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The glymphatic system (GS) is a newly discovered brain anatomy. Its discovery improves our understanding of brain fluid flow and waste removal paths and provides an anatomical basis for the flow of cerebral interstitial fluid (ISF) and cerebrospinal fluid (CSF). GS occurs through a normal exchange within perivascular space (PVS), facilitating the elimination of metabolic wastes generated by nerve cells from the brain. Therefore, the GS is mainly responsible for the removal of metabolic waste. Reduced GS activity has been observed to be associated with central nervous system disorders such as cerebral small vessel disease (CSVD) and neurodegenerative diseases. Its activity is expected to be an indicator for diagnosing diseases and predicting their prognosis. This review introduces the magnetic resonance imaging (MRI) technology related to the GS suitable for clinical use and the difference in the system's activity in normal and abnormal states. Through a summary of previous research, imaging methods suitable for monitoring the activity of the GS in the clinic were proposed, and their diagnostic effect on different brain disorders was analyzed. This review aims to clarify ideas for the clinical translation of basic research focusing on GS and provide future clinical research directions and perspectives.

IntroductionClinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. However, existing research platforms for medical image informatics have limited support for Artificial Intelligence (AI) algorithms and clinical applications.MethodsTo this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios. It has several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc.Results and discussionSenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. It also facilitates the data annotation and model training processes, which makes it easier for clinical researchers to develop and deploy customized AI models. In addition, it is clinic-oriented and supports various clinical applications such as diagnosis and surgical planning for lung cancer, liver tumor, coronary artery disease, etc. By simplifying AI-based medical image analysis, SenseCare has a potential to promote clinical research in a wide range of disease diagnosis and treatment applications.

There is a growing interest and adoption of 7 Tesla (T) magnetic resonance imaging (MRI) in the field of medicine and research. In the domain of neuroimaging, 7T MRI shows notable advantages over lower field strength MRI systems by offering improved visualization of anatomical structures, enhanced lesion conspicuity, and better characterization of pathological processes. Cerebrovascular disease, which involves a spectrum of etiologies from large artery abnormalities to small vessel disease, is a leading cause of morbidity and mortality worldwide. Imaging plays an indispensable role in the diagnosis and treatment of cerebrovascular diseases. The excellence in imaging capabilities of 7T MRI can achieve multi-scale, high-precision imaging requirements from large artery disease assessment to small vessel disease assessment, which presents a variety of clinical applications and significant potential for clinical transformation. In this review, we firstly reviewed the literature focusing on technique aspects, comparing 7T with the clinically well-established 3T and 1.5T MRI systems. Then, we reviewed published studies to showcase the state-of-the-art progress in the assessment of cerebrovascular disease at 7T. Additionally, we discussed the challenges and perspectives of 7T techniques.

IntroductionHemorrhagic transformation (HT) is a serious complication that can occur spontaneously after an acute ischemic stroke (AIS) or after a thrombolytic/mechanical thrombectomy. Our study aims to explore the potential correlations between fibrinogen levels and the occurrence of spontaneous HT (sHT) and HT after mechanical thrombectomy (tHT).MethodsA total of 423 consecutive AIS patients diagnosed HT who did not undergone thrombolysis and 423 age- and sex-matched patients without HT (non-HT) were enrolled. Fibrinogen levels were measured within 24 h of admission after stroke. The cohorts were trisected according to fibrinogen levels. The HT were further categorized into hemorrhagic infarction (HI) or parenchymal hematoma (PH) based on their imaging characteristics.ResultsIn sHT cohort, fibrinogen levels were higher in HT patients than non-HT patients (p

BackgroundImproving health care in cities with a diverse, international population is crucial for ensuring health equity, particularly for foreigners facing challenges due to cultural and language barriers. This situation is especially relevant in China, a major destination for expatriates and travelers, where optimizing health care services and incorporating international standards in the public sector are vital. Achieving this involves understanding the operational details, cultural and linguistic nuances, and advancing medical digitalization. A strategic approach focusing on cultural competence and awareness of health care systems is essential for effectively navigating health care for foreigners and expatriates in China. ObjectiveThe aim of this study was to perform an in-depth analysis of the subjective and objective experiences of local and international patients in public hospitals in China to provide a basis for enhancing the medical experience of all patients. MethodsA structured questionnaire was provided to patients at an international outpatient service of a top-tier university hospital in China. Qualitative analysis of the survey responses was performed to methodically categorize and analyze medical treatment, focusing on patient demand and satisfaction across four main category elements (“high demand, high satisfaction”; “high demand, low satisfaction”; “low demand, high satisfaction”; and “low demand, low satisfaction”), enabling a detailed cross-sectional analysis to identify areas for improvement. ResultsElements falling under “high demand, high satisfaction” for both Chinese and international patients were primarily in the realms of medical quality and treatment processes. In contrast, elements identified as “high demand, low satisfaction” were significantly different between the two patient groups. ConclusionsThe findings highlight the importance of systematic, objective research in advancing the quality of international health care services within China’s leading academic medical centers. Key to this improvement is rigorous quality control involving both patients and providers. This study highlights the necessity of certifying such centers and emphasizes the role of digital platforms in disseminating information about medical services. This strategy is expected to cater to diverse patient needs, enhancing the overall patient experience. Furthermore, by developing comprehensive diagnosis and treatment services and highlighting the superior quality and costs associated with international health care, these efforts aim to foster a sense of belonging among international patients and increase the attractiveness of China’s medical services for this demographic.

Abstract Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples, e.g., in-context learning. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks. In this paper, we aim at approaches adapting the foundation models for medical image classification and present a novel dataset and benchmark for the evaluation, i.e., examining the overall performance of accommodating the large-scale foundation models downstream on a set of diverse real-world clinical tasks. We collect five sets of medical imaging data from multiple institutes targeting a variety of real-world clinical tasks (22,349 images in total), i.e., thoracic diseases screening in X-rays, pathological lesion tissue screening, lesion detection in endoscopy images, neonatal jaundice evaluation, and diabetic retinopathy grading. Results of multiple baseline methods are demonstrated using the proposed dataset from both accuracy and cost-effective perspectives.

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction and behavioral impairment. We aimed to use principal components factor analysis to explore the association between gait domains and AD under single and dual-task gait assessments. Methods: A total of 41 AD participants and 41 healthy control (HC) participants were enrolled in our study. Gait parameters were measured using the JiBuEn® gait analysis system. The principal component method was used to conduct an orthogonal maximum variance rotation factor analysis of quantitative gait parameters. Multiple logistic regression was used to adjust for potential confounding or risk factors. Results: Based on the factor analysis, three domains of gait performance were identified both in the free walk and counting backward assessments: “rhythm” domain, “pace” domain and “variability” domain. Compared with HC, we found that the pace factor was independently associated with AD in two gait assessments; the variability factor was independently associated with AD only in the counting backwards assessment; and a statistical difference still remained after adjusting for age, sex and education levels. Conclusions: Our findings indicate that gait domains may be used as an auxiliary diagnostic index for Alzheimer’s disease.

Abstract Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.

Abstract Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

ObjectiveTo explore the common differential flora of IgAN, Kawasaki disease and IgA vasculitis by screening and analyzing the differential intestinal flora between the three disease groups of IgAN, Kawasaki disease and IgA vasculitis and their healthy controls.MethodsPapers on 16srRNA sequencing-related intestinal flora of IgAN, Kawasaki disease and IgA vasculitis were searched in databases, the literature was systematically collated and analysed, the original data was download from the relevant databases, and then the operational taxonomic unit and species classification analysis were performed. Besides, Alpha diversity analysis and Beta diversity analysis were performed to screen for IgAN, Kawasaki disease and I1gA vasculitis groups and finally compare the common intestinal differential flora among the three groups.ResultsAmong the common differential flora screened, Lachnospiracea_incertae_sedis was lower in both the IgAN and Kawasaki disease groups than in the respective healthy controls; Coprococcus was low in the IgAN group but high in the IgA vasculitis group. Fusicatenibacter was lower in both the Kawasaki disease and IgA vasculitis groups than in their respective healthy controls, and Intestinibacter was low in the Kawasaki disease group, but its expression was high in the IgA vasculitis group.ConclusionThe dysbiosis of the intestinal flora in the three groups of patients with IgAN, Kawasaki disease and IgA vasculitis, its effect on the immunity of the organism and its role in the development of each disease group remain unclear, and the presence of their common differential flora may further provide new ideas for the association of the pathogenesis of the three diseases.

ObjectiveDyslipidemia is one of the major public health problems in China. It is characterized by multisystem dysregulation and inflammation, and oxidant/antioxidant balance has been suggested as an important factor for its initiation and progression. The objective of this study was to determine the relationship between prevalence of dyslipidemia and measured changes in the levels of proinflammatory cytokines (IL-6, TNF-a, and MCP-1), thiobarbituric acid-reactant substances (TBARS), and serum total antioxidant capacity (TAC) in serum samples.Study designA cross-sectional survey with a purposive sampling of 2,631 enrolled participants (age 18–85 years) was performed using the adult population of long-term residents of the municipality of east coast China in Fujian province between the years 2017 and 2019. Information on general health status, dyslipidemia prevalence, and selected mediators of inflammation was collected through a two-stage probability sampling design according to socioeconomic level, sex, and age.MethodsThe lipid profile was conducted by measuring the levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) with an autoanalyzer. Dyslipidemia was defined according to National Cholesterol Education Program Adult Treatment Panel III diagnostic criteria, and patients with it were identified by means of a computerized database. Serum parameters including IL-6/TNF-a/MCP-1, TBARS, and TAC were measured in three consecutive years. Familial history, education level, risk factors, etc. were determined. The association between dyslipidemia and serum parameters was explored using multivariable logistic regression models. Sociodemographic, age, and risk factors were also investigated among all participants.ResultsThe mean prevalence of various dyslipidemia in the population at baseline (2017) was as follows: dyslipidemias, 28.50%; hypercholesterolemia, 26.33%; high LDL-C, 26.10%; low HDL-C, 24.44%; and hypertriglyceridemia, 27.77%. A significant effect of aging was found among all male and female participants. The mean levels of serum Il-6/TNF-a/MCP-1 were significantly higher in all the types of dyslipidemia among male participants. Female participants with all types of dyslipidemia but low HDL-C showed an elevation of IL-6 and MCP-1 levels, and those with dyslipidemias and hypercholesterolemia presented higher levels of TNF-a compared to the normal participants. The oxidative stress marker TBARS increased among all types of dyslipidemia except hypertriglyceridemia. All participants with different types of dyslipidemia had a lower total antioxidant capacity. Correlation analysis showed that cytokines and TBARS were positively associated with age, obesity, and diabetes mellitus, but not sex, sedentary leisure lifestyle, hypertension, and CVD/CHD history. The activity of TAC was negatively associated with the above parameters.ConclusionsThe correlation between the prevalence of dyslipidemia and the modification of inflammation status was statistically significant. The levels of proinflammatory cytokines, oxidative stress, and antioxidant capacity in serum may reflect the severity of the lipid abnormalities. These promising results further warrant a thorough medical screening in enhanced anti-inflammatory and reduced oxidative stress to better diagnose and comprehensively treat dyslipidemia at an early stage.

ObjectivesChronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD.MethodsDemographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively.ResultsA total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively.ConclusionRF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice.

Diabetic nephropathy (DN) is a fatal complication of diabetes and the main cause of end-stage renal disease. Due to the suboptimal effects of current treatments, there is an urgent need to develop new therapeutic strategies for DN. Trametenolic acid (TA), a lanostane-type tetracyclic triterpenoid, is one of the main active ingredients extracted from the natural product Inonotus obliquus. Our study was aimed at clarifying the potential protective effects of TA on DN and its underlying mechanism. In this research, C57BLKS/db (db/db) mice were used as the spontaneous DN model, and TA (10 mg/kg/d) was intraperitoneally injected for 4 consecutive weeks. Ratio of right kidney weight/body weight was calculated, and the contents of serum creatinine (Scr), blood urea nitrogen (BUN), and urine albumin were detected. The activities of superoxide dismutase (SOD) and catalase (CAT) and the contents of reductive glutathione (GSH) and malondialdehyde (MDA) were measured. The histopathological changes of renal tissues were observed by hematoxylin and eosin (HE), periodic acid-Schiff (PAS), and Masson staining. The protein expressions of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), NAD(P)H:quinone oxidoreductase-1 (NQO-1), nuclear factor kappa B (NF-κB), proinflammation cytokine tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), Nephrin, and Podocin were detected by western blot assay. Immunohistochemistry was utilized to detect expressions of collagen III (COL-III) and fibronectin (FN). Our results showed that TA administration significantly reduced the ratio of right kidney weight/body weight, BUN, Scr, and urine albumin levels and alleviated the histopathological changes of DN mice. Moreover, TA administration remarkably increased GSH content and SOD and CAT activities and decreased MDA content. Western blot assay demonstrated that TA activated Nrf2 signaling and increased the expression of downstream antioxidant enzymes HO-1 and NQO-1. Further studies illustrated that NF-κB signaling was inhibited, and downstream proinflammation cytokine expressions of TNF-α, IL-6, and IL-1β were also downregulated. In addition, we also found that TA administration significantly increased the expression of nephrin and podocin proteins and reduced the protein expression of COL-III and FN. These findings suggested that TA exhibited a renoprotective effect by ameliorating oxidative stress and inflammation via Nrf2/HO-1 and NF-κB signaling pathways.

In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view clustering (IMC) problem. Up until now, most efforts for the IMC problem have been made on the learning of consensus representations or graphs, while many missing views are ignored, making it impossible to capture the information hidden in the missing view. To overcome this drawback, we first analyzed the low-rank relationship existing inside each graph and among all graphs, and then propose a novel method for the IMC problem via low-rank graph tensor completion. Specifically, we first stack all similarity graphs into a third-order graph tensor and then exploit the low-rank relationship from each mode using the matrix nuclear norm. In this way, the connection hidden between the missing and available instances can be recovered. The consensus representation can be learned from all completed graphs via multi-view spectral clustering. To obtain the optimal multi-view clustering result, incomplete graph recovery and consensus representation learning are integrated into a joint framework for optimization. Extensive experimental results on several incomplete multi-view datasets demonstrate that the proposed method can obtain a better clustering performance in comparison with state-of-the-art incomplete multi-view clustering methods.

BackgroundIncreased oxidative stress has been suggested as one of the underlying mechanisms in iodide excess-induced thyroid disease. Metallothioneins (MTs) are regarded as scavengers of reactive oxygen species (ROS) in oxidative stress. Our aim is to investigate the effects of propylthiouracil (PTU), a thyroid peroxidase inhibitor, perchlorate (KClO4), a competitive inhibitor of iodide transport, and thyroid stimulating hormone (TSH) on mitochondrial superoxide production instigated by high concentrations of iodide in the thyroids of MT-I/II knockout (MT-I/II KO) mice.MethodsEight-week-old 129S7/SvEvBrd-Mt1tm1Bri Mt2tm1Bri/J (MT-I/II KO) mice and background-matched wild type (WT) mice were used.ResultsBy using a mitochondrial superoxide indicator (MitoSOX Red), lactate dehydrogenase (LDH) release, and methyl thiazolyl tetrazolium (MTT) assay, we demonstrated that the decreased relative viability and increased LDH release and mitochondrial superoxide production induced by potassium iodide (100 µM) can be relieved by 300 µM PTU, 30 µM KClO4, or 10 U/L TSH in the thyroid cell suspensions of both MT-I/II KO and WT mice (P

The Jurassic coal-measure source rocks in the Junggar Basin have drawn considerable attention in recent years. In our hydrocarbon thermal simulation experiments of these rocks, we found that the dark mudstone evaluated as good source rock, had a much lower hydrocarbon generation capacity than the coal and carbonaceous mudstone, evaluated as poor source rock. Based on this background, we performed Fourier transform infrared spectroscopy (FTIR) and combined the results of semi-open thermal simulation experiments to explore the association between the molecular structure and hydrocarbon production capacity, with the aim of obtaining a new understanding of hydrocarbon potential of Jurassic coal-measure source rocks from the perspective of molecular structure. The results indicate that coals exhibit lower condensation of aromatic structures and higher relative abundance of aliphatic structures with a higher degree of branched chaining than mudstones and carbonaceous mudstones. Apparent aromaticity (fa), aromatic abundance parameter I, and degree of condensation (DOC) are negatively correlated with organic matter abundance. The aliphatic structural parameter H demonstrates a substantial positive correlation with organic matter abundance. Furthermore, aliphatic relative abundance factor A is associated with the type of organic matter; the better is the type of the organic matter, the larger is the A value. The combination of the molecular structures with the thermal simulation results shows that the aliphatic hydrogen enrichment of selected carbonaceous mudstone is similar to that of coal. However, the relative abundance of the aliphatic group of it is high, and the DOC of the aromatic structure is low, making the hydrocarbon generation base stronger and easier to crack. Thus, the hydrocarbon generation capacity of carbonaceous mudstone is slightly higher than that of coal. Mudstone has low H and I values, and the DOC is high, indicating that its hydrocarbon base is low, so it has low hydrocarbon generation capacity. Therefore, the molecular structure is closely associated with the hydrocarbon potential of coal-measure source rocks. When evaluating the qualities of coal-measure source rocks, the influence of molecular structure on these rocks should be considered. [ABSTRACT FROM AUTHOR]

Background and purpose Individuals with intracranial artery occlusion have high rates of ischaemic events and recurrence. It has been challenging to identify patients who had high-risk stroke using a simple, valid and non-invasive screening approach. This study aimed to investigate whether fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), a specific imaging sign on the FLAIR sequence, could be a predictor of ischaemic events in a population with internal carotid artery (ICA) or middle cerebral artery (MCA) occlusion.Methods We retrospectively analysed 147 patients (mean 60.43±12.83 years) with 149 lesions, including 37 asymptomatic and 112 symptomatic cases of ICA or MCA occlusion. Symptomatic occlusion was considered if ischaemic events were present in the relevant territory within 90 days. FVH Alberta Stroke Program Early Computed Tomography Score (FVH-ASPECTS: 0–7, with 0 indicating absence of FVH and 7 suggesting prominent FVH) and collateral circulation grade were assessed for each participant. Multivariable logistic regression analysis was performed to detect independent markers associated with symptomatic status.Results A lower FVH-ASPECTS was associated with a more favourable collateral circulation grade (rho=−0.464, p