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.

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 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

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.

Orbital disorders include conditions originating from the orbital bones, surrounding tissues, and post-orbital septum. They also include systemic ailments affecting the orbit. Different clinical symptoms make up the complex range of orbital disorders. Because these disorders mostly impact the orbital area instead of the intraocular compartment, there is little diagnostic usefulness for typical ophthalmic visual tests. As such, the primary instruments for diagnosing and evaluating orbital illnesses have become ophthalmic imaging modalities, including ocular ultrasonography(B-scan), computed tomography(CT), and magnetic resonance imaging(MRI). One way to improve the precision and promptness of diagnosing orbital diseases is to standardize the functioning of widely used imaging equipment and define the radiological features of orbital abnormalities. Such programs are crucial for the care of patients with orbital disorders since they considerably reduce the number of misdiagnoses and missed diagnoses in these individuals. The underlying concepts, operational techniques, and normal and pathological imaging findings associated with common diagnostic tools for orbital illnesses are all thoroughly reviewed in this guideline. The objective is to improve primary healthcare settings' diagnostic competence in the field of orbital pathology and to standardize procedures for diagnosing orbital disorders.

Objective To investigate the efficacy of red and blue lights combined with Yufa Shengfa solution and 5% minoxidil solution in treating Ludwig type Ⅰ female androgenetic alopecia. Methods A total of 160 patients with Ludwig type Ⅰ female androgenetic alopecia were randomly divided into group A (Yufa Shengfa solution combined with 5% minoxidil solution), group B (red and blue lights therapy combined with Yufa Shengfa solution), group C (red and blue lights therapy combined with 5% minoxidil solution) and group D (red and blue lights therapy combined with Yufa Shengfa solution and 5% minoxidil solution), with 40 cases in each group. All the patients orally took compound glycyrrhizin tablets and Centrum multivitamins, and the therapeutic period was 3 months. Differences in hair diameter, hair density, and the number of hair follicles with multiple hairs were compared before and after treatment. Results The hair density, hair diameter, and the number of hair follicles with multiple hairs improved significantly in 4 groups compared with those before treatment, and group D showed the best improvement in these parameters, with significant between-group differences (P < 0.001). There were no serious adverse events or side effects during treatment in all the patients. Conclusion Red and blue lights therapy combined with Yufa Shengfa solution and 5% minoxidil solution is an effective non-surgical treatment for Ludwig type Ⅰ female androgenetic alopecia.

Objective:To explore the effect of family empowerment model on the improvement of swallowing care ability and care preparedness of primary caregivers of first-episode stroke dysphagia patients, further to explore its impact on patients′s wallowing function and life quality.Methods:This study was a randomized controlled study. From January 2021 to December 2022, 80 main caregivers of patients with dysphagia caused by manual stroke admitted to the Department of Acupuncture and Moxibustion, Shenzhen Hospital of Traditional Chinese Medicine were selected as the research objects, and 40 cases in the control group and 40 cases in the observation group were selected by random number table method. The control group were treated with conventional nursing care of first-episode stroke dysphagia patients in the acupuncture and moxibustion Department. On the basis of the conventional care in the control group, the observation group were treated with family empowerment model intervention for 14 days and was followed up for 28 days. Primary caregivers′ swallowing care ability, Caregiver Preparedness Scale (CPS), patients′ swallowing function rate, Swallowing Related Quality of Life (SWALQOL) were used to evaluate the effects before intervention and at the end of intervention.Results:There were 18 males and 19 females primary caregivers in the control group, aged (55.61 ± 7.43) years old. There were 18 males and 21 females primary caregivers in the observation group, aged (58.23 ± 8.22) years old. The swallowing care ability score showed a statistically significant difference between the observation group (143.47 ± 3.96) and the control group (107.74 ± 1.43) ( t=-26.76, P<0.05). After intervention, the caregiver preparedness scale was (26.11 ± 3.81) in the observation group, and (18.35 ± 4.54) in the control group, and the difference was statistically significant ( t=-4.11, P<0.05).The patients′ swallowing function rate and SWALQOL score were respectively 97.44% (38/39) and (91.41 ± 8.08) points in the observation group, and 72.97% (27/37) and (80.33 ± 4.21) points in the control group, and the difference was both statistically significant ( χ2=10.76, t=-2.54, both P<0.05). Conclusions:The implementation of family empowerment model could enhance the swallowing care ability and care preparedness of primary caregivers of the first-episode stroke dysphagia patients, which could further improve patients′ swallowing function and life quality.

Objective To analyze the values of renal resistance index(RRI),cystatin C(CysC),blood β2-microglobulin(β2-MG)and urinary N-acetyl-β-glucosamine glycosidase(NAG)in early prediction of contrast-induced acute kidney injury(CI-AKI).Methods A retrospective cohort analysis on 207 postoperative patients after intervention therapy was conducted.The patients were divided into AKI group(18 patients)and non-AKI group(189 patients)based on whether CI-AKI occurred.General and clinical data were collected and compared.Accord-ing to the time of diagnosis of AKI(D0 on the day of surgery or D1 on the first day after surgery),the AKI group was divided into AKI(D0)group and AKI(D1)group.Indicators RRI,CysC,and blood β2-MG,serum creatinine(sCr),and urinary NAG were compared between the two groups.The risk factors of CI-AKI were explored using logistic regression and linear regression.Results In the AKI group,males,preoperative sCr,acute physiological and chronic health(APACHⅡ)score and sequential organ failure(SOFA)score,surgical duratrion,sCr,CysC,blood β2-MG,urinary NAG on the day of surgery and the first day after surgery,and RRI were higher than those in the non-AKI group;Higher APACHEⅡ and SOFA scores and higher CysC level on D1 were independent risk factors for the occurrence of CI-AKI(P<0.05).Levels of CysC and urinay NAG on D0 were higher in the AKI(D0)group than in the AKI(D1)group(P<0.05).RRI,urinary NAG and blood β2-MG were not independent risk factors for CI-AKI.Conclusions CysC and urinary NAG are powerful predictors for the prediction of CI-AKI,and RRI and blood β2-MG cannot predict the occurrence of CI-AKI early.