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Background: Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and completeness of clinical data required for training deep learning models remains a significant challenge. This study aims to explore a new method that reduces data dependency and achieves predictive performance comparable to existing research. Methods: This study proposed DAPNet, a deep learning-based disease progression prediction model that solely utilizes the comorbidity duration (without relying on multi-modal data or comprehensive medical records) and disease associations from biomedical knowledge graphs to deliver high-performance prediction. DAPNet is the first to apply multi-view graph contrastive learning to disease progression prediction tasks. Compared with other studies on comorbidities, DAPNet innovatively integrates molecular-level disease association information, combines disease co-occurrence and ICD10, and fully explores the associations between diseases; Results: This study validated DAPNet using a de-identified clinical dataset derived from medical claims, which includes 2,714 patients and 10,856 visits. Meanwhile, a kidney dataset (606 patients) based on MIMIC-IV has also been constructed to fully validate its performance. The results showed that DAPNet achieved state-of-the-art performance on the severe pneumonia dataset (F1=0.84, with an improvement of 8.7%), and outperformed the six baseline models on the kidney disease dataset (F1=0.80, with an improvement of 21.3%). Through case analysis, we elucidated the clinical and molecular associations identified by the DAPNet model, which facilitated a better understanding and explanation of potential disease association, thereby providing interpretability for the model. Conclusions: The proposed DAPNet, for the first time, utilizes comorbidity duration and disease associations network, enabling more accurate disease progression prediction based on a multi-view graph contrastive learning, which provides valuable insights for early diagnosis and treatment of patients. Based on disease association networks, our research has enhanced the interpretability of disease progression predictions. [ABSTRACT FROM AUTHOR]

In recent years, the industry has demanded high hardness, wear resistance, corrosion resistance, and other properties for critical components made of steel and its alloys. Enhancing the surface properties of a part by applying modification techniques to its surface is considered to be an economical and efficient method. Laser hardening, as a new surface modification technology, has received more and more attention due to its high hardening efficiency, fast cooling rate, and excellent surface properties. Laser hardening technology has good application prospects in surface modification, and significant progress has been made in the exploration of laser hardening strengthening mechanisms, optimization of process parameters, high-quality preparation of hardened layers, and theoretical simulation. However, some problems need to be addressed in the practical application of the technology. This paper reviews the research progress of the laser surface hardening process for steel and its alloys, including numerical investigation, process optimization, hardening performance analysis, and external field-assisted laser hardening technology. It also discusses the problems and challenges faced by the technology and gives an outlook on its development trend. [ABSTRACT FROM AUTHOR]

Branch retinal vein occlusion (BRVO) is the most prevalent retinal vascular disease that constitutes a threat to vision due to increased venous pressure caused by venous effluent in the space, leading to impaired visual function. Optical Coherence Tomography Angiography (OCTA) is an innovative non-invasive technique that offers high-resolution three-dimensional structures of retinal blood vessels. Most publicly available datasets are collected from single visits with different patients, encompassing various eye diseases for distinct tasks and areas. Moreover, due to the intricate nature of eye structure, professional labeling not only relies on the expertise of doctors but also demands considerable time and effort. Therefore, we have developed a BRVO-focused dataset named Soul (Source of ocular vascular) and propose a human machine collaborative annotation framework (HMCAF) using scrambled retinal blood vessels data. Soul is categorized into 6 subsets based on injection frequency and follow-up duration. The dataset comprises original images, corresponding blood vessel labels, and clinical text information sheets which can be effectively utilized when combined with machine learning. [ABSTRACT FROM AUTHOR]

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene. [ABSTRACT FROM AUTHOR]

A new aptamer-based method has been developed for interferon-γ (IFN-γ) detection by utilizing interface reactivity-modulated fluorescent metal–organic frameworks (MOFs). Specifically, the binding of IFN-γ to its aptamer decreases the interface reactivity between the biotin-labeled aptamer and the streptavidin-functionalized magnetic beads by generating significant steric effects. As a result, several biotin-labeled aptamers escape from the enrichment of magnetic beads and remain in the supernatant, which subsequently undergo the terminal deoxynucleotidyl transferase-catalyzed polymerization elongation. Along with the elongation, pyrophosphate is continuously produced as the by-product, triggering the decomposition of fluorescent MOFs to generate a remarkable fluorescent response with the excitation/emission wavelength of 610 nm/685 nm. Experimental results show that the method enables the detection of IFN-γ in the range 0.06 fM to 6 pM with a detection limit of 0.057 fM. The method also displays high specificity and repeatability with an average relative standard deviation of 2.04%. Moreover, the method demonstrates satisfactory recoveries from 96.3 to 105.5% in serum samples and excellent utility in clinical blood samples. Therefore, this work may provide a valuable tool for IFN-γ detection and is expected to be of high potential in tuberculosis diagnosis in the future. [ABSTRACT FROM AUTHOR]

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development. [ABSTRACT FROM AUTHOR]

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F -score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient's symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient's clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM) and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine. [ABSTRACT FROM AUTHOR]

Introduction: Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi‐morbidity/treatment complexity. Some headway has been made to identify genetic and non‐genetic risk factors for T2DM. However, longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi‐morbidity. Methods: This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre‐ and post‐T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n = 237 vs matched non‐T2DM controls n = 44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. Results: For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis, a greater indication of ischaemic heart disease proportions was observed. Post‐T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. Conclusion: These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores. [ABSTRACT FROM AUTHOR]

Chinese medicine (CM) was extensively used to treat COVID-19 in China. We aimed to evaluate the real-world effectiveness of add-on semi-individualized CM during the outbreak. A retrospective cohort of 1788 adult confirmed COVID-19 patients were recruited from 2235 consecutive linked records retrieved from five hospitals in Wuhan during 15 January to 13 March 2020. The mortality of add-on semi-individualized CM users and non-users was compared by inverse probability weighted hazard ratio (HR) and by propensity score matching. Change of biomarkers was compared between groups, and the frequency of CMs used was analyzed. Subgroup analysis was performed to stratify disease severity and dose of CM exposure. The crude mortality was 3.8% in the semi-individualized CM user group and 17.0% among the non-users. Add-on CM was associated with a mortality reduction of 58% (HR = 0.42, 95% CI: 0.23 to 0.77, p = 0.005) among all COVID-19 cases and 66% (HR = 0.34, 95% CI: 0.15 to 0.76, p = 0.009) among severe/critical COVID-19 cases demonstrating dose-dependent response, after inversely weighted with propensity score. The result was robust in various stratified, weighted, matched, adjusted and sensitivity analyses. Severe/critical patients that received add-on CM had a trend of stabilized D-dimer level after 3–7 days of admission when compared to baseline. Immunomodulating and anti-asthmatic CMs were most used. Add-on semi-individualized CM was associated with significantly reduced mortality, especially among severe/critical cases. Chinese medicine could be considered as an add-on regimen for trial use. [ABSTRACT FROM AUTHOR]

Entropy has been an important tool for the complexity analysis of time series from various fields. Based on studying all the template mismatches, a modified sample entropy (SE) method, named as inverse sample entropy (ISE), for investigating the complexity of financial time series is proposed in this paper. Different from SE, ISE considers the far neighbors of templates; it also provides more comprehensive information combined with SE. Stock markets usually fluctuate with the economy policies; ISE allows us to detect the financial crisis by the change of complexity. By experiments on both simulated data and real-world stock data, ISE shows that the threshold r is more flexible compared with that of SE, which allows ISE to be applied not only to limited type of data. Besides, it is more robust to high dimension m , so ISE can be extended to the application of high dimension analysis. For studying the impact of embedding dimension m under multiple scales on both artificial and real-world data, we made a comparison on the use of SE and ISE. Both SE and ISE are able to distinguish time series with different features and characteristics. While SE is sensitive to high dimension analysis, ISE shows robustness. [ABSTRACT FROM AUTHOR]

White matter hyperintensity (WMH) is a common finding in aging population and considered to be a contributor to cognitive decline. Our study aimed to characterize the spatial patterns of WMH in different severities and explore its impact on cognition and brain microstructure in non-demented elderly. Lesions were both qualitatively (Fazekas scale) and quantitatively assessed among 321 community-dwelled individuals with MRI scanning. Voxel- and atlas-based analyses of the whole-brain white matter microstructure were performed. The WMH of the same severities was found to occur uniformly with a specific pattern of lesions. The severity of WMH had a significant negative association with the performance of working and episodic memory, beginning to appear in Fazekas 3 and 4. The white matter tracts presented significant impairments in Fazekas 3, which showed brain-wide changes above Fazekas 4. Lower FA in the superior cerebellar peduncle and left posterior thalamic radiation was mainly associated with episodic memory, and the middle cerebellar peduncle was significantly associated with working memory. These results support that memory is the primary domain to be affected by WMH, and the effect may potentially be influenced by tract-specific WM abnormalities. Fazekas scale 3 might be the critical stage predicting a future decline in cognition. [ABSTRACT FROM AUTHOR]

Patients with type 2 diabetes mellitus (T2DM) have a considerably high risk of developing dementia, especially for those with mild cognitive impairment (MCI). The investigation of the microstructural change of white matter (WM) between T2DM with amnesic MCI (T2DM-aMCI) and T2DM with normal cognition (T2DM-NC) and their relationships to cognitive performances can help to understand the brain variations in T2DM-related amnesic cognitive impairment. In the current study, 36 T2DM-aMCI patients, 40 T2DM-NC patients, and 40 healthy control (HC) individuals underwent diffusion tensor image and T1-weighted MRI scans and comprehensive cognition assessments. All of these cognitive functions exhibited intergroup ranking differences in patients. The T2DM-NC patients and HC individuals did not reveal any significant differences in WM integrity. The T2DM-aMCI patients showed disrupted integrity in multiple WM tracts compared with HC and T2DM-NC. Specifically, the damaged WM integrity of the right inferior fronto-occipital fasciculus and the right inferior longitudinal fasciculus exhibited significant correlations with episodic memory and attention function impairment in T2DM patients. Furthermore, cognitive impairment-related WM microstructural damage was associated with the degeneration of cortex connected to the affected WM tract. These findings indicate that degeneration exists extensively in WM tracts in T2DM-aMCI, whereas no brain WM damage is evident in T2DM-NC. [ABSTRACT FROM AUTHOR]

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility. [ABSTRACT FROM AUTHOR]

The paper mainly introduces a method combining detrended fluctuation analysis (DFA) with moving fitting window to analyze the heartbeat time series in different pathologic states. Compared to traditional approaches, the estimation of the local scale exponent method shows more details of scale properties and provides a reliable analysis. We also quantify the effects of outliers, subseries length and gender on scale spectrum α( s). Comparing the results of the healthy subjects, patients with congestive heart failure (CHF), and patients with atrial fibrillation (AF) indicate clearly that at small scales, the exponents show great volatility and they all have their own scale pattern. In addition, the length of the series should be relatively longer to avoid properties losing. For the effect of gender, contrary to healthy results, the scale exponents of CHF male subjects are continuously greater than female. The spectrum of DFA scale exponents provides a new way to measure the heartbeat series and distinguishes healthy and pathologic groups. [ABSTRACT FROM AUTHOR]

There has been considerable interest in quantifying the complexity of different time series, such as physiologic time series, traffic time series. However, these traditional approaches fail to account for the multiple time scales inherent in time series, which have yielded contradictory findings when applied to real-world datasets. Then multi-scale entropy analysis (MSE) is introduced to solve this problem which has been widely used for physiologic time series. In this paper, we first apply the MSE method to different correlated series and obtain an interesting relationship between complexity and Hurst exponent. A modified MSE method called multiscale permutation entropy analysis (MSPE) is then introduced, which replaces the sample entropy (SampEn) with permutation entropy (PE) when measuring entropy for coarse-grained series. We employ the traditional MSE method and MSPE method to investigate complexities of different traffic series, and obtain that the complexity of weekend traffic time series differs from that of the workday time series, which helps to classify the series when making predictions. [ABSTRACT FROM AUTHOR]

The paper mainly applies the multiscale entropy (MSE) to analyze the financial time series. The MSE is used to examine the complexity of a quantified system. Based on MSE, we propose multiscale cross-sample entropy (MSCE) to analyze the complexity and correlation of two time series. By comparing with the results, we find that both results present remarkable scaling characterization and the value of each log return of financial time series decreases with a increasing scale factor. From the results of MSE, we also find that the entropy of the Europe markets is lower than that of the Asia, but higher than that of the Americas. It means the MSE can distinguish different areas markets. The results of MSCE show that financial plate have high synchrony with the plate of Electron, IT and Realty. The MSCE can distinguish the highly synchronous plates. [ABSTRACT FROM AUTHOR]