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.

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.

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.

The purpose of this study was to explore the effect and mechanism of Xihuang Pills on rats with precancerous lesions of the breast. Of 48 healthy female rats, 8 were randomly selected as blank group, and the other 40 were treated with 7,12-dimethylbenzanthracene(DMBA) combined with estrogen and progestin to establish a model of precancerous lesions of the breast. The successfully modeled rats were randomly divided into a model group, a tamoxifen group(1.8 mg·kg~(-1)·d~(-1)), a Xihuang Pills low-dose group(0.3 g·kg~(-1)·d~(-1)), a medium-dose group(0.6 g·kg~(-1)·d~(-1)) and a high-dose group(1.2 g·kg~(-1)·d~(-1)). After 30 days of admi-nistration, the histopathological changes of viscera and breast were observed by haematoxylin and eosin(HE) staining, and the visceral index was calculated. Enzyme linked immunosorbent assay(ELISA) was used to detect the contents of estradiol(E_2) and progesterone(P) in serum. The protein expressions of vascular endothelial growth factor(VEGF) and fibroblast growth factor 2(FGF2) were detected by immunohistochemistry. The protein expressions of VEGF, vascular endothelial growth factor receptor 2(VEGFR2), phosphorylated-vascular endothelial growth factor receptor 2(p-VEGFR2), B-cell lymphoma-2(Bcl-2), and Bcl-2 associated X protein(Bax) were detected by Western blot and the mRNA expressions of VEGF, FGF2, CXC-chemokine receptor 4(CXCR4), cysteine aspartic acid-specific protease(caspase-3), and stromal cell-derived factor 1(SDF-1) were detected by real-time polymerase chain reaction(RT-PCR). HE staining revealed that the model group had some liver and kidney damages and severe hyperplastic mammary tissue, while the Xihuang Pills high-dose group had mild hyperplasia. Compared with the model group, the Xihuang Pills groups had lo-wer ovarian coefficient(P<0.05 or P<0.01) and Xihuang Pills high-dose group had lower uterine coefficient(P<0.01). ELISA results showed that compared with the model group, expressions of E_2 and P in Xihuang Pills high-dose group were significantly decreased(P<0.05 or P<0.01). Immunohistochemistry, Western blot and RT-PCR indicated that compared with the conditions in the model group, the protein and mRNA expressions of VEGF and FGF2 in the Xihuang Pills groups were down-regulated(P<0.05 or P<0.01), and the protein expression of Bcl-2 was lowered(P<0.01); there was a decrease in the protein expressions of VEGFR2 and p-VEGFR2(P<0.01), a down-regulation in the mRNA expressions of CXCR4 and SDF-1(P<0.01), while an increase in the mRNA expression of caspase-3(P<0.01) in both Xihuang Pills medium-dose and high-dose groups; the protein expression of Bax in Xihuang Pills high-dose group was increased(P<0.01). The above results indicated that Xihuang Pills can effectively intervene in precance-rous lesions of the breast, and the mechanism may be related to the regulation of E_2 and P secretion as well as the inhibition of angiogenesis and chemokine receptor expression, thus controlling the occurrence of precancerous lesions of the breast in rats.
Rats ; Female ; Animals ; Rats, Sprague-Dawley ; bcl-2-Associated X Protein ; Vascular Endothelial Growth Factor A/metabolism* ; Caspase 3 ; Vascular Endothelial Growth Factor Receptor-2 ; Fibroblast Growth Factor 2 ; Proto-Oncogene Proteins c-bcl-2 ; 9,10-Dimethyl-1,2-benzanthracene/toxicity* ; Precancerous Conditions ; Hyperplasia ; Receptors, Chemokine ; RNA, Messenger

Based on data mining technology, the acupoints compatibility rules of acupuncture for depression diseases were explored. The randomized controlled trial (RCT) articles regarding acupuncture for depression diseases published from establishment of database to September 2nd, 2022 were searched in CNKI database, Wangfang database, VIP database, SinoMed database, PubMed, EMbase, Web of Science and Cochrane Library. The use frequency of acupoints, meridian tropism, selection of special acupoints and acupoint association rules for five common depression diseases, including primary depression, post-stroke depression, menopausal syndrome, psychoneurosis and anxiety disorder, were analyzed by Python programming language. Cytoscape software was used to analyze the acupoint association and the disease-acupoint co-occurrence network. As a result, totally 387 articles were included, and 319 acupoints prescriptions for the above five common depression diseases were extracted, involving 159 acupoints. The use frequency of acupoints was 2 574 times in total. The frequently-used acupoints were Baihui (GV 20), Sanyinjiao (SP 6), Taichong (LR 3), Neiguan (PC 6), Shenmen (HT 7), Yintang (GV 24⁺), Zusanli (ST 36), Hegu (LI 4), Sishencong (EX-HN 1) and Taixi (KI 3), etc. The frequently involved meridians were the governor vessel, foot-taiyang bladder meridian, foot-taiyin spleen meridian, and foot-jueyin liver meridian. The frequency of the special acupoints from high to low was crossing points, five-shu points, yuan-primary points, back-shu points, luo-connecting points, and eight confluent points, etc, which were often used in combination with "Baihui (GV 20)-Yintang (GV 24⁺)" (the highest degree of association). At the same time, the analysis of the co-occurrence network of depression diseases and acupoints showed that the core acupoints group of acupuncture for depression diseases were Baihui (GV 20), Taichong (LR 3), Shenmen (HT 7), Zusanli (ST 36), Neiguan (PC 6) and Sanyinjiao (SP 6). In conclusion, acupuncture treatment for depression diseases has gradually formed a rule of acupoint compatibility, with special acupoint as the main body and "unblocking the governor vessel, and regulating the spirit and qi " as the main therapeutic principle.
Acupuncture Points ; Acupuncture Therapy ; Data Mining ; Depression ; Meridians ; Randomized Controlled Trials as Topic

The UPLC-MS/MS was established for the determination of acetyl-11-keto-beta-boswellic acid(AKBA) and β-boswellic acid(β-BA), the main active components of Olibanum and Myrrha extracts in Xihuang Formula, in rat plasma and urine. The effects of compatibility on the pharmacokinetic behaviors of AKBA and β-BA in rats were investigated, and the differences in pharmacokinetic behaviors between healthy rats and rats with precancerous lesions of breast cancer were compared. The results showed that compared with RM-NH and RM-SH groups, the AUC_(0-t) and AUC_(0-∞) of β-BA increased(P<0.05 or P<0.01), T_(max) decreased(P<0.05 or P<0.01), and C_(max) increased(P<0.01) after compatibility. The trends of AKBA and β-BA were the same. Compared with RM-SH group, the T_(max) decreased(P<0.05), C_(max) increased(P<0.01), and the absorption rate increased in the normal group of Xihuang Formula. The results of urinary excretion showed that there was a decreasing trend in the urinary excretion rate and total urinary excretion of β-BA and AKBA after compatibility, but there was no statistical difference. Compared with normal group of Xihuang Formula, the AUC_(0-t) and AUC_(0-∞) of β-BA increased(P<0.05), T_(max) increased(P<0.05), and the clearance rate decreased in the breast precancerous lesion group. AUC_(0-t) and AUC_(0-∞) of AKBA showed an increasing trend, the in vivo retention time was prolonged, and the clearance rate was reduced, but there was no significant difference compared with the normal group. The cumulative urinary excretion and urinary excretion rate of β-BA and AKBA decreased under pathological conditions, indicating that pathological conditions could affect the in vivo process of β-BA and AKBA, and reduce their excretion in the form of prototype drugs, showing different pharmacokine-tic characteristics from normal physiological conditions. In this study, UPLC-MS/MS analysis method was established, which was sui-table for in vivo pharmacokinetic analysis of β-BA and AKBA. This study laid a foundation for the development of new dosage forms of Xihuang Formula.
Rats ; Animals ; Chromatography, Liquid ; Tandem Mass Spectrometry ; Drugs, Chinese Herbal ; Precancerous Conditions ; Triterpenes/pharmacology*

Objective: To explore the prognostic factors of extracellular NK/T cell lymphoma (ENKTL) treated with pegaspargase/L-asparaginase. Methods: The clinical data of 656 ENKTL patients diagnosed at 11 medical centers in the Huaihai Lymphoma Working Group from March 2014 to April 2021 were retrospectively analyzed. The patients were randomly divided into two groups: a training set (460 cases) and a validation set (196 cases) at 7∶3, and the prognostic factors of the patients were analyzed. A prognostic scoring system was established, and the predictive performance of different models was compared. Results: Patients' median age was 46 (34, 57) years, with 456 males (69.5% ) and 561 nasal involvement (85.5% ). 203 patients (30.9% ) received a chemotherapy regimen based on L-asparaginase combined with anthracyclines, and the 5-year overall survival rate of patients treated with P-GEMOX regimen (pegaspargase+gemcitabine+oxaliplatin) was better than those treated with SMILE regimen (methotrexate+dexamethasone+cyclophosphamide+L-asparaginase+etoposide) (85.9% vs 63.8% ; P=0.004). The results of multivariate analysis showed that gender, CA stage, the Eastern Cooperative Oncology Group performance status (ECOG PS) score, HGB, and EB virus DNA were independent influencing factors for the prognosis of ENKTL patients (P<0.05). In this study, the predictive performance of the prognostic factors is superior to the international prognostic index, Korean prognostic index, and prognostic index of natural killer lymphoma. Conclusion: Gender, CA stage, ECOG PS score, HGB, and EB virus DNA are prognostic factors for ENKTL patients treated with pegaspargase/L-asparaginase.
Male ; Humans ; Middle Aged ; Asparaginase/therapeutic use* ; Prognosis ; Retrospective Studies ; Lymphoma, Extranodal NK-T-Cell/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Etoposide ; Cyclophosphamide ; Methotrexate/therapeutic use* ; DNA/therapeutic use* ; Treatment Outcome