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.

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.

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.

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.

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 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

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 distribution of airborne pollen in Cangzhou,to analyze the correlation between pollen characteristics,meteorological factors and the rate of allergic rhinitis visits,and to provide a reference basis for the precise prevention and treatment of allergic rhinitis in Cangzhou.METHODS The gravity deposition method was applied to collect daily airborne pollen from the monitoring sites in Cangzhou from March to October 2022 and record the pollen species and quantities.We also collected information on allergic rhinitis patients who visited the ENT Department of Cangzhou Central Hospital during the same period and meteorological data(daily maximum temperature,minimum temperature,maximum relative humidity,maximum precipitation,and maximum wind speed at ground level)during the same period,and analyzed the relationship between the number of AR patients'visits,the amount of pollen,and the meteorological factors.RESULTS 1.A total of 229 pollen exposures were collected from March to October 2022,totaling 19 368 pollen grains,of which 18 750 grains of pollen could be identified in 19 families and 618 grains were difficult to identify the families.2.There were two peaks of pollen during the period,in April and September respectively.Spring pollen to tree-based category,to the pine family,willow willow family,Xylariaceae(ash)most often;summer pollen volume has declined,but still tree-based category;fall pollen to herbaceous category,to the Asteraceae Artemisia,mulberry,Gramineae most often.3.The pollen concentration in Cangzhou City was negatively correlated with maximum humidity,maximum precipitation,maximum temperature,and minimum temperature.Maximum wind speed at ground level was positively correlated with pollen concentration.4.Pollen concentration was positively correlated with the number of visits.The correlation analysis between daily medical visits from June to October and air pollen concentration showed a significant positive correlation,while the results from March to May showed no significant correlation.CONCLUSION The peak of airborne pollen dispersal in April and September in Cangzhou.Spring and summer pollen is dominated by tree species,and the dominant pollen is Pinaceae,Willow genus of Populus,and Xylariaceae(ash genus);fall pollen is dominated by herbaceous species,and the dominant pollen is Artemisia,Asteraceae,Mulberry,and Gramineae.Meteorological factors are important factors affecting pollen concentration.Within a certain range,the lower the maximum humidity,the lowest maximum precipitation,the lower the highest or the lowest temperature,the higher the pollen concentration,and the pollen concentration is more affected by the lowest temperature.There is a correlation between pollen concentration and the number of AR patients'visits from June to October,which can be used as an environmental early warning indicator of the prevalence of AR.