A comparison between hospital follow‐up and collaborative follow‐up in patients with acute heart failure

AIMS: There are no previous studies focusing on collaborative follow-ups between hospitals and clinics for patients discharged after acute heart failure (AHF) in Japan. The purpose of this study was to determine the status of collaboration between hospitals and clinics for patients with AHF in Japan and to compare patient characteristics and clinical outcomes using a large Japanese observational database. METHODS AND RESULTS: Of 4056 consecutive patients hospitalized for AHF in the Kyoto Congestive Heart Failure registry, we analysed 2862 patients discharged to go home, who were divided into 1674 patients (58.5%) followed up at hospitals with index hospitalization (hospital follow-up group) and 1188 (41.5%) followed up in a collaborative fashion with clinics or other general hospitals (collaborative follow-up group). The primary outcome was a composite of all-cause death or heart failure (HF) hospitalization within 1 year after discharge. Previous hospitalization for HF and length of hospital stay longer than 15 days were associated with hospital follow-up. Conversely, ≥80 years of age, hypertension, and cognitive dysfunction were associated with collaborative follow-up. The cumulative 1-year incidence of the primary outcome, all cause death, and cardiovascular death were similar between the hospital and collaborative follow-up groups (31.6% vs. 29.6%, P = 0.51, 13.1% vs, 13.9%, P = 0.35, 8.4% vs. 8.2%, P = 0.96). Even after adjusting for confounders, the difference in risk for patients in the hospital follow-up group relative to those in the collaborative follow-up group remained insignificant for the primary outcome, all-cause death, and cardiovascular death (HR: 1.11, 95% CI: 0.97-1.27, P = 0.14, HR: 1.10, 95% CI: 0.91-1.33, P = 0.33, HR: 0.96, 95% CI: 0.87-1.05, P = 0.33). The cumulative 1-year incidence of HF hospitalization was higher in the hospital follow-up group than in the collaborative follow-up group (25.5% vs. 21.3%, P = 0.02). The risk of HF hospitalization was higher in the hospital follow-up group than in the collaborative follow-up group (HR: 1.19, 95% CI: 1.01-1.39, P = 0.04). CONCLUSIONS: In patients hospitalized for AHF, 41.5% received collaborative follow-up after discharge. The risk of HF hospitalization was higher in the hospital follow-up group than in the collaborative follow-up, although risk of the primary outcome, all-cause death, and cardiovascular death were similar between groups

Public assistance in patients with acute heart failure: a report from the KCHF registry

AIMS: There is a scarcity of data on the post-discharge prognosis in acute heart failure (AHF) patients with a low-income but receiving public assistance. The study sought to evaluate the differences in the clinical characteristics and outcomes between AHF patients receiving public assistance and those not receiving public assistance. METHODS AND RESULTS: The Kyoto Congestive Heart Failure registry was a physician-initiated, prospective, observational, multicentre cohort study enrolling 4056 consecutive patients who were hospitalized due to AHF for the first time between October 2014 and March 2016. The present study population consisted of 3728 patients who were discharged alive from the index AHF hospitalization. We divided the patients into two groups, those receiving public assistance and those not receiving public assistance. After assessing the proportional hazard assumption of public assistance as a variable, we constructed multivariable Cox proportional hazard models to estimate the risk of the public assistance group relative to the no public assistance group. There were 218 patients (5.8%) receiving public assistance and 3510 (94%) not receiving public assistance. Patients in the public assistance group were younger, more frequently had chronic coronary artery disease, previous heart failure hospitalizations, current smoking, poor medical adherence, living alone, no occupation, and a lower left ventricular ejection fraction than those in the no public assistance group. During a median follow-up of 470 days, the cumulative 1 year incidences of all-cause death and heart failure hospitalizations after discharge did not differ between the public assistance group and no public assistance group (13.3% vs. 17.4%, P = 0.10, and 28.3% vs. 23.8%, P = 0.25, respectively). After adjusting for the confounders, the risk of the public assistance group relative to the no public assistance group remained insignificant for all-cause death [hazard ratio (HR), 0.97; 95% confidence interval (CI), 0.69-1.32; P = 0.84]. Even after taking into account the competing risk of all-cause death, the adjusted risk within 180 days in the public assistance group relative to the no public assistance group remained insignificant for heart failure hospitalizations (HR, 0.93; 95% CI, 0.64-1.34; P = 0.69), while the adjusted risk beyond 180 days was significant (HR, 1.56; 95% CI, 1.07-2.29; P = 0.02). CONCLUSIONS: The AHF patients receiving public assistance as compared with those not receiving public assistance had no significant excess risk for all-cause death at 1 year after discharge or a heart failure hospitalization within 180 days after discharge, while they did have a significant excess risk for heart failure hospitalizations beyond 180 days after discharge. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02334891 (NCT02334891) and https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000017241 (UMIN000015238)

A Genome-Wide Association Study Identified AFF1 as a Susceptibility Locus for Systemic Lupus Eyrthematosus in Japanese

Systemic lupus erythematosus (SLE) is an autoimmune disease that causes multiple organ damage. Although recent genome-wide association studies (GWAS) have contributed to discovery of SLE susceptibility genes, few studies has been performed in Asian populations. Here, we report a GWAS for SLE examining 891 SLE cases and 3,384 controls and multi-stage replication studies examining 1,387 SLE cases and 28,564 controls in Japanese subjects. Considering that expression quantitative trait loci (eQTLs) have been implicated in genetic risks for autoimmune diseases, we integrated an eQTL study into the results of the GWAS. We observed enrichments of cis-eQTL positive loci among the known SLE susceptibility loci (30.8%) compared to the genome-wide SNPs (6.9%). In addition, we identified a novel association of a variant in the AF4/FMR2 family, member 1 (AFF1) gene at 4q21 with SLE susceptibility (rs340630; P = 8.3×10−9, odds ratio = 1.21). The risk A allele of rs340630 demonstrated a cis-eQTL effect on the AFF1 transcript with enhanced expression levels (P<0.05). As AFF1 transcripts were prominently expressed in CD4+ and CD19+ peripheral blood lymphocytes, up-regulation of AFF1 may cause the abnormality in these lymphocytes, leading to disease onset

Genetics of rheumatoid arthritis contributes to biology and drug discovery

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Skin Mycobiome of Psoriasis Patients is Retained during Treatment with TNF and IL-17 Inhibitors

BACKGROUND: Biological treatment relieves refractory skin lesions in patients with psoriasis; however, changes in the fungal microbiome (the mycobiome) on the skin are unclear. METHODS: The skin mycobiome of psoriasis patients treated with TNF inhibitors (TNFi, n = 5) and IL-17 inhibitors (IL-17i, n = 7) was compared with that of patients not receiving systemic therapy (n = 7). Skin swab samples were collected from non-lesional post-auricular areas. Fungal DNA was sequenced by ITS1 metagenomic analysis and taxonomic classification was performed. RESULTS: An average of 37543 reads/sample were analyzed and fungi belonging to 31 genera were detected. The genus Malassezia accounted for >90% of reads in 7/7 samples from the no-therapy group, 4/5 from the TNFi group, and 5/7 from the IL-17i group. Biodiversity was low in those three groups. Few members of the genus trichophyton were detected; the genus Candida was not detected at all. Among the Malassezia species, M. restricta was the major species in 6/7 samples from the no-therapy group, 4/5 from the TNFi group, and 5/7 from the IL-17i group whose the other largest species revealed M. globosa. CONCLUSIONS: The mycobiome is retained on post-auricular skin during systemic treatment with TNF and IL-17 inhibitors

Predicting the future direction of cell movement with convolutional neural networks.

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatically identify those morphological features that influence future cell movement

Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs

Regenerative medicine using neural stem cells (NSCs), which self-renew and have pluripotency, has recently attracted a lot of interest. Much research has focused on the transplantation of differentiated NSCs to damaged tissues for the treatment of various neurodegenerative diseases and spinal cord injuries. However, current approaches for distinguishing differentiated from non-differentiated NSCs at the single-cell level have low reproducibility or are invasive to the cells. Here, we developed a fully automated, non-invasive convolutional neural network-based model to determine the differentiation status of human NSCs at the single-cell level from phase-contrast photomicrographs; after training, our model showed an accuracy of identification greater than 94%. To understand how our model distinguished between differentiated and non-differentiated NSCs, we evaluated the informative features it learned for the two cell types and found that it had learned several biologically relevant features related to NSC shape during differentiation. We also used our model to examine the differentiation of NSCs over time; the findings confirmed our model&rsquo;s ability to distinguish between non-differentiated and differentiated NSCs. Thus, our model was able to non-invasively and quantitatively identify differentiated NSCs with high accuracy and reproducibility, and, therefore, could be an ideal means of identifying differentiated NSCs in the clinic