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 design a morning quality assurance method for pencil-beam scanning proton system to achieve integrated measurement of multiple parameters.Methods:A functionally partitioned morning check phantom was designed and manufactured, which was fixed to a specific position on the treatment bed with a 3D-printed clip, along with the two-dimensional matrix ionization chamber, for consistency checks of proton field and beam-related parameters. Additionally, a groove for an imaging phantom was reserved on one side of the clip for the functional check of the onboard imaging guidance system. The sensitivity and specificity of the aforementioned morning check method were tested, demonstrating its effectiveness. The morning check data from a rotating beam treatment room at the Hefei Ion Medical Center over a continuous period of 7 months (126 d) were analyzed. The output, field flatness, symmetry, field size and the duration of morning check were observed.Results:The results showed that the changes in the output, field flatness, and symmetry were all within 1%, the change in the field size was within 0.5 mm, and the range variations for both 155 MeV and 240 MeV energy levels were within 1 mm. The changes in the spot size for the four energy levels of 100 MeV, 130 MeV, 160 MeV, and 190 MeV were all within 2%, and the spot position deviations were within 1.5 mm. The entire morning check process could be completed within 20 min.Conclusions:The morning check method designed and manufactured in this study specifically for pencil-beam scanning proton therapy can efficiently and integrally measure various proton system parameters and can be used as an implementation method for clinical proton therapy morning check.

In the earliest existing pharmacological monograph in China,Shennong Bencao Jing,there is a record of a parasitic plant known as"desert ginseng"——Cistanche deserticola.It exerts beneficial effects on the function of the heart,kidney,spleen,and lung.As research into its pharmacological properties has progressed,the active components of Cistanche deserticola also have shown promise for treating intestinal disorders.Due to the complex pathological mechanisms of sepsis,which often accompany multiple organ dysfunction,aligns closely with the multi-target pharmacological characteristics of Cistanche deserticola's.Furthermore,because Cistanche deserticola is both edible and medicinal,and exhibits a wide therapeutic window,it has a natural advantage in the clinical transformation and application after drug development.Currently,to expand its medicinal range,a review of the main active components of Cistanche deserticola and their related pharmacological effects is conducted,and combined with the specific characteristics of intestinal injury in sepsis,the potential value of Cistanche deserticola in intestinal barrier protection,microbial coordination,and intestinal motility regulation is further elaborated,providing new ideas and a theoretical basis for the application of Cistanche deserticola in sepsis-induced intestinal injury.

Objective:To analyze the screening results of the Urban Cancer Early Diagnosis and Treatment Project in Qinghai Province from 2019 to 2024.Methods:A summary and statistical analysis were conducted on six years of screening data from the Urban Cancer Early Dia-gnosis and Treatment Program in Qinghai Province,with the high-risk rate,screening rate,and detection rate calculated separately for each type of cancer.Results:From 2019 to 2024,56,882 high-risk individuals were identified.The high-risk rates for lung,colorectal,breast,up-per gastrointestinal,and liver cancer were 22.02%,21.57%,14.23%,13.52%,and 6.10%,respectively.Overall,13,592 individuals com-pleted clinical screening,with detection rates of 0.32%for lung cancer,0.41%for liver cancer,0.08%for precancerous gastric lesions,3.63%for precancerous colorectal lesions,0.08%for esophageal cancer,0.16%for gastric cancer,and 0.14%for colorectal cancer.Conclusions:The implementation of the Urban Cancer Early Diagnosis and Treatment Program in Qinghai Province aids in the early detection of cancer,improves early diagnosis and survival rates,and reduces mortality.Nevertheless,due to low public awareness and limited participation,en-hancements in program management and public outreach are required.

Objective To explore the value of diffusion-weighted imaging(DWI)and apparent diffusion coefficient(ADC)in magnetic resonance imaging(MRI)in differential diagnosis of benign and malignant cervical lymph nodes in patients with nasopharyngeal carcinoma.Methods Clinical data of 98 patients diagnosed with nasopharyngeal carcinoma were retrospectively analyzed.This cohort included 65 patients with pathologically confirmed malignant lymph nodes and 33 patients with benign lymph nodes.Prior to pathological diagnosis,all patients underwent routine MRI scans and DWI.ADC values of both benign and malignant lymph nodes were recorded.The diagnostic performance was evaluated using receiver operating characteristic(ROC)curve analysis.Additionally,the diagnostic efficacy of MRI and ADC values in distinguishing benign and malignant lymph nodes of nasopharyngeal carcinoma was compared.Results Of the 65 patients with malignant lymph nodes,42 cases were accompanied by liquefactive necrosis,and 13 cases were accompanied by extracapsular invasion.Most benign lymph nodes showed no signal on DWI sequence,while malignant lymph nodes showed obvious high signal or mixed signals.The ADC value of benign lymph nodes was(1.724±0.365)×10-3 mm2/s,which was higher than that of malignant nodes(1.022±0.210)×10-3 mm2/s(P＜0.01).The ROC curve analysis results showed that the area under the curve of ADC value for diagnosing benign and malignant lymph nodes was 0.843(95%CI:0.782-0.904),with a cutoff value of 1.363×10-3 mm2/s.At this point,the sensitivity and specificity for differentiating benign and malignant lymph nodes were 0.856 and 0.849,respectively,and Youden index was 0.705.When the ADC value was lower than the cutoff value,the accuracy,sensitivity and specificity for differentiating cervical lymph nodes in nasopharyngeal carcinoma patients were 0.878,0.892 and 0.848,respectively,which were superior to MRI(P＜0.05).Conclusion The DWI images of malignant lymph nodes in patients with nasopharyngeal carcinoma show high signal and low ADC,and ADC has high efficiency in the differential diagnosis of benign and malignant cervical lymph nodes.

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 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.

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.