Objective To investigate the correlation between the anterior ethmoidal artery(AEA)and anatomical variations of the anterior cranial base,and to analyze the predictive factors for AEA suspension.Methods Sinus CT imaging data of 159 patients undergoing endoscopic sinus sur-gery(ESS)were retrospectively analyzed.Mimics 21.0 software was utilized for three-dimensional reconstruction,measuring parameters of AEA and anterior cranial base anatomy and performing classi-fication.Pearson and Spearman correlation analyses were used to evaluate the correlations among vari-ous anatomical parameters and their classifications.Multivariate binary logistic regression analysis was performed to screen for independentpredictive factors of AEA suspension.Results The rates of AEA suspension differed significantly across different Keros classifications(P＜0.001),with an increase rate as the Keros classification level increased(P＜0.001).The transverse diameter,height and vol-ume of supraorbital ethmoid cells(SOEC),olfactory fossa depth,lateral lamella of the cribriform plate(LLCP)length and frontal sinus pneumatization classification grade were positively correlated with the distance from AEA to the cranial base(P＜0.05).Multivariate binary Logistic regression analysis showed that the presence of SOEC(OR=4.178,95％CI,2.517 to 6.935,P＜0.001),in-creased olfactory fossa depth(OR=1.433,95％CI,1.197 to 1.715,P＜0.001),and higher frontal sinus pneumatization classification grade(OR=1.621,95％CI,1.121 to 2.345,P=0.01)were independent predictive factors for AEA suspension.Conclusion Detailed preoperative CT imaging assessment,especially the analysis of SOEC,olfactory fossa depth and frontal sinus pneumatization classification,aids in accurately assessing the anatomical position of AEA,thereby effectively reduc-ing the risk of AEA injury,and improving the safety and success rate of surgery.

Background and Purpose:Previous studies have investigated the prognostic significance of skeletal muscle and adipose tissue composition and distribution in colorectal cancer patients,yet most have not differentiated between rectal and colon cancer patient cohorts.This study aimed to explore the relationship between body composition and long-term prognosis,and to develop a postoperative predictive model.Methods:Clinical data of rectal cancer patients who underwent surgical treatment at Qingdao University Affiliated Hospital from January 2018 to December 2021 were retrospectively collected.Inclusion criteria:①Age＞18 years;② Preoperative colonoscopy and pathological diagnosis of colorectal cancer;③ Complete surgical resection;④Abdominal computed tomography(CT)scan 1 month before surgery.Exclusion criteria:① Clinical data is missing;② Multiple metastases of tumors;③ Tumor T stage 0 or carcinoma in situ;④ Severe artifacts lead to poor quality CT imaging,making it difficult to distinguish between fat and muscle;⑤ Inability to obtain follow-up results.This study has been approved by the Medical Ethics Committee of the Affiliated Hospital of Qingdao University(approval number:QYFYWZLL30313),and informed consent has been waived in the ethical approval process.The skeletal muscle index(SMI)and subcutaneous adipose tissue index(SATI)were calculated by dividing the areas of skeletal muscle and subcutaneous fat observed on CT scans by the square of the patient's height.Univariate and multivariate COX regression analyses were conducted to identify risk factors influencing recurrence-free survival(RFS)and overall survival(OS)in rectal cancer patients.Based on the results of the multivariate analysis,a nomogram prediction model was developed,its predictive power and accuracy were assessed using the receiver operating characteristic(ROC)curve,calibration plots and decision curve analysis(DCA),and internal validation was conducted.Results:A total of 696 patients were included in this study,with 96(13.8%)patients experiencing postoperative recurrence and 89(12.8%)patients dying.Multivariate COX regression analysis showed that SMI,SATI,tumor T stage and N stage were independent factors affecting the postoperative RFS and OS of patients.Nomogram prediction models for RFS and OS in rectal cancer patients were constructed based on the above independent predictors.The area under ROC curve(AUC)for 3-,4-and 5-year RFS was 0.862,0.846 and 0.824,respectively;the AUC for 3-,4-and 5-year OS was 0.886,0.898 and 0.875,respectively.The models were evaluated using calibration curves and decision curves,and internal validation was performed,which showed that the prediction accuracy of the models was good.Conclusion:CT body composition is an independent predictor of RFS and OS in rectal cancer patients,and the nomogram model developed based on these factors demonstrates good predictive value for patient prognosis.

Background and Purpose:Previous studies have investigated the prognostic significance of skeletal muscle and adipose tissue composition and distribution in colorectal cancer patients,yet most have not differentiated between rectal and colon cancer patient cohorts.This study aimed to explore the relationship between body composition and long-term prognosis,and to develop a postoperative predictive model.Methods:Clinical data of rectal cancer patients who underwent surgical treatment at Qingdao University Affiliated Hospital from January 2018 to December 2021 were retrospectively collected.Inclusion criteria:①Age＞18 years;② Preoperative colonoscopy and pathological diagnosis of colorectal cancer;③ Complete surgical resection;④Abdominal computed tomography(CT)scan 1 month before surgery.Exclusion criteria:① Clinical data is missing;② Multiple metastases of tumors;③ Tumor T stage 0 or carcinoma in situ;④ Severe artifacts lead to poor quality CT imaging,making it difficult to distinguish between fat and muscle;⑤ Inability to obtain follow-up results.This study has been approved by the Medical Ethics Committee of the Affiliated Hospital of Qingdao University(approval number:QYFYWZLL30313),and informed consent has been waived in the ethical approval process.The skeletal muscle index(SMI)and subcutaneous adipose tissue index(SATI)were calculated by dividing the areas of skeletal muscle and subcutaneous fat observed on CT scans by the square of the patient's height.Univariate and multivariate COX regression analyses were conducted to identify risk factors influencing recurrence-free survival(RFS)and overall survival(OS)in rectal cancer patients.Based on the results of the multivariate analysis,a nomogram prediction model was developed,its predictive power and accuracy were assessed using the receiver operating characteristic(ROC)curve,calibration plots and decision curve analysis(DCA),and internal validation was conducted.Results:A total of 696 patients were included in this study,with 96(13.8%)patients experiencing postoperative recurrence and 89(12.8%)patients dying.Multivariate COX regression analysis showed that SMI,SATI,tumor T stage and N stage were independent factors affecting the postoperative RFS and OS of patients.Nomogram prediction models for RFS and OS in rectal cancer patients were constructed based on the above independent predictors.The area under ROC curve(AUC)for 3-,4-and 5-year RFS was 0.862,0.846 and 0.824,respectively;the AUC for 3-,4-and 5-year OS was 0.886,0.898 and 0.875,respectively.The models were evaluated using calibration curves and decision curves,and internal validation was performed,which showed that the prediction accuracy of the models was good.Conclusion:CT body composition is an independent predictor of RFS and OS in rectal cancer patients,and the nomogram model developed based on these factors demonstrates good predictive value for patient prognosis.

Objective:To investigate the impact of body composition and inflammatory nutritional indicators on postoperative complications in patients with obstructive colorectal cancer,and to develop and validate a nomogram model.Methods:This is a retrospective case series study. The clinical data of 293 patients with obstructive colorectal cancer who were treated at the Department of Gastrointestinal Surgery,the Affiliated Hospital of Qingdao University,between January 2016 and January 2024,were retrospectively collected. The cohort included 182 males and 111 females,aged (65.0±12.1) years (range: 18 to 80 years). The dataset was randomly divided into a training group ( n=196) and a validation group ( n=97) with a 7∶3 ratio. Independent sample t test and multivariate logistic regression analysis were employed to identify independent risk factors associated with postoperative complications in patients with obstructive colorectal cancer. A preoperative nomogram model was subsequently developed for predicting postoperative complications,which was further validated using a validation cohort. Results:The training group comprised 119 males and 77 females,with 68 cases experiencing postoperative complications and 128 cases without complications. The validation group included 63 males and 34 females,with 30 cases experiencing postoperative complications and 67 cases without complications.Univariate analysis and multivariate analysis revealed that low skeletal muscle index ( OR=0.867,95% CI: 0.795 to 0.947),high visceral fat index ( OR=1.058,95% CI: 1.028 to 1.089),high systemic immune inflammation index ( OR=1.002, 95% CI: 1.000 to 1.003), low prognostic nutritional index ( OR=0.847,95% CI: 0.782 to 0.917),and preoperative anemia ( OR=2.714,95% CI: 1.161 to 6.344) were independent risk factors for postoperative complications (all P<0.05). A nomogram prediction model based on these five indicators was established. The area under the receiver operating characteristic (ROC) curve for the prediction model was 0.878 (95% CI: 0.829 to 0.928) in the training group and 0.849 (95% CI:0.767 to 0.930) in the validation group. Conclusions:The preoperative nomogram model,which incorporates inflammatory and nutritional indicators,demonstrates a good accuracy in predicting postoperative complications for patients with obstructive colorectal cancer. This model can effectively assist in guiding treatment decisions.

Objective:To investigate the impact of body composition and inflammatory nutritional indicators on postoperative complications in patients with obstructive colorectal cancer,and to develop and validate a nomogram model.Methods:This is a retrospective case series study. The clinical data of 293 patients with obstructive colorectal cancer who were treated at the Department of Gastrointestinal Surgery,the Affiliated Hospital of Qingdao University,between January 2016 and January 2024,were retrospectively collected. The cohort included 182 males and 111 females,aged (65.0±12.1) years (range: 18 to 80 years). The dataset was randomly divided into a training group ( n=196) and a validation group ( n=97) with a 7∶3 ratio. Independent sample t test and multivariate logistic regression analysis were employed to identify independent risk factors associated with postoperative complications in patients with obstructive colorectal cancer. A preoperative nomogram model was subsequently developed for predicting postoperative complications,which was further validated using a validation cohort. Results:The training group comprised 119 males and 77 females,with 68 cases experiencing postoperative complications and 128 cases without complications. The validation group included 63 males and 34 females,with 30 cases experiencing postoperative complications and 67 cases without complications.Univariate analysis and multivariate analysis revealed that low skeletal muscle index ( OR=0.867,95% CI: 0.795 to 0.947),high visceral fat index ( OR=1.058,95% CI: 1.028 to 1.089),high systemic immune inflammation index ( OR=1.002, 95% CI: 1.000 to 1.003), low prognostic nutritional index ( OR=0.847,95% CI: 0.782 to 0.917),and preoperative anemia ( OR=2.714,95% CI: 1.161 to 6.344) were independent risk factors for postoperative complications (all P<0.05). A nomogram prediction model based on these five indicators was established. The area under the receiver operating characteristic (ROC) curve for the prediction model was 0.878 (95% CI: 0.829 to 0.928) in the training group and 0.849 (95% CI:0.767 to 0.930) in the validation group. Conclusions:The preoperative nomogram model,which incorporates inflammatory and nutritional indicators,demonstrates a good accuracy in predicting postoperative complications for patients with obstructive colorectal cancer. This model can effectively assist in guiding treatment decisions.

Diabetic peripheral neuropathy（DPN） is a neurodegenerative disease of diabetes mellitus involving peripheral nervous system damage， which is characterized by axonal degenerative necrosis， Schwann cell apoptosis and demyelination of nerve myelin sheath as the main pathological features， this disease is highly prevalent and is a major cause of disability in diabetic patients. Currently， the pathogenesis of DPN may be related to oxidative stress， inflammatory response， metabolic abnormality， and microcirculation disorder. The treatment of DPN in modern medicine mainly starts from controlling blood glucose， nourishing nerves and improving microcirculation， which can only alleviate the clinical symptoms of patients， and it is difficult to fundamentally improve the pathological damage of peripheral nerves. Mitochondrial quality control refers to the physiological mechanisms that can maintain the morphology and functional homeostasis of mitochondria， including mitochondrial biogenesis， mitochondrial dynamics， mitochondrial oxidative stress and mitochondrial autophagy， and abnormal changes of which may cause damage to peripheral nerves. After reviewing the literature， it was found that traditional Chinese medicine（TCM） can improve the low level of mitochondrial biogenesis in DPN， maintain the balance of mitochondrial dynamics， inhibit mitochondrial oxidative stress and mitochondrial autophagy， and delay apoptosis of Schwann cells and neural axon damage， which has obvious effects on the treatment of DPN. With the deepening of research， mitochondrial quality control may become one of the potential targets for the research of new anti-DPN drugs， therefore， this paper summarized the research progress of TCM in treating DPN based on four aspects of mitochondrial quality control， with the aim of providing a theoretical research basis for the discovery of new drugs.

Objective:To explore the improvement of image quality of different acceleration factors in breath-hold three-dimensional magnetic resonance cholangiopancreatography (3D MRCP) using deep learning (DL) and compressed sensing (CS) technology.Methods:A total of 68 patients who underwent upper abdominal 3D MRCP examination at West China Hospital of Sichuan University from March to August 2023 were prospectively included. The patients were subdivided into three groups randomly with the following paramters: CS group with an acceleration factor of 24 (CS-24); DL-CS group with acceleration factors 24 (DL-CS-24) and 33 (DL-CS-33) respectively. The signal-to-noise ratio (SNR), contrast ratio (CR) and contrast-to-noise ratio (CNR) of the three sets of images were measured, and the overall image quality, background suppression, artifacts, and visibility of bile ducts and pancreatic ducts at all levels were subjectively evaluated. Chi-square test and Friedman test were used to perform statistical analysis on the number of unsatisfactory diagnostic images and subjective and objective indicators of the three groups of sequences respectively.Results:The scanning time of the DL-CS-33 group (9 s) was 30% shorter than that of the CS-24 group and DL-CS-24 group (13s). The images of DL-CS-33 group from 68 patients all met the clinical diagnostic requirements and statistically differences were found between the images from CS-24 group and DL-CS-24 group (all P<0.05). There were no statistically differences in SNR, CR, CNR, overall image quality, artifacts, and visibility scores of bile ducts and pancreatic ducts at all levels between the DL-CS-33 group and the CS-24 group (all P>0.05). The SNR, CR, CNR, intrahepatic bile duct, main pancreatic duct and overall image quality of the DL-CS -24 group were better than those of the CS-24 group (all P<0.05). Conclusions:DL-CS technology could improves breath-hold 3D MRCP image quality with the 24 acceleration factor with no additioanl scanning time. DL-CS technology combined with a high acceleration factor of 33 further reduces scanning time while ensuring overall image quality, providing a fast breath-hold scanning solution.

Objective:To explore the impact of artificial intelligence compressed sensing technology (CS-AI) on image quality in three-dimensional proton density weighted imaging (3D PDWI) of the unilateral hip joint.Methods:High-resolution unilateral hip imaging was conducted on 67 healthy volunteers at West China Hospital of Sichuan University from January to July 2023. Imaging was performed by using CS-AI 3D PDWI sequence with acceleration factors (AF) of 4, 6, 8, and 10, respectively. According to the AF, all subjects were divided into 4 groups: CS-AI 4, CS-AI 6, CS-AI 8 and CS-AI 10, with CS-AI 4 serving as a reference. Recording the scan time, the signal and noise intensity of the femoral head, muscle, and subcutaneous fat were measured by a senior radiologist and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were then calculated. Additionally, two observers provided ratings for overall image quality and artifacts in the 4 groups, and statistical analysis was performed using the Friedman rank-sum test.Results:The acquisition times for CS-AI 4, CS-AI 6, CS-AI 8, and CS-AI 10 were 5 min 49 s, 3 min 54 s, 2 min 56 s and 2 min 22 s, respectively. Compared to CS-AI 4, the scanning time for CS-AI 6, CS-AI 8, CS-AI 10 were reduced by 32.95%, 50.14%, 59.31%, respectively. The objective evaluation revealed that the SNR and CNR of the femoral head and muscle in groups CS-AI 6, CS-AI 8, and CS-AI 10 were slightly lower than those in group CS-AI 4 ( P<0.05), and the differences were statistically significant. However, no statistically significant differences were found among the 3 groups ( P>0.05). The subjective evaluation indicated that the overall image quality scores of group CS-AI 8 [3 (3,4)] did not significantly differ from those of group CS-AI 4 and CS-AI 6( P>0.05); The mean scores of group CS-AI 4 and CS-AI 6 were 4 (4, 4); Scores of group CS-AI 10 was 3(3, 3), which statistically significant differ from those of the other groups ( P<0.05). The artifacts rating for groups CS-AI 4, CS-AI 6, CS-AI 8 and CS-AI 10 were 4 (4, 4), 4 (4, 4), 3 (3, 4), and 2 (2, 3) respectively. When AF was set to 10, the images exhibited the most severe artifacts ( P<0.05). For other AF values, artifact ratings did not differ significantly ( P>0.05). Conclusion:The CS-AI 3D-PDWI sequence with acceleration factor 8 can acquire high-resolution images of the unilateral hip joint that meet clinical diagnostic requirements while reducing scanning time.

Objective:To assess the efficacy and safety of programmed death-1 (PD-1) inhibitor combined with radiotherapy in advanced and relapsed / refractory extranodal NK/T cell lymphoma (ENKTL).Methods:Clinical data of 26 patients with advanced and recurrent / refractory ENKTL admitted to Sun Yat-sen University Cancer Center from January 2019 to December 2021 were retrospectively analyzed. All patients were treated with the PD-1 inhibitor combined with radiotherapy. The treatment responses, survival rate and and adverse reactions of the regimen were analyzed. The Kaplan-Meier method was used to estimate the 1- and 2-year progression-free survival (PFS) rate and overall survival (OS) rate, and the Cox proportional risk model was used for univariate prognostic factorial analysis for PFS and OS.Results:The median follow-up time of 26 patients was 29 months (10-49 months). The objective response rate (ORR) was 85%. The complete and partial remission rates were 77% and 8%. The median PFS time was 25 months. The 1- and 2- year PFS rates were 73.1% and 53.3%. The 1- and 2- year OS rates were 88.5% and 75.3%. The main adverse reaction was acute mucositis with an incidence rate of 31% (8/26), followed by hematological toxicity. The incidence of immune-related adverse events in lung, liver and thyroid were low. Only 1 patient developed grade 3 acute mucositis, 1 patient developed grade 4 immune pneumonitis, and the remaining patients had grade 1-2 toxicities. All patients showed good tolerance. The univariate analysis showed that elevated lactate dehydrogenase, Epstein-Barr virus DNA positive after treatment, and less than 6 cycles of anti-PD-1 immunotherapy were prognostic factors for poor OS.Conclusion:The regimen of PD-1 inhibitor combined with radiotherapy demonstrates promising efficacy and well tolerance in patients with advanced and relapsed / refractory ENKTL.

Objective To analyze gene expression profiles of biopsy specimens from breast cancer patients who were treated with neoadjuvant chemotherapy(NAC) after biopsies, and to identify the genes which are closely associated with the efficacy of neoadjuvant chemotherapy with T/FAC [docetaxel(Taxotere), 5-fluorouracil, doxorubicin and cyclophosphamide] or T/FEC (Taxotere, 5-fluorouracil, epirubicin and cyclophosphamide) regimen.Methods We retrieved and collected gene expression profiles from publicly available databases.Four datasets, a total of 844 samples, were finally retained because all the patients had received a uniform neoadjuvant chemotherapy regimen.Response to neoadjuvant chemotherapy was categorized as a pathological complete response (pCR) or residual invasive cancer (RD).The differentially expressed genes (adjusted P-value<0.05) and therapeutic efficacy were analyzed and explored.Results After differential analysis, genes whose expressions were higher or lower in pCR group than in RD group were identified in each of the four datasets, respectively.There were 34 and 42 genes which were simultaneously more highly expressed or more lowly expressed in pCR group than in RD group in the four datasets.The unsupervised clustering, based on the 76 intersection genes, showed that the pCR specimens tended to form one cluster and the RD tended to form the other.Conclusion The seventy-six differentially expressed genes are associated with the efficacy of neoadjuvant chemotherapy and are likely to be novel predictive biomarkers for the efficacy of neoadjuvant chemotherapy.