ABSTRACT
BACKGROUND: The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response. METHODS: This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001. RESULTS: For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized. CONCLUSION: The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction. CRITICAL RELEVANCE STATEMENT: This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy. KEY POINTS: This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction.
ABSTRACT
The anti-aging agent TiO2-polyacrylonitrile (PAN) and the mechanical strengthening agent CSW-PAN were prepared by radical polymerization using rutile nano-titanium dioxide (TiO2) and anhydrous calcium sulfate whisker (CSW) as raw materials. The structures of TiO2-PAN and CSW-PAN were characterized using Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Simultaneously, the mechanical properties, aging properties, and thermal stability of TiO2-PAN/CSW-PAN/polypropylene (PP) composites were studied, and the results showed that the surfaces of nano-titanium dioxide and calcium sulfate whiskers were successfully grafted with acrylonitrile. Owing to the introduction of new elements, such as acrylonitrile, nano-titanium dioxide and calcium sulfate whiskers have anti-aging properties. In comparison of the impact strength and tensile strength of TiO2-PAN/PP and TiO2-PAN/CSW-PAN/PP before aging, it can be proven that adding CSW-PAN can significantly enhance the mechanical properties of TiO2-PAN/CSW-PAN/PP. After 1000 h of aging, the tensile strength of the ternary composite TiO2-PAN/CSW-PAN/PP was 19.88 MPa when the addition amount of TiO2-PAN and CSW-PAN was 3%. Moreover, the impact strength of the ternary composite material TiO2-PAN/CSW-PAN/PP after 1000 h of aging is even better than that of non-aging pure PP materials, proving that the service life of improved PP products is extended, unnecessary waste and environmental pollution can be relieved, and the needs of specific engineering fields can be met.
ABSTRACT
Oncogenic KRAS mutations drive an approximately 25 % of all human cancers. Son of Sevenless 1 (SOS1), a critical guanine nucleotide exchange factor, catalyzes the activation of KRAS. Targeting SOS1 degradation has engaged as a promising therapeutic strategy for KRAS-mutant cancers. Herein, we designed and synthesized a series of novel CRBN-recruiting SOS1 PROTACs using the pyrido[2,3-d]pyrimidin-7-one-based SOS1 inhibitor as the warhead. One representative compound 11o effectively induced the degradation of SOS1 in three different KRAS-mutant cancer cell lines with DC50 values ranging from 1.85 to 7.53 nM. Mechanism studies demonstrated that 11o-induced SOS1 degradation was dependent on CRBN and proteasome. Moreover, 11o inhibited the phosphorylation of ERK and displayed potent anti-proliferative activities against SW620, A549 and DLD-1 cells. Further optimization of 11o may provide us promising SOS1 degraders with favorable drug-like properties for developing new chemotherapies targeting KRAS-driven cancers.
Subject(s)
Antineoplastic Agents , Cell Proliferation , Drug Design , SOS1 Protein , Humans , SOS1 Protein/metabolism , SOS1 Protein/antagonists & inhibitors , Antineoplastic Agents/pharmacology , Antineoplastic Agents/chemical synthesis , Antineoplastic Agents/chemistry , Cell Proliferation/drug effects , Structure-Activity Relationship , Cell Line, Tumor , Molecular Structure , Drug Screening Assays, Antitumor , Dose-Response Relationship, Drug , Pyrimidines/pharmacology , Pyrimidines/chemical synthesis , Pyrimidines/chemistry , Pyrimidinones/pharmacology , Pyrimidinones/chemical synthesis , Pyrimidinones/chemistry , Proteolysis Targeting ChimeraABSTRACT
The intensive aquaculture model has resulted in a heightened prevalence of diseases among farmed animals. It is imperative to identify healthy and efficacious alternatives to antibiotics for the sustainable progression of aquaculture. In this investigation, a strain of Lactobacillus acidophilus AC was introduced into the cultural water at varying concentrations (105 CFU/mL, 106 CFU/mL, 107 CFU/mL) to nourish zebrafish (Danio rerio). The findings revealed that L. acidophilus AC effectively increased the growth performance of zebrafish, improved the ion exchange capacity of gills, and enhanced hepatic antioxidant and immune-enzyme activities. Furthermore, L. acidophilus AC notably enhanced the intestinal morphology and augmented the activity of digestive enzymes within the intestinal tract. Analysis of intestinal flora revealed that L. acidophilus AC exerted a significant impact on the intestinal flora community, manifested by a reduction in the relative abundance of Burkholderiales, Candidatus_Saccharibacteria_bacterium, and Sutterellaceae, coupled with an increase in the relative abundance of Cetobacterium. Metabolomics analysis demonstrated that L. acidophilus AC significantly affected intestinal metabolism of zebrafish. PG (i-19:0/PGE2) and 12-Hydroxy-13-O-d-glucuronoside-octadec-9Z-enoate were the metabolites with the most significant up- and down-regulation folds, respectively. Finally, L. acidophilus AC increased the resistance of zebrafish to Aeromonas hydrophila. In conclusion, L. acidophilus AC was effective in enhancing the health and immunity of zebrafish. Thus, our findings suggested that L. acidophilus AC had potential applications and offered a reference for its use in aquaculture.
Subject(s)
Gastrointestinal Microbiome , Lactobacillus acidophilus , Probiotics , Zebrafish , Animals , Zebrafish/immunology , Probiotics/pharmacology , Animal Feed/analysis , Diet/veterinaryABSTRACT
Conventional perovskite solar cells (PSC) built on transparent conductive oxide (TCO) glass face a fundamental challenge to retain fill factor (FF) for large-area upscaling due to series resistance loss. Building a perovskite solar cell on metal has the potential to reduce this FF loss and is promising for flexible applications. However, their efficiency and stability lag far behind their TCO counterparts. Herein, findings on the complex chemical reactions and degradation-promoting processes at different perovskite/metal (Cu, Au, Ag, and Mo) interfaces, which are closely linked with the inherent stability; and the interlayer engineering for perovskite/metal interface's band alignment, which plays an essential role in achieving high efficiency, are reported. Leveraging these findings, 21% power conversion efficiency (PCE) is achieved for 1 cm2 perovskite solar cells using a p-i-n top-illumination structure on a molybdenum substrate, the highest reported for a PSC built on metal. Notably, the FF and PCE losses due to area upscaling are remarkably reduced by one order of magnitude relative to the counterparts on conventional TCO glass, highlighting an alternative pathway for PSC upscaling and module design.
Subject(s)
Calcium Compounds , Metals , Oxides , MolybdenumABSTRACT
Inserting an ultrathin low-conductivity interlayer between the absorber and transport layer has emerged as an important strategy for reducing surface recombination in the best perovskite solar cells. However, a challenge with this approach is a trade-off between the open-circuit voltage (Voc) and the fill factor (FF). Here, we overcame this challenge by introducing a thick (about 100 nanometers) insulator layer with random nanoscale openings. We performed drift-diffusion simulations for cells with this porous insulator contact (PIC) and realized it using a solution process by controlling the growth mode of alumina nanoplates. Leveraging a PIC with an approximately 25% reduced contact area, we achieved an efficiency of up to 25.5% (certified steady-state efficiency 24.7%) in p-i-n devices. The product of Voc × FF was 87.9% of the Shockley-Queisser limit. The surface recombination velocity at the p-type contact was reduced from 64.2 to 9.2 centimeters per second. The bulk recombination lifetime was increased from 1.2 to 6.0 microseconds because of improvements in the perovskite crystallinity. The improved wettability of the perovskite precursor solution allowed us to demonstrate a 23.3% efficient 1-square-centimeter p-i-n cell. We demonstrate here its broad applicability for different p-type contacts and perovskite compositions.
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Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).
Subject(s)
Gadolinium , Myocardial Infarction , Benchmarking , Contrast Media , Heart , Humans , Magnetic Resonance Imaging/methods , Myocardial Infarction/diagnostic imaging , Myocardium/pathologyABSTRACT
BACKGROUND: Acute lung injury (ALI) always leads to severe inflammation. As inflammation and oxidative stress are the common pathological basis of endotoxin-induced inflammatory injury and ischemic reperfusion injury (IRI), we speculate that remote ischemic preconditioning (RIPC) can be protective for ALI when used as remote inflammatory preconditioning (RInPC). METHOD: A total of 21 Sprague-Dawley rats were used for the animal experiments. Eighteen rats were equally and randomly divided into the control (NS injection), LPS (LPS injection), and RInPC groups. The RInPC was performed prior to the LPS injection via tourniquet blockage of blood flow to the right hind limb and adopted three cycles of 5 min tying followed by 5 min untying. Animals were sacrificed 24 hours later. There were 2 rats in the LPS group and 1 in the RInPC group who died before the end of the experiment. Supplementary experiments in the LPS and RInPC groups were conducted to ensure that 6 animals in each group reached the end of the experiment. RESULTS: In the present study, we demonstrated that the RInPC significantly attenuated the LPS-induced ALI in rats. Apoptotic cells were reduced significantly by the RInPC, with the simultaneous improvement of apoptosis-related proteins. Reduction of MPO and MDA and increasing of SOD activity were found significantly improved by the RInPC. Increasing of TNF-α, IL-1ß, and IL-6 induced by the LPS was inhibited, while IL-10 was significantly increased by RInPC, compared to the LPS group. CONCLUSION: RInPC could inhibit inflammation and attenuate oxidative stress, thereby reducing intrinsic apoptosis and providing lung protection in the LPS-induced ALI in rats.
Subject(s)
Acute Lung Injury/immunology , Apoptosis/immunology , Ischemic Preconditioning/methods , Lung/immunology , Signal Transduction/immunology , Acute Lung Injury/chemically induced , Acute Lung Injury/metabolism , Animals , Caspases/immunology , Caspases/metabolism , Cytokines/immunology , Cytokines/metabolism , Lipopolysaccharides , Lung/metabolism , Lung/pathology , Malondialdehyde/immunology , Malondialdehyde/metabolism , Peroxidase/immunology , Peroxidase/metabolism , Proto-Oncogene Proteins c-bcl-2/immunology , Proto-Oncogene Proteins c-bcl-2/metabolism , Rats, Sprague-Dawley , Superoxide Dismutase/immunology , Superoxide Dismutase/metabolism , bcl-2-Associated X Protein/immunology , bcl-2-Associated X Protein/metabolismABSTRACT
PURPOSE: To investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on 11C-methyl-N-2ß-carbomethoxy-3ß-(4-fluorophenyl)-tropanel (11C-CFT) positron emission tomography (PET) imaging. METHODS: Patients with different forms of PDs-including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)-underwent dopamine transporter (DAT) imaging with 11C-CFT PET. A novel multistep computer-aided classification framework-consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification-was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages. RESULTS: Accuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively-with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype. CONCLUSIONS: The proposed computer-aided classification framework based on 11C-CFT PET imaging holds promise for improving the differential diagnosis of PDs.
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It is generally accepted that primary spontaneous pneumothorax (PSP) is found mostly in tall, thin young males. However, the precise etiology of PSP is unclear. We compared some thoracic structural angles of PSPs and controls in young males to determine the predominant factor. CT data of 43 male PSPs (age 21.88 ± 5.30) and 30 controls (age 21.47 ± 5.47) were collected. The angle of thoracic vertebrae slope (â α), sternal slope angle (â ß), and tracheal-sternal stem angle (â γ) were measured in the sagittal position. Carina angle (â δ) was measured in the coronal position. After a propensity score match (PSM) of age and smoking history, the angles were comparatively evaluated. There were significant differences in all four angles between two unmatched groups, while only in three (â ß, â γ, and â δ) after PSM was performed. The correlation between â ß and PSP was most significant, and R2 was 0.456. The prediction accuracy of â ß was 81.3%, OR was 1.386, 95% confidence interval was (1.095-1.754). The sternal slope angles in PSPs are greater than controls indicate the sternal slope angle is associates with the development of PSP and it could be a thoracic structural index to predict the risk of PSP occurrence. Clin. Anat. 33:605-609, 2020. © 2019 Wiley Periodicals, Inc.
Subject(s)
Pneumothorax , Sternum/anatomy & histology , Thoracic Vertebrae/anatomy & histology , Adolescent , Adult , Female , Humans , Male , Propensity Score , Sternum/diagnostic imaging , Thoracic Vertebrae/diagnostic imaging , Tomography, X-Ray Computed , Young AdultABSTRACT
BACKGROUND: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the criteria of PD diagnosis are relatively well defined, current diagnostic procedures using medical images are labor-intensive and expertise-demanding. Hence, highly integrated automatic diagnostic algorithms are desirable. METHODS: In this work, we propose an end-to-end multi-modality diagnostic framework, including segmentation, registration, feature extraction and machine learning, to analyze the features of striatum for PD diagnosis. Multi-modality images, including T1-weighted MRI and 11C-CFT PET, are integrated into the proposed framework. The reliability of this method is validated on a dataset with the paired images from 49 PD subjects and 18 Normal (NL) subjects. RESULTS: We obtained a promising diagnostic accuracy in the PD/NL classification task. Meanwhile, several comparative experiments were conducted to validate the performance of the proposed framework. CONCLUSION: We demonstrated that (1) the automatic segmentation provides accurate results for the diagnostic framework, (2) the method combining multi-modality images generates a better prediction accuracy than the method with single-modality PET images, and (3) the volume of the striatum is proved to be irrelevant to PD diagnosis.
ABSTRACT
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.
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Although invasive thymoma commonly infiltrates neighbouring mediastinal structures, its extension into the superior vena cava (SVC) and consequent SVC occlusion are rare. In such cases, the urgent removal of the thymoma and radical resection of the infiltrated SVC representreasonable options, since induction therapy is time-consuming and useless for symptom resolution. A case of invasive thymoma extending into the SVC and right atrium (RA) with SVC syndrome is reported. The patient underwent a combined resection of the invasive tumor and SVC under cardiopulmonary bypass (CPB), and the SVC and bilateral brachiocephalic vein (BCV) were reconstructed with an autologous pericardial 'Y' conduit. After 40 months of follow-up, the patient showed a patent graft and no tumor recurrence.
Subject(s)
Heart Atria/surgery , Superior Vena Cava Syndrome/surgery , Thymoma/surgery , Thymus Neoplasms/surgery , Aged , Follow-Up Studies , Heart Atria/pathology , Humans , Male , Neoplasm Invasiveness , Prognosis , Superior Vena Cava Syndrome/pathology , Thymoma/pathology , Thymus Neoplasms/pathology , Tomography, X-Ray ComputedABSTRACT
OBJECTIVE: The present study employed 5-aza-2'-deoxycytidine (5-Aza-CdR) to treat non-small cell lung cancer (NSCLC) cell line A549 to investigate the effects on proliferation and expression of the TFPI-2 gene. METHODS: Proliferation was assessed by MTT assay after A549 cells were treated with 0, 1, 5, 10 µmol/L 5-Aza-CdR, a specific demethylating agent, for 24 ,48 and 72h. At the last time point cells were also analyzed by flow cytometry (FCM) to identify any change in their cell cycle profiles. Methylation-specific polymerase chain reaction (MSPCR), real time polymerase chain reaction(real-time PCR) and western blotting were carried out to determine TFPI-2 gene methylation status, mRNA expression and protein expression. RESULTS: MTT assay showed that the growth of A549 cells which were treated with 5-Aza-CdR was significantly suppressed as compared with the control group (0 µmol/L 5-Aza-CdR). After treatment with 0, 1, 5, 10 µmol/L 5-Aza-CdR for 72h, FCM showed their proportion in G0/G1 was 69.7±0.99%, 76.1±0.83%, 83.8±0.35%, 95.5±0.55% respectively (P<0.05), and the proportion in S was 29.8±0.43%, 23.7±0.96%, 15.7±0.75%, 1.73±0.45%, respectively (P<0.05), suggesting 5-Aza-CdR treatment induced G0/G1 phase arrest. MSPCR showed that hypermethylation in the promoter region of TFPI-2 gene was detected in control group (0 µmol/L 5-Aza-CdR), and demethylation appeared after treatment with 1, 5, 10 µmol/L 5-Aza-CdR for 72h. Real-time PCR showed that the expression levels of TFPI-2 gene mRNA were 1±0, 1.49±0.14, 1.86±0.09 and 5.80±0.15 (P<0.05) respectively. Western blotting analysis showed the relative expression levels of TFPI-2 protein were 0.12±0.01, 0.23±0.02, 0.31±0.02, 0.62±0.03 (P<0.05). TFPI-2 protein expression in A549 cells was gradually increased significantly with increase in the 5-Aza-CdR concentration. CONCLUSIONS: TFPI-2 gene promoter methylation results in the loss of TFPI-2 mRNA and protein expression in the non-small cell lung cancer cell line A549, and 5-Aza-CdR treatment could induce the demethylation of TFPI-2 gene promoter and restore TFPI-2 gene expression. These findings provide theoretic evidence for clinical treatment of advanced non-small cell lung cancer with the demethylation agent 5-Aza-CdR. TFPI-2 may be one molecular marker for effective treatment of advanced non-small cell lung cancer with 5-Aza-CdR.