Objective:To investigate the prevalence of baseline dyslipidemia in HIV-infected people before starting antiviral therapy (ART) in China.Methods:The data were collected from HIV/AIDS ART database of Chinese Disease Prevention and Control Information System. A national sample of HIV- infected people who initiated ART from 2018 to 2023 was used to collect baseline information, including sociodemographic characteristics and laboratory test results. According to the Chinese Lipid Management Guidelines (2023) and the National Cholesterol Education Program Adult Treatment Panel Ⅲ guidelines, triglyceride (TG) ≥1.7 mmol/L or total cholesterol (TC) ≥5.2 mmol/L were identified as dyslipidemia. Statistical analysis was performed with software SAS 9.4. An unconditional logistic regression model was used to analyze the factors influencing TG and TC abnormalities in HIV-infected patients before ART.Results:A total of 359 952 adults infected with HIV were included in this study, the prevalence rate of dyslipidemia was 38.41% (138 263/359 952). The abnormal rates of TG and TC were 31.40% (113 041/359 952) and 13.75% (49 494/359 952), respectively. In all age groups except for the 25-44 age groups, the abnormal rates of TG and TC were higher in HIV-infected women than in HIV-infected men. In HIV-infected patients, women, those aged 45-64 years, those lived in northeast region, those had heterosexual transmission, and those with BMI ≥28.0 kg/m 2, CD4 +T lymphocytes counts ≥500 cells/μl had higher rates of baseline dyslipidemia (all P<0.05). Conclusions:The abnormal rate of TG in HIV-infected people before ART was higher in China from 2018 to 2023, especially in HIV-infected women, and the abnormal rate of TG and TC increased with age. Attention should be paid to the clinical diagnosis and ART selection in the treatment of HIV infection.

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.

To explore the differences in biological gas production in the waterlogged zone of a coal seam fire-affected area, in this study the in-situ gas production experiment was conducted with the mine water from aquitard layers in coal seam fire zones in Xinjiang. The results showed that the biogas production first increased and then decreased with the increase in distance, and the highest gas production reached 216.55 mL. The changes in key metabolic pathways during the anaerobic fermentation of coal were analyzed, which showed that as the distance from the aquitard layer in the coal seam fire zone increased, the methanogenesis pathways gradually shifted from acetic acid decarboxylation and carbon dioxide reduction to acetic acid decarboxylation and methylamine methanogenesis. The significant variability in the in-situ mine water reservoir conditions contributed to the differences. In addition, the reservoir pressure and temperature increased as the distance from the fire zone became longer, and the salinity of the farthest mine water in the reverse fault was the highest due to the lack of groundwater supply. Pearson correlation analysis revealed significant correlations of microbial communities with key functional genes and the types and concentrations of ions. The ions significantly influencing microbial enzymatic metabolic activities included Al³⁺, Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺, Mg²⁺, PO₄^3-, and Mo⁶⁺. The differences in metabolic pathways were attributed to the integrated effects of a co-occurring environment with multiple ions. The gas production simulation experiments and metagenomic analyses provide data support for the practical application of in-situ biogas experiments, laying a foundation for engineering applications.
Biofuels ; Coal ; Methane/biosynthesis* ; Fires ; Groundwater ; Coal Mining ; Fermentation ; China ; Anaerobiosis

Objective:To investigate the prevalence of baseline dyslipidemia in HIV-infected people before starting antiviral therapy (ART) in China.Methods:The data were collected from HIV/AIDS ART database of Chinese Disease Prevention and Control Information System. A national sample of HIV- infected people who initiated ART from 2018 to 2023 was used to collect baseline information, including sociodemographic characteristics and laboratory test results. According to the Chinese Lipid Management Guidelines (2023) and the National Cholesterol Education Program Adult Treatment Panel Ⅲ guidelines, triglyceride (TG) ≥1.7 mmol/L or total cholesterol (TC) ≥5.2 mmol/L were identified as dyslipidemia. Statistical analysis was performed with software SAS 9.4. An unconditional logistic regression model was used to analyze the factors influencing TG and TC abnormalities in HIV-infected patients before ART.Results:A total of 359 952 adults infected with HIV were included in this study, the prevalence rate of dyslipidemia was 38.41% (138 263/359 952). The abnormal rates of TG and TC were 31.40% (113 041/359 952) and 13.75% (49 494/359 952), respectively. In all age groups except for the 25-44 age groups, the abnormal rates of TG and TC were higher in HIV-infected women than in HIV-infected men. In HIV-infected patients, women, those aged 45-64 years, those lived in northeast region, those had heterosexual transmission, and those with BMI ≥28.0 kg/m 2, CD4 +T lymphocytes counts ≥500 cells/μl had higher rates of baseline dyslipidemia (all P<0.05). Conclusions:The abnormal rate of TG in HIV-infected people before ART was higher in China from 2018 to 2023, especially in HIV-infected women, and the abnormal rate of TG and TC increased with age. Attention should be paid to the clinical diagnosis and ART selection in the treatment of HIV infection.

Volume overload is one of the major causes of recurrent hospital admissions in patients with heart failure, and proper volume management is critical to patient treatment and prognosis. This paper introduces the concept of mobile health, describes the application forms and effects of mobile health in volume management of heart failure patients, summarizes the shortcomings of existing research and prospects for the future, so as to provide reference for further promoting the application of mobile health in volume management of heart failure patients in China.

Volume overload is one of the major causes of recurrent hospital admissions in patients with heart failure, and proper volume management is critical to patient treatment and prognosis. This paper introduces the concept of mobile health, describes the application forms and effects of mobile health in volume management of heart failure patients, summarizes the shortcomings of existing research and prospects for the future, so as to provide reference for further promoting the application of mobile health in volume management of heart failure patients in China.

Objective:To investigate the clinical value of referral colposcopy in cervical high-risk human papillomavirus (HR-HPV) positive women in cervical cancer screening.Methods:Totally 2 445 cases, which were referred for colposcopic cervical biopsy for cervical HR-HPV positive in Karamay Central Hospital from January 2018 to November 2021 were collected. The status of cervical HR-HPV positive transferred colposcopy in different situations to identify high-grade squamous intraepithelial lesions (HSIL) and above (HISL+ ) was analyzed. The value of referral colposcopy in cervical HR-HPV positive women under different conditions was evaluated.Results:2 445 HR-HPV positive women were referred for colposcopic cervical biopsy, which confirmed 1 447 cases of negative for intraepithelial lesion or malignancy (NILM), 362 cases of low grade squamous intraepithelia lesion (LSIL), 510 cases of HSIL and 126 cases of squamous cell carcinoma (SCC); The complete coincidence rate between colposcopy diagnosis and pathological diagnosis was 67.08%(1 640/2 445), and the Kappa value of consistency test was 0.489. The sensitivity and specificity of colposcopy in the diagnosis of LSIL+ were 91.28% and 69.38%, and HSIL+ were 74.52% and 93.15%. The detection rates of HSIL+ in HPV16/18 positive and other 12 HPV positive patients with abnormal cervical liquid based cytology (TCT) were 64.78%(103/159) and 78.79%(364/462), respectively. The positive rates of HPV16/18 and 12 other HPV positive HSIL+ with normal TCT were 16.46%(82/498) and 6.56%(87/1 326), respectively. The rate of detecting HSIL+ in abnormal areas under colposcopy was 44.69%(534/1 195), and that in routine biopsy was 8.16%(102/1 250).Conclusions:Among the referred for colposcopic cases, the detection rate of HSIL+ was higher in cases with cervical HR-HPV positive and TCT abnormalities. Colposcopy has obvious value in identifying cervical lesions. The accurate diagnosis of cervical lesions is based on cervical biopsy under colposcopy.

Cell Cycle Checkpoints/genetics* ; Checkpoint Kinase 1/metabolism* ; Heterozygote ; Humans ; Mitosis/genetics* ; Mutation ; Zygote/metabolism*