Case Analysis of Misdiagnosis in Emergency Internal Medicine

Objective: This paper attempts to determine the clinical factors associated with the diagnosis of stroke in young adults. Method: We registered and examined young patients from the hospital stroke center. Demographic data, past medical history, 3 hours diagnosis time within the time frame, and the outcomes were assessed. We compared the patients with misdiagnosis and those who were correctly diagnosed in order to identify the factors associated with acute stroke. Results: From 2005−2011, 57 patients (16−50 years) were enrolled in the registry. 8 patients (14%; 4 men and 4 women, mean age 38 years) were misdiagnosed. Of these 8 patients, seven were initially discharged from the emergency department. Patients aged less than 35 years old (p = 0.05) and posterior circulation stroke patients (p = 0.006) were more likely to be misdiagnosed. Conclusion: First aid department needs to popularize the awareness of stroke among young adults. Misdiagnosis may cause the patient to lose the best chance of thrombolysis

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management

Integrative single-cell transcriptomic investigation unveils long non-coding RNAs associated with localized cellular inflammation in psoriasis

Psoriasis is a complex, chronic autoimmune disorder predominantly affecting the skin. Accumulating evidence underscores the critical role of localized cellular inflammation in the development and persistence of psoriatic skin lesions, involving cell types such as keratinocytes, mesenchymal cells, and Schwann cells. However, the underlying mechanisms remain largely unexplored. Long non-coding RNAs (lncRNAs), known to regulate gene expression across various cellular processes, have been particularly implicated in immune regulation. We utilized our neural-network learning pipeline to integrate 106,675 cells from healthy human skin and 79,887 cells from psoriatic human skin. This formed the most extensive cell transcriptomic atlas of human psoriatic skin to date. The robustness of our reclassified cell-types, representing full-layer zonation in human skin, was affirmed through neural-network learning-based cross-validation. We then developed a publicly available website to present this integrated dataset. We carried out analysis for differentially expressed lncRNAs, co-regulated gene patterns, and GO-bioprocess enrichment, enabling us to pinpoint lncRNAs that modulate localized cellular inflammation in psoriasis at the single-cell level. Subsequent experimental validation with skin cell lines and primary cells from psoriatic skin confirmed these lncRNAs’ functional role in localized cellular inflammation. Our study provides a comprehensive cell transcriptomic atlas of full-layer human skin in both healthy and psoriatic conditions, unveiling a new regulatory mechanism that governs localized cellular inflammation in psoriasis and highlights the therapeutic potential of lncRNAs in this disease’s management

FragGeneScan: predicting genes in short and error-prone reads

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database

Intramyocardial Transplantation of Undifferentiated Rat Induced Pluripotent Stem Cells Causes Tumorigenesis in the Heart

BACKGROUND: Induced pluripotent stem cells (iPSCs) are a novel candidate for use in cardiac stem cell therapy. However, their intrinsic tumorigenicity requires further investigation prior to use in a clinical setting. In this study we investigated whether undifferentiated iPSCs are tumorigenic after intramyocardial transplantation into immunocompetent allogeneic recipients. METHODOLOGY/PRINCIPAL FINDINGS: We transplanted 2 × 10(4), 2 × 10(5), or 2 × 10(6) cells from the established rat iPSC line M13 intramyocardially into intact or infarcted hearts of immunocompetent allogeneic rats. Transplant duration was 2, 4, or 6 weeks. Histological examination with hematoxylin-eosin staining confirmed that undifferentiated rat iPSCs could generate heterogeneous tumors in both intracardiac and extracardiac sites. Furthermore, tumor incidence was independent of cell dose, transplant duration, and the presence or absence of myocardial infarction. CONCLUSIONS/SIGNIFICANCE: Our study demonstrates that allogeneic iPSC transplantation in the heart will likely result in in situ tumorigenesis, and that cells leaked from the beating heart are a potential source of tumor spread, underscoring the importance of evaluating the safety of future iPSC therapy for cardiac disease

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Research on Construction and Application for the Model of Multistage Job Shop Scheduling Problem

Based on the practical application of an enterprise, we address the multistage job shop scheduling problem with several parallel machines in the first stage (production), a few parallel machines in the second stage (processing and assembly), and one machine in the following stages (including joint debugging, testing, inspection, and packaging). First, we establish the optimization objective model for the first two stages. Then, based on the design of the sequencing algorithm in the first two stages, a correction algorithm is designed between the first stage and the second stage to solve this problem systematically. Finally, we propose two benchmark approaches to verify the performance of our proposed algorithm. Verification of numerical experiments shows that the model and algorithm constructed in this paper effectively improve the production efficiency of the enterprise