Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

Objective:To explore the clinical value of the three-dimensional fat suppression fast spoiled gradient echo (3D-FS-FSPGR) MRI sequence in the preoperative evaluation of congenital radial polydactyly.Methods:The data of children with congenital radial polydactyly who underwent surgical treatment in the Department of Orthopedics at Shanxi Children’s Hospital from May 2021 to April 2022 were retrospectively analyzed. Preoperative X-ray examinations and MRI 3D-FS-FSPGR sequence scans were performed on the children. Radiologists first described the morphological characteristics of the articular cartilage, and then orthopedic surgeons performed the Wassel classification based on the X-ray and MRI imaging result, focusing on the bifurcation level, morphology, and articular surface of the phalangeal and metacarpal cartilage. The corresponding surgical method was selected according to the Wassel classification, and intraoperative incision exploration was used as the gold standard. Six months after surgery, the surgical outcome was evaluated using the modified Tada scoring system [with a total score of 0-7, and classified as excellent: >5 points, good: 3-5 points, and poor: <3 points; the excellent and good rate = (excellent + good) cases/total number of cases × 100%]. The appearance, function and recurrence of the operated finger were evaluated 1 year after surgery. Descriptive statistics were performed using SPSS 26.0 software, and the Kappa coefficient was used to evaluate the consistency of the Wassel classification result between radiographs, MRI 3D-FS-FSPGR sequences and intraoperative exploration respectively.Results:A total of 45 children (55 fingers) with congenital radial polydactyly were enrolled, including 25 males and 20 females, aged 5 to 60 months, with the median age of 9 months. Unilateral findings were seen in 35 cases and bilateral findings in 10 cases. MRI 3D-FS-FSPGR imaging sequences clearly demonstrated the level of cartilage bifurcation and bone tissue growth and development, which were consistent with intraoperative exploration findings. The accuracy of the MRI 3D-FS-FSPGR Wassel classification was 100% (55/55), and the accuracy of the X-ray Wassel classification was 81.8% (45/55). Disagreements were found in the classifications of five fingers: three with X-ray classifications of Wassel type Ⅳ but actually classified as type Ⅲ, and two with X-ray classifications of Wassel type Ⅳ but actually classified as type Ⅴ. The Kappa coefficients were all >0.85. All patients were followed up for 1 year. The modified Tada score showed excellent in 41 fingers, good in 6 fingers, and poor in 8 fingers, for an excellent and good rate of 85.5% (47/55). At final follow-up, the reconstructed thumbs showed significant improvement in appearance, with normal bone axis restoration, no deviation of the digits, and normal nail appearance. There was no significant scarring or contracture. Functions of thumb flexion, extension, grasping, and opposition were good. There was no postoperative deformity or recurrence.Conclusion:MRI 3D-FS-FSPGR sequences can accurately classify congenital radial polydactyly preoperatively, optimize the surgical incision and osteotomy alignment, and achieve excellent surgical outcomes.

Objective:To explore the clinical value of the three-dimensional fat suppression fast spoiled gradient echo (3D-FS-FSPGR) MRI sequence in the preoperative evaluation of congenital radial polydactyly.Methods:The data of children with congenital radial polydactyly who underwent surgical treatment in the Department of Orthopedics at Shanxi Children’s Hospital from May 2021 to April 2022 were retrospectively analyzed. Preoperative X-ray examinations and MRI 3D-FS-FSPGR sequence scans were performed on the children. Radiologists first described the morphological characteristics of the articular cartilage, and then orthopedic surgeons performed the Wassel classification based on the X-ray and MRI imaging result, focusing on the bifurcation level, morphology, and articular surface of the phalangeal and metacarpal cartilage. The corresponding surgical method was selected according to the Wassel classification, and intraoperative incision exploration was used as the gold standard. Six months after surgery, the surgical outcome was evaluated using the modified Tada scoring system [with a total score of 0-7, and classified as excellent: >5 points, good: 3-5 points, and poor: <3 points; the excellent and good rate = (excellent + good) cases/total number of cases × 100%]. The appearance, function and recurrence of the operated finger were evaluated 1 year after surgery. Descriptive statistics were performed using SPSS 26.0 software, and the Kappa coefficient was used to evaluate the consistency of the Wassel classification result between radiographs, MRI 3D-FS-FSPGR sequences and intraoperative exploration respectively.Results:A total of 45 children (55 fingers) with congenital radial polydactyly were enrolled, including 25 males and 20 females, aged 5 to 60 months, with the median age of 9 months. Unilateral findings were seen in 35 cases and bilateral findings in 10 cases. MRI 3D-FS-FSPGR imaging sequences clearly demonstrated the level of cartilage bifurcation and bone tissue growth and development, which were consistent with intraoperative exploration findings. The accuracy of the MRI 3D-FS-FSPGR Wassel classification was 100% (55/55), and the accuracy of the X-ray Wassel classification was 81.8% (45/55). Disagreements were found in the classifications of five fingers: three with X-ray classifications of Wassel type Ⅳ but actually classified as type Ⅲ, and two with X-ray classifications of Wassel type Ⅳ but actually classified as type Ⅴ. The Kappa coefficients were all >0.85. All patients were followed up for 1 year. The modified Tada score showed excellent in 41 fingers, good in 6 fingers, and poor in 8 fingers, for an excellent and good rate of 85.5% (47/55). At final follow-up, the reconstructed thumbs showed significant improvement in appearance, with normal bone axis restoration, no deviation of the digits, and normal nail appearance. There was no significant scarring or contracture. Functions of thumb flexion, extension, grasping, and opposition were good. There was no postoperative deformity or recurrence.Conclusion:MRI 3D-FS-FSPGR sequences can accurately classify congenital radial polydactyly preoperatively, optimize the surgical incision and osteotomy alignment, and achieve excellent surgical outcomes.

Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Varicella is a common infectious disease caused by varicella-zoster virus (VZV), with high transmissibility and pathogenicity, and vaccination is a better preventive measure. The clinical diagnosis of varicella, the clinical immunogenicity evaluation of varicella vaccine, the continuous monitoring of the immune effect after marketing and the epidemiological investigation all require the detection of the antibody level of VZV in patients. Fluorescent antibody to membrane antigen method (FAMA) is the gold standard for detection of VZV antibody in serum, possessing advantages of high sensitivity and good specificity, playing an important role in detection of VZV antibody. However, the defects of FAMA operation complexity and low throughput are also troubling the clinical testing personnels. This article reviews research progress both at home and abroad on the technology of VZV serum antibody detection. The advantages and limitations of the current detection methods are discussed and the future development direction are highlighted on the dectection of VZV serum antibody IgG.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Varicella is a common infectious disease caused by varicella-zoster virus (VZV), with high transmissibility and pathogenicity, and vaccination is a better preventive measure. The clinical diagnosis of varicella, the clinical immunogenicity evaluation of varicella vaccine, the continuous monitoring of the immune effect after marketing and the epidemiological investigation all require the detection of the antibody level of VZV in patients. Fluorescent antibody to membrane antigen method (FAMA) is the gold standard for detection of VZV antibody in serum, possessing advantages of high sensitivity and good specificity, playing an important role in detection of VZV antibody. However, the defects of FAMA operation complexity and low throughput are also troubling the clinical testing personnels. This article reviews research progress both at home and abroad on the technology of VZV serum antibody detection. The advantages and limitations of the current detection methods are discussed and the future development direction are highlighted on the dectection of VZV serum antibody IgG.