[Risk factors for embolism in children with refractory Mycoplasmapneumoniae pneumonia and construction of a nomogram model for prediction of embolism]. / éš¾æ²»æ€§è‚ºç‚Žæ”¯åŽŸä½“è‚ºç‚Žæ‚£å„¿åˆå¹¶æ “å¡žçš„å±é™©å› ç´ åˆ†æžåŠåˆ—çº¿å›¾é¢„æµ‹æ¨¡åž‹çš„æž„å»º.

OBJECTIVES: To study the risk factors for embolism in children with refractory Mycoplasma pneumoniae pneumonia (RMPP) and to construct a nomogram model for prediction of embolism. METHODS: This retrospective study included 175 children diagnosed with RMPP at Children's Hospital Affiliated toZhengzhou University from January 2019 to October 2023. They were divided into two groups based on the presence of embolism: the embolism group (n=62) and the non-embolism group (n=113). Multivariate logistic regression analysis was used to screen for risk factors of embolism in children with RMPP, and the R software was applied to construct the nomogram model for prediction of embolism. RESULTS: Multivariate logistic regression analysis indicated that higher levels of D-dimer, interleukin-6 (IL-6) and neutrophil to lymphocyte ratio (NLR), lung necrosis, and pleural effusion were risk factors for embolism in children with RMPP (P<0.05). The area under the curve of the nomogram model for prediction of embolism constructed based on the aforementioned risk factors was 0.912 (95%CI: 0.871-0.952, P<0.05). The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit with the actual situation (P<0.05). Calibration and decision curve analysis indicated that the model had high predictive efficacy and clinical applicability. CONCLUSIONS: Higher levels of D-dimer, IL-6 and NLR, lung necrosis, and pleural effusion are risk factors for embolism in children with RMPP. The nomogram model based on these risk factors has high clinical value for predicting embolism in children with RMPP.

Enhancing Dental Material Performance: Tung Oil-Infused Polyurea Microcapsule Coatings for Self-Healing and Antimicrobial Applications.

Within the realm of dental material innovation, this study pioneers the incorporation of tung oil into polyurea coatings, setting a new precedent for enhancing self-healing functionality and durability. Originating from an ancient practice, tung oil is distinguished by its outstanding water resistance and microbial barrier efficacy. By synergizing it with polyurea, we developed coatings that unite mechanical strength with biological compatibility. The study notably quantifies self-healing efficiency, highlighting the coatings' exceptional capacity to mend physical damages and thwart microbial incursions. Findings confirm that tung oil markedly enhances the self-repair capabilities of polyurea, leading to improved wear resistance and the inhibition of microbial growth, particularly against Streptococcus mutans, a principal dental caries pathogen. These advancements not only signify a leap forward in dental material science but also suggest a potential redefinition of dental restorative practices aimed at prolonging the lifespan of restorations and optimizing patient outcomes. Although this study lays a substantial foundation for the utilization of natural oils in the development of medical-grade materials, it also identifies the critical need for comprehensive cytotoxicity assays. Such evaluations are essential to thoroughly assess the biocompatibility and the safety profile of these innovative materials for clinical application. Future research will concentrate on this aspect, ensuring that the safety and efficacy of the materials align with clinical expectations for dental restorations.

Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging.

OBJECTIVE: To create an automated machine learning model using sacroiliac joint MRI imaging for early sacroiliac arthritis detection, aiming to enhance diagnostic accuracy. METHODS: We conducted a retrospective analysis involving 71 patients with early sacroiliac arthritis and 85 patients with normal sacroiliac joint MRI scans. Transverse T1WI and T2WI sequences were collected and subjected to radiomics analysis by two physicians. Patients were randomly divided into training and test groups at a 7:3 ratio. Initially, we extracted the region of interest on the sacroiliac joint surface using ITK-SNAP 3.6.0 software and extracted radiomic features. We retained features with an Intraclass Correlation Coefficient > 0.80, followed by filtering using max-relevance and min-redundancy (mRMR) and LASSO algorithms to establish an automatic identification model for sacroiliac joint surface injury. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated. Model performance was assessed by accuracy, sensitivity, and specificity. RESULTS: We evaluated model performance, achieving an AUC of 0.943 for the SVM-T1WI training group, with accuracy, sensitivity, and specificity values of 0.878, 0.836, and 0.943, respectively. The SVM-T1WI test group exhibited an AUC of 0.875, with corresponding accuracy, sensitivity, and specificity values of 0.909, 0.929, and 0.875, respectively. For the SVM-T2WI training group, the AUC was 0.975, with accuracy, sensitivity, and specificity values of 0.933, 0.889, and 0.750. The SVM-T2WI test group produced an AUC of 0.902, with accuracy, sensitivity, and specificity values of 0.864, 0.889, and 0.800. In the SVM-bimodal training group, we achieved an AUC of 0.974, with accuracy, sensitivity, and specificity values of 0.921, 0.889, and 0.971, respectively. The SVM-bimodal test group exhibited an AUC of 0.964, with accuracy, sensitivity, and specificity values of 0.955, 1.000, and 0.875, respectively. CONCLUSION: The radiomics-based detection model demonstrates excellent automatic identification performance for early sacroiliitis.

Two transcription factors, AcREM14 and AcC3H1, enhance the resistance of kiwifruit Actinidiachinensis var. chinensis to Pseudomonas syringae pv. actinidiae.

Kiwifruit bacterial canker is a global disease caused by Pseudomonas syringae pv. actinidiae (Psa), which poses a major threat to kiwifruit production worldwide. Despite the economic importance of Actinidia chinensis var. chinensis, only a few resistant varieties have been identified to date. In this study, we screened 44 kiwifruit F1 hybrid lines derived from a cross between two A. chinensis var. chinensis lines and identified two offspring with distinct resistance to Psa: resistant offspring RH12 and susceptible offspring SH14. To identify traits associated with resistance, we performed a comparative transcriptomic analysis of these two lines. We identified several highly differentially expressed genes (DEGs) associated with flavonoid synthesis, pathogen interactions, and hormone signaling pathways, which play essential roles in disease resistance. Additionally, using weighted gene co-expression network analysis, we identified six core transcription factors. Moreover, qRT-PCR results demonstrated the high expression of AcC3H1 and AcREM14 in Psa-induced highly resistant hybrid lines. Ultimately, Overexpression of AcC3H1 and AcREM14 in kiwifruit enhanced disease resistance, and this was associated with upregulation of enzymatic activity and gene expression in the salicylic acid (SA) signaling pathway. Our study elucidates a molecular mechanism underlying disease resistance in kiwifruit and contributes to the advancement of research on kiwifruit breeding.

Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images.

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201's performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model.

The anticancer mechanisms of exopolysaccharide from Weissella cibaria D-2 on colorectal cancer via apoptosis induction.

Exopolysaccharide (EPS) from Weissella cibaria has been devoted to the study of food industry. However, the anticancer activity of W. cibaria derived EPS has not yet been investigated. In this study, we obtained the EPS from W. cibaria D-2 isolated from the feces of healthy infants and found that D-2-EPS, a homopolysaccharide with porous web like structure, could effectively inhibit the proliferation, migration, invasion and induce cell cycle arrest in G0/G1 phase of colorectal cancer (CRC) cells. In HT-29 tumor xenografts, D-2-EPS significantly retarded tumor growth without obvious cytotoxicity to normal organs. Furthermore, we revealed that D-2-EPS promoted the apoptosis of CRC cells by increasing the levels of Fas, FasL and activating Caspase-8/Caspase-3, indicating that D-2-EPS might induce apoptosis through the extrinsic Fas/FasL pathway. Taken together, the D-2-EPS has the potential to be developed as a nutraceutical or drug to prevent and treat colorectal cancer.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography.

Background and Objective: Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and Methods: Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. Results: The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. Conclusion: Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.

Exopolysaccharide from Weissella confusa J4-1 inhibits colorectal cancer via induction of cell cycle arrest.

Exopolysaccharide (EPS), a bioproduct of lactic acid bacteria (LAB), has various health-promoting biological activities that may be beneficial for cancer therapy. This in vivo and in vitro study aimed to elucidate the anti-colorectal cancer (CRC) capacity of a homopolysaccharide EPS obtained from Weissella confusa J4-1 (EPSJ4-1) isolated from the faeces of healthy infants. We confirmed that EPSJ4-1 contained glucose and effectively suppressed the proliferation, migration, and invasion of CRC cells. EPSJ4-1 treatment significantly retarded the growth of HT-29 tumour xenografts without causing cytotoxicity to normal organs. EPSJ4-1 exerts an inhibitory effect on cell proliferation by inducing G0/G1 phase cell cycle arrest in CRC cells. Furthermore, EPSJ4-1 upregulated p21 levels and downregulated mutant p53 and cyclin kinase 2 levels. This is the first study to demonstrate the antitumour effects of EPS from W. confusa on CRC via cell cycle arrest and inhibition of cell migration and invasion, suggesting that EPSJ4-1 has the potential to be developed as a nutraceutical or pharmaceutical drug to prevent and treat CRC.

Electromagnetic fields ameliorate hepatic lipid accumulation and oxidative stress: potential role of CaMKKß/AMPK/SREBP-1c and Nrf2 pathways.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, and is related to disturbed lipid metabolism and redox homeostasis. However, a definitive drug treatment has not been approved for this disease. Studies have found that electromagnetic fields (EMF) can ameliorate hepatic steatosis and oxidative stress. Nevertheless, the mechanism remains unclear. METHODS: NAFLD models were established by feeding mice a high-fat diet. Simultaneously, EMF exposure is performed. The effects of the EMF on hepatic lipid deposition and oxidative stress were investigated. Additionally, the AMPK and Nrf2 pathways were analysed to confirm whether they were activated by the EMF. RESULTS: Exposure to EMF decreased the body weight, liver weight and serum triglyceride (TG) levels and restrained the excessive hepatic lipid accumulation caused by feeding the HFD. The EMF boosted CaMKKß protein expression, activated AMPK phosphorylation and suppressed mature SREBP-1c protein expression. Meanwhile, the activity of GSH-Px was enhanced following an increase in nuclear Nrf2 protein expression by PEMF. However, no change was observed in the activities of SOD and CAT. Consequently, EMF reduced hepatic reactive oxygen species (ROS) and MDA levels, which means that EMF relieved liver damage caused by oxidative stress in HFD-fed mice. CONCLUSIONS: EMF may activate the CaMKKß/AMPK/SREBP-1c and Nrf2 pathways to control hepatic lipid deposition and oxidative stress. This investigation indicates that EMF may be a novel therapeutic method for NAFLD.

Value of Radiomic Analysis Combined With Diffusion Tensor Imaging in Early Diagnosis of HIV-Associated Neurocognitive Disorders.

BACKGROUND: The combination of radiomics and diffusion tensor imaging (DTI) may have potential clinical value in the early stage of HIV-associated neurocognitive disorders (HAND). PURPOSE: To investigate the value of DTI-based radiomics in the early stage of HAND in people living with HIV (PLWH). STUDY TYPE: Retrospective. POPULATION: A total of 138 male PLWH were included, including 68 with intact cognition (IC) and 70 with asymptomatic neurocognitive impairment (ANI). Seventy healthy controls (HCs) were recruited for tract-based spatial statistics (TBSS) analysis. All PLWHs were randomly divided into training and validation cohorts at a 7:3 ratio. FIELD STRENGTH/SEQUENCE: A 3 T, single-shot spin-echo echo planar imaging (EPI). ASSESSMENT: The differences between the PLWH groups were compared using TBSS and region of interest (ROI) analysis. Radiomic features were extracted from the corpus callosum (CC) on DTI postprocessed images, including fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD). The performance of the radiomic signatures was evaluated by ROC curve analysis. The radiomic signature with the highest area under the curve (AUC) was combined with clinical characteristics to construct a nomogram. Decision curve analysis (DCA) was performed to evaluate the ability of different methods in discriminating ANI. STATISTICAL TESTS: Chi-square test, independent-samples t test, Kruskal-Wallis test, Mann-Whitney U test, threshold-free cluster enhancement (TFCE), ROC curve analysis, DCA, multivariate logistic regression analysis, Hosmer-Lemeshow test. P < 0.05 with TFCE corrected and P < 0.0001 without TFCE corrected were considered statistically significant. RESULTS: The ANI group showed lower FA and higher AD than the IC group. In the validation cohort, the AUCs of the FA-, AD-, MD- and RD-based radiomic signatures and the clinicoradiomic nomogram were 0.829, 0.779, 0.790, 0.864, and 0.874, respectively. DCA revealed that the nomogram was of greater clinical value than TBSS analysis, the clinical models, and the RD-based radiomic signature. DATA CONCLUSION: The combination of DTI and radiomics is correlated with early stage of HAND in PLWH. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Significantly Affected Lubrication Behavior of Silicone Oil Lubricated Si3N4/Glass Contact after Cleaning with Different Solvents.

Conventional methyl silicone oils have poor lubricating properties in boundary lubrication regions, particularly for ceramic/oxide point contact lubrication. In this study, the residues of various organic solvents on the surfaces of Si3N4 spheres/glass disks were used to determine their effect on the lubricating properties of silicone oil 200. The minute ethanol residues significantly enhanced the antifriction and antiwear properties of silicone oil. Compared to the blank sample, the coefficient of friction (COF) and wear volume of silicone oil 200 with the residual ethanol friction pair were reduced by >40% and >98%, respectively. Being immiscible with silicone oil, the minute ethanol residues also removed impurities from the glass surface and maintained a clean interface, thus effectively blocking direct interactions between the friction pair interfaces. In addition, the residual ethanol reduced the atomic force microscope probe-to-glass surface adhesive force in the silicone oil 200 environment, thus allowing it to maintain low COF and wear rates over a broader range of speeds, loads, and times. In contrast to previous work, this study is the first to effectively regulate the lubrication properties of silicone oil using a residual organic solvent. The findings further verified that the adsorption of vapor molecules can significantly alter the surface forces between interfaces. Thus, adjusting the adhesion force through trace amounts of organic solvent residues may provide novel research inputs, thereby guiding the expansion and scope of silicone oil lubrication applications.

[Automatic identification algorithm of meniscus tear based on radiomics of knee MRI].

Objective: To establish a classification model based on knee MRI radiomics, realize automatic identification of meniscus tear, and provide reference for accurate diagnosis of meniscus injury. Methods: A total of 228 patients (246 knees) with meniscus injury who were admitted between July 2018 and March 2021 were selected as the research objects. There were 146 males and 82 females; the age ranged from 9 to 76 years, with a median age of 53 years. There were 210 cases of meniscus injury in one knee and 18 cases in both knees. All the patients were confirmed by arthroscopy, among which 117 knees with meniscus tear and 129 knees with meniscus non-tear injury. The proton density weighted-spectral attenuated inversion recovery (PDW-SPAIR) sequence images of sagittal MRI were collected, and two doctors performed radiomics studies. The 246 knees were randomly divided into training group and testing group according to the ratio of 7∶3. First, ITK-SNAP3.6.0 software was used to extract the region of interest (ROI) of the meniscus and radiomic features. After retaining the radiomic features with intraclass correlation coefficient (ICC)>0.8, the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for filtering the features to establish an automatic identification model of meniscus tear. The receiver operator characteristic curve (ROC) and the corresponding area under the ROC curve (AUC) was obtained; the model performance was comprehensively evaluated by calculating the accuracy, sensitivity, and specificity. Results: A total of 1 316-dimensional radiomic features were extracted from the meniscus ROI, and the ICC within the group and ICC between the groups of the 981-dimensional radiomic features were both greater than 0.80. The redundant information in the 981-dimensional radiomic features was eliminated by mRMR, and the 20-dimensional radiomic features were retained. The optimal feature subset was further selected by LASSO, and 8-dimensional radiomic features were selected. The average ICC within the group and the average ICC between the groups were 0.942 and 0.920, respectively. The AUC of the training group was 0.889±0.036 [95% CI (0.845, 0.942), P<0.001], and the accuracy, sensitivity, and specificity were 0.873, 0.869, and 0.842, respectively; the AUC of the testing group was 0.876±0.036 [95% CI (0.875, 0.984), P<0.001], and the accuracy, sensitivity, and specificity were 0.862, 0.851, and 0.845, respectively. Conclusion: The model established by the radiomics method has good automatic identification performance of meniscus tear.

Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN.

BACKGROUND: The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis. PURPOSE: This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears. MATERIALS AND METHODS: Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis. RESULTS: Our model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results. CONCLUSIONS: 3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Results: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.

Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning.

Background: Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today's clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network's labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation. Results: We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors. Conclusion: Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.

Estimation of leak area-pressure relationship for cracks on water pipes using models based on linear-elastic fracture mechanics.

The assessment and control of the real losses from water distribution systems require the accurate estimation of the flow rate from an individual leak as a function of the internal pressure. The lack of analytical models able to accurately describe the relationship between the area of the leak and the pressure head is the key problem. This paper utilized the linear-elastic fracture mechanics (LEFM) theory for thin shells to derive models for both longitudinal and circumferential cracks. The models were validated by both finite element (FE) simulations and laboratory experiments under varying crack and pipe parameters. Both fluid-structure interaction (FSI) and traditional FE simulations were performed, and the results were compared to quantify the effect of leakage hydraulics on leak area. In the laboratory experiments, an image analysis technology was utilized to measure the leak area and flow rate simultaneously, so that the effect of the discharge coefficient could be excluded. In addition, the leak area was systematically measured under the effect of different parameters. The results revealed that the values predicted by the derived models were in good agreement with the experimental and FE simulation values for both types of cracks. The LEFM theory and the phenomena observed in this study can improve our understanding of the leak behavior and enable the development of effective pressure management strategies for water distribution systems.

Motor neuron TDP-43 proteinopathy in progressive supranuclear palsy and corticobasal degeneration.

TDP-43 is mislocalized from the nucleus and aggregates within the cytoplasm of affected neurons in cases of amyotrophic lateral sclerosis. TDP-43 pathology has also been found in brain tissues under non-amyotrophic lateral sclerosis conditions, suggesting mechanistic links between TDP-43-related amyotrophic lateral sclerosis and various neurological disorders. This study aimed to assess TDP-43 pathology in the spinal cord motor neurons of tauopathies. We examined 106 spinal cords from consecutively autopsied cases with progressive supranuclear palsy (n = 26), corticobasal degeneration (n = 12), globular glial tauopathy (n = 5), Alzheimer's disease (n = 21) or Pick's disease (n = 6) and neurologically healthy controls (n = 36). Ten of the progressive supranuclear palsy cases (38%) and seven of the corticobasal degeneration cases (58%) showed mislocalization and cytoplasmic aggregation of TDP-43 in spinal cord motor neurons, which was prominent in the cervical cord. TDP-43 aggregates were found to be skein-like, round-shaped, granular or dot-like and contained insoluble C-terminal fragments showing blotting pattern of amyotrophic lateral sclerosis or frontotemporal lobar degeneration. The lower motor neurons also showed cystatin-C aggregates, although Bunina bodies were absent in haematoxylin-eosin staining. The spinal cord TDP-43 pathology was often associated with TDP-43 pathology of the primary motor cortex. Positive correlations were shown between the severities of TDP-43 and four-repeat (4R)-tau aggregates in the cervical cord. TDP-43 and 4R-tau aggregates burdens positively correlated with microglial burden in anterior horn. TDP-43 pathology of spinal cord motor neuron did not develop in an age-dependent manner and was not found in the Alzheimer's disease, Pick's disease, globular glial tauopathy and control groups. Next, we assessed SFPQ expression in spinal cord motor neurons; SFPQ is a recently identified regulator of amyotrophic lateral sclerosis/frontotemporal lobar degeneration pathogenesis, and it is also reported that interaction between SFPQ and FUS regulates splicing of MAPT exon 10. Immunofluorescent and proximity-ligation assays revealed altered SFPQ/FUS-interactions in the neuronal nuclei of progressive supranuclear palsy, corticobasal degeneration and amyotrophic lateral sclerosis-TDP cases but not in Alzheimer's disease, Pick's disease and globular glial tauopathy cases. Moreover, SFPQ expression was depleted in neurons containing TDP-43 or 4R-tau aggregates of progressive supranuclear palsy and corticobasal degeneration cases. Our results indicate that progressive supranuclear palsy and corticobasal degeneration may have properties of systematic motor neuron TDP-43 proteinopathy, suggesting mechanistic links with amyotrophic lateral sclerosis-TDP. SFPQ dysfunction, arising from altered interaction with FUS, may be a candidate of the common pathway.

Mechanical and Processing Enhancement of a Recycled HDPE/PPR-Based Double-Wall Corrugated Pipe via a POE-g-MAH/CaCO3/HDPE Polymer Composite.

With the people's awareness of the "3Rs" in recent years, using recycled high-density polyethylene (HDPE) and random copolymer polypropylene (PPR) as the base materials for piping fabrication has become a mainstream in scholastic path and industrial engineering. In this study, the modified maleic anhydride-grafted polyethylene (POE-g-MAH) compatibilizer was fabricated to increase the interfacial adhesion and dispersion. With the surface modification of calcium carbonate, a POE-g-MAH/CaCO3/HDPE polymer composite has been prepared. Such modified polymer composites can further reinforce the processing performance and mechanical properties of recycled HDPE and PPR materials. The results indicated that with the introduction of the polymer composite, significant enhancement of the recycled materials in the aspects of processability, tensile strength, flexural performance, and impact force could be obtained, and the POE-g-MAH/CaCO3/HDPE polymer composite would contribute to the impressive balance between high rigidity and toughness. In addition, the feasibility and mechanical properties of the recycled HDPE-PPR-POE-g-MAH/CaCO3/HDPE blended system were also studied: with the help of a composite microcapsule, the gap of mechanical capacity between recycled and non-recycled materials was further reduced, and such a blended system was capable of being commercialized in the piping industry.

Genetic analysis of ATP10B for Parkinson's disease in Japan.

Compound heterozygosity of ATP10B is thought to be a risk factor for young-onset Parkinson's disease (PD). We genetically screened 245 patients with young-onset sporadic PD and 33 patients with autosomal recessive PD for ATP10B. All 13 identified gene variants were heterozygous with little evidence of the pathogenicity.