Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

With the deepening of the modernization of traditional Chinese medicine(TCM),the analysis of mineralogical features of mineralogical TCM,as an important part of TCM,has received more and more attention.At present,the research of mineral-based traditional Chinese medicine is faced with problems such as confusion of medicinal resources,unclear material basis,and low exclusivity of quality standards,while common analytical techniques have certain limitations in quantitative analysis,sensitivity and comprehensive characterization,which restrict the modernization research of mineral-based medicines and its clinical applications.The advanced mineral identification and characterization system integrates energy spectrum and spectral analysis techniques,combined with efficient data processing algorithms,which can rapidly and accurately analyze mineral components qualitatively and quantitatively,but its application in mineral medicine research is still in its infancy.This article reviews the common analytical techniques for mineral medicine and the potential application of advanced mineral identification and characterization system in the identification of mineral Chinese medicine matrix and quality control.The advanced mineral identification and characterization system can not only accurately distinguish the mineralogical characteristics of mineral medicines,such as the mineral composition,elemental state and embedded characteristics,and provide data support for the research of mineral medicine resources,but also provide scientific basis for the establishment of systematic quality standards,the analysis of the preparation mechanism,the revelation of its potential medicinal effect of the material basis and the control of the risk of heavy metals.

With the deepening of the modernization of traditional Chinese medicine(TCM),the analysis of mineralogical features of mineralogical TCM,as an important part of TCM,has received more and more attention.At present,the research of mineral-based traditional Chinese medicine is faced with problems such as confusion of medicinal resources,unclear material basis,and low exclusivity of quality standards,while common analytical techniques have certain limitations in quantitative analysis,sensitivity and comprehensive characterization,which restrict the modernization research of mineral-based medicines and its clinical applications.The advanced mineral identification and characterization system integrates energy spectrum and spectral analysis techniques,combined with efficient data processing algorithms,which can rapidly and accurately analyze mineral components qualitatively and quantitatively,but its application in mineral medicine research is still in its infancy.This article reviews the common analytical techniques for mineral medicine and the potential application of advanced mineral identification and characterization system in the identification of mineral Chinese medicine matrix and quality control.The advanced mineral identification and characterization system can not only accurately distinguish the mineralogical characteristics of mineral medicines,such as the mineral composition,elemental state and embedded characteristics,and provide data support for the research of mineral medicine resources,but also provide scientific basis for the establishment of systematic quality standards,the analysis of the preparation mechanism,the revelation of its potential medicinal effect of the material basis and the control of the risk of heavy metals.

Objective: To quantitatively analyze the content of glucose in glucose injection by near infrared spectroscopy to control the quality of the product.Methods: A quantitative model was established by near infrared reflectance spectroscopy and the injection came from pharmaceutical enterprises with different concentrations of glucose and the solution samples came from laboratories with different concentrations of glucose.The liquid sample accessories were selected, a quantitative model was established by a partial least squares method, and the effect of environmental temperature on the model was studied as well.Results: According to the quantitative model, the correlation coefficient reached up to 0.999 3, and RMS deviation (RMSECV) was 0.077 4.In the verification test, the results of various liquid formulas containing glucose were similar to those of the laboratory results.The prediction error was less than 5% within the temperature range of 20-35℃.Conclusion: The model of near-infrared partial least squares (PLS) can accurately predict the content of glucose in glucose products without sodium chloride, and can be used for the quality control of glucose intermediates and the finished products in the large infusion manufacturers

Objective:To assess the genetic diversity and phylogenetic relationship of Desmodium styraeifolium from fifteen regions of Guangdong and Guangxi Zhuang autonomous region. Methods:The molecular technique ISSR((inter-simple sequence repeat))was applied to investigate the genetic diversity of Desmodium styraeifolium from fifteen regions of Guangdong and Guangxi Zhuang autonomous region. The data was analyzed with Popgene 1. 32,and a cluster diagram was presented by UPGMA. Results:Totally 51 amplified fragments were obtained by 7 ISSR primers. The results analyzed by Popgene 1. 32 showed that the Shannon diversity index(I)was 0. 3,the NEI’s genetic diversity coefficient(H)was 0. 246 4,the coefficient of genetic differentiation (GST)was 0. 123 8,and the gene flow(Nm)was 3. 539 7. Conclusion:The above mentioned results exhibit that Desmodium styraeifolium from Guangdong,Guangxi and some wild herbal populations has high genetic diversity. The clustering results illustrate that the genetic distance of Desmodium styraeifolium originated from Guangdong and Guangxi is related with geographic distance.

Compared with that for botanical drugs and animal-derived drugs, the identification study for mineral traditional Chi-nese medicines is relatively weak. The traditional identification methods can’ t meet the quality control requirements of mineral tradi-tional Chinese medicines, and the application of modern analysis techniques are needed urgently in the systematic research of mineral traditional Chinese medicines. In the paper, the identification of traditional methods combined with some modern analysis techniques such as X-ray diffraction, near infrared spectroscopy and Raman spectroscopy for mineral traditional Chinese medicines was summarized and analyzed to provide basic idea and methods for the systematic identification construction of mineral traditional Chinese medicines.