Objective To explore the current status of digital health literacy among elderly orthopedic perioperative patients and its influencing factors, and to provide a basis for optimizing perioperative management and improving patients’ health management level. Methods Utilizing convenience sampling, elderly orthopedic perioperative patients from China-Japan Union Hospital of Jilin University were chosen as the subjects between January and April 2024. For the purpose of the questionnaire investigation, the orthopedic patient functional exercise compliance scale, eHealth Literacy Scale, Social Support Rating Scale, and general information questionnaire were utilized. We employed univariate analysis and multiple linear stratified regression to investigate the factors influencing digital health literacy. Pearson correlation analysis was utilized to explore the interrelationships among variables. Results A total of 143 patients were investigated. Among them, there were 53 males and 90 females. The average age was (69.91±6.35) years old. The average score of the eHealth Literacy Scale was (18.25±9.45) points, the average score of the Social Support Rating Scale was (38.44±7.76) points, and the average score of the orthopedic patient functional exercise compliance scale was (55.16±14.28) points. The determinants of digital health literacy in older orthopedic perioperative patients were social support and educational attainment (P<0.05). Social support and functional exercise adherence were mediated, in part, by digital health literacy (P<0.001). Conclusions The digital health literacy in elderly orthopedic perioperative is generally at a low level. Healthcare professionals need to pay particular attention to patients with lower levels of education. Meanwhile, efforts should be made to enhance patients’ social support from multiple dimensions, in order to improve their digital health literacy and lay a solid foundation for the precise implementation of digital health management during the perioperative.
Objective To evaluate the utility of collagen-gel droplet embedded-culture drug sensitivity test (CD-DST) in pancreatic carcinoma cell by compared with WST-8. Methods The chemosensitivity to 5-fluorouracil (5-FU), gemzar (GEM) and oxaliplatin (OXA) of pancreatic adenocarcinoma cells SW1990, PCT-3 and ASPC-1 were tested by WST-8 and CD-DST respectively. Results In a certain living cell number range (500-10 000), there was a linear correlation (r=0.991 1, P＜0.05) between the integral optical density in CD-DST and the cell number. The inhibition ratios of three kinds of cell growth tested by CD-DST were higher than those tested by WST-8 (P＜0.05). The results of drug chemosensitivity to 5-FU， GEM and OXA detected by two methods were uniform. Conclusion The CD-DST can be used to assay the drug chemosensitivity in vitro for pancreatic carcinoma.
Objective To summarize the blood supply to the sources and characteristics of advanced breast cancer，and explore the method，efficacy，and clinical applications of preoperative super-selective arterial catheterization chemoembolization under DSA for it. Methods Sixty patients with advanced breast cancer confirmed by the aspiration biopsy from February 2007 to October 2011 in this hospital were selected. Seldinger method was used，distributing of the tumor blood supply artery was identified and intubated the target artery by super-selective arterial catheterization via the femoral artery puncture under the DSA. Then，pirarubicin 60 mg plus paclitaxel 120 mg of two chemotherapy drugs was injected into slowly the target artery and the intervention infusion chemotherapy was performed，finally the tumor blood supply artery was embolizated by gelatin sponge particle. Results A total of 112 conclusive blood supply artery in 60 patients with DSA were found，including eight cases of single blood supply artery，52 cases of multiple blood supply arteries，mainly in the lateral thoracic artery and （or） internal thoracic artery-based. The complete remission rate was 25.0% （15/60），partial remission rate was 73.3% （44/60），stable disease rate was 1.7% （1/60），the total effective rate was 98.3% （59/60）. There was no progression disease. The median remission duration was 19 months，median survival time was 40 months. Conclusions The location of the original foci of breast cancer is closely related to blood supply arteries. The tumor in the lateral of the breast mainly dominates by the lateral thoracic artery blood supply. The tumor in the inner breast mainly dominates by the internal thoracic artery blood supply. The preoperative super-selective arterial catheterization chemoembolization under DSA can obviously improve the therapeutic effect，long-term survival，and the target of interventional chemoembolization.
There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model’s perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.
The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.
In recent years, the task of object detection and segmentation in medical image is the research hotspot and difficulty in the field of image processing. Instance segmentation provides instance-level labels for different objects belonging to the same class, so it is widely used in the field of medical image processing. In this paper, medical image instance segmentation was summarized from the following aspects: First, the basic principle of instance segmentation was described, the instance segmentation models were classified into three categories, the development context of the instance segmentation algorithm was displayed in two-dimensional space, and six classic model diagrams of instance segmentation were given. Second, from the perspective of the three models of two-stage instance segmentation, single-stage instance segmentation and three-dimensional (3D) instance segmentation, we summarized the ideas of the three types of models, discussed the advantages and disadvantages, and sorted out the latest developments. Third, the application status of instance segmentation in six medical images such as colon tissue image, cervical image, bone imaging image, pathological section image of gastric cancer, computed tomography (CT) image of lung nodule and X-ray image of breast was summarized. Fourth, the main challenges in the field of medical image instance segmentation were discussed and the future development direction was prospected. In this paper, the principle, models and characteristics of instance segmentation are systematically summarized, as well as the application of instance segmentation in the field of medical image processing, which is of positive guiding significance to the study of instance segmentation.
Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.
Objective To further comprehend the definition， molecular mechanism， and clinical significance of perineural invasion （PNI） so as to explore new therapy for the tumors. Methods The literatures about the definition， molecular mechanism， and clinical study of PNI were reviewed and analyzed. Results At present， widely accepted definition of PNI was that at least 33% of the circumference of the nerve should be surrounded by tumor cells or tumor cells within any of three layers of the nerve sheath. The newest theory on molecular mechanism of PNI was that PNI was more like infiltration， invasion， not just diffusion. “Path of low-resistance” and “Reciprocal signaling interactions” were the main theories. More recently， the studies had demonstrated that “Reciprocal signaling interactions” could more clearly explain the mechanism of PNI. Stromal elements， including fibroblasts， seemed to play a key role in the complex signaling interactions driving PNI. Neurotrophins and axonal guidance molecules had been implicated in promoting the progress of PNI. PNI was a prognosis index in the cancers of the head and neck， stomach， pancreas， colon and rectum， and prostate， which was positive indicated that the patients would have a poor prognosis and a low 5-year survival rate. Conclusions The mechanism of PNI is very complex， and its clear mechanism is still undefined. Keeping on researching the mechanism of PNI could provide theoretical foundation to disclose the mechanism and the therapy of PNI.
Remarkable results have been realized by the U-Net network in the task of medical image segmentation. In recent years, many scholars have been researching the network and expanding its structure, such as improvement of encoder and decoder and improvement of skip connection. Based on the optimization of U-Net structure and its medical image segmentation techniques, this paper elucidates in the following: First, the paper elaborates on the application of U-Net in the field of medical image segmentation; Then, the paper summarizes the seven improvement mechanism of U-Net: dense connection mechanism, residual connection mechanism, multi-scale mechanism, ensemble mechanism, dilated mechanism, attention mechanism, and transformer mechanism; Finally, the paper states the ideas and methods on the U-Net structure improvement in a bid to provide a reference for later researches, which plays a significant part in advancing U-Net.
ObjectiveProlonged mechanical ventilation (PMV) is a prognostic marker for short-term adverse outcomes in patients after lung transplantation.The risk of prolonged mechanical ventilation after lung transplantation is still not clear. The study to identify the risk factors of prolonged mechanical ventilation (PMV) after lung transplantation.Methods This retrospective observational study recruited patients who underwent lung transplantation in Wuxi People’s Hospital from January 2020 to December 2022. Relevant information was collected from patients and donors, including recipient data (gender, age, BMI, blood type, comorbidities), donor data (age, BMI, time of endotracheal intubation, oxygenation index, history of smoking, and any comorbidity with multidrug-resistant bacterial infections), and surgical data (surgical mode, incision type, operation time, cold ischemia time of the donor lung, intraoperative bleeding, and ECMO support), and postoperative data (multi-resistant bacterial lung infection, multi-resistant bacterial bloodstream infection, and mean arterial pressure on postoperative admission to the monitoring unit). Patients with a duration of mechanical ventilation ≤72 hours were allocated to the non-prolonged mechanical ventilation group, and patients with a duration of mechanical ventilation>72 hours were allocated to the prolonged mechanical ventilation group. LASSO regression analysis was applied to screen risk factors., and a clinical prediction model for the risk of prolonged mechanical ventilation after lung.ResultsPatients who met the inclusion criteria were divided into the training set and the validation set. There were 307 cases in the training set group and 138 cases in the validation set group. The basic characteristics of the training set and the validation set were compared. There were statistically significant differences in the recipient’s BMI, donor’s gender, CRKP of the donor lung swab, whether the recipient had pulmonary infection before the operation, the type of transplantation, the cold ischemia time of the donor lung, whether ECMO was used during the operation, the duration of ECMO assistance, CRKP of sputum, and the CRE index of the recipient's anal test (P<0.05). 2. The results of the multivariate logistic regression model showed that female recipients, preoperative mechanical ventilation in recipients, preoperative pulmonary infection in recipients, intraoperative application of ECMO, and the detection of multi-drug resistant Acinetobacter baumannii, multi-drug resistant Klebsiella pneumoniae and maltoclomonas aeruginosa in postoperative sputum were independent risk factors for prolonged mechanical ventilation after lung transplantation. The AUC of the clinical prediction model in the training set and the validation set was 0.838 and 0.828 respectively, suggesting that the prediction model has good discrimination. In the decision curves of the training set and the validation set, the threshold probabilities of the curves in the range of 0.05-0.98 and 0.02-0.85 were higher than the two extreme lines, indicating that the model has certain clinical validity.ConclusionsFemale patients, Preoperative pulmonary infection, preoperative mechanical ventilation,blood type B, blood type O, application of ECMO assistance, multi-resistant Acinetobacter baumannii infection, multi-resistant Klebsiella pneumoniae infection, and multi-resistant Stenotrophomonas maltophilia infection are independent risk factors for PMV (prolonged mechanical ventilation) after lung transplantation.