Objective To explore and establish the bacterial endotoxin limit value and testing method of the active pharmaceutical ingredient （API） of pentetic acid, and conduct methodological investigation. Methods Three batches of pentetic acid were used to establish the method for the determination of bacterial endotoxin, and interference test was conducted simultaneously. The sample was dissolved with commercially available alkaline regulator to a concentration of 4 mg/ml or lower, and then diluted with water for bacterial endotoxin test. Limulus reagent with sensitivity of 0.125 EU/ml or higher was selected and redissolved with commercially available magnesium ion buffer solution. And the endotoxin test was performed by gel method. Results The endotoxin limit of API of pentetic acid was determined as: less than 0.125 EU/mg. Conclusion The method established could be used for the control of bacterial endotoxin in API of pentetic acid.

Objective:To identify the relationship between the CT arterial radiomics score and the treatment response to neoadjuvant therapy for pancreatic cancer.Methods:The clinical data of 243 pancreatic cancer patients who received surgical resection after neo-adjuvant therapy in the First Affiliated Hospital of Naval Medical University from March 2017 to March 2023 were retrospectively analyzed. Based on the tumor regression grade (TRG), the patients were divided into good response group (TRG 0-1, n=30) and non-good response group (TRG 2-3, n=213). The clinical, radiological and pathological features were compared between two groups. Fully-automated segmentation tool was used for segmenting the arterial CT scan of pancreatic tumor before and after treatment. Python package was applied to extract the radiomics features of tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the arterial radiomics score. Univariate and multivariate logistic regression models were used to analyze the association between arterial radiomics score and treatment response to neoadjucant therapy. Receiver operating-characteristics (ROC) curve was drawn and area under curve (AUC), specificity, sensitivity and accuracy for evaluating the treatment response were calculated. The clinical usefulness of arterial radiomics score for diagnosing the response of neoadjuvant treatment for pancreatic cancer were determined by decision curve analysis (DCA) . Results:A total of 330 arterial radiomics CT features were obtained, and 9-selected arterial phase features associated with treatment response were determined after being reduced by the Lasso logistic regression algorithm. Univariate analysis showed that the arterial radiomics score, three-dimensional diameter after neoadjuvant therapy, pancreatic contour, T stage, N stage, Peri-pancreatic nerve invasion, lymph-vascular space invasion (LVSI) and invasion of duodenum were all associated with treatment response (all P value <0.05). Multivariate logistic regression analyses confirmed that arterial radiomics score was obviously associated with the neoadjuvant treatment response ( P<0.001). At the cut-off value of 1.93, AUC of the arterial radiomics score for diagnosing neoadjuvant treatment response was 0.92, and the specificity, sensitivity and accuracy was 86.7%, 84.5% and 84.8%. DCA demonstrated that when the percentage for predicting the treatment response by using the arterial radiomics score was >0.2, the patients could benefit from the application of arterial radiomics score for evaluating neoadjuvant therapy response. Conclusions:The arterial radiomics score was strongly correlated with the neoadjuvant treatment response of pancreatic cancer, and can accurately predict neoadjuant treatment efficacy.

Objective Nowadays, radioactive xenon isotopes, including ^131mXe, ^133mXe, ¹³³Xe, and ¹³⁵Xe, are primarily released into the atmosphere through various reactor operation and major accidents of reactors. To improve the online monitoring capability of xenon in nuclear facilities and their gaseous effluents, a highly sensitive online xenon monitoring system was developed to monitor, warn, and alarm the activity concentration of radioactive xenon. Methods The online monitoring system for radioactive xenon gas in nuclear facilities was established using xenon membrane separation and concentration, xenon high-efficiency selective adsorption, and low-background gamma-ray spectrometry analysis methods. Results Under the operation mode of one-hour sampling and one-hour measuring, the minimum detectable activity concentration of the radioactive xenon online monitoring system for ¹³³Xe was approximately (1.43 ± 0.03) Bq/m³. Conclusion This system can be effectively used for online monitoring of xenon activity concentration in nuclear facilities such as nuclear power plants and isotope production reactors, as well as in gaseous effluents. It helps improve the safety level of personnel, the environment, and nuclear facilities.

Objective:To explore the mediating effect of appearance functional internalization between social comparison tendency and body image distress of college students.Methods:From June to September 2023, a cross-sectional survey was conducted among 308 college students with the social comparison tendency scale, the functional internalization questionnaire of college students' appearance and the body image anxiety scale for adolescent students.SPSS 22.0 software was used for common method bias test, descriptive statistics and correlation analysis, Mplus 8.0 was used for structural equation construction, and Bootstrap method was used for intermediary effect analysis.Results:Body image distress (43.86±8.78) was positively correlated with social comparison tendency (35.20±6.04) and appearance functional internalization (38.35±9.68) ( r=0.35, 0.33, both P<0.01).There was also a positive correlation between social comparison tendency and appearance functional internalization ( r=0.51, P<0.01). The effect of social comparison tendency on body image distress was mediated by appearance functional internalization, and the mediating effect value was 0.137, accounted for 35.13%(0.137/0.390) of the total effect. Conclusion:The appearance functional internalization mediates the relationship between social comparison tendency and body image distress.Social comparison tendency can not only directly predict body image distress, but also can predict body image distress, indirectly through appearance functional internalization.

Objective:To explore the relationship between social exclusion, rumination, executive function and mobile phone addiction among college students.Methods:From November to December 2023, a total of 516 college students were investigated by social exclusion questionnaire for undergraduate, ruminative responses scale, the Geurten-questionnaire of executive functioning in Chinese college students and mobile phone addiction tendency scale. SPSS 26.0 statistical software was used for common method bias test, descriptive statistics, correlation analysis, and PROCESS 3.5 macro program was used to test the mediation effect.Results:Social exclusion (31.21±12.69), rumination (42.85±12.38), executive function (71.46±9.41), and college students' mobile phone addiction tendency (43.53±11.74) were all significantly and positively correlated with each other ( r=0.299-0.500, all P<0.01). The direct effect of social exclusion on mobile phone addiction was significant (effect size=0.138, 95% CI=0.048-0.228), accounting for 37.91%(0.138/0.364) of the total effect. Rumination had a mediating effect between social exclusion and mobile phone addiction (effect size=0.053, 95% CI=0.001-0.112), accounting for 14.56%(0.053/0.364) of the total effect. Executive function mediates the relationship between social exclusion and mobile phone addiction (effect size=0.137, 95% CI=0.091-0.188), accounting for 37.64%(0.137/0.364) of the total effect.Rumination and executive function has a chain mediating effect between social exclusion and mobile phone addiction (effect size=0.036, 95% CI=0.016-0.061), accounting for 9.89%(0.036/0.364) of the total effect. Conclusion:Social exclusion can directly affect mobile phone addiction in college students and can also influence mobile phone addiction in college students through the independent mediating effects of rumination and executive function, as well as the chain mediating effect of rumination and executive function.

Objective:To construct radiomics models of micro-calcification in carotid plaques, and compare their diagnostic values.Methods:Fifty-two patients with large atherosclerotic cerebral infarction admitted to Department of Neurology, Third Affiliated Hospital of Soochow University from May 2017 to November 2019 were enrolled. All patients underwent conventional carotid artery Doppler ultrasound to detect carotid plaques and Micropure? ultrasound to detect micro-calcifications in the plaques. A cross-section image with maximum numbers of micro-calcifications was chosen when there were micro-calcifications in carotid plaques; otherwise, a cross-section image with the largest area of the plaque was chosen. After all images were normalized by Photoshop software, the plaques were delineated as regions of interest using MaZda 4.6 software and 283 texture features of the plaques were automatically extracted. The texture features with the strongest predictive value were selected through consistency analysis (intrclass correlation coefficient [ICC]>0.75), two-sample t-test, Least absolute shrinkage and selection operator (Lasso) regression. The predictive models were constructed by RandomForest (RF) and Support vector machine (SVM) classifiers. The training set and test set were divided by 7: 3 to analyze the classification accuracy. Receiver operating characteristic (ROC) curves were used to calculate the area under the curve (AUC) to evaluate the diagnostic values of the models. Delong test was used to compare the difference between the diagnostic values of the 2 classifiers in test set. Results:A total of 148 plaque images from 52 patients were enrolled, including 104 plaques with micro-calcification and 44 plaques without micro-calcification. Nine texture features were finally selected after ICC analysis, T test and Lasso regression: 5 image gray histogram features were mean, variance, skewness, kurtosis and 99 th percentile (Perc. 99%); 1 autoregressive model feature was Teta3, and 3 wavelet transform features were WavEnLH_s-3, WavEnLH_s-4, and WavEnLH_s-6. With RF classifier, accuracy of the diagnostic model was 0.93, enjoying AUC of 0.92; with SVM classifier, that was 0.91, enjoying AUC of 0.90; Delong test showed that the diagnostic values of the 2 classifiers in test set were significantly different ( Z=1.000, P=0.320). Conclusion:Radiomic models constructed by RF and SVM classifiers can identify micro-calcification in carotid plaques, and the 2 classifiers share equivalent diagnostic values.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

ObjectiveTo establish the frozen section method of Ferula ferulaeoides， and to study the histochemical localization of volatile oil and coumarins in different organs of F. ferulaeoides. MethodThe roots， stems， petioles and leaves of F. ferulaeoides were used as materials to investigate the concentration of sucrose protectant， liquid nitrogen flash-freezing time， embedding conditions， section thickness， freezing temperature and time and post-treatment methods， the most suitable section conditions were screened by comparing the integrity， microscopic effect， elongation and clarity of frozen sections. Sudan Ⅳ staining method and fluorescence microscopy were used to locate the volatile oil and coumarins of F. ferulaeoides. ResultThe optimal conditions for frozen sections of the roots， stems， petioles and leaves of F. ferulaeoides were as follows：10%， 15% and 20% gradient sucrose as the protectant for roots， 10%， 20% and 30% gradient sucrose as the protectant for stems and petioles， 20%， 25% and 30% gradient sucrose as the protectant for leaves， glue-water （2∶1） as the embedding agent， quick-freeze in liquid nitrogen for 20 s， warmed up at -25 ℃ for 30 min， sliced at -20 ℃ with the thickness of 25 μm， rinsed with the same concentration of sucrose solution （gradient sucrose solution selected the last concentration）， and the slices placed on the ice pack for a period of time and stored at room temperature. Among them， the concentration of sucrose protectant was the most important factor. The results of histochemical localization showed that volatile oil and coumarins in four organs of F. ferulaeoides were mainly distributed in resin canal. ConclusionFrozen section of F. ferulaeoides is established for the first time with high rate of slicing and simplified steps， its volatile oil and coumarins are mainly accumulated in resin canal.

Apparently balanced chromosomal structural rearrangements are known to cause male infertility and account for approximately 1% of azoospermia or severe oligospermia. However, the underlying mechanisms of pathogenesis and etiologies are still largely unknown. Herein, we investigated apparently balanced interchromosomal structural rearrangements in six cases with azoospermia/severe oligospermia to comprehensively identify and delineate cryptic structural rearrangements and the related copy number variants. In addition, high read-depth genome sequencing (GS) (30-fold) was performed to investigate point mutations causative of male infertility. Mate-pair GS (4-fold) revealed additional structural rearrangements and/or copy number changes in 5 of 6 cases and detected a total of 48 rearrangements. Overall, the breakpoints caused truncations of 30 RefSeq genes, five of which were associated with spermatogenesis. Furthermore, the breakpoints disrupted 43 topological-associated domains. Direct disruptions or potential dysregulations of genes, which play potential roles in male germ cell development, apoptosis, and spermatogenesis, were found in all cases (n = 6). In addition, high read-depth GS detected dual molecular findings in case MI6, involving a complex rearrangement and two point mutations in the gene DNAH1. Overall, our study provided the molecular characteristics of apparently balanced interchromosomal structural rearrangements in patients with male infertility. We demonstrated the complexity of chromosomal structural rearrangements, potential gene disruptions/dysregulation and single-gene mutations could be the contributing mechanisms underlie male infertility.
Azoospermia/genetics* ; Chromosome Aberrations ; Humans ; Infertility, Male/genetics* ; Male ; Oligospermia/genetics* ; Translocation, Genetic

Objective:To rapidly evaluate the level of healthcare resource demand for laboratory testing and prevention and control of corona virus disease 2019 (COVID-19) in different epidemic situation, and prepare for the capacity planning, stockpile distribution, and funding raising for infectious disease epidemic response.Methods:An susceptible, exposed, infectious, removed infectious disease dynamics model with confirmed asymptomatic infection cases and symptomatic hospitalized patients was introduced to simulate different COVID-19 epidemic situation and predict the numbers of hospitalized or isolated patients, and based on the current COVID-19 prevention and control measures in China, the demands of resources for laboratory testing and prevention and control of COVID-19 were evaluated.Results:When community or local transmission or outbreaks occur and total population nucleic acid testing is implemented, the need for human resources is 3.3-89.1 times higher than the reserved, and the current resources of medical personal protective equipment and instruments can meet the need. The surge in asymptomatic infections can also increase the human resource demand for laboratory testing and pose challenge to the prevention and control of the disease. When vaccine protection coverage reach ≥50%, appropriate adjustment of the prevention and control measures can reduce the need for laboratory and human resources.Conclusions:There is a great need in our country to reserve the human resources for laboratory testing and disease prevention and control for the response of the possible epidemic of COVID-19. Challenges to human resources resulted from total population nucleic acid testing and its necessity need to be considered. Conducting non-pharmaceutical interventions and encouraging more people to be vaccinated can mitigate the shock on healthcare resource demand in COVID-19 prevention and control.