[Objective] To investigate the antigen and gene frequency distribution of the MNS blood group system in the Han population of voluntary blood donors in Suzhou, and to explore the polymorphism of rare MNS blood group genes, in order to improve the construction of the local rare blood group database. [Methods] A total of 8 034 whole blood samples were randomly collected from Han blood donors at our station from October 2023 to June 2024. The MNS blood group phenotypes were identified using serological methods. Gene frequencies were analyzed and compared with those of ethnic populations in other regions. Rare MNS phenotype samples were subjected to gene sequencing. [Results] The distribution of MNS blood group system phenotypes within the population was as follows: the MM, NN, and MN phenotypes accounted for 23.00%, 27.12%, and 49.88% respectively; the SS, ss, and Ss phenotypes accounted for 0.30%, 90.99%, and 8.70% respectively. The gene frequencies of M, N, S, and s were 0.4794, 0.5206, 0.0465, and 0.9534 respectively. Chi-squared tests confirmed adherence to Hardy-Weinberg equilibrium with P-values of 0.997 and 0.349, showing statistical significance compared to some other regional ethnic populations (P<0.05). Additionally, one rare serological phenotype, S-s-, with a frequency of 0.01%, was identified. [Conclusion] The MNS blood group system in the Han population of voluntary blood donors in Suzhou exhibits polymorphism and regional distribution characteristics. Gene frequencies differ from those observed in other regions of China. It is essential to enhance the establishment of a rare blood type database in Suzhou to provide data support for precise clinical transfusion.

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.

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.

This study aimed to identify the related substances of phloroglucinol injection by two-dimensional liquid chromatography quadrupole time-of-flight mass spectrometry (2D-LC-Q-TOF/MS). The first-dimensional separation was carried out on an HSS T3 (250 mm × 4.6 mm, 5 μm) column by gradient elution using 1.36 g·L^-1 potassium dihydrogen phosphate buffer solution (pH adjusted to 3.0 with diluted phosphoric acid) and acetonitrile as the mobile phases. The separated components were then trapped in switch valve tube lines respectively and delivered to the second-dimensional desalting gradient elution which was performed with a BDS C18 (100 mm × 4.6 mm, 2.4 μm) column using 0.1% formic acid and methanol as the mobile phases. After rapid desalting, electrospray-ionization quadrupole time-of-flight high resolution mass spectrometry was used for determining the accurate masses and elemental compositions of the parents and their product ions for both phloroglucinol and its related substance. Structures of the related substances were then figured out by mass spectrometry elucidation, organic reaction mechanism analysis, and/or comparison with reference substances. Under the established analytical conditions, phloroglucinol and its related substances were adequately separated, 17 main related substances were detected and identified in the injection and its stressed samples for the first time. The identification results can provide reference for the quality control of phloroglucinol injection.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Purpose::Shaping and assembling contemporary external fixators rapidly for the severe mandibular fractures remains a challenge, especially in emergency circumstance. We designed a novel external fixator that incorporates universal joints to provide the stabilization for mandibular comminuted fractures. This study aims to confirm the efficacy of this novel external fixator through biomechanical tests in vitro and animal experiments. Methods::In vitro biomechanical tests were conducted using 6 fresh canine with mandibular defect to simulate critical comminuted fractures. Three mandibles were stabilized by the novel external fixator and other mandibles were fixed by 2.5 mm reconstruction plates. All fixed mandibles were subjected to loads of 350 N on the anterior regions of teeth and 550 N on the first molar of the unaffected side. The stability was evaluated based on the maximum displacement and the slope of the load-displacement curve. In animal experiments, 9 beagles with comminuted mandibular fractures were divided into 3 groups, which were treated with the novel external fixation, reconstruction plate, and dental arch bar, respectively. The general observation, the changes in animals’ weight, and the surgical duration were recorded and compared among 3 groups. The CT scans were performed at various intervals of 0 day (immediately after the surgery), 3 days, 7 days, 14 days, 21 days, and 28 days to analyze the displacement of feature points on the canine mandible and situation of fracture healing at 28 days. The statistical significance was assessed by the two-way analysis of variance test followed by the Bonferroni test, enabling multiple comparisons for all tests using GraphPad Prism10.1.0 (GraphPad Inc, USA). Results::The outcomes of the biomechanical tests indicated that no statistically significant differences were found in terms of the maximum displacement ( p = 0.496, 0.079) and the slope of load displacement curves ( p = 0.374, 0.349) under 2 load modes between the external and internal fixation groups. The animal experiment data showed that there were minor displacements of feature points between the external and internal fixation groups without statistic difference, while the arch bar group demonstrated inferior stability. The CT analysis revealed that the best fracture healing happened in the internal fixation group, followed by the external fixation and arch baring at 28 days after fixation. The external fixation group had the shortest fixation duration (25.67 ± 3.79) min compared to internal fixation ((70.67 ± 4.51) min, p < 0.001) and arch baring ((42.00 ± 3.00) min, p = 0.046). Conclusion::The conclusion of this study highlighted the efficacy and reliability of this novel external fixator in managing mandibular fractures rapidly, offering a viable option for the initial stabilization of comminuted mandibular fractures in the setting of emergency rescue.

Objective To establish the pharmacokinetic model of linezolid in neonates,and to optimize the administration regimen. Methods A prospective study was conducted among 64 neonates with sepsis who received linezolid as anti-infective therapy,and liquid chromatography-tandem mass spectrometry was used to measure the plasma concentration of the drug. Clinical data were collected,and nonlinear mixed effects modeling was used to establish a population pharmacokinetic (PPK) model. Monte Carlo simulation and evaluation was performed for the optimal administration regimen of children with different features. Results The pharmacokinetic properties of linezolid in neonates could be described by a single-compartment model with primary elimination,and the population typical values for apparent volume of distribution and clearance rate were 0.79 L and 0.34 L/h,respectively. The results of goodness of fit,visualization verification,and the Bootstrap method showed that the model was robust with reliable results of parameter estimation and prediction. Monte Carlo simulation results showed that the optimal administration regimen for linezolid in neonates was as follows:6 mg/kg,q8h,at 28 weeks of gestational age (GA);8 mg/kg,q8h,at 32 weeks of GA;9 mg/kg,q8h,at 34-37 weeks of GA;11 mg/kg,q8h,at 40 weeks of GA. Conclusions The PPK model established in this study can provide a reference for individual administration of linezolid in neonates. GA and body weight at the time of administration are significant influencing factors for the clearance rate of linezolid in neonates.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.