Biomechanical comparison of the therapeutic effect of a novel proximal femoral bionic intramedullary nail and traditional inverted triangle hollow screw on femoral neck fracture.

OBJECTIVE: A novel Proximal Femoral Bionic Nail (PFBN) has been developed by a research team for the treatment of femoral neck fractures. This study aims to compare the biomechanical properties of the innovative PFBN with those of the conventional Inverted Triangular Cannulated Screw (ITCS) fixation method through biomechanical testing. METHODS: Sixteen male femoral specimens preserved in formalin were selected, with the donors' age at death averaging 56.1 ± 6.3 years (range 47-64 years), and a mean age of 51.4 years. The femurs showed no visible damage and were examined by X-rays to exclude diseases affecting bone quality such as tumors, severe osteoporosis, and deformities. The 16 femoral specimens were randomly divided into an experimental group (n = 8) and a control group (n = 8). All femurs were prepared with Pauwels type III femoral neck fractures, fixed with PFBN in the experimental group and ITCS in the control group. Displacement and stress limits of each specimen were measured through cyclic compression tests and failure experiments, and vertical displacement and strain values under a 600 N vertical load were measured in all specimens through vertical compression tests. RESULTS: In the vertical compression test, the average displacement at the anterior head region of the femur was 0.362 mm for the PFBN group, significantly less than the 0.480 mm for the ITCS group (p < 0.001). At the fracture line area, the average displacement for the PFBN group was also lower than that of the ITCS group (0.196 mm vs. 0.324 mm, p < 0.001). The difference in displacement in the shaft area was smaller, but the average displacement for the PFBN group (0.049 mm) was still significantly less than that for the ITCS group (0.062 mm, p = 0.016). The situation was similar on the posterior side of the femur. The average displacements in the head area, fracture line area, and shaft area for the PFBN group were 0.300 mm, 0.168 mm, and 0.081 mm, respectively, while those for the ITCS group were 0.558 mm, 0.274 mm, and 0.041 mm, with significant differences in all areas (p < 0.001). The average strain in the anterior head area for the PFBN group was 4947 µm/m, significantly less than the 1540 µm/m for the ITCS group (p < 0.001). Likewise, in the fracture line and shaft areas, the average strains for the PFBN group were significantly less than those for the ITCS group (p < 0.05). In the posterior head area, the average strain for the PFBN group was 4861 µm/m, significantly less than the 1442 µm/m for the ITCS group (p < 0.001). The strain conditions in the fracture line and shaft areas also showed the PFBN group was superior to the ITCS group (p < 0.001). In cyclic loading experiments, the PFBN fixation showed smaller maximum displacement (1.269 mm vs. 1.808 mm, p < 0.001), indicating better stability. In the failure experiments, the maximum failure load that the PFBN-fixated fracture block could withstand was significantly higher than that for the ITCS fixation (1817 N vs. 1116 N, p < 0.001). CONCLUSION: The PFBN can meet the biomechanical requirements for internal fixation of femoral neck fractures. PFBN is superior in biomechanical stability compared to ITCS, particularly showing less displacement and higher failure resistance in cyclic load and failure experiments. While there are differences in strain performance in different regions between the two fixation methods, overall, PFBN provides superior stability.

Intestinal Cckbr-specific knockout mouse as a novel model of salt-sensitive hypertension via sodium over-absorption.

OBJECTIVES: To investigate the value of CCKBRfl/fl villin-Cre mice as a mouse model of salt-sensitive hypertension (SSH). METHODS: In the first part, 2-month-old CCKBRfl/fl villin-Cre mice (CKO) and control CCKBRfl/fl mice (WT) were fed with normal diet (0.4% NaCl) or high salt diet (4% NaCl), separately for 6 weeks. In the rescue study, one week of hydrochlorothiazide or saline injection were treated with the CKO mice fed high salt diet. The blood pressure, biochemical indexes, and the expression of small intestinal sodium transporters (NHE3, NKCC1, eNaC) was detected. The organ injury markers (MMP2/MMP9) and the histopathological changes of kidneys were observed, whereas the changes of duodenal sodium absorption were detected by small intestinal perfusion in vivo. RESULTS: The CCKBRfl/fl villin-Cre mice with high salt intake exhibited high blood pressure, increased duodenal sodium absorption and urinary sodium excretion, and with renal injury. The protein expression of NHE3, NKCC1 and eNaC were also significant increase in the intestine of CKO-HS mice. Treatment with hydrochlorothiazide remarkably attenuated the elevated blood pressure by high salt absorption in the CCKBRfl/fl villin-Cre mice, but no significant histopathological changes were observed. CONCLUSIONS: These results support a crucial role of intestinal Cckbr deficiency on SSH development and the diuretic antihypertension effect in CCKBRfl/fl villin-Cre mice. The CCKBRfl/fl villin-Cre mice with the high salt intake may serve as a stable model of salt-sensitive hypertensive induced by sodium overloading.

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.

COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation.

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.

Effects of off-pump coronary artery bypass graft on the clinical efficacy, surgical indicators, and cardiac function of patients with coronary heart disease.

BACKGROUND: Coronary heart disease (CHD) is a common clinical cardiovascular disease. This study aimed to analyze the effects of off-pump coronary artery bypass graft on the clinical efficacy, surgical indicators, and cardiac function of patients with CHD. METHODS: We retrospectively analyzed the clinical data of 120 patients with CHD who were treated in our hospital from May 2017 to May 2020. And they were divided into the control group (extracorporeal coronary artery bypass graft) and the observation group (off-pump coronary artery bypass graft). The clinical efficacy, surgical indicators, cardiac function, myocardial injury, the degree of cardiac autonomic nerve imbalance, incidence of complications and quality of life one year after the operation in the 2 groups were compared. RESULTS: The total effective rate of the observation group was significantly higher than that of the control group. Intraoperative blood loss, operation time, intraoperative blood transfusion, and hospital stay in the observation group were significantly better than those in the control group. After treatment, the levels of cardiac index (CI), ejection fraction (EF), stroke volume (SV), and cardiac output (CO) in the observation group and the control group were higher than those before treatment, especially in the observation group. Compared with those before operation, CK-MB and cTnI of the two groups significantly increased at all time points after surgery. After treatment, SDNN, LF, HF, and TP of patients in the two groups increased, which was significant in the observation group. The incidence of complications such as myocardial infarction, ischemic changes, respiratory insufficiency, and intraoperative ventricular fibrillation in the observation group was significantly lower than that in the control group. The score of quality of life in the observation group was significantly higher than the control group. CONCLUSIONS: In the treatment of patients with CHD, off-pump coronary artery bypass graft has good clinical effects, which can significantly improve the heart function, and cardiac autonomic nerve imbalance of patients, reduce myocardial damage, decrease the incidence of complications, and improve the quality of life. Therefore, off-pump coronary artery bypass graft is worthy of clinical application.

The roles of PDGFRα signaling in the postnatal development and functional maintenance of the SMC-ICC-PDGFRα+ cell (SIP) syncytium in the colon.

BACKGROUND: The SIP syncytium in the gut consists of smooth muscle cells, interstitial cells of Cajal, and PDGFRα+ cells. We studied the fate of SIP cells after blocking PDGFRα receptor to explore the roles of PDGFRα signaling in the postnatal development and functional maintenance of the SIP syncytium. METHODS: Crenolanib was administered to mice from P0, P10, or P50. The morphological changes in SIP cells were examined by immunofluorescence. Protein expression in SIP cells was detected by Western blotting. Moreover, colonic transit was analyzed by testing the colonic bead expulsion time. KEY RESULTS: A dose of 5 mg(kg•day)-1 crenolanib administered for 10 days beginning on P0 apparently hindered the development of PDGFRα+ cells in the colonic longitudinal muscularis and myenteric plexus without influencing their proliferative activity and apoptosis, but this result was not seen in the colonic circular muscularis. SMCs were also inhibited by crenolanib. A dose of 7.5 mg(kg•day)-1 crenolanib administered for 15 days beginning on P0 caused reductions in both PDGFRα+ cells and ICC in the longitudinal muscularis, myenteric plexus, and circular muscularis. However, when crenolanib was administered at a dose of 5 mg(kg•day)-1 beginning on P10 or P50, it only noticeably decreased the number of PDGFRα+ cells in the colonic longitudinal muscularis. Crenolanib also caused PDGFRα+ cells to transdifferentiate into SMC in adult mice. Colonic transit was delayed after administration of crenolanib. CONCLUSIONS & INFERENCES: Therefore, PDGFRα signaling is essential for the development and functional maintenance of the SIP cells, especially PDGFRα+ cells.

Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network.

The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy. Graphical abstract ᅟ.

pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.

MOTIVATION: Cells are deemed the basic unit of life. However, many important functions of cells as well as their growth and reproduction are performed via the protein molecules located at their different organelles or locations. Facing explosive growth of protein sequences, we are challenged to develop fast and effective method to annotate their subcellular localization. However, this is by no means an easy task. Particularly, mounting evidences have indicated proteins have multi-label feature meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Unfortunately, most of the existing computational methods can only be used to deal with the single-label proteins. Although the 'iLoc-Animal' predictor developed recently is quite powerful that can be used to deal with the animal proteins with multiple locations as well, its prediction quality needs to be improved, particularly in enhancing the absolute true rate and reducing the absolute false rate. RESULTS: Here we propose a new predictor called 'pLoc-mAnimal', which is superior to iLoc-Animal as shown by the compelling facts. When tested by the most rigorous cross-validation on the same high-quality benchmark dataset, the absolute true success rate achieved by the new predictor is 37% higher and the absolute false rate is four times lower in comparison with the state-of-the-art predictor. AVAILABILITY AND IMPLEMENTATION: To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc-mAnimal/, by which users can easily get their desired results without the need to go through the complicated mathematics involved. CONTACT: xxiao@gordonlifescience.org or kcchou@gordonlifescience.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.

Recommended by the World Health Organization (WHO), drug compounds have been classified into 14 main ATC (Anatomical Therapeutic Chemical) classes according to their therapeutic and chemical characteristics. Given an uncharacterized compound, can we develop a computational method to fast identify which ATC class or classes it belongs to? The information thus obtained will timely help adjusting our focus and selection, significantly speeding up the drug development process. But this problem is by no means an easy one since some drug compounds may belong to two or more than two ATC classes. To address this problem, using the DO (Drug Ontology) approach based on the ChEBI (Chemical Entities of Biological Interest) database, we developed a predictor called iATC-mDO. Subsequently, hybridizing it with an existing drug ATC classifier, we constructed a predictor called iATC-mHyb. It has been demonstrated by the rigorous cross-validation and from five different measuring angles that iATC-mHyb is remarkably superior to the best existing predictor in identifying the ATC classes for drug compounds. To convenience most experimental scientists, a user-friendly web-server for iATC-mHyd has been established at http://www.jci-bioinfo.cn/iATC-mHyb, by which users can easily get their desired results without the need to go through the complicated mathematical equations involved.

iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals.

Motivation: Given a compound, can we predict which anatomical therapeutic chemical (ATC) class/classes it belongs to? It is a challenging problem since the information thus obtained can be used to deduce its possible active ingredients, as well as its therapeutic, pharmacological and chemical properties. And hence the pace of drug development could be substantially expedited. But this problem is by no means an easy one. Particularly, some drugs or compounds may belong to two or more ATC classes. Results: To address it, a multi-label classifier, called iATC-mISF, was developed by incorporating the information of chemical­chemical interaction, the information of the structural similarity, and the information of the fingerprintal similarity. Rigorous cross-validations showed that the proposed predictor achieved remarkably higher prediction quality than its cohorts for the same purpose, particularly in the absolute true rate, the most important and harsh metrics for the multi-label systems. Availability and Implementation: The web-server for iATC-mISF is accessible at http://www.jci-bioinfo.cn/iATC-mISF. Furthermore, to maximize the convenience for most experimental scientists, a step-by-step guide was provided, by which users can easily get their desired results without needing to go through the complicated mathematical equations. Their inclusion in this article is just for the integrity of the new method and stimulating more powerful methods to deal with various multi-label systems in biology. Contact: xxiao@gordonlifescience.org Supplementary Information: Supplementary data are available at Bioinformatics online.

Curcumin attenuates insulin resistance in hepatocytes by inducing Nrf2 nuclear translocation.

BACKGROUND/AIMS: NF-E2-Related Factor-2 (Nrf2) is a transcription factor that plays a crucial role in the cellular protection against oxidative stress. Curcumin has been reported to induce Nrf2 nuclear translocation and upregulate the expression of numerous reactive oxygen species (ROS) detoxifying and antioxidant genes in hepatocytes. This study was designed to investigate whether curcumin-induced Nrf2 nuclear translocation could reduce ROS-mediated insulin resistance in cultured LO2 hepatocytes. METHODOLOGY: Human LO2 hepatocytes were incubated with curcumine and glucose oxidase (GO) in the presence/absence of wortmannin (a phosphatidyinositol 3-kinase (PI3K) inhibitor), oxidative stress, cellular damage, Nrf2 nuclear translocation and insulin resistance were measured. RESULTS: GO exposure significantly increased intracellular ROS, glutathione (GSH) depletion, malondialdehyde (MDA) formation, and increased activities of cellular lactate dehydrogenase (LDH) and aspartate amino transferase (AST), as well as causing insulin resistance. Curcumin pretreatment significantly attenuated these disturbances in intracellular ROS, liver enzyme activity and significantly antagonized the lipid peroxidation, GSH depletion and insulin resistance induced by GO in LO2 hepatocytes. These effects paralleled Nrf2 nuclear translocation induced by curcumin. Wortmannin partially blocked curcumin-induced Nrf2 nuclear translocation. In addition, wortmannin prevented curcumin-induced improvements in intracellular ROS, MDA formation, GSH depletion, liver enzyme activity and insulin resistance in cultured LO2 hepatocytes. CONCLUSIONS: These findings suggest that curcumin could reduce ROS-mediated insulin resistance in hepatocytes, at least in part through nuclear translocation of Nrf2.