In January 2023, a patient with soft tissue degloving defect of right index, middle, ring and little fingers was treated in the Department of Hand Surgery, Suzhou Ruihua Orthopaedic Hospital. Seven free flaps from a leg were harvested to reconstruct the defected wound of fingers in primary surgery. Flap thinning and plastic surgery were performed in stage-II surgery. Over the 22 months of postoperative follow-up, the flaps in right index, middle, ring and little fingers survived well with the colour and texture close to proximal skin. There was no obvious swelling of the flaps and sensation of the flaps recovered to S 3. The donor sites healed well and the donor leg walked normally.

Purpose To analyze the value of a deep learning(DL)radiomics model based on dynamic contrast-enhanced MRI images in predicting the expression of Ki-67 status in breast cancer.Materials and Methods A retrospective analysis of 152 breast cancer patients confirmed by pathological results at Guangdong Women and Children Hospital,MRI images and clinical pathological data were reviewed,and based on postoperative immunohistochemistry results,the images of the high and low expression groups of Ki-67 were randomly sampled in a ratio of 8∶2 to form a training set of 122 cases and a validation set of 30 cases.Single-factor and multi-factor Logistic regression analyses of clinical data were performed to select independent predictors of breast cancer expressing Ki-67 status.The ResNet-18 model was used as the basic model for DL feature extraction.Hand-crafted radiomic features and DL features were extracted.Eight machine learning models were constructed based on clinical features,hand-crafted radiomic features,DL features,and their combinations.The area under the receiver operating characteristic curve was used to evaluate the predictive performance of the models,and the best model was determined as the output model.Results The progesterone receptor status(OR=0.764,P=0.040)and human epidermal growth factor receptor-2 status(OR=1.187,P=0.046)were independent clinical predictors of breast cancer expressing Ki-67 status.The combined feature models demonstrated superior performance over the individual feature models,and the support vector machine algorithm had the highest prediction performance in the validation set,with an area under the curve of 0.847.Conclusion The DL radiomics model based on dynamic contrast-enhanced MRI images can effectively predict the expression of Ki-67 status in breast cancer.The support vector machine algorithm combined with feature model is the best,which can help the clinical diagnosis and treatment of breast cancer and prognosis evaluation.

Objective:To compare the effects of erector spinae plane block at T 2 (T 2-ESPB) and nerve root block at C 5 (C 5-NRB) in patients undergoing arthroscopic shoulder surgery with general anesthesia. Methods:This was a randomized, controlled, non-inferiority study. Sixty American Society of Anesthesiologists Physical Status classification I or Ⅱ patients, aged 45-75 yr, with body mass index ≤35 kg/m 2, scheduled for elective arthroscopic shoulder surgery at Jining No. 1 People′s Hospital from April 2023 to February 2024, were included and divided into 2 groups ( n=30 each) using a random number table method: C 5-NRB group (group C) and T 2-ESPB group (group T). In group C, C 5-NRB was carried out by injecting 0.5% ropivacaine 5 ml. In group T, T 2-ESPB was performed by injecting 0.25% ropivacaine 30 ml. The efficacy of nerve block was assessed using a prick test at 30 min after administration, and then total intravenous anesthesia was performed in both groups. The time to first rescue analgesia (the non-inferiority boundary Δ =2 h), requirement for rescue analgesia within 24 h after operation and intraoperative consumption of anesthetics were recorded. The motor function of the affected limb during shoulder abduction, elbow flexion and elbow extension was assessed and scored using the modified Bromage scale (MBS) at 30 min and 4 and 12 h after nerve block. The diaphragmatic excursion was measured and recorded using M-mode ultrasound before nerve block and at 30 min after nerve block to evaluate the occurrence of diaphragmatic paralysis. Complications such as local anesthetic toxicity, recurrent laryngeal nerve block and pneumothorax were also recorded. Results:The mean difference (95% confidence interval) for the time to first rescue analgesia between the two groups was 5.551 (1.875-9.148) h, with the upper limit exceeding the non-inferiority boundary. Compared with group T, the intraoperative consumption of remifentanil was significantly reduced, the time to first rescue analgesia was prolonged, the consumption of morphine for rescue analgesia was decreased, MBS scores during shoulder abduction, elbow flexion and elbow extension were decreased at 30 min after block, and MBS scores during shoulder abduction and elbow flexion were decreased at 4 and 12 h after block in group C ( P<0.05). There was no significant difference in the diaphragmatic excursion, incidence of diaphragm paralysis and incidence of complications before and after block in the two groups ( P>0.05). Conclusions:C 5-NRB provides superior efficacy compared to T 2-ESPB when used for arthroscopic shoulder surgery under general anesthesia.

Purpose To analyze the value of a deep learning(DL)radiomics model based on dynamic contrast-enhanced MRI images in predicting the expression of Ki-67 status in breast cancer.Materials and Methods A retrospective analysis of 152 breast cancer patients confirmed by pathological results at Guangdong Women and Children Hospital,MRI images and clinical pathological data were reviewed,and based on postoperative immunohistochemistry results,the images of the high and low expression groups of Ki-67 were randomly sampled in a ratio of 8∶2 to form a training set of 122 cases and a validation set of 30 cases.Single-factor and multi-factor Logistic regression analyses of clinical data were performed to select independent predictors of breast cancer expressing Ki-67 status.The ResNet-18 model was used as the basic model for DL feature extraction.Hand-crafted radiomic features and DL features were extracted.Eight machine learning models were constructed based on clinical features,hand-crafted radiomic features,DL features,and their combinations.The area under the receiver operating characteristic curve was used to evaluate the predictive performance of the models,and the best model was determined as the output model.Results The progesterone receptor status(OR=0.764,P=0.040)and human epidermal growth factor receptor-2 status(OR=1.187,P=0.046)were independent clinical predictors of breast cancer expressing Ki-67 status.The combined feature models demonstrated superior performance over the individual feature models,and the support vector machine algorithm had the highest prediction performance in the validation set,with an area under the curve of 0.847.Conclusion The DL radiomics model based on dynamic contrast-enhanced MRI images can effectively predict the expression of Ki-67 status in breast cancer.The support vector machine algorithm combined with feature model is the best,which can help the clinical diagnosis and treatment of breast cancer and prognosis evaluation.

In January 2023, a patient with soft tissue degloving defect of right index, middle, ring and little fingers was treated in the Department of Hand Surgery, Suzhou Ruihua Orthopaedic Hospital. Seven free flaps from a leg were harvested to reconstruct the defected wound of fingers in primary surgery. Flap thinning and plastic surgery were performed in stage-II surgery. Over the 22 months of postoperative follow-up, the flaps in right index, middle, ring and little fingers survived well with the colour and texture close to proximal skin. There was no obvious swelling of the flaps and sensation of the flaps recovered to S 3. The donor sites healed well and the donor leg walked normally.

With the rapid increase of MRI systems in hospitals in China,national multi-sectoral strategies have been put forward to clarify requirements for improving image quality of MRI systems and preventing application risks in clinic.Quality control of MRI systems becomes an important task for regulators as well as hospital radiology departments.The tools used for quality control include imaging performance phantom and specialized function phantom,which can realize detection or calibration for parameters such as high contrast resolution,image uniformity and relaxation time.This article mainly reviews the research progress of the operation principles,common types and clinical applications for these two types of phantoms mentioned above.

With the rapid increase in the ownership of MRI equipment in China,quality control,particularly in clinical usage aspects,has become critically important.For clinical quality control of MRI systems,it is essential to establish comprehensive workflow principles encompassing multiple elements such as personnel,equipment,standards,tools and methodologies.To advance the standardization and widespread adoption of clinical quality control for MRI equipment,efforts must focus on strengthening regulatory frameworks,advancing phantom research,development and enhancing professional expertise.Concurrently,continuous improvements in training programs and supervision mechanisms are necessary to ensure the effective implementation of MRI clinical quality control practices.Furthermore,in the era of digital healthcare,clinical quality assurance for MRI equipment is evolving toward automation and intelligent solutions,providing higher-quality and more efficient assurance for clinical applications.

Purpose To explore the feasibility and practical value of using National Institute of Metrology(NIM)calorimetry method to measure whole-body specific absorption rate(SAR)values of in-service MRI equipment.Materials and Methods A NIM calorimetry device for measuring whole-body SAR values was developed,SAR values of different MRI devices were measured by NIM calorimetry,and compared with National Electrical Manufacturers Association(NEMA)calorimetry and pulse-energy method to verify the measurement accuracy and applicability of the NIM calorimetry method.Results The NIM calorimetry device developed in this study had reliable performance,and the experimental results indicated the difference in measurement results between NIM calorimetry(1.63 W/kg)and NEMA calorimetry(1.80 W/kg)was within 10%.The difference between the SAR measurement results of multiple MRI devices based on NIM calorimetry(0.46,0.93,0.61 W/kg)and the pulse energy method(0.42,0.89,0.56 W/kg)was within 10%.Conclusion The NIM calorimetry method in this study can accurately measure whole-body SAR values and has applicability.

The tetraspanins are closely associated with the development and therapeutic prognosis of colorectal tumors. These proteins play a role in cell proliferation, metastasis, and invasion, regulate apoptosis and autophagy of colorectal tumor cells. affect immune escape by releasing exosomes, intervening the epithelial-mesenchymal transition process, and altering the tumor microenvironment, and enhance tumor stemness through specific pathways. This paper reviews the mechanisms and current research regarding the status of tetraspanins in colorectal cancer, aiming to improve early diagnosis and providing valuable insights for treatment strategies.

Objective:To explore the impact of the two-way referral model on compliance and prognosis in patients with heart failure.Methods:This bidirectional cohort study enrolled chronic heart failure (CHF) patients treated at Qilu Hospital of Shandong University or designated primary hospitals between March 2018 and March 2022. Patients were categorized into two groups based on referral status: two-way referral group (participating in the referral model with≥1 follow-up visit at primary hospitals) and the core hospital group (receiving treatment and follow-up exclusively at Qilu Hospital). Baseline clinical characteristics were collected and compared between groups. Patients underwent followed-up, with primary endpoints including follow-up rate, drug (β-blockers, angiotension converting enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blockers (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists) utilization rate and target dose achievement rate. Secondary endpoints encompassed changes from baseline in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), and N-terminal pro-brain natriuretic peptide (NT-proBNP), plus cardiovascular mortality and heart failure rehospitalization. Generalized linear mixed models analyzed longitudinal trends in LVEF, LVEDd, and NT-proBNP levels. Kaplan-Meier curves and Cox regression evaluated LVEF recovery rates, supplemented by subgroup analyses. Multivariate logistic regression was used to identify factors influencing target dose achievement rate for β-blockers and ACEI/ARB/ARNI therapies in CHF patients.Results:A total of 357 patients were enrolled, aged 53 (41, 63) years, including 256 males (71.7%). 157 patients were in the two-way referral group and 200 patients in the core hospital-treated group. Compared with the core hospital-treated group, the two-way referral group had lower baseline LVEF (28 (22, 34)% vs. 31 (23, 36)%, P=0.021) and systolic blood pressure (116 (104, 125) mmHg vs. 121 (109, 134) mmHg (1 mmHg=0.133 kPa), P=0.010). The 12-month follow-up rate of the two-way referral group was higher than the core hospital-treated group (73.8% vs. 56.0%, P=0.004). No significant between-group differences were observed in drug utilization rate of β-blockers, ACEI/ARB/ARNI, or sodium-glucose cotransporter 2 inhibitors during follow-up (all P>0.05), while mineralocorticoid receptor antagonists use showed a declining trend in both groups. Although the core hospital-treated group had higher target dose achievement rates for β-blockers (65.4% vs. 49.3%, P=0.042) and ACEI/ARB/ARNI (79.8% vs. 65.8%, P=0.046) than the two-way referral group, multivariate logistic regression indicated that the two-way referral model was not a negative predictor for these outcomes (all P>0.05). Both groups showed improved NT-proBNP, LVEDd, and LVEF from baseline (all P<0.001) with no significant difference in trends between groups (all P>0.05). There was no significant difference in the composite incidence (7.6% vs. 6.5%, P=0.674) and cumulative incidence (log-rank P=0.684) of cardiovascular death and heart failure rehospitalization at 12 months between two groups. Conclusion:The two-way referral model demonstrates advantages in improving medication adherence, drug utilization rates, and targetdoseachievement rates among CHF patients. This model not only promotes cardiac functional recovery but also reduces risks of cardiovascular mortality and heart failure rehospitalization, achieving comparable therapeutic and management outcomes to those observed in core hospital-treated patients.