transwell assays. The interaction between PBK and non-SMC condensin II complex subunit G2 (NCAPG2) was analyzed using co-immunoprecipitation and Western blotting. RESULTS: PBK was overexpressed in multiple cancer types, including pancreatic cancer. A high PBK expression was associated with a poor prognosis of the patients and correlated with immune infiltration and alterations in the tumor microenvironment. Elevated PBK expression was positively correlated with the sensitivity to MEK inhibitors (Trametinib) and EGFR inhibitors (Afatinib) but negatively with the sensitivity to Bcl-2 inhibitors (TW37) and niclosamide. In BXPC-3 cells, PBK knockdown significantly suppressed NCAPG2 expression and inhibited cell proliferation, migration, and invasion. Co-immunoprecipitation confirmed a direct binding between PBK and NCAPG2. CONCLUSIONS PBK is a key regulator of pancreatic cancer and interacts with NCAPG2 to promote tumor progression, suggesting its value as a potential biomarker and therapeutic target for pancreatic cancer.
Humans ; Pancreatic Neoplasms/genetics* ; Prognosis ; Biomarkers, Tumor/genetics* ; Cell Line, Tumor ; Cell Proliferation ; Adenocarcinoma/metabolism* ; Tumor Microenvironment ; Cell Movement ; Mitogen-Activated Protein Kinase Kinases

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

Objective:To explore the effect and application of interventional measures with concept of risk management in adverse events of medical devices,and assess their effectiveness in reducing the incidence of adverse events and in enhancing patients'safety.Methods:The intervention measures with concept of risk management was adopted to carry out risk assessment and hierarchical management for medical devices,and the operating status of medical devices was monitored in real time,and the standard operating procedures of using medical device were formulated for conducting risk management for medical devices.A total of 2,222 conventional medical devices in clinical use at Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University from January 2022 to March 2024 were selected.Among them,1,064 devices(with random values≤0.48)were managed using conventional methods by stratified random sampling without proportional allocation.Another 1,158 devices(random value>0.48)applied the risk intervention management method.A comparative analysis was conducted on the incidence of adverse events of medical devices,the qualified rate of medical device cleaning and the wet package rate of the two management methods,as well as the score of medical device knowledge level of nursing staff.Results:Among the 1,158 medical devices that adopted the risk intervention management method,the incidences of adverse events in non-surgical departments,surgical departments and diagnosis-related departments were 0.69%(8/1,158),1.90%(22/1,158)and 3.11%(36/1,158),respectively.All were lower than 3.85%(41/1,064),4.98%(53/1,064),and 8.93%(95/1,064)of 1,064 medical devices under the conventional management method,respectively.The differences were statistically significant(x2=25.716,16.143,33.852,P<0.05).The incidence of adverse events at different risk levels of medical devices was lower than that of conventional management method,and the difference was statistically significant(x2=7.625,P<0.05).The qualified rate of cleaning medical device was higher than that of the conventional management method,while the rate of wetting package was lower than that of the conventional management method,and the differences were statistically significant(x2=9.091,17.540,P<0.05).The knowledge levels and total scores of nursing staffs for the composition of medical devices,usage norms,cleaning and sterilization,defect examination,maintenance and precautions of management method with risk intervention were all higher than those of conventional management method,and the differences were statistically significant(t=2.158,2.422,2.544,2.391,2.152,4.528,P<0.05).Conclusion:The intervention measure with the concept of risk management can significantly reduce the incidence of adverse events of medical device,and improve the qualified rate of cleaning medical device,and improve the knowledge level of nursing staffs for medical device.

Objective:To report the preliminary clinical exploration result of spatially fractionated radiation therapy (SFPT) using the pencil-beam-scanning (PBS) technique at a single center for the treatment of patients with bulky tumors.Methods:Data on the clinical characteristics, tumor characteristics, and dosimetric parameters were retrospectively collected from patients with bulky tumors at the Radiation Oncology Department, Ion Medical Center, First Affiliated Hospital of University of Science and Technology of China (i.e., the Hefei Ion Medical Center) from April 2024 to December 2024. Three-dimensional lattice radiotherapy (LRT) was primarily utilized in the SFRT, with multi-field robust optimization performed using pencil beam scanning. SFRT target volumes (STVs) were defined as 1.0 cm-diameter spheres. Primary observation indicators included the remission rate of tumor-associated symptoms, followed by the local tumor control rate.Results:A total of 12 patients were enrolled in this study, with a median age of 61 years (28-85 years). The primary tumors included hepatocellular carcinoma (six patients), sarcomas (three patients), and lung cancer (two patients). Eight patients received concurrent systemic therapy. The SFRT plans showed a median gross tumor volume (GTV) of 429.63 cm 3 (120.60-2 053.30 cm 3), a median STV number of 8 (3-20), a single-fraction dose to STVs of 10 GyE, a median irradiation quantity of 6 (3-8), a median STV of 8 cm 3 (3-20 cm 3), a median STV proportion of 2.09% (0.62%-3.30%), a median GTV corresponding to a single STV of 52.91 cm 3 (30.25-159.82 cm 3), and a median peak-to-valley dose ratio of 3.37 (2.29-7.60). All patients received conventionally fractionated proton therapy (CFPT), with a median prescription dose of 50 GyE (40-60 GyE). Furthermore, these patients showed a median follow-up time of 174 d (133-235 d), a remission rate of tumor-associated symptoms of 75%, and a local control rate of 100%. Four patients experienced grade 1-2 treatment-related adverse events, suggesting high overall tolerability of the patients. Conclusions:SFRT represents a promising technique with high control rates and tolerability for bulky tumors, providing the possibility for quick symptom relief and the control of tumor progression.

Objective:To evaluate the levels and source of cadmium exposure by dietary pathway among middle-aged and elderly people ≥40 in cadmium-contaminated areas of China.Methods:A total of 7 193 people aged 40-89 years from four typical cadmium-contaminated areas in China were selected as the study subjects. Food Frequency Questionnaire (FFQ), Total Diet Study (TDS) and a 3-day-24-hour dietary recall survey were conducted. Dietary cadmium intake and food sources through dietary pathways were assessed based on cadmium content in foods, consumption amounts and intake frequencies.Results:The mean age of the participants was 63.39±12.21 years, with 50.05% being males. The average monthly dietary cadmium intake was 7.39 μg/(kg·BW). Staple foods and vegetables were the primary sources of dietary cadmium intake, accounting for 57.51% and 32.48%, respectively. The monthly dietary cadmium intake in all surveyed regions did not exceed the Provisional Tolerable Monthly Intake (PTMI) recommended by the Joint FAO/WHO Expert Committee on Food Additives (JECFA).Conclusion:The monthly dietary cadmium intake among middle-aged and elderly people in cadmium-contaminated areas of China is relatively low, with the risk remaining at an acceptable level. Staple foods and vegetables are the most significant contributors to dietary cadmium intake.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.