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.

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.

This study was aimed to establish X-ray diffraction (XRD) Fourier fingerprint of mineral Chinese medicine Actinolitum, in order to provide a new method for evaluating the quality of Actinolitum. Actinolitum samples were analyzed by the technology of powder XRD. And the XRD Fourier fingerprint was determined. Accord to the fingerprints and the intensity of each characteristic peak in XRD patterns of Actinolitum, the similarity of different samples were calculated using the law of cosines and the correlation coefficient method. Systematic cluster analysis was also used for the data. The results showed that XRD patterns of 10 certified products, 3 doping products and 2 counterfeits of Actinolitum were obtained. The geometric and topological characteristics of 10 certified products were consistent. XRD fingerprint of Actinolitum from 10 certified products had 18 common characteristic peaks. The similarity analysis showed that the similarity of XRD patterns common peak of certified products were higher among 15 samples (> 0.98). The similarity of doping products was slightly lower (0.85-0.97). And the counterfeits had the lowest similarity (< 0.2). These three had significant differences which can be distinguished. The results of cluster analysis were consistent with the similarity analysis results. It was concluded that XRD fingerprint had good specificity and feasible. It was accurate and reliable, which can be used to distinguish and evaluate Actinolitum.

Objective:To develop a qualitative analysis model for the fast identification of Dens Draconis and its adulterants by NIR correlation coefficient method. Methods:On the basis of the traditional morphological identification, the spectra were collected u-sing the fiber accessory of a near-infrared spectroradiometer. The reference spectra were set up using the NIR spectra of certified Dens Draconis. The characteristic spectral section was chosen and the appropriate threshold was set to establish a qualitative analysis model for the rapid identification of Dens Draconis and its adulterant. Results:The spectral section of 5 000-4 200 cm-1 was selected as the characteristic spectral section, the correlation coefficient of Dens Draconis and its adulterant was calculated in training set samples, and 92. 67% was used as the threshold. Totally 10 batches of validation set samples were validated the qualitative analysis model, and the prediction accuracy was 90%. Conclusion:The method has good prediction ability, and can be used in the rapid identification of Dens Draconis and its adulterant.