A BRD4-Targeting Photothermal Agent for Controlled Protein Degradation.

BRD4 protein plays a pivotal role in cell cycle regulation and differentiation. Disrupting the activity of BRD4 has emerged as a promising strategy for inhibiting the growth and proliferation of cancer cells. Herein, we introduced a BRD4-targeting photothermal agent for controlled protein degradation, aiming to enhance low-temperature photothermal therapy (PTT) for cancer treatment. By incorporating a BRD4 protein inhibitor into a cyanine dye scaffold, the photothermal agent specifically bond to the bromodomain of BRD4. Upon low power density laser irradiation, the agent induced protein degradation, directly destroying the BRD4 structure and inhibiting its transcriptional regulatory function. This strategy not only prolonged the retention time of the photothermal agent in cancer cells but also confined the targeted protein degradation process solely to the tumor tissue, minimizing side effects on normal tissues through the aid of exogenous signals. This work established a simple and feasible platform for future PTT agent design in clinical cancer treatment.

A Golgi Apparatus-Targeted Photothermal Agent with Protein Anchoring for Enhanced Cancer Photothermal Therapy.

The Golgi apparatus (GA) is central in shuttling proteins from the endoplasmic reticulum to different cellular areas. Therefore, targeting the GA to precisely destroy its proteins through local heat could induce apoptosis, offering a potential avenue for effective cancer therapy. Herein, a GA-targeted photothermal agent based on protein anchoring is introduced for enhanced photothermal therapy of tumor through the modification of near-infrared molecular dye with maleimide derivative and benzene sulfonamide. The photothermal agent can actively target the GA and covalently anchor to its sulfhydryl proteins, thereby increasing its retention within the GA. Under laser irradiation, the heat generated by the photothermal agent efficiently disrupts sulfhydryl proteins in situ, leading to GA dysfunction and ultimately inducing cell apoptosis. In vivo experiments demonstrate that the photothermal agent can precisely treat tumors and significantly reduce side effects.

[A multimodal medical image contrastive learning algorithm with domain adaptive denormalization].

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.

[A meta-learning based method for segmentation of few-shot magnetic resonance images].

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.

[Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning].

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.

Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network.

Background: The growth and aging process of the human population has accelerated the increase in surgical procedures. Yet, the demand for increasing operations can be hardly met since the training of anesthesiologists is usually a long-term process. Closed-loop artificial intelligence (AI) model provides the possibility to solve intelligent decision-making for anesthesia auxiliary control and, as such, has allowed breakthroughs in closed-loop control of clinical practices in intensive care units (ICUs). However, applying an open-loop artificial intelligence algorithm to build up personalized medication for anesthesia still needs to be further explored. Currently, anesthesiologists have selected doses of intravenously pumped anesthetic drugs mainly based on the blood pressure and bispectral index (BIS), which can express the depth of anesthesia. Unfortunately, BIS cannot be monitored at some medical centers or operational procedures and only be regulated by blood pressure. As a result, here we aim to inaugurally explore the feasibility of a basic intelligent control system applied to drug delivery in the maintenance phase of general anesthesia, based on a convolutional neural network model with open-loop design, according to AI learning of existing anesthesia protocols. Methods: A convolutional neural network, combined with both sliding window sampling method and residual learning module, was utilized to establish an "AI anesthesiologist" model for intraoperative dosing of personalized anesthetic drugs (propofol and remifentanil). The fitting degree and difference in pumping dose decision, between the AI anesthesiologist and the clinical anesthesiologist, for these personalized anesthetic drugs were examined during the maintenance phase of anesthesia. Results: The medication level established by the "AI anesthesiologist" was comparable to that obtained by the clinical anesthesiologist during the maintenance phase of anesthesia. Conclusion: The application of an open-loop decision-making plan by convolutional neural network showed that intelligent anesthesia control is consistent with the actual anesthesia control, thus providing possibility for further evolution and optimization of auxiliary intelligent control of depth of anesthesia.

Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery.

Objective: To explore the application of machine learning algorithm in the prediction and evaluation of cesarean section, predicting the amount of blood transfusion during cesarean section and to analyze the risk factors of hypothermia during anesthesia recovery. Methods: (1)Through the hospital electronic medical record of medical system, a total of 600 parturients who underwent cesarean section in our hospital from June 2019 to December 2020 were included. The maternal age, admission time, diagnosis, and other case data were recorded. The routine method of cesarean section was intraspinal anesthesia, and general anesthesia was only used for patients' strong demand, taboo, or failure of intraspinal anesthesia. According to the standard of intraoperative bleeding, the patients were divided into two groups: the obvious bleeding group (MH group, N = 154) and nonobvious hemorrhage group (NMH group, N = 446). The preoperative, intraoperative, and postoperative indexes of parturients in the two groups were analyzed and compared. Then, the risk factors of intraoperative bleeding were screened by logistic regression analysis with the occurrence of obvious bleeding as the dependent variable and the factors in the univariate analysis as independent variables. In order to further predict intraoperative blood transfusion, the standard cases of recesarean section and variables with possible clinical significance were included in the prediction model. Logistic regression, XGB, and ANN3 machine learning algorithms were used to construct the prediction model of intraoperative blood transfusion. The area under ROC curve (AUROC), accuracy, recall rate, and F1 value were calculated and compared. (2) According to whether hypothermia occurred in the anesthesia recovery room, the patients were divided into two groups: the hypothermia group (N = 244) and nonhypothermia group (N = 356). The incidence of hypothermia was calculated, and the relevant clinical data were collected. On the basis of consulting the literatures, the factors probably related to hypothermia were collected and analyzed by univariate statistical analysis, and the statistically significant factors were analyzed by multifactor logistic regression analysis to screen the independent risk factors of hypothermia in anesthetic convalescent patients. Results: (1) First of all, we compared the basic data of the blood transfusion group and the nontransfusion group. The gestational age of the transfusion group was lower than that of the nontransfusion group, and the times of cesarean section and pregnancy in the transfusion group were higher than those of the non-transfusion group. Secondly, we compared the incidence of complications between the blood transfusion group and the nontransfusion group. The incidence of pregnancy complications was not significantly different between the two groups (P > 0.05). The incidence of premature rupture of membranes in the nontransfusion group was higher than that in the transfusion group (P < 0.05). There was no significant difference in the fetal umbilical cord around neck, amniotic fluid index, and fetal heart rate before operation in the blood transfusion group, but the thickness of uterine anterior wall and the levels of Hb, PT, FIB, and TT in the blood transfusion group were lower than those in the nontransfusion group, while the number of placenta previa and the levels of PLT and APTT in the blood transfusion group were higher than those in the nontransfusion group. The XGB prediction model finally got the 8 most important features, in the order of importance from high to low: preoperative Hb, operation time, anterior wall thickness of the lower segment of uterus, uterine weakness, preoperative fetal heart, placenta previa, ASA grade, and uterine contractile drugs. The higher the score, the greater the impact on the model. There was a linear correlation between the 8 features (including the correlation with the target blood transfusion). The indexes with strong correlation with blood transfusion included the placenta previa, ASA grade, operation time, uterine atony, and preoperative Hb. Placenta previa, ASA grade, operation time, and uterine atony were positively correlated with blood transfusion, while preoperative Hb was negatively correlated with blood transfusion. In order to further compare the prediction ability of the three machine learning methods, all the samples are randomly divided into two parts: the first 75% training set and the last 25% test set. Then, the three models are trained again on the training set, and at this time, the model does not come into contact with the samples in any test set. After the model training, the trained model was used to predict the test set, and the real blood transfusion status was compared with the predicted value, and the F1, accuracy, recall rate, and AUROC4 indicators were checked. In terms of training samples and test samples, the AUROC of XGB was higher than that of logistic regression, and the F1, accuracy, and recall rate of XGB of ANN were also slightly higher than those of logistic regression and ANN. Therefore, the performance of XGB algorithm is slightly better than that of logistic regression and ANN. (2) According to the univariate analysis of hypothermia during the recovery period of anesthesia, there were significant differences in ASA grade, mode of anesthesia, infusion volume, blood transfusion, and operation duration between the normal body temperature group and hypothermia group (P < 0.05). Logistic regression analysis showed that ASA grade, anesthesia mode, infusion volume, blood transfusion, and operation duration were all risk factors of hypothermia during anesthesia recovery. Conclusion: In this study, three machine learning algorithms were used to analyze the large sample of clinical data and predict the results. It was found that five important predictive variables of blood transfusion during recesarean section were preoperative Hb, expected operation time, uterine weakness, placenta previa, and ASA grade. By comparing the three algorithms, the prediction effect of XGB may be more accurate than that of logistic regression and ANN. The model can provide accurate individual prediction for patients and has good prediction performance and has a good prospect of clinical application. Secondly, through the analysis of the risk factors of hypothermia during the recovery period of cesarean section, it is found that ASA grade, mode of anesthesia, amount of infusion, blood transfusion, and operation time are all risk factors of hypothermia during the recovery period of cesarean section. In line with this, the observation of this kind of patients should be strengthened during cesarean section.

A Protein-Binding Molecular Photothermal Agent for Tumor Ablation.

Photothermal therapy usually requires a high power density to activate photothermal agent for effective treatment, which inevitably leads to damage to normal tissues and inflammation in tumor tissues. Herein, we rationally design a protein-binding strategy to build a molecular photothermal agent for photothermal ablation of tumor. The synthesized photothermal agent can covalently bind to the thiol groups on the intracellular proteins. The heat generated by the photothermal agent directly destroyed the bioactive proteins in the cells, effectively reducing the heat loss and the molecular leakage. Under a low power density of 0.2 W cm-2 , the temperature produced by the photothermal agent was sufficient to induce apoptosis. In vitro and in vivo experiments showed that the therapeutic effect of photothermal therapy can be efficiently improved with the protein-binding strategy.

Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning.

BACKGROUND: Interactive echocardiography translation is an efficient educational function to master cardiac anatomy. It strengthens the student's understanding by pixel-level translation between echocardiography and theoretically sketch images. Previous research studies split it into two aspects of image segmentation and synthesis. This split makes it hard to achieve pixel-level corresponding translation. Besides, it is also challenging to leverage deep-learning-based methods in each phase where a handful of annotations are available. METHODS: To address interactive translation with limited annotations, we present a two-step transfer learning approach. Firstly, we train two independent parent networks, the ultrasound to sketch (U2S) parent network and the sketch to ultrasound (S2U) parent network. U2S translation is similar to a segmentation task with sector boundary inference. Therefore, the U2S parent network is trained with the U-Net network on the public segmentation dataset of VOC2012. S2U aims at recovering ultrasound texture. So, the S2U parent network is decoder networks that generate ultrasound data from random input. After pretraining the parent networks, an encoder network is attached to the S2U parent network to translate ultrasound images into sketch images. We jointly transfer learning U2S and S2U within the CGAN framework. Results and conclusion. Quantitative and qualitative contrast from 1-shot, 5-shot, and 10-shot transfer learning show the effectiveness of the proposed algorithm. The interactive translation is achieved with few-shot transfer learning. Thus, the development of new applications from scratch is accelerated. Our few-shot transfer learning has great potential in the biomedical computer-aided image translation field, where annotation data are extremely precious.

[Medical computer-aided detection method based on deep 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.

[Left ventricle segmentation in echocardiography based on adaptive mean shift].

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, K-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.

[The clinical investigation of the potential complications of H-UPPP surgery in removing the partial pharyngeal muscle].

OBJECTIVE: In order to improve the postoperative effect of modified UPPP, removing the partial pharyngeal muscle in surgery, we investigate the postoperative effect, the characteristics of pharyngeal cavity and the potential complications in OSAHS patients. METHOD: To choose 82 OSAHS patients with obstructive oropharyngeal plane diagnosed by Apneagraphy (AG), Fibre nasopharyngoscope combined with Müller examination and nasopharyngeal 3D-CT, which had completed clinical data inpatients in the anesthesia underwent of the partial pharyngeal muscles in the postoperative, divided into a control group of 26 cases, operating the H-UPPP surgery which did not remove partial pharyngeal muscle; The experimental group of 56 cases did a H-UPPP surgical which removed partial pharyngeal muscle of possible concurrent symptoms such as nasal regurgitation, Eustachian tube dysfunction and other follow-up study in six months after the monthly telephone follow-up or outpatient exams to understand the disease. Patients were evaluated the sleepiness by ESS(Epworth sleepiness scale) in 6 months after the surgery, compared with the preoperative ESS scores, do a t test for statistical analysis. AG can be used to evaluate effects of the UPPP after 6 months. By measuring uvula length (L1), extent from free edge of soft palate to postpharyngeal (L2) and stenosis of nasopharynx width (L3) mean, we investigate the characteristics of pharyngeal cavity using the multiple linear regression to do the hypothesis test and evaluate the association between measuring mean and effect. Using SPSS19.0 software do the preoperative contrast analysis. RESULT: After 6 months in surgery, 56 cases in the experimental group, effect in 50 cases (89.29%), effective in 6 cases (10.71%); ESS score: Preoperative 11.74 +/- 2.48, after the first 6 months 3.84 +/- 2.05. Twenty-six cases in control group,effect in 19 cases (73.08%), effective in 7 cases (26.92%); ESS score: Preoperative 11.91 +/- 2.40, after the first 6 months 6.92 +/- 2.47, t-test P value of less than 0.05 between the experimental group and the control group; There are no ear fullness, hearing loss, increase their own sound which reflect eustachian tube dysfunction and other complications in two groups; The function of pharyngeal cavity could be recovered normal lever after 6 months; After 6 months of the operation, in the experimental group and the control group L1 mean was respectively (5.91 +/- 3.38) mm and (6.20 +/- 3.76) mm (P>0.05); L2 mean was respectively (15.70 +/- 3.29)mm and (15.35 +/- 1.44) mm (P> 0.05); L3 mean was respectively (20.54 +/- 3.33) mm and (16.43 +/- 2.21) mm (P<0.05). Nasal fauces pitch mean was significantly widened. By the multiple linear regression analysis, the postoperative effect has the linear correlation between L2 and 1,3 residual mean with the negative correlation. Due to the standardized coefficient, L3 residual mean has the most influence on the postoperative effect. CONCLUSION: Modified UPPP surgery removing the partial pharyngeal muscle is in favor of upgrading the postoperative effect with significantly increasing the width of postoperative nasal pharyngeal isthmus area, then there are not occur the eustachian tube dysfunction, the soft palate function, swallowing and articulation function disabled.

[Research on failure reasons of surgical treatment of obstructive sleep apnea hypopnea syndrome and reoperation method].

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.

[Significance of AG in OSAHS operation treatment guidance and evaluation of postoperative efficacy].

OBJECTIVE: Apneagraph can be used to discuss which the best operation scheme is for OSAHS. Effects of uvulopalatopharyngoplasty can be assessed by Apneagraph in obstructive sleep apnea hypopnea syndrome (OSAHS) patients. METHOD: Fifty-six patients with OSAHS received the modified UPPP operation were randomly selected in our hospital. The AG and PSG were applied for diagnosis and evaluation of operation effects. The sleepiness state was assessed by ESS (Epworth sleepiness scale) 6 months after the surgery, compared with the preoperative ESS scores using attest for statistical analysis. We used the SPSS19.0 software to carry our data analysis. RESULT: After 6 months, the evaluation of postoperative efficacy came out to be completely controlled in 42 cases (75%), significantly effective in 14 cases (25%), and uncured in 0 cases. Correlation between the transpalatal obstruction proportion and the AHI reduction percentage was significantly positive (r = 0.667). There were 38 patients with oropharynx obstruction percentage more than 73.35% presented completely controlled in 34 cases (89.47%), significantly effective in 4 cases (10.33%), and uncured in 0 cases. CONCLUSION: AG has the dual functions of analyzing sleep-related respiratory disturbance events and determining upper airway obstruction sites. AG application in the postoperative evaluation of modified uppp has significantly objective guide significance. The modified UPPP for treatment of OSAHS can improve the operation effect. Patients with oropharynx obstruction percentage more than 73.5% don't need to receive the operation for treatment of retroglottal region.