Near-infrared BODIPY photosensitizers for two-photon excited singlet oxygen generation and tumor cell photodynamic therapy.

In this paper, two near-infrared BODIPY photosensitizers, Id-BDPI and Cz-BDPI, were obtained by modifying the indole and carbazole aromatic heterocycles in the core of BODIPY. The maximum absorption wavelengths of Id-BDPI and Cz-BDPI were 694 nm and 722 nm, and their singlet oxygen yields were 48% and 48.4%, respectively. In the simulated tumor cell photodynamic therapy, Id-BDPI and Cz-BDPI could effectively inhibit the growth of A549 tumor cells under near-infrared light. Meanwhile, the lysosomal co-localization coefficients of Id-BDPI and Cz-BDPI with A549 tumor cells were 0.94 and 0.89, respectively, showing high lysosomal targeting ability and biocompatibility. The two-photon absorption cross sections measured at 1050 nm by the Z-scanning method were 661.8 GM and 715.6 GM, respectively, and Cz-BDPI was further successfully applied to two-photon fluorescence imaging and two-photon excited singlet oxygen generation in zebrafish. The above results indicate that the introduction of aromatic heterocycles can effectively enhance the photodynamic efficacy of BODIPY photosensitizers, and the larger two-photon absorption cross section also brings potential for two-photon photodynamic therapy applications.

In Silico Analyses, Experimental Verification and Application in DNA Vaccines of Ebolavirus GP-Derived pan-MHC-II-Restricted Epitopes.

(1) Background and Purpose: Ebola virus (EBOV) is the causative agent of Ebola virus disease (EVD), which causes extremely high mortality and widespread epidemics. The only glycoprotein (GP) on the surface of EBOV particles is the key to mediating viral invasion into host cells. DNA vaccines for EBOV are in development, but their effectiveness is unclear. The lack of immune characteristics resides in antigenic MHC class II reactivity. (2) Methods: We selected MHC-II molecules from four human leukocyte antigen II (HLA-II) superfamilies with 98% population coverage and eight mouse H2-I alleles. IEDB, NetMHCIIpan, SYFPEITHI, and Rankpep were used to screen MHC-II-restricted epitopes with high affinity for EBOV GP. Further immunogenicity and conservation analyses were performed using VaxiJen and BLASTp, respectively. EpiDock was used to simulate molecular docking. Cluster analysis and binding affinity analysis of EBOV GP epitopes and selected MHC-II molecules were performed using data from NetMHCIIpan. The selective GP epitopes were verified by the enzyme-linked immunospot (ELISpot) assay using splenocytes of BALB/c (H2d), C3H, and C57 mice after DNA vaccine pVAX-GPEBO immunization. Subsequently, BALB/c mice were immunized with Protein-GPEBO, plasmid pVAX-GPEBO, and pVAX-LAMP/GPEBO, which encoded EBOV GP. The dominant epitopes of BALB/c (H-2-I-AdEd genotype) mice were verified by the enzyme-linked immunospot (ELISpot) assay. It is also used to evaluate and explore the advantages of pVAX-LAMP/GPEBO and the reasons behind them. (3) Results: Thirty-one HLA-II-restricted and 68 H2-I-restricted selective epitopes were confirmed to have high affinity, immunogenicity, and conservation. Nineteen selective epitopes have cross-species reactivity with good performance in MHC-II molecular docking. The ELISpot results showed that pVAX-GPEBO could induce a cellular immune response to the synthesized selective peptides. The better immunoprotection of the DNA vaccines pVAX-LAMP/GPEBO coincides with the enhancement of the MHC class II response. (4) Conclusions: Promising MHC-II-restricted candidate epitopes of EBOV GP were identified in humans and mice, which is of great significance for the development and evaluation of Ebola vaccines.

Dilated transformer: residual axial attention for breast ultrasound image segmentation.

Background: The segmentation of breast ultrasound (US) images has been a challenging task, mainly due to limited data and the inherent image characteristics involved, such as low contrast and speckle noise. Although convolutional neural network-based (CNN-based) methods have made significant progress over the past decade, they lack the ability to model long-range interactions. Recently, the transformer method has been successfully applied to the tasks of computer vision. It has a strong ability to capture distant interactions. However, most transformer-based methods with excellent performance rely on pre-training on large datasets, making it infeasible to directly apply them to medical images analysis, especially that of breast US images with limited high-quality labels. Therefore, it is of great significance to find a robust and efficient transformer-based method for use on small breast US image datasets. Methods: We developed a dilated transformer (DT) method which mainly uses the proposed residual axial attention layers to build encoder blocks and the introduced dilation module (DM) to further increase the receptive field. We evaluated the proposed method on 2 breast US image datasets using the 5-fold cross-validation method. Dataset A was a public dataset with 562 images, while dataset B was a private dataset with 878 images. Ground truth (GT) was delineated by 2 radiologists with more than 5 years of experience. The evaluation was followed by related ablation experiments. Results: The DT was found to be comparable with the state-of-the-art (SOTA) CNN-based method and outperformed the related transformer-based method, medical transformer (MT), on both datasets. Especially on dataset B, the DT outperformed the MT on metrics of Jaccard index (JI) and Dice similarity coefficient (DSC) by 2.67% and 4.68%, respectively. Meanwhile, when compared with Unet, the DT improved JI and DSC by 4.89% and 4.66%, respectively. Moreover, the results of the ablation experiments showed that each add-on part of the DT is important and contributes to the segmentation accuracy. Conclusions: The proposed transformer-based method could achieve advanced segmentation performance on different small breast US image datasets without pretraining.

Accurate segmentation of breast tumor in ultrasound images through joint training and refined segmentation.

Objective.This paper proposes an automatic breast tumor segmentation method for two-dimensional (2D) ultrasound images, which is significantly more accurate, robust, and adaptable than common deep learning models on small datasets.Approach.A generalized joint training and refined segmentation framework (JR) was established, involving a joint training module (Jmodule) and a refined segmentation module (Rmodule). InJmodule, two segmentation networks are trained simultaneously, under the guidance of the proposed Jocor for Segmentation (JFS) algorithm. InRmodule, the output ofJmoduleis refined by the proposed area first (AF) algorithm, and marked watershed (MW) algorithm. The AF mainly reduces false positives, which arise easily from the inherent features of breast ultrasound images, in the light of the area, distance, average radical derivative (ARD) and radical gradient index (RGI) of candidate contours. Meanwhile, the MW avoids over-segmentation, and refines segmentation results. To verify its performance, the JR framework was evaluated on three breast ultrasound image datasets. Image dataset A contains 1036 images from local hospitals. Image datasets B and C are two public datasets, containing 562 images and 163 images, respectively. The evaluation was followed by related ablation experiments.Main results.The JR outperformed the other state-of-the-art (SOTA) methods on the three image datasets, especially on image dataset B. Compared with the SOTA methods, the JR improved true positive ratio (TPR) and Jaccard index (JI) by 1.5% and 3.2%, respectively, and reduces (false positive ratio) FPR by 3.7% on image dataset B. The results of the ablation experiments show that each component of the JR matters, and contributes to the segmentation accuracy, particularly in the reduction of false positives.Significance.This study successfully combines traditional segmentation methods with deep learning models. The proposed method can segment small-scale breast ultrasound image datasets efficiently and effectively, with excellent generalization performance.

Elevated serum AST and LDH levels are associated with infant death in premature babies with neonatal leukemoid reaction: a cohort study.

Background: Neonatal leukemoid reaction (NLR) is often accompanied by infectious or non-infectious diseases, a low birth weight, sepsis, prematurity, ventricular hemorrhage, and bronchial dysplasia. It has an incidence rate of 1.3-15% and a mortality rate of about 41.4%. Previous studies on NLR have largely focused on its pathogenesis and clinical cases, but little is known about its prognostic laboratory indicators. We found that some of the NLR exhibited obviously elevation in liver function tests like aspartate transaminase (AST) and lactate dehydrogenase (LDH) which were not took by all the LR infants. The necessity for liver function tests for the prognosis of NLR was still unclear. Methods: A total of 39 premature infants with NLR at the First Hospital of Jilin University between March 2016 and March 2017 were included in this retrospective cohort study. The infants were divided into death and cured group based on the clinical outcomes. Premature infants with LR and death were defined as the case group (n=14), while infants without death were defined as the control group (n=25). Confounding factors such as age and gender between the two groups were controlled. Blood routine tests, including the white blood cell (WBC) count and subtypes, and liver function, and clinical features were recorded and analyzed. T tests were used to examine the differences in the laboratory indicators between the NLR and control groups. Receiver operating characteristic curves (ROCs) and areas under the curve (AUCs) were used to examine laboratory indicators for prognosis. Results: For predicting clinical outcomes, the ROC curves showed that the cut-off values for AST and LDH were 279 and 1,412 U/L, respectively. The sensitivity and specificity for AST were 92% and 71.43%, respectively, with an AUC of 0.894, while the sensitivity and specificity for LDH were 88% and 78.57%, respectively, with an AUC of 0.911. Conclusions: This innovative study investigated the NLR prognosis depending on laboratory tests. We found that serum AST and LDH levels had reliable predictive value in determining adverse outcomes of NLR.

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images.

BACKGROUND: The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. RESULTS: The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. CONCLUSIONS: The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. METHODS: The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

Using impression data to improve models of online social influence.

Influence, the ability to change the beliefs and behaviors of others, is the main currency on social media. Extant studies of influence on social media, however, are limited by publicly available data that record expressions (active engagement of users with content, such as likes and comments), but neglect impressions (exposure to content, such as views) and lack "ground truth" measures of influence. To overcome these limitations, we implemented a social media simulation using an original, web-based micro-blogging platform. We propose three influence models, leveraging expressions and impressions to create a more complete picture of social influence. We demonstrate that impressions are much more important drivers of influence than expressions, and our models accurately identify the most influential accounts in our simulation. Impressions data also allow us to better understand important social media dynamics, including the emergence of small numbers of influential accounts and the formation of opinion echo chambers.

Lesion segmentation in breast ultrasound images using the optimized marked watershed method.

BACKGROUND: Breast cancer is one of the most serious diseases threatening women's health. Early screening based on ultrasound can help to detect and treat tumours in the early stage. However, due to the lack of radiologists with professional skills, ultrasound-based breast cancer screening has not been widely used in rural areas. Computer-aided diagnosis (CAD) technology can effectively alleviate this problem. Since breast ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD systems, is challenging. RESULTS: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 open-source BUS images. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model RDAU-NET. Its accuracy (Acc), Dice similarity coefficient (DSC) and Jaccard index (JI) reached 96.25%, 78.4% and 65.34% on dataset A, and its Acc, DSC and sensitivity reached 97.96%, 86.25% and 88.79% on dataset B, respectively. CONCLUSIONS: We proposed an adaptive morphological snake based on marked watershed (AMSMW) algorithm for BUS image segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. METHODS: The proposed method consists of two steps. In the first step, contrast limited adaptive histogram equalization (CLAHE) and a side window filter (SWF) are used to preprocess BUS images. Lesion contours can be effectively highlighted, and the influence of noise can be eliminated to a great extent. In the second step, we propose adaptive morphological snake (AMS). It can adjust the working parameters adaptively according to the size of the lesion. Its segmentation results are combined with those of the morphological method. Then, we determine the marked area and obtain candidate contours with a marked watershed (MW). Finally, the best lesion contour is chosen by the maximum average radial derivative (ARD).

Partial splenic embolization has beneficial effects for the management of gastroesophageal variceal hemorrhage.

BACKGROUND/AIMS: Partial splenic embolization (PSE) is used in the management of gastroesophageal variceal hemorrhage (GEVH). However, it is uncertain whether it has beneficial effects for GEVH patients in preventing variceal recurrence and variceal hemorrhage, as well as promoting overall survival (OS), when it is combined with conventional therapies. MATERIALS AND METHODS: The databases including PubMed, EMBASE, Web of Science, Google scholar, and Cochrane Central Register of Controlled Trials were searched up to 11th of November, 2015. Meta-analyses were performed by using Review Manager 5.3 software for analyzing the risk of bias, Newcastle-Ottawa Scale for assessing the bias of cohort studies, and GRADEprofiler software for assessing outcomes obtained from the meta-analyses. RESULTS: A total of 1505 articles were reviewed, and 1 randomized controlled trial and 5 cohort studies with 244 participants were eligible for inclusion. The pooled hazard ratio (HR) of variceal recurrence is 0.50 (95% confidence interval (CI) 0.37, 0.68; P< 0.00001; I2 = 0%). The pooled HR of variceal hemorrhage is 0.24 (95% CI 0.15, 0.39; P< 0.00001; I2 = 0%). The pooled HR of OS is 0.50 (95% CI 0.33, 0.67; P< 0.00001; I2 = 0%). Meta-analyses demonstrated statistically significant superiority of combinational therapies over conventional therapies in preventing variceal recurrence and variceal hemorrhage and prolonging OS. The complications related to PSE were mild or moderate and nonfatal. CONCLUSIONS: The results indicate that PSE has beneficial effects for GEVH patients, however, future investigation with a larger number of subjects in clinical trials is warranted.

[Correlation analysis on the disorders of patella-femoral joint and torsional deformity of tibia].

OBJECTIVE: To reveal the possible mechanism involved in patella-femoral degenerative arthritis (PFDA) in- duced by torsion-deformity of tibia via analyzing the relationship between torsion-deformity of the tibia in patients with PFDA and the disorder of patella-femoral joint under the static and dynamic conditions. METHODS: From October 2009 to October 2010, 50 patients (86 knees, 24 knees of male patients and 62 knees of female patients) with PFDA were classified as disease group and 16 people (23 knees, 7 knees of males and 16 knees of females) in the control group. The follow indexes were measured: the torsion-angle of tibia on CT scanning imagings, the patella-femoral congruence angle and lateral patella-femoral angle under static and dynamic conditions when the knee bent at 30 degrees of flexion. Based on the measurement results, the relationship between the torsion-deformity of tibias and the disorders of patella-femoral joints in patients with PFDA were analyzed. Finally,the patients were divided into three groups including large torsion-angle group, small torsion-angle group and normal group according to the size of torsion-angle, in order to analyze the relationship between torsion-deformity and disorders of patella-femoral joint, especially under the dynamic conditions. RESULTS: Compared with patients without PFDA, the ones with PFDA had bigger torsion-angle (30.30 ± 7.11)° of tibia, larger patella-femoral congruence angle (13.20 ± 3.94)° and smaller lateral patella-femoral angle (12.30 ± 3.04)°. The congruence angle and lateral patella-femoral angle under static and dynamic conditions had statistical differences respectively in both too-big torsion-angle group and too-small torsion-angle group. The congruence angle and lateral patella-femoral angle under static and dynamic conditions had no statistical differences in normal torsion-angle group. CONCLUSION: Torsion-deformity of tibia is the main reason for disorder of patella-femoral joint in the patients with PFDA. Torsion-deformity of tibia is always accompanied by instability of patella-femoral joint,especially under the dynamic condition, thus causing PFDA. It can not only provide arrangement information and degenerative condition of patella-femoral joint,but also provide guidance through the analysis on the relationship for better clinical prevention and early treatment of degenerative bone and joint disease.

[Comparison of the status of STAT4 tyrosine phosphorylation in peripheral T-lymphocytes induced by IL-12 in rheumatoid arthritis and osteoarthritis].

OBJECTIVE: To compare the levels of STAT4 tyrosine phosphorylation in peripheral T-lymphocytes induced by IL-12 in rheumatoid arthritis (RA) and osteoarthritis (OA). METHODS: From May 2007 to August 2009, peripheral blood mononuclear cells (PBMCs) were isolated from RA patients [RA group, all the cases were female, the age was from 28 to 55 years with an average of (45.0 +/- 13.0) years] and OA patients [OA group, all the cases also were female; the age was from 55 to 75 years with an average of (67.0 +/- 9.6) years]. The purity of T-lymphocytes from PBMCs was accredited by flow cytometry. The IL-12 of 50 ng/ml added in T-lymphocytes, the levels of STAT4 tyrosine phosphorylation were detected by western blot after different time intervals (0, 10, 30, 60 min). RESULTS: The purity of T-lymphocytes were above 91% through diremption and depuration for peripheral blood monouclear cells. The levels of STAT4 tyrosine phosphorylation in T-lymphocytes from RA induced by IL-12 were higher than that from OA in the different times (10, 30, 60 min); after 30 min, its levels from RA and OA achieved to crest value. CONCLUSION: STAT4 in peripheral T-lymphocytes of rheumatoid arthritis was more easily to be activated than osteoarthritis.

[Treatment of proximal fracture of humerus by percutaneous antegrade Kirschner pinning].

OBJECTIVE: To summarize and analyze the clinical indication, surgical techniques and results of percutaneous antegrade Kirschner pinning in the treatment of proximal fracture of humerus. METHODS: Thirty-two patients with displaced fractures of the proximal humerus were treated with 2 to 3 Kirschner wires by percutaneous antegrade transfixation. Among 32 casas, there were 7 male and 25 female, with a mean age of 49.25 years (range, from 28 to 75 years). According to the Neer fracture classification, there were 20 cases of 2-part, 9 of 3-part, and 3 of 4-part fractures. RESULTS: All patients were followed up for 8 to 34 months with an average of 13.5 months. According to Constant-Murley evaluation, the results were excellent in 21 cases, good in 9, fair in 2. CONCLUSION: Percutaneous antegrade transfixaton is a good technique for the management of displaced 2-part fractures of the proximal humerus and also alternative to 3-part or 4-part fractures.