Association between dietary intake of saturated fatty acid subgroups and breast cancer risk.

The impact of dietary saturated fatty acids (SFAs) on breast cancer risk may vary depending on their carbon chain lengths, attributable to the discrepancy in their dietary sources and biological activities. The associations between SFA subgroups classified by chain length and breast cancer risk remain controversial. In this case-control study, we aimed to investigate the association between the dietary intake of SFA subgroups, classified by chain lengths, and odds of breast cancer in China. This study included 1661 cases of breast cancer (confirmed as primary and histologically) and 1674 frequency-matched controls. Face-to-face interviews were used to collect basic information, while dietary intake information was obtained by a food frequency questionnaire. The unconditional logistic regression model was used to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs). All SFA subgroups were inversely associated with odds of breast cancer. The adjusted ORs (95% CIs) were 0.78 (0.61-0.99) for medium-chain SFAs, 0.50 (0.31-0.83) for long even-chain SFAs, 0.69 (0.54-0.88) for long odd-chain, and 0.67 (0.48-0.95) for very long-chain SFAs, respectively. In the restricted cubic spline (RCS) models, a non-linear M-shaped association was observed between long odd-chain SFAs and odds of breast cancer (Pnon-linearity = 0.007). However, the associations of medium-chain SFAs, long even-chain SFAs, and very long-chain SFAs did not reach statistical significance (Pnon-linearity > 0.05). No significant interactions were observed between all these four subgroups of SFAs and menopausal status or BMI. Our findings emphasize the significance of elucidating the associations of dietary SFAs according to chain lengths, providing insights into the etiology as well as the potential benefits of SFA-rich food intake in reducing the risk of breast cancer. Further prospective cohort studies and intervention studies are warranted to confirm these findings and identify the underlying mechanisms of the association between dietary SFAs and breast cancer.

Heritable transcriptional defects from aberrations of nuclear architecture.

Transcriptional heterogeneity due to plasticity of the epigenetic state of chromatin contributes to tumour evolution, metastasis and drug resistance1-3. However, the mechanisms that cause this epigenetic variation are incompletely understood. Here we identify micronuclei and chromosome bridges, aberrations in the nucleus common in cancer4,5, as sources of heritable transcriptional suppression. Using a combination of approaches, including long-term live-cell imaging and same-cell single-cell RNA sequencing (Look-Seq2), we identified reductions in gene expression in chromosomes from micronuclei. With heterogeneous penetrance, these changes in gene expression can be heritable even after the chromosome from the micronucleus has been re-incorporated into a normal daughter cell nucleus. Concomitantly, micronuclear chromosomes acquire aberrant epigenetic chromatin marks. These defects may persist as variably reduced chromatin accessibility and reduced gene expression after clonal expansion from single cells. Persistent transcriptional repression is strongly associated with, and may be explained by, markedly long-lived DNA damage. Epigenetic alterations in transcription may therefore be inherently coupled to chromosomal instability and aberrations in nuclear architecture.

Meta-optics empowered vector visual cryptography for high security and rapid decryption.

Optical encryption is a promising approach to protecting secret information owing to the advantages of low-power consumption, parallel, high-speed, and multi-dimensional processing capabilities. Nevertheless, conventional strategies generally suffer from bulky system volume, relatively low security level, redundant measurement, and/or requirement of digital decryption algorithms. Here, we propose a general optical security strategy dubbed meta-optics-empowered vector visual cryptography, which fully exploits the abundant degrees of freedom of light as well as the spatial dislocation as key parameters, significantly upgrading the security level. We also demonstrate a decryption meta-camera that can implement the reversal coding procedure for real-time imaging display of hidden information, avoiding redundant measurement and digital post-processing. Our strategy features the merits of a compact footprint, high security, and rapid decryption, which may open an avenue for optical information security and anti-counterfeiting.

Clinical efficacy of combination therapy of FuXi-Tiandi-Wuxing Decoction and anti-viral drugs in the treatment of novel coronavirus pneumonia: A prospective interventional study.

Introduction: The National Administration of Traditional Chinese Medicine of the People's Republic of China (NATCM) and the State Administration of Traditional Chinese medicine (TCM) advocated a combination therapy of TCM and anti-viral drugs for novel coronavirus pneumonia (NCP) to improve the efficacy of clinical treatment. Methods: Forty-six patients diagnosed with NCP were sequentially divided into intent-to-treat population: the experimental group (combination of FuXi-Tiandi-Wuxing Decoction and anti-viral drugs; n = 23) and the control group (anti-viral drugs only) (n = 23). The two groups were compared in terms of duration of fever, cough symptom score, fatigue, appetite, dyspnea, out-of-bed activities, chest computer tomography (CT) recovery, virological clearance, average length of hospital stay, and clinical effective rate of drug. After 6 days of observation, patients from the control group were divided into as-treated population: experimental subgroup (n = 14) to obtain clinical benefit and control subgroup (n = 9). Results: There was a significant improvement in the duration of fever (1.087 ± 0.288 vs 4.304 ± 2.490), cough (0.437 ± 0.589 vs 2.435 ± 0.662; P < 0.05), chest CT evaluation (82.6% vs 43.4%; P < 0.05), and virological clearance (60.8% vs 8.7%; P < 0.05) in patients of the experimental group compared with patients in the control group. Further observation in as-treated population reported that cough (0.742 ± 0.463 vs 1.862 ± 0.347; P < 0.05) and fatigue (78.5% vs 33.3%; P < 0.05) were significantly relieved after adding FuXi-Tiandi-Wuxing Decoction to the existing treatment. Conclusion: An early treatment with combination therapy of FuXi-Tiandi-Wuxing Decoction and anti-viral drugs significantly relieves the clinical symptoms of NCP, shows improvement in chest CT scan, improves virological clearance, shortens average length of hospital stay, and reduces the risk of severe illness. The effect of FuXi-Tiandi-Wuxing Decoction in NCP may be clinically important and require further consideration.

Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis.

PURPOSE: Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification. METHODS: We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification. RESULTS: The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD. CONCLUSION: Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks.

Efficient exosome subpopulation isolation and proteomic profiling using a Sub-ExoProfile chip towards cancer diagnosis and treatment.

Exosomes have been extensively studied as liquid biopsy biomarkers in the past decade. However, the origin and molecular heterogeneity of exosomes hinder the research development moving from proof-of-concept to clinical applications. Herein, we report an integrated microfluidic platform termed Sub-ExoProfile chip, to achieve the selective isolation and subsequent proteomic profiling of multiplex exosome subpopulations simultaneously. The Sub-ExoProfile chip comprises three cylindrical self-assembled nanopillars, on which specific exosome capture antibodies (CD81, EpCAM, HER2) were immobilized to capture and sort different exosome subpopulations. It is worth noting that the 3D porous nanopillars afford enhanced interface binding efficiency; thus, a tumor-specific exosome subpopulation with lowly-expressed surface marker was still isolated with satisfactory capture efficiency. Moreover, the amphiphilic mesoporous silica nanoparticle self-assembled nanopillars also served as a nanoreactor for the enrichment and in situ digestion of exosomal proteins, providing improved performance for the mass-spectrometry based molecular characterization of exosome subpopulations. The Sub-ExoProfile chip was investigated on standard exosome samples from different breast cancer cell lines. The isolation and quantitative detection of exosome subpopulations were in line with the molecular subtype of breast cancer cell lines, and the molecular makeup was confirmed using classic microplate ELISA. Clinical samples from HER2-positive and triple-negative breast cancer patients were also examined using the Sub-ExoProfile chip. The quantitative results of three exosome subpopulations distinguished the three subtypes of breast cancer significantly. Most importantly, the molecular characterization of three exosome subpopulations revealed that the distinct exosome subpopulation participated in a different signaling pathway and performed distinct biological functions. It is envisioned that the analysis of the exosome subpopulation on the Sub-ExoProfile chip will facilitate the screening of tumor-specific exosomal biomarkers and open a new avenue for exosome-based liquid biopsy.

Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification.

The diagnosis of chest diseases is a challenging task for assessing thousands of radiology subjects. Their diagnosis decisions heavily rely on the expert radiologists' manual annotations. It is important to develop automated analysis methods for the computer-aided diagnosis of chest diseases on chest radiography. To explore the label relationship and improve the diagnosis performance, we present an end-to-end multi-label learning framework for jointly modeling the global and local label correlation, called GL-MLL that (1) explores the label correlation from a globally static view and a locally adaptive view, (2) considers the imbalanced class distribution, and (3) focuses on capturing label-specific features in image-level representation. We validate the performance of the proposed framework on the CheXpert dataset. The results demonstrate that the proposed GL-MLL outperforms state-of-the-art approaches. The code is available at https://github.com/llt1836/GL-MLL.

Genetic Analysis of Patients With Early-Onset Parkinson's Disease in Eastern China.

Background: Genetic factors play an important role in the pathogenesis of early-onset Parkinson's disease (EOPD). To date, more than 20 pathogenic genes associated with Parkinson's disease (PD) have been identified. This study aims to explore the mutation spectrum of EOPD and the clinical characteristics of mutation carriers in eastern China. Methods: We recruited 155 unrelated EOPD patients, including 8 familial and 147 sporadic EOPD (age at onset ≤ 50 years). Overall, 24 known PD-associated genes were detected by whole exome sequencing and multiplex ligation-dependent probe amplification (MLPA) from patient samples. The genetic and clinical characteristics of pathogenic/likely pathogenic (P/LP) loci in this cohort were analyzed. Results: Overall, 14 (9.03%) patients were detected with P/LP variants distributed in seven genes. The most frequent mutation occurred in PRKN (7/155, 4.52%), followed by LRRK2 (2/155, 1.29%), SNCA, CHCHD2, TMEM230, DNAJC13 and PLA2G6 (1/155, 0.64%, respectively). Exon rearrangement mutations accounted for 57.9% (11/19) of all mutations in PRKN. Four novel variants were detected: c.14T > C (p.M5T) in SNCA, c.297C > A (p.Y99X) in CHCHD2, c.2578C > T (p.R860C) in DNAJC13 and c.4C > T (p.Q2X) in TMEM230. We found the first case of LRRK2 c.6055G > A (p.G2019S) mutation in Chinese population. The median onset age of patients with P/LP mutations in autosomal recessive genes (PRKN and PLA2G6) was about 18.0 years earlier than patients without mutation. The proportion of patients with mutations were 63.64%, 27.03% and 9.68% when patients were stratified according to the age of onset at ≤ 30, ≤ 40 and ≤ 50 years, respectively. Conclusion: Early-onset Parkinson's disease patients from eastern China present a regional specific mutation spectrum. Analysis of larger patient cohorts is required to support these findings, and mechanistic studies of the four novel missense/non-sense mutations will clarify their role in the pathogenicity of EOPD.

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson's Disease.

Background: Depression, one of the most frequent non-motor symptoms in Parkinson's disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression. Methods: We performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD. Results: We observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN. Conclusion: Our results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.

Factors predicting the occurrence of net adverse clinical and cerebral events in patients with acute coronary syndrome treated with clopidogrel or ticagrelor in combination with aspirin: a real-world study.

Background: This study aimed to establish the factors influencing the clinical benefits of ticagrelor and clopidogrel for acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) surgery. Methods: A multicenter, retrospective, real-world study was conducted on patients with ACS whose data were sourced from 3 databases, namely the BRIC-ACS(I) study, COSTIC study, and 301 Hospital PCI patient database from January 2014 to October 2017. The primary endpoint was net adverse clinical and cerebral events (NACCE). Results: A total of 7,236 ACS patients were included, of which 4,444 patients (61.4%) and 2,792 patients (38.6%) were in the clopidogrel dual antiplatelet therapy (DAPT) group and ticagrelor DAPT group, respectively. The hazard ratio (HR) for NACCE was significantly higher in patients aged ≥65 years than those aged ≤65 years in the clopidogrel DAPT group (HR: 2.15, 95% CI: 1.68-2.76) and ticagrelor DAPT group (HR: 1.75, 95% CI: 1.34-2.29). In patients treated with clopidogrel DAPT, patients with unstable angina had a significantly lower HR for NACCE than patients with ST-elevation myocardial infarction. Use of beta blockers (HR: 0.77, 95% CI: 0.60-0.99) was an influencing factor in patients treated with clopidogrel DAPT, whereas in patients treated with ticagrelor DAPT, only smoking status (HR: 0.75, 95% CI: 0.57-0.99) was a significant influencing factor. Conclusions: Age, hypertension status, and presence or absence of unstable angina were factors influencing the composite outcome of NACCE. The selection of patients to be treated with either clopidogrel DAPT or ticagrelor DAPT depending on the presence or absence of factors influencing treatment outcome may improve therapeutic management.

Differential functional connectivity of insular subdivisions in de novo Parkinson's disease with mild cognitive impairment.

The insula, consisting of functionally diverse subdivisions, plays a significant role in Parkinson's disease (PD)-related cognitive disorders. However, the functional connectivity (FC) patterns of insular subdivisions in PD remain unclear. Our aim is to investigate the changes in FC patterns of insular subdivisions and their relationships with cognitive domains. Three groups of participants were recruited in this study, including PD patients with mild cognitive impairment (PD-MCI, n = 25), PD patients with normal cognition (PD-NC, n = 13), and healthy controls (HCs, n = 17). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in insular subdivisions of the three groups. Moreover, all participants underwent a neuropsychological battery to assess cognition so that the relationship between altered FC and cognitive performance could be elucidated. Compared with the PD-NC group, the PD-MCI group exhibited increased FC between the left dorsal anterior insular (dAI) and the right superior parietal gyrus (SPG), and altered FC was negatively correlated with memory and executive function. Compared with the HC group, the PD-MCI group showed significantly increased FC between the right dAI and the right median cingulate and paracingulate gyri (DCG), and altered FC was positively related to attention/working memory, visuospatial function, and language. Our findings highlighted the different abnormal FC patterns of insular subdivisions in PD patients with different cognitive abilities. Furthermore, dysfunction of the dAI may partly contribute to the decline in executive function and memory in early drug-naïve PD patients.

TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis.

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.

Characterizing mild cognitive impairment in prodromal Parkinson's disease: A community-based study in China.

OBJECTIVE: The International Parkinson and Movement Disorder Society (MDS) has published research criteria for prodromal Parkinson's disease (pPD), which includes cognitive impairment as a prodromal marker. However, the clinical features of mild cognitive impairment (MCI) in pPD remain unknown. Our study aimed to evaluate the frequency and clinical features of mild cognitive impairment of pPD in the elderly in China. METHODS: The cross-sectional community-based study recruited 2688 participants aged ≥50 years. Subjects were diagnosed with pPD according to the MDS criteria. Overall, 39 pPD and 22 healthy controls underwent comprehensive clinical and neuropsychological assessment. MCI was also diagnosed by the MDS criteria. Next, we investigated the relationship between clinical factors and cognition. RESULTS: Among the 2,663 dementia-free and Parkinson disease (PD)-free participants, 55 met the criteria for pPD (2.1%) and 23 pPD met the criteria for MCI. Memory, attention/working memory, and executive function were the most frequent impaired domains, and amnestic MCI multidomain phenotype was the most frequent MCI subtype (69.57%) in pPD. Additionally, correlation analysis revealed that the global cognitive performance was negatively related to UPDRS-III score (r = -0.456, p = 0.004). CONCLUSION: MCI, specifically impairment in memory, attention/working memory, and executive domain, is present at the prodromal stage of PD. In addition, cognitive performance is correlated with motor symptoms in pPD. Our results reflect that cognitive profile, combined with motor symptoms, can help clinicians to identify individuals with pPD early, as those would be the optimal candidates for neuroprotective therapy.

Comparison of Net Clinical Benefit Between Clopidogrel and Ticagrelor Following Percutaneous Coronary Intervention in Patients in China With Acute Coronary Syndrome.

INTRODUCTION: The objective of the present study was to evaluate the difference in net clinical benefit of clopidogrel plus aspirin compared with ticagrelor plus aspirin after 12 months in patients in mainland China with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) with newer-generation drug-eluting stents (DESs). METHODS: In this multicenter, retrospective, real-world study, the data were sourced from three databases: BRIC-ACS(I) study, COSTIC study, and 301 Hospital PCI patient database from January 2014 to October 2017. The primary endpoint of the study was net adverse clinical and cerebral events (NACCE) comprised of all-cause death, non-fatal myocardial infarction (MI), non-fatal stroke or Bleeding Academic Research Consortium (BARC) type ≥ 2 (excluding BARC type = 4) bleeding, whereas the secondary end point was evaluation of major adverse cardiovascular events (MACE) and BARC type ≥ 2 bleeding events. RESULTS: A total of 7862 ACS patients were included in the final analysis, of whom propensity score matching (PSM) analysis yielded 2165 patients in each cohort. After PSM analysis, cumulative incidence of NACCE was significantly lower with clopidogrel and aspirin than with ticagrelor and aspirin [117 (5.4%) vs. 180 (8.3%), P < 0.001] at 12 months. Effect estimates showed reduced risk of NACCE occurrence in patients treated with clopidogrel and aspirin [adjusted hazard ratio (aHR): 0.61, 95% CI 0.48-0.77, P < 0.001]. Incidence of bleeding was significantly lower in the clopidogrel cohort than in the ticagrelor cohort (aHR: 0.48, 95% CI 0.35-0.66, P < 0.001). Clopidogrel and aspirin therapy was comparable to ticagrelor and aspirin in reducing the incidence of MACE after PSM analysis. CONCLUSION: In Chinese ACS patients who underwent PCI with second-generation DESs, outpatient use of clopidogrel dual antiplatelet therapy (DAPT) was associated with reduction in NACCE and bleeding.

Modelled Economic Analysis for Dacomitinib-A Cost Effectiveness Analysis in Treating Patients With EGFR-Mutation-Positive Non-Small Cell Lung Cancer in China.

OBJECTIVES: To establish the cost-effectiveness of dacomitinib compared to gefitinib from the Chinese healthcare system perspective. PATIENTS: Advanced non-small cell lung cancer (NSCLC) harbouring epidermal growth factor receptor (EGFR) mutations. METHODS: Partitioned survival analysis was undertaken to examine the cost-effectiveness of dacomitinib utilising individual patient data (IPD) from the pivotal randomised controlled trial (RCT) (ARCHER 1050). The three health states modelled were progression-free, post-progression, and death. Parametric survival distributions were fitted to IPD against the Kaplan-Meier survival curves corresponding to progression-free survival (PFS) and overall survival (OS) outcomes by randomised groups. Costs included drug acquisition and administration, outpatient management (outpatient consultation and examinations), and best supportive care costs. Utility weights were sourced from the pivotal trial and other published literature. The incremental cost-effectiveness ratio (ICER) was calculated with costs and quality-adjusted life years (QALYs) discounted at an annual rate of 5%. Both deterministic and probabilistic sensitivity analyses were undertaken. RESULTS: In the base case, dacomitinib (CNY 265,512 and 1.95 QALY) was associated with higher costs and QALY gains compared to gefitinib (CNY 247,048 and 1.61 QALYs), resulting in an ICER of CNY 58,947/QALY. Using the empirical WTP/QALY threshold, dacomitinib is a cost-effective treatment strategy for patients with EGFR-mutation-positive advanced NSCLC. The probabilistic sensitivity analysis suggested that dacomitinib had a 97% probability of being cost-effective. CONCLUSIONS: Dacomitinib is a cost-effective treatment strategy in treating patients with EGFR-mutation-positive NSCLC from the Chinese healthcare system perspective. The uncertainty around the cost-effectiveness of dacomitinib could be reduced if long-term survival data become available. CLINICAL TRIAL REGISTRATION: NCT01024413.

Serum miR-214 Serves as a Biomarker for Prodromal Parkinson's Disease.

Circulating microRNAs (miRNAs) have been proposed to be accessible biomarkers for Parkinson's disease (PD). However, there is a lack of known miRNAs that can serve as biomarkers for prodromal PD (pPD). We previously identified that miR-31 and miR-214 were dysregulated in PD. The aim of this study was to explore the roles of miR-31 and miR-214 in pPD. We recruited 25 pPD patients, 20 patients with de novo PD (dnPD), 24 advanced PD (aPD) patients and 21 controls. Next, we investigated the expression of miR-31 and miR-214. Compared to controls, miR-214 was found to be significantly upregulated in pPD patients while miR-31 was significantly upregulated in aPD patients. In addition, the expression of miR-214 was lower in aPD patients compared to both dnPD or pPD patients, while the expression of miR-31 was higher in aPD patients compared to dnPD patients. In order to predict pPD via miRNA expression, the receiver operating characteristic curve was constructed and the area under curve (AUC) was calculated. For pPD prediction by miR-214, the AUC was 0.756. The optimal cut-off value of miR-214 was 0.1962, and the sensitivity and specificity were 72.0% and 76.2%, respectively. On the other hand, the AUC for aPD detection by miR-31 was 0.744. The optimal cut-off value for miR-31 was 0.0148, with a sensitivity of 87.5% and a specificity of 71.4%. In conclusion, miR-214 can distinguish pPD patients from controls and may be used as a potential biomarker for pPD diagnosis.

Cost-effectiveness Analysis of Prophylaxis Versus On-demand Treatment for Children With Hemophilia B Without Inhibitors in China.

PURPOSE: Hemophilia B (HB) is a hereditary bleeding disorder caused by a deficiency of coagulation factor IX (FIX), which represents 15% to 20% of all patients with hemophilia. Clinical studies have found significant benefits of prophylaxis treatment with FIX versus on-demand (OD) treatment. However, these benefits are associated with an increase in FIX consumption and a considerable increase in cost. Most Chinese children with HB receive OD treatment. Only a small proportion of patients with HB receive prophylaxis treatment in China. The patients with inhibitors could result in more complicated bleeding events, joint status, or treatment patterns. The objective of this study is to assess the cost-effectiveness of prophylaxis compared with OD treatment in children with HB without inhibitors from the Chinese health care perspective. METHODS: A Markov model was used to analyze cost-effectiveness by comparing prophylaxis with OD treatment. The model uses a 17-year time horizon with a yearly cycle. Transition probabilities and utility weights were estimated using published studies. The cost data for patients with HB were collected from Beijing Children's Hospital and Capital Medical University. One-way and probabilistic sensitivity analyses were performed to assess the robustness of the results. FINDINGS: The model projects that prophylaxis has an incremental 1.23 quality-adjusted life-years (QALYs). The incremental cost per QALY gained for individuals with HB receiving prophylaxis was ¥155,709.80 ($23,530.36) compared with the OD group, which is under the willingness-to-pay threshold (3 times the gross domestic product per capital according to the World Health Organization recommendations) in China of ¥193,932 ($29,306.37). Moreover, 1-way sensitivity analysis found that the results were sensitive to the utility of nonarticular bleeding. The lower this parameter is, the higher the probability is of the incremental cost-effectiveness ratio for prophylaxis not being cost-effective. This finding infers that when the patients have a better QALY (higher utility) at the beginning, the cost for benefit from prophylaxis treatment is lower. The results of the probabilistic sensitivity analyses indicate that the probability of prophylaxis being cost-effective is 89.3%. IMPLICATIONS: Although prophylaxis is a costly treatment, the results of this study indicate that it is cost-effective compared with OD treatment for children with HB in China.

MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.

Lung cancer is one of the most common and deadly malignant cancers. Accurate lung tumor segmentation from CT is therefore very important for correct diagnosis and treatment planning. The automated lung tumor segmentation is challenging due to the high variance in appearance and shape of the targeting tumors. To overcome the challenge, we present an effective 3D U-Net equipped with ResNet architecture and a two-pathway deep supervision mechanism to increase the network's capacity for learning richer representations of lung tumors from global and local perspectives. Extensive experiments on two real medical datasets: the lung CT dataset from Liaoning Cancer Hospital in China with 220 cases and the public dataset of TCIA with 422 cases. Our experiments demonstrate that our model achieves an average dice score (0.675), sensitivity (0.731) and F1-score (0.682) on the dataset from Liaoning Cancer Hospital, and an average dice score (0.691), sensitivity (0.746) and F1-score (0.724) on the TCIA dataset, respectively. The results demonstrate that the proposed 3D MSDS-UNet outperforms the state-of-the-art segmentation models for segmenting all scales of tumors, especially for small tumors. Moreover, we evaluated our proposed MSDS-UNet on another challenging volumetric medical image segmentation task: COVID-19 lung infection segmentation, which shows consistent improvement in the segmentation performance.

Consistency and Stability of Motor Subtype Classifications in Patients With de novo Parkinson's Disease.

OBJECTIVE: Patients with Parkinson's disease (PD) are commonly classified into subtypes based on motor symptoms. The aims of the present study were to determine the consistency between PD motor subtypes, to assess the stability of PD motor subtypes over time, and to explore the variables influencing PD motor subtype stability. METHODS: This study was part of a longitudinal study of de novo PD patients at a single center. Based on three different motor subtype classification systems proposed by Jankovic, Schiess, and Kang, patients were respectively categorized as tremor-dominant/indeterminate/postural instability and gait difficulty (TD/indeterminate/PIGD), TD S /mixed S /akinetic-rigid S (ARS), or TD K /mixed K /AR K at baseline evaluation and then re-assessed 1 month later. Demographic and clinical characteristics were recorded at each evaluation. The consistency between subtypes at baseline evaluation was assessed using Cohen's kappa coefficient (κ). Additional variables were compared between PD subtype groups using the two-sample t-test, Mann-Whitney U-test or Chi-squared test. RESULTS: Of 283 newly diagnosed, untreated PD patients, 79 were followed up at 1 month. There was fair agreement between the Jankovic, Schiess, and Kang classification systems (κ S = 0.383 ± 0.044, κ K = 0.360 ± 0.042, κ SK = 0.368 ± 0.038). Among the three classification systems, the Schiess classification was the most stable and the Jankovic classification was the most unstable. The non-motor symptoms questionnaire (NMSQuest) scores differed significantly between PD patients with stable and unstable subtypes based on the Jankovic classification (p = 0.008), and patients with a consistent subtype had more severe NMSQuest scores than patients with an inconsistent subtype. CONCLUSION: Fair consistency was observed between the Jankovic, Schiess, and Kang classification systems. For the first time, non-motor symptoms (NMSs) scores were found to influence the stability of the TD/indeterminate/PIGD classification. Our findings support combining NMSs with motor symptoms to increase the effectiveness of PD subtypes.

Altered Dynamic Functional Connectivity in de novo Parkinson's Disease Patients With Depression.

BACKGROUND: Depression is one of the most prevalent and disturbing non-motor symptoms in Parkinson's disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD). METHODS: We performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics. RESULTS: Four distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network. CONCLUSIONS: Our study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.