Objective To analyze the trends in the disease burden of schistosomiasis in China from 1992 to 2021, and to project the disease burden of schistosomiasis in China from 2022 to 2030, so as to provide insights into the elimination of schistosomiasis in China. Methods The prevalence, age-standardized prevalence, disability-adjusted life year (DALYs) rate and age-standardized DALYs rate of schistosomiasis, as well as the years lost due to disability (YLDs) rate and age-standardized YLDs rate of anemia attributable to Schistosoma infections in China, the world and different socio-demographic index (SDI) regions were captured from the Global Burden of Disease Study 2021 (GBD 2021) data resources, and the trends in the disease burden due to schistosomiasis were evaluated with estimated annual percentage change (EAPC) and its 95% confidence interval (CI). In addition, the age, period and cohort effects on the prevalence of schistosomiasis were examined in China using an age-period-cohort (APC) model, and the disease burden of schistosomiasis was predicted in China from 2022 to 2030 using a Bayesian age-period-cohort (BAPC) model. Results The age-standardized prevalence and DALYs rate of schistosomiasis, and the age-standardized YLDs rate of anemia attributable to Schistosoma infections were 761.32/10⁵, 5.55/10⁵ and 0.38/10⁵ in China in 2021. These rates were all lower than the global levels (1 914.30/10⁵, 21.90/10⁵ and 3.36/10⁵, respectively), as well as those in the medium SDI regions (1 413.61/10⁵, 12.10/10⁵ and 1.93/10⁵, respectively), low-medium SDI regions (2 461.03/10⁵, 26.81/10⁵ and 4.48/10⁵, respectively), and low SDI regions (5 832.77/10⁵, 94.48/10⁵ and 10.65/10⁵, respectively), but higher than those in the high SDI regions (59.47/10⁵, 0.49/10⁵ and 0.05/10⁵, respectively) and high-medium SDI regions (123.11/10⁵, 1.20/10⁵ and 0.12/10⁵, respectively). The prevalence and DALYs rate of schistosomiasis were higher among men (820.79/10⁵ and 5.86/10⁵, respectively) than among women (697.96/10⁵ and 5.23/10⁵, respectively) in China in 2021, while the YLDs rate of anemia attributable to Schistosoma infections was higher among women (0.66/10⁵) than among men (0.12/10⁵). The prevalence of schistosomiasis peaked at ages of 30 to 34 years among both men and women, while the DALYs rate of schistosomiasis peaked among men at ages of 15 to 19 years and among women at ages of 20 to 24 years. The age-standardized prevalence of schistosomiasis showed a moderate decline in China from 1992 to 2021 relative to different SDI regions [EAPC = -1.51%, 95% CI: (-1.65%, -1.38%)], while the age-standardized DALYs rate [EAPC = -3.61%, 95% CI: (-3.90%, -3.33%)] and age-standardized YLDs rate of anemia attributable to Schistosoma infections [EAPC = -4.16%, 95% CI: (-4.38%, -3.94%)] appeared the fastest decline in China from1992 to 2021 relative to different SDI regions. APC modeling showed age, period, and cohort effects on the trends in the prevalence of schistosomiasis in China from 1992 to 2021, and the prevalence of schistosomiasis appeared a rise followed by decline with age, and reduced with period and cohort. BAPC modeling revealed that the age-standardized prevalence and age-standardized DALYs rate of schistosomiasis, and age-standardized YLDs rate of anemia attributable to Schistosoma infections all appeared a tendency towards a decline in China from 2022 to 2030, which reduced to 722.72/10⁵ [95% CI: (538.74/10⁵, 906.68/10⁵)], 5.19/10⁵ [95% CI: (3.54/10⁵, 6.84/10⁵)] and 0.30/10⁵ [95% CI: (0.21/10⁵, 0.39/10⁵)] in 2030, respectively. Conclusions The disease burden of schistosomiasis appeared a tendency towards a decline in China from 1992 to 2021, and is projected to appear a tendency towards a decline from 2022 to 2030. There are age, period and cohort effects on the prevalence of schistosomiasis in China. Precision schistosomiasis control is required with adaptations to current prevalence and elimination needs.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

【Objective】 To establish an effective quality monitoring indicator system for blood quality control in blood banks, in order to analyze the quality control indicators for blood collection and supply, and evaluate blood quality control process, thus promoting continuous improvement and standardizing management of blood quality control in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation services, component preparation, blood testing, blood supply and quality control was established. The Questionnaire of Quality Monitoring Indicators for Blood Collection and Supply Process was distributed to 17 blood banks in Shandong, which clarified the definition and calculation formula of indicators. The quality monitoring indicator data from January to December 2022 in each blood bank were collected, and 20 quality control indicators data were analyzed by SPSS25.0 software. 【Results】 The average pass rate of key equipment monitoring, environment monitoring, key material monitoring, and blood testing item monitoring of 17 blood banks were 99.47%, 99.51%, 99.95% and 98.99%, respectively. Significant difference was noticed in the pass rate of environment monitoring among blood banks of varied scales(P<0.05), and the Pearson correlation coefficient (r) between the total number of blood quality testing items and the total amount of blood component preparation was 0.645 (P<0.05). The average discarding rates of blood testing or non-blood testing were 1.14% and 3.36% respectively, showing significant difference among blood banks of varied scales (P<0.05). The average discarding rate of lipemic blood was 3.07%, which had a positive correlation with the discarding rate of non testing (r=0.981 3, P<0.05). There was a statistically significant difference in the discarding rate of lipemic blood between blood banks with lipemic blood control measures and those without (P<0.05). The average discarding rate of abnormal color, non-standard volume, blood bag damage, hemolysis, blood protein precipitation and blood clotting were 0.20%, 0.14%, 0.06%, 0.06%, 0.02% and 0.02% respectively, showing statistically significant differences among large, medium and small blood banks(P<0.05).The average discarding rates of expired blood, other factors, confidential unit exclusion and unqualified samples were 0.02%, 0.05%, 0.003% and 0.004%, respectively. The discarding rate of blood with air bubbles was 0.015%, while that of blood with foreign body and unqualified label were 0. 【Conclusion】 The quality control indicator system of blood banks in Shandong can monitor weak points in process management, with good applicability, feasibility, and effectiveness. It is conducive to evaluate different blood banks, continuously improve the quality control level of blood collection and supply, promote the homogenization and standardization of blood quality management, and lay the foundation for comprehensive evaluation of blood banks in Shandong.

ObjectiveTo investigate the effect of mucin 1 (MUC1) on the proliferation and apoptosis of nasopharyngeal carcinoma (NPC) and its regulatory mechanism. MethodsThe 60 NPC and paired para-cancer normal tissues were collected from October 2020 to July 2021 in Quanzhou First Hospital. The expression of MUC1 was measured by real-time quantitative PCR (qPCR) in the patients with PNC. The 5-8F and HNE1 cells were transfected with siRNA control (si-control) or siRNA targeting MUC1 (si-MUC1). Cell proliferation was analyzed by cell counting kit-8 and colony formation assay, and apoptosis was analyzed by flow cytometry analysis in the 5-8F and HNE1 cells. The qPCR and ELISA were executed to analyze the levels of TNF-α and IL-6. Western blot was performed to measure the expression of MUC1, NF-кB and apoptosis-related proteins (Bax and Bcl-2). ResultsThe expression of MUC1 was up-regulated in the NPC tissues, and NPC patients with the high MUC1 expression were inclined to EBV infection, growth and metastasis of NPC. Loss of MUC1 restrained malignant features, including the proliferation and apoptosis, downregulated the expression of p-IкB、p-P65 and Bcl-2 and upregulated the expression of Bax in the NPC cells. ConclusionDownregulation of MUC1 restrained biological characteristics of malignancy, including cell proliferation and apoptosis, by inactivating NF-κB signaling pathway in NPC.

A high performance liquid chromatography (HPLC) method utilizing correction factors was established for the quantitative detection of related substances in flumazenil. Separation was achieved using an Agilent Pursuit XRs C18 column (250 mm × 4.6 mm, 5 μm) with an isocratic elution of dilute phosphoric acid, methanol, and tetrahydrofuran as the mobile phases. Correction factors calculated from a standard curve method were applied to determine the impurity content. The quantification of impurities in flumazenil was conducted using both external standard and correction factor methods, followed by validation and comparison of the two. For the identification of degradation products, a forced degradation approach was employed to prepare a flumazenil degradation solution, and the resulting impurities were confirmed by LC-MS analysis. The separation of flumazenil and its impurities was found to be efficient. The limits of quantification for impurities A, B, D, and E were established at 0.169 9, 0.314 7, 0.143 9, and 0.270 8 ng, respectively, with the limits of detection at 0.055 8, 0.096 9, 0.048 8, and 0.089 0 ng. These impurities demonstrated a strong linear relationship across the concentration ranges of 0.034 9-7.847 0, 0.038 7-8.710 7, 0.034 6-7.794 1, and 0.032 4-7.292 8 µg·mL^-1, respectively (n = 7). The method achieved average recoveries between 98.25% and 99.42%, with an RSD of less than 2.0% (n = 9), indicating high accuracy. The external standard and correction factor methods were used to determine the related substances in flumazenil, and the results of the two methods were consistent. The established HPLC method is characterized by its high accuracy, sensitivity, and repeatability, and is suitable for determining related substances in flumazenil.

Objective To investigate the significance of intratumoral and peritumoral radiomics models in predicting occult lymph node metastasis in stage T1 non-small cell lung cancer(NSCLC)and to compare the predictive accuracy in different peritumoral radiomics models.Methods The CT images and clinical data of 211 patients without lymph node metastasis on preoperative CT examination and pathologically confirmed NSCLC after surgery were collected.The radiomics features were derived from the three-dimensional volume of interest(VOI)of the intratumoral and peritumoral at 3-,5-,and 10-mm following lesion segmentation on CT images of each patient.The feature data of all nidus were radomly divide into training set and validation set with a ratio of 7︰3.The Pearson or Spearman correlation test was performed to remove redundancy.Dimensionality was reduced by the least absolute shrinkage and selection operator(LASSO)regression analysis.The linear combination of selected features and corresponding coefficients were used to construct the Radiomics score(Radscore).The clinical model and comprehensive model were constructed by logistic regression analysis.The conprehensive model was visualized with the nomogram,and its performance was evaluated.Results Among the peritumoral radiomics models,the peritumoral 5-mm model showed the best predictive efficacy[validation set,area under the curve(AUC)0.771].The comprehensive model containing Radscore,CT image features and CEA exhibited the best performance(validation set,AUC 0.850).Conclusion Intratumoral and peritumoral radiomics models perform efficiently in predicting occult lymph node metastasis in stage T1 NSCLC,and nomogram can effectively and noninvasively predict occult lymph node metastasis in NSCLC.