Proteolysis-targeting chimeras (PROTACs) have emerged as a promising therapeutic strategy for targeted protein degradation. However, the clinical application of PROTACs may be hindered by off-target toxicity resulting from non-tissue-specific protein degradation and ingenious prodrug strategies may open new avenues to addressing this concern. Herein, we propose a light-induced positive feedback strategy to use photodynamic therapy (PDT) to improve the activation efficiency of PROTAC prodrugs, monitor PROTAC release, and combine PROTAC to induce tumor cell apoptosis. In the hypoxic tumor microenvironment, the azo bond in AZO-PRO selectively cleaves, triggering the release of the potent protein degrader PRO and the multifunctional photosensitizer. Once activated, the fluoresce signal of the photosensitizer dramatically recovers, allowing monitoring of prodrug activation. Additionally, upon irradicating the tumor site using near-infrared (NIR) laser, PDT exacerbates tumor hypoxia, further promoting AZO-PRO activation. Our work introduces a novel approach to efficiently track and activate PROTAC prodrugs, enhance their antitumor efficacy, and mitigate off-target systemic toxicity.

Objective:To construct a medical quality evaluation index system for inpatients in public hospitals based on the life cycle theory,starting from the entire process of medical treatment.Method:A comprehensive study was conducted on the screening of medical quality evaluation indicators for hospitalized patients using literature analysis,key informant interviews,and expert inquiry methods.Results:The effective recovery rate of the two rounds of expert consultation was 100%,with high enthusiasm from the experts.The authority coefficient was above 0.7,and Kendall's W coordination coefficient was 0.267(P＜0.05).The evaluation indicators for medical quality of hospitalized patients,including 3 primary indicators,10 secondary indicators,and 51 tertiary indicators,were determined.Conclusions:The medical quality evaluation index system for inpatients in public hospitals based on the life cycle theory has certain scientificity and reliability.However,the weight analysis of the index system has not been carried out and is still in the theoretical exploration stage.Further empirical research is needed for verification and improvement.

Objective:To construct a medical quality evaluation index system for inpatients in public hospitals based on the life cycle theory,starting from the entire process of medical treatment.Method:A comprehensive study was conducted on the screening of medical quality evaluation indicators for hospitalized patients using literature analysis,key informant interviews,and expert inquiry methods.Results:The effective recovery rate of the two rounds of expert consultation was 100%,with high enthusiasm from the experts.The authority coefficient was above 0.7,and Kendall's W coordination coefficient was 0.267(P＜0.05).The evaluation indicators for medical quality of hospitalized patients,including 3 primary indicators,10 secondary indicators,and 51 tertiary indicators,were determined.Conclusions:The medical quality evaluation index system for inpatients in public hospitals based on the life cycle theory has certain scientificity and reliability.However,the weight analysis of the index system has not been carried out and is still in the theoretical exploration stage.Further empirical research is needed for verification and improvement.

When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.
Algorithms ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
Humans ; Algorithms ; Brain/diagnostic imaging* ; Brain Neoplasms/diagnostic imaging* ; Recognition, Psychology

Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.
Humans ; Carcinoma, Hepatocellular ; Liver Neoplasms ; Learning

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

The use of echocardiography ventricle segmentation can obtain ventricular volume parameters, and it is helpful to evaluate cardiac function. However, the ultrasound images have the characteristics of high noise and difficulty in segmentation, bringing huge workload to segment the object region manually. Meanwhile, the automatic segmentation technology cannot guarantee the segmentation accuracy. In order to solve this problem, a novel algorithm framework is proposed to segment the ventricle. Firstly, faster region-based convolutional neural network is used to locate the object to get the region of interest. Secondly, -means is used to pre-segment the image; then a mean shift with adaptive bandwidth of kernel function is proposed to segment the region of interest. Finally, the region growing algorithm is used to get the object region. By this framework, ventricle is obtained automatically without manual localization. Experiments prove that this framework can segment the object accurately, and the algorithm of adaptive mean shift is more stable and accurate than the mean shift with fixed bandwidth on quantitative evaluation. These results show that the method in this paper is helpful for automatic segmentation of left ventricle in echocardiography.

Objective To evaluate the safety,feasibility,and clinical outcome of anterior percutaneous endoscopic cervical discectomy (PECD).Methods The study involved 28 patients undergone PECD.Visual analogue scale (VAS) and MacNab scale were recorded before operation and at 3 days,1,3,6,12 and 18 months after operation.In addition,MRI examination was conducted at postoperative l month,3 months and 12 months.After data collection,single-factor T test with SAS software was performed.Results Follow-up (range,18-24 months,mean 19 months) was achieved in 25 patients.When compared to the preoperative score,VAS and MacNab scale presented improvement at postoperative 3 days (P ＞ 0.05) and great improvement at postoperative 1,3,6,12,18 and 24 months (P ＜ 0.01).VAS and MacNab scale at postoperative 3 days presented statistical differences as compared to those at postoperative 3,6,12 and 18 months (P ＜0.05),but the differences were not statistically insignificant at postoperative 3,6,12,and 18 months (P ＞ 0.05).Moreover,VAS and MacNab scale showed significant improvement at postoperative 24 months as compared to those before operation (P ＜0.01) and those at postoperative day 3 (P ＜ 0.01).Conclusion Anterior PECD is effective in treatment of cervical soft or partial hard disc herniation.

OBJECTIVE: To analyze failure reasons of surgical treatment of obstructive sleep apnea hypopnea syndrome (OSAHS), and explore the methods of reoperation. METHOD: By selecting 27 patients, who accepted surgical treatment for OSAHS and recurred, we analyzed failure reasons and obstructive location by apneagraph, nasopharyngeal 3D-CT, electronic nasopharynlaryngoscope. Among them, 14 patients accepted reoperation, such as uvulopalatopharyngoplasty (UPPP), nasoendoscopic surgery, adenoidectomy, partial glossectomy, tracheotomy were applied matching to differential obstructive location. AHI, lowest SaO2, Epworth sleepiness scale (ESS), complication were recorded after 6 months. RESULT: After 6 months, their AHI decreased from 48.19 +/- 13.11 to 11.32 +/- 4. 42, ESS scores decreased from 12.93 +/- 4.60 to 4.93 +/- 1.44, P<0.05. Two of the 14 patients were cured, while the other 12 were efficient. No complications were observed. CONCLUSION Obstructive location judgement and proper surgical operation are the keys of the treatment. Preoperative AG sleep monitoring, nasopharyngeal 3D CT, electronic nasopharynlaryngoscope examination for determining blocking plane, the decision of surgery which is significant.
Adult ; Female ; Humans ; Male ; Palate ; surgery ; Palate, Soft ; surgery ; Pharynx ; surgery ; Recurrence ; Reoperation ; Sleep Apnea, Obstructive ; prevention & control ; surgery ; Treatment Outcome ; Uvula ; surgery