A review of studies on visual behavior analysis aided diagnosis of autism spectrum disorders / 生物医学工程学杂志

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. With the rapid development of computer vision, visual behavior analysis aided diagnosis of ASD has got more and more attention. This paper reviews the research on visual behavior analysis aided diagnosis of ASD. First, the core symptoms and clinical diagnostic criteria of ASD are introduced briefly. Secondly, according to clinical diagnostic criteria, the interaction scenes are classified and introduced. Then, the existing relevant datasets are discussed. Finally, we analyze and compare the advantages and disadvantages of visual behavior analysis aided diagnosis methods for ASD in different interactive scenarios. The challenges in this research field are summarized and the prospects of related research are presented to promote the clinical application of visual behavior analysis in ASD diagnosis.

A multi-behavior recognition method for macaques based on improved SlowFast network / 生物医学工程学杂志

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.

Conjunto mínimo de dados e o vírus da imunodeficiência humana / Minimum data set and human immunodeficiency virus / Conjunto mínimo de satos y el virus de inmunodeficiencia humana

Objetivo: Investigar dados dos sistemas de informação em saúde do HIV e sua relação com o conjunto mínimo de dados da atenção à saúde (CMD) brasileiro. Métodos: Realizou-se estudo descritivo e transversal em janeiro/2019, a partir dos dados coletados nos formulários dos sistemas que registram o tratamento antirretroviral e exames laboratoriais. Resultados: Foram analisados 282 campos dos formulários. Após agregação dos campos comuns, restaram 83 variáveis, 17 (20,5%) consideradas aplicáveis ao CMD. Conclusão: O CMD coleta dados sobre consultas e exames de seguimento dos contatos assistenciais do HIV, porém não registra tratamento antirretroviral. A maioria das variáveis coletadas nos sistemas do HIV poderão compor o modelo de informação clínica do HIV para Registro Eletrônico de Saúde.

Objective: It was to investigate data of the health information systems of HIV and its relationship with Brazilian minimum data set of health care (MDS). Methods: A descriptive and cross-sectional study was carried out in January/2019, based on data collected in the forms of the systems that register the antiretroviral treatment and laboratory tests. Results: Were analyze 282 fields of the forms, after aggregation of the common ones, 83 variables remained, 17 (20.5%) considered applicable to MDS. Conclusion: The MDS collects data on consultations and follow-up examinations of HIV care, does not register antiretroviral treatment. Most of the variables collected in HIV systems may be part of the HIV clinical information model for the Electronic Health Record.

Objetivo: Investigar datos de los sistemas de información en salud del VIH y su relación con el conjunto mínimo de datos de la atención a la salud (CMD) brasileño. Métodos: Se realizó estudio descriptivo y transversal en enero/2019, a partir de los formularios de los sistemas del tratamiento antirretroviral y exámenes de laboratorio. Resultados: Se analizaron 282 campos de los formularios, después de la agregación de los comunes, quedaron 83 variables, 17 (20,5%) consideradas aplicables al CMD. Conclusión: El CMD recoge datos sobre consultas y exámenes de seguimiento de la asistencia del VIH, no registra tratamiento antirretroviral. La mayoría de las variables del VIH podrán componer modelo de información clínica del Registro Electrónico de Salud.

IgA nephropathy standard dataset and quality control / 中华肾脏病杂志

Objective:To establish a IgA nephropathy (IgAN) standard dataset for the structured and standardization of IgAN clinical information, which will be beneficial to the integration and utilization of clinical information among different medical institutions. Therefore, the IgAN Expert Collaboration Group composed the "IgA Nephropathy Standard Dataset".Methods:Referring to the domestic information standards, guidelines, data standard and consensus of related fields, based on electronic medical history, the patient identification number was used as the primary key of the system to collect information. By standardizing each data element in the data set, the standardization of the management system in data and information exchange, data collaboration and sharing was ensured, and a quality control system was developed.Results:This standard dataset included 607 data elements and 8 business domains, which were patient information, medical history information, physical examination, laboratory examination, assistant examination, renal pathology, drug treatment, and follow-up, respectively. Each module was composed of module name, data element name, English name, definition, range, reference standard, etc. At the same time, a corresponding quality control system was formulated to evaluate data quality from multiple dimensions such as completeness, standardization, accuracy, timeliness, and security for ensuring the high quality and security of the data.Conclusion:The IgAN standard dataset is established, which will contribute to the structuration and standardization of clinical information of IgAN patients.

Triple-negative breast cancer cells respond to T cells severely at the alternative splicing layer

BACKGROUND: Cross talk of tumor­immune cells at the gene expression level has been an area of intense research. However, it is largely unknown at the alternative splicing level which has been found to play important roles in the tumor­immune microenvironment. RESULTS: Here, we re-exploited one transcriptomic dataset to gain insight into tumor­immune interactions from the point of AS level. Our results showed that the AS profiles of triple-negative breast cancer cells co-cultured with activated T cells were significantly changed but not Estrogen receptor positive cells. We further suggested that the alteration in AS profiles in triple-negative breast cancer cells was largely caused by activated T cells rather than paracrine factors from activated T cells. Biological pathway analyses showed that translation initiation and tRNA aminoacylation pathways were most disturbed with T cell treatment. We also established an approach largely based on the AS factor­AS events associations and identified LSM7, an alternative splicing factor, may be responsible for the major altered events. CONCLUSIONS: Our study reveals the notable differences of response to T cells among breast cancer types which may facilitate the development or improvement of tumor immunotherapy.

Minimum dataset standard for cohort study of high-risk population of stroke based on regional big data platform: a consensus / 预防医学

@#A large cohort study of high-risk population of stroke based on the real world is of great significance for stroke prevention and control. However, the data element structures, variable definitions and scopes of regional big data platforms are inconsistent, which will be an obstacle for data sharing, summary, and analysis among different regions. In this study, we formed an expert consensus on a unified minimum dataset standard for the cohort study of high-risk population of stroke, considering the categories and definitions of risk factors of stroke, and the existing database of the regional big data platforms. The consensus shall provide a reference for the comparison, integration, and sharing of real world data within and between regions, and play an important role in the cohort study on risk factors of stroke, as well as the implementation and evaluation of prevention and control measures.

Decision Tree Based Salp Swarm Optimization for Multi Medical Data Classification with Feature Reduction Technique

Abstract The ambitious task in the domain of medical informatics is medical data classification. From medical datasets, intention to ameliorate human burden with the medical data classification entails to taking in classification designs. The medical data classification is the major focus of this paper, where a Decision Tree based Salp Swarm Optimization (DT-SWO) algorithm is proposed. After pre-processingthe hybrid feature selection method selects the medical data features. The high dimensional features are reduced by Discriminant Independent Component Analysis (DICA) and DT-SWO is to classify the most relevant class of medical data. The details of four datasets namely Leukemia, Diffuse Larger B-cell Lymphomas (DLBCL), Lung cancer and Colon relating to four diseases for heart, liver, cancer and lungs are collected from the UCI machine learning repository. Ultimately, the experimental outcomes demonstrated that the proposed DT-SWO algorithm is suitable for medical data classification than other algorithms.

Utilizing benchmarked dataset and gene regulatory network to investigate hub genes in postmenopausal osteoporosis

Objective: The objective of this paper was to investigate hub genes of postmenopausal osteoporosis (PO) utilizing benchmarked dataset and gene regulatory network (GRN). Materials and Methods: To achieve this goal, the first step was to benchmark the dataset downloaded from the ArrayExpress database by adding local noise and global noise. Second, differentially expressed genes (DEGs) between PO and normal controls were identified using the Linear Models for Microarray Data package based on benchmarked dataset. Third, five kinds of GRN inference methods, which comprised Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT), and GEne Network Inference with Ensemble of trees (Genie3), were described and evaluated by receiver operating characteristic (ROC) and precision and recall (PR) curves. Finally, GRN constructed according to the method with best performance was implemented to conduct topological centrality (closeness) for the purpose of investigate hub genes of PO. Results:A total of 236 DEGs were obtained based on benchmarked dataset of 20,554 genes. By assessing Zscore, GeneNet, CLR, PCIT, and Genie3 on the basis of ROC and PR curves, Genie3 had a clear advantage than others and was applied to construct the GRN which was composed of 236 nodes and 27,730 edges. Closeness centrality analysis of GRN was carried out, and we identified 14 hub genes (such as TTN, ACTA1, and MYBPC1) for PO. Conclusion: In conclusion, we have identified 14 hub genes (such as TN, ACTA1, and MYBPC1) based on benchmarked dataset and GRN. These genes might be potential biomarkers and give insights for diagnose and treatment of PO

The two-way regulation molecular network mechanism of Taohechengqi decoction in prevention and treatment of stroke based on vascular endothelial growth factor signaling pathway/tumor necrosis factor signaling pathway / 药学学报

Stroke has been harmful to human health for a long time, and there is no satisfactory treatment strategy because of its complex pathogenesis. Taohechengqi decoction has been effective in the treatment of stroke. In this study, the components were collected by TCMSP, TCMIP, BATMAN-TCM and TCMID databases, the targets were predicted and screened by PharmMapper and BATMAN-TCM databases, and the functional enrichment analysis of the targets was carried out by using R language package clusterProfiler. Finally, the key targets are verified by GEO database and molecular docking. The results showed that 51 active components of Taohechengqi decoction may regulate 15 key targets such as nitric oxide synthase, endothelial (NOS3), prostaglandin G/H synthase 2 (PTGS2), matrix metalloproteinase-9 (MMP9), affecting vascular endothelial growth factor signaling pathway and other pathways to play a role in the prevention of stroke, affecting tumor necrosis factor signaling pathway and other pathways to play a role in the treatment of stroke. GEO data analysis showed that androgen receptor (AR), caspase-8 (CASP8), intercellular adhesion molecule 1 (ICAM1), interleukin-1 beta (IL1B), mitogen-activated protein kinase 14 (MAPK14), MMP9, myeloperoxidase (MPO), peroxisome proliferator-activated receptor gamma (PPARG), PTGS2 and cellular tumor antigen p53 (TP53) were up-regulated genes, while serum albumin (ALB), estrogen receptor 1 (ESR1), NOS3, transcription factor p65 (RELA) and proto-oncogene tyrosine-protein kinase Src (SRC) were down-regulated genes. GEO analysis explained that Taohechengqi decoction may prevent stroke by down-regulating ESR1, NOS3, and treat stroke by up-regulating ICAM1, IL1B, MAPK14, MMP9, PPARG, PTGS2, TP53, and down-regulating RELA and SRC. The study found that in the process of prevention and treatment of stroke, Taohechengqi decoction played a two-way regulation role through multi-genes and multiple ways, which provided a new strategy for the treatment of stroke.

Injury surveillance information system: A review of the system requirements / 中华创伤杂志(英文版)

PURPOSE@#An injury surveillance information system (ISIS) collects, analyzes, and distributes data on injuries to promote health care delivery. The present study aimed to review the data elements and functional requirements of this system.@*METHOD@#This study was conducted in 2019. Studies related to injury surveillance system were searched from January 2000 to September 2019 via the databases of PubMed, Web of Knowledge, ScienceDirect, and Scopus. Articles related to the epidemiology of injury, population survey, and letters to the editor were excluded, while the review and research articles related to ISISs were included in the study. Initially 324 articles were identified, and finally 22 studies were selected for review. Having reviewed the articles, the data needed were extracted and the results were synthesized narratively.@*RESULTS@#The results showed that most of the systems reviewed in this study used the minimum data set suggested by the World Health Organization injury surveillance guidelines along with supplementary data. The main functions considered for the system were injury track, data analysis, report, data linkage, electronic monitoring and data dissemination.@*CONCLUSION@#ISISs can help to improve healthcare planning and injury prevention. Since different countries have various technical and organizational infrastructures, it is essential to identify system requirements in different settings.

The Role and Requirement of Datasets in the Quality Control of Artifcial Intelligence Medical Device / 中国医疗器械杂志

Artificial intelligence is a blooming branch of medical device. Its development and quality control all rely on high quality clinical data. Since there is no established standard or guidance yet, it is important to study how to build and utilize a dataset appropriately and scientifically, especially for the decrease of clinical trial expense. With reference to the current status of premarket review and related guidance in developed countries, this paper analyzes the role and requirement of datasets in the quality control of AI medical device, providing useful information for regulation agencies and the development of public datasets for AI.

Deep learning for prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

Socio-Demographic Characteristics Of Male Contraceptive Use In Indonesia

@#Low contraceptive uptake among men remains significant issues in Indonesia. Hence, this study seeks to understand the association between socio-demographic factors and men’s contraceptive use in Indonesia by utilizing the 2012 Indonesia Demographic and Health Survey (IDHS), couple dataset. Bivariate analysis was conducted by performing a chi-squared test of independence to analyse the relationship between selected socio-demographic factors and the dependent variable. A binary logistic regression model was considered to identify the effects of covariates. Place of residence, husbands’ approval on family planning, husbands and wives knew family planning from newspaper/magazine, and the perception that contraception is woman’s business were significant predictors according to the IDHS. Programs related to gender-sensitive campaign about family planning and gender-sensitive curricula in schools are needed for encouraging men to use contraception.

Machine Learning Cases in Clinical and Biomedical Domains

The aim of this paper is twofold: Firstly, to provide introductory knowledge to the reader who has little orno knowledge of machine learning with examples of applications in clinical and biomedical domains, andsecondly, to compare and contrast the concept of Artificial Neural Network (ANN) and the Qur’anic conceptof intellect (aql) in the Qur’an. Learning algorithm can generally be categorised into supervised andunsupervised learning. To better understand the machine learning concept, hypothetical data of glaucomacases are presented. ANN is then selected as an example of supervised learning and the underlyingprinciples in ANN are presented with general audience in mind with an attempt to relate the mechanismemployed in the algorithm with Qur’anic verses containing the verbs derived from aql. The applications ofmachine learning in clinical and biomedical domains are briefly demonstrated based on the author’s ownresearch and most recent examples available from University of California, Irvine Machine LearningRepository. Selected verses which indicate motivation to use the intellect in positive manners and rebuke tothose who do not activate the intellect are presented. The evidence found from the verses suggests thatANN shares similar learning process to achieve belief (iman) by analysing the similitudes (amsal) introducedto the algorithm.

Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management / 대한의료정보학회지

OBJECTIVES: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. METHODS: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. RESULTS: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. CONCLUSIONS: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

Construction of System Framework of Basic Dataset of Nursing Management Information for Hospital of TCM / 医学信息学杂志

Starting with the concepts of basic dataset and basic dataset of nursing management information of hospital of Tradtional Chinese Medicine (TCM),the paper introduces the research purpose,construction principle,construction method,construction contents and system framework of the basic dataset standard system of nursing management information of hospital of TCM,in order to lay a foundation for achieving nursing management data resource sharing and exchange of hospital of TCM.

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma

BACKGROUND/AIMS: The integration of multiple profiling data and the construction of a transcriptional regulatory network may provide additional insights into the molecular mechanisms of hepatocellular carcinoma (HCC). The present study was conducted to investigate the deregulation of genes and the transcriptional regulatory network in HCC. METHODS: An integrated analysis of HCC gene expression datasets was performed in Gene Expression Omnibus. Functional annotation of the differentially expression genes (DEGs) was conducted. Furthermore, transcription factors (TFs) were identified, and a global transcriptional regulatory network was constructed. RESULTS: An integrated analysis of eight eligible gene expression profiles of HCC led to 1,835 DEGs. Consistent with the fact that the cell cycle is closely related to various tumors, the functional annotation revealed that genes involved in the cell cycle were significantly enriched. A transcriptional regulatory network was constructed using the 62 TFs, which consisted of 872 TF-target interactions between 56 TFs and 672 DEGs in the context of HCC. The top 10 TFs covering the most downstream DEGs were ZNF354C, NFATC2, ARID3A, BRCA1, ZNF263, FOXD1, GATA3, FOXO3, FOXL1, and NR4A2. This network will appeal to future investigators focusing on the development of HCC. CONCLUSIONS: The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.

Possibility of the Use of Public Microarray Database for Identifying Significant Genes Associated with Oral Squamous Cell Carcinoma

There are lots of studies attempting to identify the expression changes in oral squamous cell carcinoma. Most studies include insufficient samples to apply statistical methods for detecting significant gene sets. This study combined two small microarray datasets from a public database and identified significant genes associated with the progress of oral squamous cell carcinoma. There were different expression scales between the two datasets, even though these datasets were generated under the same platforms - Affymetrix U133A gene chips. We discretized gene expressions of the two datasets by adjusting the differences between the datasets for detecting the more reliable information. From the combination of the two datasets, we detected 51 significant genes that were upregulated in oral squamous cell carcinoma. Most of them were published in previous studies as cancer-related genes. From these selected genes, significant genetic pathways associated with expression changes were identified. By combining several datasets from the public database, sufficient samples can be obtained for detecting reliable information. Most of the selected genes were known as cancer-related genes, including oral squamous cell carcinoma. Several unknown genes can be biologically evaluated in further studies.

International Spinal Cord Injury Data Sets: Basic Pain Data Set / 中国康复理论与实践

@#Chronic pain is one of the most frequently reported reasons for reduced quality of life following spinal cord injury. Now a standardized way to collect data concerning pain in persons with SCI is lacking, which is important for the studying of pain mechanism and the evaluating of the outcome of treatment. The purpose of the International Spinal Cord Injury Pain Data Set is to standardize the collection and reporting of pain in the SCI population. This article describes each variable included in the Basic Pain Data Set in detail and how to fill in the Basic Pain Data Set forms by using 3 cases.

Unfolding for Color Volume Dataset Using the Difference of Segmented Contours / 대한의료정보학회지

OBJECTIVE: Unfolding is a rendering method to visualize organs at a glance by virtually incising them. Although conventional methods exploit gray-scale volume datasets such as CT or MR images, we use the Visible Korean Human dataset preserving actual color. This can be helpful for the study of anatomical knowledge. Segmented images of Visible Korean Human dataset store the boundary of organs. Since medical experts manually perform the segmentation from anatomical color images, it is very time-consuming. In general, therefore, some images selectively sampled with interval from entire color images are segmented. When we generate a segment volume dataset with the selected images, final results are deteriorated due to lack of segmentation information for missed images. In this paper, we solve this problem by generating intermediate images without performing a manual segmentation. METHODS: Firstly, after comparing differences of organ's contours in between two consecutive segmented images, we represent the differences as a user-defined value in the intermediate images. This procedure is repeated for all pairs of manually segmented images to reconstruct entire volume data consist of manually segmented images and their intermediate images. In rendering stage, we perform the radial volume ray casting along with the central path of target organ. If a ray reaches to a region having the user-defined values, we advance over the region without compositions to the boundary of that region. Then the color composition is begun by performing backtracking, since the advanced region is regarded to the thickness of it. RESULTS: As a result, we can produce high quality unfolding images for the stomach, colon, bronchus, and artery of the Visible Korea Human dataset. CONCLUSION: Since our approach can be applied to virtual dissection including actual human colors, it is helpful for the endoscopy and anatomy studies.