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.

ObjectiveTo investigate the value of Fried Frailty Phenotype （FFP）， liver frailty index （LFI）， and Short Physical Performance Battery （SPPB） in predicting 2-year all-cause mortality and decompensation events in patients with liver cirrhosis. MethodsA total of 277 patients with liver cirrhosis who were hospitalized in Beijing Friendship Hospital， Capital Medical University， from December 2020 to December 2021 were enrolled， and FFP， LFI， and SPPB were used to assess the state of frailty. Based on the scores of each tool， these patients were divided into frail and non-frail groups. These three tools were compared in terms of consistency and independent predictive performance. The primary endpoints were 2-year all-cause mortality rate and composite endpoints （death+decompensation events）， and the Cox regression analysis， the receiver operating characteristic （ROC） curve， net reclassification index （NRI）， and integrated discrimination improvement （IDI） index were used to analyze the predictive value of the three tools. Normally distributed continuous data were compared between two groups using the independent samples t-test， while non-normally distributed continuous data were compared using the Mann-Whitney U test. Categorical data were compared between groups using the chi-square test or Fisher’s exact test. The agreement among different frailty tools was evaluated using Cohen’s Kappa statistic. The Kaplan-Meier survival curve was plotted， and a survival analysis was performed using the log-rank test. ResultsThe prevalence rate of frailty assessed by FFP， LFI， and SPPB was 37.2%， 22.4%， and 20.2%， respectively， with a moderate consistency between FFP and LFI/SPPB （κ=0.57， 95% confidence interval ［CI］： 0.47 — 0.67； κ=0.51， 95%CI： 0.41 — 0.62） and a relatively high consistency between LFI and SPPB （κ=0.87， 95%CI： 0.80 — 0.94）. Compared with the non-frailty group， the frailty group had significantly higher all-cause mortality rate and incidence rate of composite endpoints （P0.001）. After multivariate adjustment， FFP， LFI， and SPPB had a hazard ratio of 2.42（95%CI： 1.51 — 5.11）， 2.21（95%CI： 1.11 — 4.42）， and 2.21（95%CI： 1.14 — 4.30）， respectively， in predicting all-cause mortality， as well as a hazard ratio of 2.51（95%CI： 1.61 — 3.91）， 2.40（95%CI： 1.51 — 3.80）， and 2.20（95%CI： 1.39 — 3.47）， respectively， in predicting composite endpoints. Compared with Child-Pugh score， FFP had a significantly greater area under the ROC curve （AUC） in predicting all-cause mortality （0.79 vs 0.69， P=0.032） and composite endpoints （0.75 vs 0.68， P=0.044）. Frailty assessment tools combined with Child-Pugh score significantly improved the performance in predicting all-cause mortality and composite endpoints， with an AUC of 0.81 — 0.82 and 0.77 — 0.78， respectively （P0.05）. NRI and IDI analyses further confirmed the improvement of the combined model in classification （all P0.001）. ConclusionFFP， LFI， and SPPB can independently predict adverse outcomes in patients with liver cirrhosis， among which FFP has the best predictive performance， and the combination of frailty assessment tools with Child-Pugh score can significantly enhance the accuracy of prognostic evaluation.

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.

The applications of nuclear technology in industries, medicine, and other fields have increased the risk of radiation injury. Although some small-molecule drugs for radiation injury treatment have been applied clinically or are in preclinical research, their therapeutic efficacy is significantly limited by short circulation time and rapid metabolism. Nanomaterials have attracted growing attention with their outstanding bioactivity, chemical stability, tissue compatibility, and targeted delivery capabilities, therefore having the promise of offering the potential solutions to the limitations of current small-molecule drugs. However, their biosafety and clinical efficacy require further investigation. This review summarizes the design strategies and classifications of nanomaterials for radiation injury treatment, analyzes current research progress in their therapeutic applications, and introduces nanomaterial-based approaches for enhancing the elimination of internal radionuclide contamination. Finally, the challenges and future prospects of nanomaterials in radiation injury treatment are discussed. This review aims to provide researchers with a comprehensive understanding of recent advances in nanomaterial-based radiation injury therapeutics, thereby promoting further development in this field.

Objective:To explore the mechanism for the occurrence and phenotypic characteristics of Turner syndrome based on a prenatally diagnosed case of a mosaic karyotype containing double pseudo-isodicentric X chromosome and a review of relevant literature.Methods:A fetus who was diagnosed with increased risk of trisomy 21 at the Provincial Hospital Affiliated to Shandong First Medical University in August 2023 was selected as the study subject. Clinical data of the fetus was collected. Following amniocentesis, chromosomal G-banding karyotype analysis and chromosomal microarray analysis (CMA) were carried out. This study has been approved by the Ethics Committee of the Hospital (Ethics No.: SWYX No. 2022-287).Results:The early-trimester screening suggested a high risk of trisomy 21(1/19), with free β-hCG of 116 ng/mL (MoM value 2.35), PAPP-A of 0.394 ng/mL (MoM value 0.12), and NT value of 1.3 mm, though no abnormality was found in the fetus at 19 weeks gestation. The karyotype of amniocyte was determined as 46, X, psu idic(X)(p11.21)[55]/45, X[27]/47, X, psu idic(X)(p11.21)×2[5]/46, XX[13]. CMA has yielded a result of arr[GRCh37] Xp22.33p11.21(168552_55585678)×1[0.67], Xp11.21q28(55703291_155233098)×3[0.5].Conclusion:Karyotypes of Turner syndrome are complex and diverse, and a rare 46, X, psu idic(X)(p11.21)[55]/45, X[27]/47, X, psu idic(X)(p11.21)×2[5]/46, XX[13] mosaic karyotype with double pseudo-isodicentric X chromosome has been identified. Literature review suggested that this karyotype may lead to phenotypic diversification and a risk of reduced sensitivity to hormone therapy.

OBJECTIVE: To explore the mechanism for the occurrence and phenotypic characteristics of Turner syndrome based on a prenatally diagnosed case of a mosaic karyotype containing double pseudo-isodicentric X chromosome and a review of relevant literature. METHODS: A fetus diagnosed with increased risk for trisomy 21 at the Provincial Hospital Affiliated to Shandong First Medical University in August 2023 was selected as the study subject. Clinical data of the fetus was collected. Following amniocentesis, chromosomal G-banding karyotype analysis and chromosomal microarray analysis (CMA) were carried out. This study has been approved by the Ethics Committee of the Hospital (Ethics No.: SWYX No. 2022-287). RESULTS: The early-trimester screening suggested a high risk of trisomy 21 (1/19), with free β-hCG of 116 ng/mL (MoM value 2.35), PAPP-A of 0.394 ng/mL (MoM value 0.12), and NT value of 1.3 mm, though no abnormality was found in the fetus at 19 weeks gestation. The karyotype of amniocyte was determined as 46,X,psu idic(X)(p11.21)[55]/45,X[27]/47,X,psu idic(X)(p11.21)×2[5]/46,XX[13]. CMA has yielded a result of arr[GRCh37] Xp22.33p11.21(168552_55585678)×1[0.67],Xp11.21q28(55703291_155233098)×3[0.5]. CONCLUSION Karyotypes of Turner syndrome are complex and diverse, and a rare 46,X,psu idic(X)(p11.21)[55]/45,X[27]/47,X,psu idic(X)(p11.21)×2[5]/46,XX[13] mosaic karyotype with double pseudo-isodicentric X chromosome has been identified. Literature review suggested that this karyotype may lead to phenotypic diversification and a risk of reduced sensitivity to hormone therapy.
Humans ; Turner Syndrome/diagnosis* ; Female ; Pregnancy ; Chromosomes, Human, X/genetics* ; Mosaicism ; Prenatal Diagnosis ; Karyotyping ; Adult ; Karyotype ; Amniocentesis

The applications of nuclear technology in industries, medicine, and other fields have increased the risk of radiation injury. Although some small-molecule drugs for radiation injury treatment have been applied clinically or are in preclinical research, their therapeutic efficacy is significantly limited by short circulation time and rapid metabolism. Nanomaterials have attracted growing attention with their outstanding bioactivity, chemical stability, tissue compatibility, and targeted delivery capabilities, therefore having the promise of offering the potential solutions to the limitations of current small-molecule drugs. However, their biosafety and clinical efficacy require further investigation. This review summarizes the design strategies and classifications of nanomaterials for radiation injury treatment, analyzes current research progress in their therapeutic applications, and introduces nanomaterial-based approaches for enhancing the elimination of internal radionuclide contamination. Finally, the challenges and future prospects of nanomaterials in radiation injury treatment are discussed. This review aims to provide researchers with a comprehensive understanding of recent advances in nanomaterial-based radiation injury therapeutics, thereby promoting further development in this field.

Objective:To explore the mechanism for the occurrence and phenotypic characteristics of Turner syndrome based on a prenatally diagnosed case of a mosaic karyotype containing double pseudo-isodicentric X chromosome and a review of relevant literature.Methods:A fetus who was diagnosed with increased risk of trisomy 21 at the Provincial Hospital Affiliated to Shandong First Medical University in August 2023 was selected as the study subject. Clinical data of the fetus was collected. Following amniocentesis, chromosomal G-banding karyotype analysis and chromosomal microarray analysis (CMA) were carried out. This study has been approved by the Ethics Committee of the Hospital (Ethics No.: SWYX No. 2022-287).Results:The early-trimester screening suggested a high risk of trisomy 21(1/19), with free β-hCG of 116 ng/mL (MoM value 2.35), PAPP-A of 0.394 ng/mL (MoM value 0.12), and NT value of 1.3 mm, though no abnormality was found in the fetus at 19 weeks gestation. The karyotype of amniocyte was determined as 46, X, psu idic(X)(p11.21)[55]/45, X[27]/47, X, psu idic(X)(p11.21)×2[5]/46, XX[13]. CMA has yielded a result of arr[GRCh37] Xp22.33p11.21(168552_55585678)×1[0.67], Xp11.21q28(55703291_155233098)×3[0.5].Conclusion:Karyotypes of Turner syndrome are complex and diverse, and a rare 46, X, psu idic(X)(p11.21)[55]/45, X[27]/47, X, psu idic(X)(p11.21)×2[5]/46, XX[13] mosaic karyotype with double pseudo-isodicentric X chromosome has been identified. Literature review suggested that this karyotype may lead to phenotypic diversification and a risk of reduced sensitivity to hormone therapy.

Objective:To develop a prediction tool for 6-year incident risk of frailty among Chinese older adults aged 65 years or above.Methods:Data from the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018 was used, including 13 676 older adults aged 65 years or above who were free of frailty at baseline. Key predictors of frailty were identified via the least absolute shrinkage and selection operator (LASSO) method, and were thereafter used to predict the incident frailty based on the Cox proportional hazards regression model. The model was internally validated by 2 000 Bootstrap resamples and evaluated for the performance of discrimination and calibration using the area under the receiver operating characteristic curve (AUC) and calibration curve, respectively. The net benefit of the developed prediction tool was evaluated by decision-curve analysis.Results:The M( Q1, Q3) age and follow-up time of the participants were 81.0 (71.0, 90.0) years and 6.0 (4.1, 9.2) years, respectively. A total of 4 126 older persons (30.2%) were recorded with frailty incidents during the follow-up, with the corresponding incidence density of 41.8/1 000 person-years. A total of 15 key predictors of frailty were selected by LASSO, namely, age, sex, race, education years, meat consumption, tea drinking, performing housework, raising domestic animals, playing cards or mahjong, and baseline status of visual function, activities of the daily living score, instrumental activities of the daily living score, hypertension, heart disease, and self-rated health. The prediction model was internally validated with an AUC of 0.802, with the max Youden's index of 0.467 at a risk threshold of 19.0%. The calibration curve showed high consistency between predicted probabilities and observed proportions of frailty events. The decision curve indicated that higher net benefits could be obtained via the prediction model than did strategies based on intervention in all or none participants for any risk threshold less than 59%, and the model-based net benefit was estimated to be 0.10 at a risk threshold of 19.0%. Conclusions:The herein developed 6-year incident risk prediction model of frailty, based on easily accessible questionnaires and physical examination variables, has good predictive performance. It has application potential in identifying populations at high risk of incident frailty.