Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke.

Adipocytes normally accumulate in the epicardial and pericardial layers around the human heart, but their infiltration into the myocardium can be proarrhythmic. METHODS AND RESULTS: Human adipose derived stem/stromal cells and human induced pluripotent stem cells (hiPSC) were differentiated, respectively into predominantly white fat-like adipocytes (hAdip) and ventricular cardiomyocytes (CMs). Adipocytes cultured in CM maintenance medium (CM medium) maintained their morphology, continued to express adipogenic markers, and retained clusters of intracellular lipid droplets. In contrast, hiPSC-CMs cultivated in adipogenic growth medium displayed abnormal cell morphologies and more clustering across the monolayer. Pre-plated hiPSC-CMs co-cultured in direct contact with hAdips in CM medium displayed prolonged action potential durations, increased triangulation, slowed conduction velocity, increased conduction velocity heterogeneity, and prolonged calcium transients. When hAdip-conditioned medium was added to monolayer cultures of hiPSC-CMs, results similar to those recorded with direct co-cultures were observed. Both co-culture and conditioned medium experiments resulted in increases in transcript abundance of SCN10A, CACNA1C, SLC8A1, and RYR2, with a decrease in KCNJ2. Human adipokine immunoblots revealed the presence of cytokines that were elevated in adipocyte-conditioned medium, including MCP-1, IL-6, IL-8 and CFD that could induce electrophysiological changes in cultured hiPSC-CMs. CONCLUSIONS: Co-culture of hiPSC-CMs with hAdips reveals a potentially pathogenic role of infiltrating human adipocytes on myocardial tissue. In the absence of structural changes, hAdip paracrine release alone is sufficient to cause CM electrophysiological dysfunction mirroring the co-culture conditions. These effects, mediated largely by paracrine mechanisms, could promote arrhythmias in the heart.

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Shortening the time to diagnosis and initiating early treatment are imperative to improve outcomes in patients with systemic lupus erythematosus (SLE). The aim of this case-control study, based on the data from the Taiwan\'s National Health Insurance Research Database (NHIRD), was to investigate the patterns of diagnoses of disease phenotypes in female patients with SLE up to eight years prior to its definitive diagnosis. The 547 cases were selected from the 2000-2012 NHIRD catastrophic illness datafile and frequency-matched with 2188 controls. The primary diagnosis based on the first ICD-9-CM code for each outpatient visit was converted to Phecodes. Separate regression models, based on least absolute shrinkage and selection operator (lasso) regularization, with seven different lag periods from 1-2 to 7-8 years, were conducted. Results showed that SLE was associated with 46 disease phenotypes in a lag period of 2-3 years, but fewer in other lag periods. A number of SLE-associated disease phenotypes, such as primary thrombocytopenia, thyroid diseases, Raynaud\'s syndrome, renal disease, and several infectious diseases, occurred mainly in the first few years prior to SLE diagnosis. In conclusion, SLE should be suspected when the disease phenotypes identified in the present study occurred concomitantly.

The past few years have seen a significant increase in availability of whole genome sequencing information, allowing for its incorporation in predictive modeling for foodborne pathogens to account for inter- and intra-species differences in their virulence. However, this is hindered by the inability of traditional statistical methods to analyze such large amounts of data compared to the number of observations/isolates. In this study, we have explored the applicability of machine learning (ML) models to predict the disease outcome, while identifying features that exert a significant effect on the prediction. This study was conducted on Salmonella enterica, a major foodborne pathogen with considerable inter- and intra-serovar variation. WGS of isolates obtained from various sources (i.e., human, chicken, and swine) were used as input in four machine learning models (logistic regression with ridge, random forest, support vector machine, and AdaBoost) to classify isolates based on disease severity (extraintestinal vs. gastrointestinal) in the host. The predictive performances of all models were tested with and without Elastic Net regularization to combat dimensionality issues. Elastic Net-regularized logistic regression model showed the best area under the receiver operating characteristic curve (AUC-ROC; 0.86) and outcome prediction accuracy (0.76). Additionally, genes coding for transcriptional regulation, acidic, oxidative, and anaerobic stress response, and antibiotic resistance were found to be significant predictors of disease severity. These genes, which were significantly associated with each outcome, could possibly be input in amended, gene-expression-specific predictive models to estimate virulence pattern-specific effect of Salmonella and other foodborne pathogens on human health.

The aim of this study is to explore the role of labial minor salivary gland (LMSG) focus score (FS) in stratifying Sjögren\'s Syndrome (SS) patients, lymphoma development prediction and to facilitate early lymphoma diagnosis. Ιn an integrated cohort of 1997 patients, 618 patients with FS ≥ 1 and at least one-year elapsing time interval from SS diagnosis to lymphoma diagnosis or last follow up were identified. Clinical, laboratory and serological features were recorded. A data driven logistic regression model was applied to identify independent lymphoma associated risk factors. Furthermore, a FS threshold maximizing the difference of time interval from SS until lymphoma diagnosis between high and low FS lymphoma subgroups was investigated, to develop a follow up strategy for early lymphoma diagnosis. Of the 618 patients, 560 were non-lymphoma SS patients while the other 58 had SS and lymphoma. FS, cryoglobulinemia and salivary gland enlargement (SGE) were proven to be independent lymphoma associated risk factors. Lymphoma patients with FS ≥ 4 had a statistically significant shorter time interval from SS to lymphoma diagnosis, compared to those with FS < 4 (4 vs 9 years, respectively, p = 0,008). SS patients with FS ≥ 4 had more frequently B cell originated manifestations and lymphoma, while in patients with FS < 4, autoimmune thyroiditis was more prevalent. In the latter group SGE was the only lymphoma independent risk factor. A second LMSG biopsy is patients with a FS ≥ 4, 4 years after SS diagnosis and in those with FS < 4 and a history of SGE, at 9-years, may contribute to an early lymphoma diagnosis. Based on our results we conclude that LMSG FS, evaluated at the time of SS diagnosis, is an independent lymphoma associated risk factor and may serve as a predictive biomarker for the early diagnosis of SS-associated lymphomas.

Background and Aim: Serum immunoglobulins were reported to be associated with clinical characteristics of inflammatory bowel disease. However, whether a difference exists in the serum immunoglobulins levels in patients with Crohn\'s disease (CD) with different disease location and behavior phenotypes remains unclear. Therefore, this study aimed to explore the associations of serum immunoglobulins levels with specific CD phenotypes. Methods: Patients with CD having recorded serum immunoglobulins levels were recruited through multicenter collaborative efforts. The associations between serum immunoglobulins levels and distinct phenotypes of CD were evaluated using multiple logistic regression models. Results: A total of 608 patients with CD were included in the study. Elevated (above the upper limit of normal) serum immunoglobulin G (IgG), IgA, IgM, and IgG4 were identified in 24.5, 17.4, 2.1, and 8.2% of patients, respectively. Elevated serum IgG4 levels negatively correlated with complicated disease behavior [odds ratio (OR) 0.49, 95% confidence interval (CI) 0.26-0.92]. Elevated serum IgG was linked to isolated ileal disease with an OR of 0.37 (95% CI 0.23-0.61). The ORs of isolated ileal disease progressively reduced across increasing quartiles of IgG (P for trend < 0.001). The adjusted ORs of isolated ileal disease for increasing quartiles of IgM were 1.82 (1.07-3.1), 1.92 (1.14-3.24), 1.17 (0.69-1.98), and 1 (P for trend = 0.008). Besides, serum IgA and IgG levels significantly correlated with several disease activity indices. Conclusions: These results suggested that certain serum immunoglobulins were associated with specific disease phenotypes of CD. Further investigations to account for the associations are warranted.

Two siblings with CF are homozygous for F508del (referred to as Subject A and Subject B). Despite having the same CFTR genotype and similar environment, these two subjects exhibited different disease phenotypes. We analyzed their medical records and CF Foundation Registry data and measured inflammatory protein mediators in their sputum samples. Then, we examined the longitudinal relationships between inflammatory markers and disease severity for each subject and compared between them. Subject A presented a more severe disease than Subject B. During the study period, Subject A had two pulmonary exacerbations (PEs) whereas Subject B had one mild PE. The forced expiratory volume in 1 s (FEV1, % predicted) values for Subject A were between 34-45% whereas for Subject B varied between 48-90%. Inflammatory protein mediators associated with neutrophils, Th1, Th2, and Th17 responses were elevated in sputum of Subject A compared with Subject B, and also in samples collected prior to and during PEs for both subjects. Neutrophilic elastase (NE) seemed to be the most informative biomarkers. The infectious burden between these two subjects was different.

Mutations of different genes often result in clinically similar diseases. Among the datasets of similar diseases, we analyzed the \'phenotypic series\' from Online Mendelian Inheritance in Man and examined the similarity of the diseases that belong to the same phenotypic series, because we hypothesize that clinical similarity may unveil shared pathogenic mechanisms.
Specifically, for each pair of diseases, we quantified their similarity, based on both number and information content of the shared clinical phenotypes. Then, we assembled the disease similarity network, in which nodes represent diseases and edges represent clinical similarities.
On average, diseases have high similarity with other diseases of their own phenotypic series, even though about one third of diseases have their maximal similarity with a disease of another series. Consequently, the network is assortative (i.e., diseases belonging to the same series link preferentially to each other), but the series differ in the way they distribute within the network. Specifically, heterophobic series, which minimize links to other series, form islands at the periphery of the network, whereas heterophilic series, which are highly inter-connected with other series, occupy the center of the network.
The finding that the phenotypic series display not only internal similarity (assortativity) but also varying degrees of external similarity (ranging from heterophobicity to heterophilicity) calls for investigation of biological mechanisms that might be shared among different series. The correlation between the clinical and biological similarities of the phenotypic series is analyzed in Part II of this study1.

Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching, and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.