Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm.

The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception-ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception-ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system's calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.

The relationship between acrosome reaction and polyunsaturated fatty acid composition in boar sperm.

This study investigated the relationship between acrosome reactions and fatty acid composition with respect to fertility in boar sperm. The acrosome reaction was induced more than 85% by 60 mM methyl-beta-cyclodextrin (MBCD), and plasma membrane integrity was significantly reduced dependent on the MBCD level in boar sperm (p < .05). The acrosome-reacted sperm exhibited significantly higher saturated fatty acids (SFAs) and lower polyunsaturated fatty acids (PUFAs) composition compared to the non-acrosome reaction group (p < .0001). In addition, the PUFAs, C22:5n-6 (docosapentaenoic acid [DPA]; p < .01) and C22:6n-3 (docosahexaenoic acid [DHA]; p < .0001) were significantly decreased, and cleavage and blastocyst formation of oocytes were significantly (p < .0001) decreased in acrosome-reacted sperm relative to non-acrosome-reacted sperm. Moreover, acrosome reaction was positively correlated with SFAs, whereas negatively correlated with PUFAs, of the PUFAs, the DPA (p = .0005) and DHA (p = <.0001) were negatively correlated with the acrosome reaction. Therefore, these results suggest that the PUFAs composition of sperm is closely involved in acrosome reaction in pigs.

uEFS: An efficient and comprehensive ensemble-based feature selection methodology to select informative features.

Feature selection is considered to be one of the most critical methods for choosing appropriate features from a larger set of items. This task requires two basic steps: ranking and filtering. Of these, the former necessitates the ranking of all features, while the latter involves filtering out all irrelevant features based on some threshold value. In this regard, several feature selection methods with well-documented capabilities and limitations have already been proposed. Similarly, feature ranking is also nontrivial, as it requires the designation of an optimal cutoff value so as to properly select important features from a list of candidate features. However, the availability of a comprehensive feature ranking and a filtering approach, which alleviates the existing limitations and provides an efficient mechanism for achieving optimal results, is a major problem. Keeping in view these facts, we present an efficient and comprehensive univariate ensemble-based feature selection (uEFS) methodology to select informative features from an input dataset. For the uEFS methodology, we first propose a unified features scoring (UFS) algorithm to generate a final ranked list of features following a comprehensive evaluation of a feature set. For defining cutoff points to remove irrelevant features, we subsequently present a threshold value selection (TVS) algorithm to select a subset of features that are deemed important for the classifier construction. The uEFS methodology is evaluated using standard benchmark datasets. The extensive experimental results show that our proposed uEFS methodology provides competitive accuracy and achieved (1) on average around a 7% increase in f-measure, and (2) on average around a 5% increase in predictive accuracy as compared with state-of-the-art methods.

Hepatocyte-specific clusterin overexpression attenuates diet-induced nonalcoholic steatohepatitis.

Clusterin is a multifunctional glycoprotein that plays important roles and is up-regulated in liver diseases such as hepatitis and hepatocellular carcinoma. However, little is known about the significance of clusterin in the pathogenesis of non-alcoholic steatohepatitis (NASH). The aim of this study is to examine the role of clusterin in progression of steatohepatitis in mice fed a methionine and choline deficient (MCD) diet. We generated hepatocyte-specific clusterin overexpression (hCLU-tg) mice, and hCLU-tg mice showed lower levels of hepatic triglycerides, less infiltration of macrophages and reduction of TNF-α, activation of Nrf-2 than wild-type littermates fed the MCD diet. Also, sustained clusterin expression in liver ameliorated hepatic fibrogenesis by reducing the activation of hepatic stellate cells by MCD diet. Sustained expression of clusterin in liver functioned as a preconditioning stimulus and prevented MCD diet-induced severe steatohepatitis injury via Nrf2 activation. These results demonstrate a novel function of clusterin as an immune preconditioning regulator in various inflammatory diseases including steatohepatitis.

iCBLS: An interactive case-based learning system for medical education.

Medical students should be able to actively apply clinical reasoning skills to further their interpretative, diagnostic, and treatment skills in a non-obtrusive and scalable way. Case-Based Learning (CBL) approach has been receiving attention in medical education as it is a student-centered teaching methodology that exposes students to real-world scenarios that need to be solved using their reasoning skills and existing theoretical knowledge. In this paper, we propose an interactive CBL System, called iCBLS, which supports the development of collaborative clinical reasoning skills for medical students in an online environment. The iCBLS consists of three modules: (i) system administration (SA), (ii) clinical case creation (CCC) with an innovative semi-automatic approach, and (iii) case formulation (CF) through intervention of medical students' and teachers' knowledge. Two evaluations under the umbrella of the context/input/process/product (CIPP) model have been performed with a Glycemia study. The first focused on the system satisfaction, evaluated by 54 students. The latter aimed to evaluate the system effectiveness, simulated by 155 students. The results show a high success rate of 70% for students' interaction, 76.4% for group learning, 72.8% for solo learning, and 74.6% for improved clinical skills.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax.

OBJECTIVE: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. METHODS AND MATERIALS: A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. RESULTS: We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. CONCLUSION: Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

Multi-model-based interactive authoring environment for creating shareable medical knowledge.

OBJECTIVE: Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows. Our proposed system provides a user-friendly authoring environment to create Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making by CDSS. METHODS AND MATERIALS: Existing systems are not physician friendly and lack interoperability and shareability of knowledge. In this paper, we proposed Intelligent-Knowledge Authoring Tool (I-KAT), a knowledge authoring environment that overcomes the above mentioned limitations. Shareability is achieved by creating a knowledge base from MLMs using Arden Syntax. Interoperability is enhanced using standard data models and terminologies. However, creation of shareable and interoperable knowledge using Arden Syntax without abstraction increases complexity, which ultimately makes it difficult for physicians to use the authoring environment. Therefore, physician friendliness is provided by abstraction at the application layer to reduce complexity. This abstraction is regulated by mappings created between legacy system concepts, which are modeled as domain clinical model (DCM) and decision support standards such as virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We represent these mappings with a semantic reconciliation model (SRM). RESULTS: The objective of the study is the creation of shareable and interoperable knowledge using a user-friendly and flexible I-KAT. Therefore we evaluated our system using completeness and user satisfaction criteria, which we assessed through the system- and user-centric evaluation processes. For system-centric evaluation, we compared the implementation of clinical information modelling system requirements in our proposed system and in existing systems. The results suggested that 82.05% of the requirements were fully supported, 7.69% were partially supported, and 10.25% were not supported by our system. In the existing systems, 35.89% of requirements were fully supported, 28.20% were partially supported, and 35.89% were not supported. For user-centric evaluation, the assessment criterion was 'ease of use'. Our proposed system showed 15 times better results with respect to MLM creation time than the existing systems. Moreover, on average, the participants made only one error in MLM creation using our proposed system, but 13 errors per MLM using the existing systems. CONCLUSION: We provide a user-friendly authoring environment for creation of shareable and interoperable knowledge for CDSS to overcome knowledge acquisition complexity. The authoring environment uses state-of-the-art decision support-related clinical standards with increased ease of use.

Toll-like receptor 4 signaling is required for clusterin-induced tumor necrosis factor-α secretion in macrophage.

Clusterin is a secretory glycoprotein that is up-regulated in areas of inflammation and under increased levels of oxidative stress. Previously, we demonstrated that clusterin activates NF-κB, and up-regulates the expression of MMP-9 and TNF-α. In this research, we extend our previous findings by reporting that such clusterin-induced macrophage response is mediated via TLR4 signaling. Specifically, we found that TNF-α induced by clusterin was significantly abrogated by pretreatment of TLR4-signaling inhibitors and anti-TLR4 neutralizing antibody. Additionally, a primary culture of macrophages derived from TLR4-signal defective and knockout mice were unresponsive to clusterin, resulting in no TNF-α secretion, whereas macrophages carrying wild-type TLR4 responded to clusterin and induced TNF-α. Moreover, clusterin increased NF-κB promoter activity in HEK-Blue hTLR4 cells, but not in HEK-Blue Null2 cells. To confirm that clusterin elicits TLR4 signal transduction, recombinant clusterin was generated and purified from cell culture. Interestingly, we found that the recombinant clusterin with C-terminal HA-tag induces TNF-α secretion at a significantly lower level compared to an intact form of clusterin without C-terminal HA-tag. Removal of HA-tag from the recombinant clusterin restored its activity, suggesting that C-terminal HA-tag partially masks the domain involved in TLR4 signaling. Furthermore, clusterin enhanced TLR4 mobilization into lipid raft of plasma membrane, and TNF-α and MMP-9 secretion stimulated by clusterin was diminished by pretreatment with methyl-ß-cyclodextrin (MßCD), which was used to disrupt lipid raft. In conclusion, clusterin-induced TNF-α and MMP-9 up-regulation is most likely mediated via TLR4 recruitment into lipid rafts, and these data describe a novel role of clusterin as an endogenous regulator for TLR4 signaling.

The Mining Minds digital health and wellness framework.

BACKGROUND: The provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people's conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems. METHODS: This work presents Mining Minds, a novel framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized support. Mining Minds embraces some of the most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, as well as modern concepts and methods, such as context-awareness, knowledge bases or analytics, to holistically and continuously investigate on people's lifestyles and provide a variety of smart coaching and support services. RESULTS: This paper comprehensively describes the efficient and rational combination and interoperation of these technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support. Moreover, this work presents a realization of the key architectural components of Mining Minds, as well as various exemplary user applications and expert tools to illustrate some of the potential services supported by the proposed framework. CONCLUSIONS: Mining Minds constitutes an innovative holistic means to inspect human behavior and provide personalized health and wellness support. The principles behind this framework uncover new research ideas and may serve as a reference for similar initiatives.

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval.

Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.