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The purpose of this research was to conduct a comparative study of the impact of ResearchGate indicators on increasing citation and usage counts of hot papers in clinical medicine indexed in the Web of Science (WoS) database. This is an applied research and has been performed using scientometric methods. Article titles, the number of citations, "Usage count last 180 days", "Usage count since 2013", publication year, and authors of 583 hot papers in clinical medicine were extracted from the WoS database. Then, the readership and citation counts of articles were extracted from ResearchGate social network. The data were analyzed by descriptive and analytical statistics using Microsoft Excel and SPSS 21. The results showed that there was a significant relationship between the number of received citations, both usage counts in WoS and ResearchGate indicators (P value = 0.01). Selfarchiving in scientific social networking sites such as ResearchGate may be effective on visibility of articles that may also be related to their received citations. [ABSTRACT FROM AUTHOR]

Presents abstracts of papers and posters for presentation at the 2005 American Society for Clinical Laboratory Science Annual Meeting and Clinical Laboratory Exposition, July 26-30, 2005 in Orlando, Florida. "In Vitro Effects of Chloramine-T on Select Wound Pathogens," by Linda J. Laatsch; "Prospective Students' Preparation for a CLS Program," by Leonce H. Thierry Jr.; "Utility of Lecithin Cholesterol Acyl Transferase Mass as a Diagnostic Marker for Liver Disease and Liver Transplant," by Karen R. Murray.

• This article summarized the development course of biodegradable cardiovascular stents. • The present in vivo experimental results of Mg-based stents were briefly reviewed. • The strategies for surface endothelialization of cardiovascular stents were summarized. • The recent advances in surface endothelialization of magnesium alloy stent materials were reviewed. • The key issues and future direction of magnesium alloy stents were proposed in the present article. Magnesium and its alloy have good mechanical properties and biodegradability, and have become the hotspot of the next-generation biodegradable vascular stent materials. However, their rapid degradation in vivo and poor biocompatibility are still the bottlenecks of clinical applications for the cardiovascular stents. In particular, how to induce the repair and regeneration of the vascular endothelial with normal physiological functions on the surface of the magnesium alloy stent materials represents the key to its clinical application in the field of cardiovascular stents. It has been believed that it is an ideal way to completely solve the postoperative complications through constructing the multifunctional anti-corrosive bioactive coating on the magnesium alloy surface to induce the formation of vascular endothelium with normal physiological functions. However, how to construct a corrosion-resistant multifunctional bioactive coating with the good endothelial regeneration abilities on the magnesium alloy surface still faces a great challenge. This paper mainly focused on highlighting and summarizing the recent advances in the surface endothelialization of the magnesium alloy materials for the vascular stent, including the bio-inert coating, in-situ immobilization of bioactive molecules on the surface, polymer coating loaded with bioactive factors, novel multifunctional polymer coating, bioactive micropatterns, bioactive layer with glycocalyx-like structure, NO-releasing coating and bioactive sol-gel coating. The advantages and disadvantages of these strategies were discussed and analyzed. Finally, in the senses of future development and clinical application, this paper analyzed and summarized the development direction and prospect of surface endothelialization of the magnesium alloy vascular stents. It is anticipated that this review can give the new cues to the surface endothelialization of the cardiovascular magnesium alloy stents and promote future advancements in this field. [ABSTRACT FROM AUTHOR]

Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision. The potential applications span across multiple domains, including human–computer interaction, virtual reality, industry, and medicine. While gesture recognition has achieved remarkable accuracy, quantifying fine movements remains a hurdle, particularly in clinical applications where the assessment of hand dysfunctions and rehabilitation training outcomes necessitate precise measurements. Several novel and lightweight frameworks based on Deep Learning have emerged to address this issue; however, their performance in accurately and reliably measuring finger movements requires validation against well-established gold standard systems. In this paper, the aim is to validate the hand-tracking framework implemented by Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that exploits the depth estimation of an RGB-Depth camera to achieve more accurate tracking of 3D movements. Three dynamic exercises commonly administered by clinicians to assess hand dysfunctions, namely hand opening–closing, single finger tapping and multiple finger tapping are considered. Results demonstrate high temporal and spectral consistency of both frameworks with the gold standard. However, the enhanced GMH-D framework exhibits superior accuracy in spatial measurements compared to the baseline GMH, for both slow and fast movements. Overall, our study contributes to the advancement of hand tracking technology, and the establishment of a validation procedure as a good-practice to prove efficacy of deep-learning-based hand-tracking. Moreover, it proves that GMH-D is a reliable framework for assessing 3D hand movements in clinical applications. • Hands are vital for carrying out daily-life tasks without external aid. • Measuring objectively hand dexterity can support new digital health treatments. • Deep Learning allows for video-based hand tracking of fine motor tasks. • We validate accuracy of Google Mediapipe Hand and GMH-D against motion capture. • Both frameworks achieve good-to-high spatial and temporal hand tracking performance. [ABSTRACT FROM AUTHOR]

Atrous convolution and attention have improved the performance of the UNet architecture for segmentation purposes. However, a perfect combination of atrous convolution and attention to improve brain tumor segmentation performance is still an interesting challenge. In this paper, we propose UNet architecture with the addition of attention in the skip connection and the replacement of the processing block with two atrous convolution sequences connected to the attention unit combined with one residual path called the Multipath Residual Attention Block (MRAB). The architecture was trained using the Brain Tumor Segmentation(BraTS) 2018, 2019, 2020, and 2021 challenge datasets. The ensembled model was validated online and obtained dice scores of 77.71%, 79.77%, 89.59% for BraTS2018, 74.91%, 80.98%, 88.48% for BraTS2019, 72.91%, 80.19%, 88.57% for BraTS2020, and 77.73%, 82.19%, 89.33% for BraTS2021 validation datasets for Enhanced Tumor(ET), Tumor Core(TC), and Whole Tumor(WT) areas, respectively. These dice score performances outperformed state-of-the-art brain tumor segmentation architectures and promised to be developed for clinical application. [ABSTRACT FROM AUTHOR]

The ability to automatically segment anatomical targets on medical images is crucial for clinical diagnosis and interventional therapy. However, supervised learning methods often require a large number of pixel-wise labels that are difficult to obtain. This paper proposes a weakly supervised glottis segmentation (WSGS) method for training end-to-end neural networks using only point annotations as training labels. This method functions by iteratively generating pseudo-labels and training the segmentation network. An automatic seeded region growing (ASRG) algorithm is introduced to generate quality pseudo labels to diffuse point annotations based on network prediction and image features. Additionally, a novel loss function based on the structural similarity index measure (SSIM) is designed to enhance boundary segmentation. Using the trained network as its core, a glottis state monitor is developed to detect the motion behavior of the glottis and assist the anesthesiologist. Finally, the performance of the proposed approach was evaluated on two datasets, achieving an average mIoU and accuracy of 82.7% and 91.3%. The proposed monitor was demonstrated to be effective, which holds significance in clinical applications. • Weakly supervised glottis segmentation method only with point annotations. • Geometry-constrained diffusion strategy for quality pseudo-label generation. • Development of a glottis state monitor to detect the motion behavior of the glottis. [ABSTRACT FROM AUTHOR]

Personal data is highly affected by the witnessed digital transformation of healthcare processes. This process relies deeply on the connectivity and decentralization of healthcare systems and data repositories. In this context, value creation and quality enhancement are obviously leveraged, however both health providers and individuals could be exposed to many risks ranging from privacy violations to medical identity theft and personal harm. Hence, it is essential that healthcare stakeholders ensure privacy protection and systemic compliance to personal data regulations such as HIPPA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). Taking clinical processes as a starting point is very important to highlight the personal data in use and to assess whether such usage is justifiable and subsequently allow privacy management decisions to be made. In this paper we combine BPMN (Business Process Model and Notation) and DMN (Decision Model and Notation) to model clinical care pathways as standard business processing constituting the hospital information system. Business process modelling presents a useful mean to model clinical care pathways. It allows a complete discovery of data processing scenarios. DMN (Decision Model and Notation) is implemented in BPMN models to present the rules that lead to a decision in easy-to-read tables which are executed directly by a decision engine. In addition, the integration of verifiable security labels of the manipulated data, we make sure compliance to legislation is ensured at the level of decision rules for each decision table of the DMN. [ABSTRACT FROM AUTHOR]

BACKGROUND: During the coronavirus disease 2019 (COVID-19) outbreak, a critical care outreach team was implemented in our hospital to guarantee multidisciplinary patient assessment at admission and prompt ICU support in medical wards. In this paper, we report the activity plan results and describe the baseline characteristics of the referred subjects. METHODS: We retrospectively evaluated data from 125 subjects referred to the critical care outreach team from March 22 to April 22, 2020. We considered subjects with a ceiling of care decision, with those deemed eligible assigned to level 3 care (ward subgroup), and those deemed ineligible admitted to the ICU (ICU subgroup). Quality indicators of the outreach team plan delivery included number of cardiac arrest calls, number of intubations in level 2 areas, and ineffective palliative support. RESULTS: We enrolled 125 consecutive adult subjects with a confirmed diagnosis of COVID-19. We did not report any emergency endotracheal intubations in the clinical ward. In the care ceiling subgroup, we had 2 (3.3%) emergency calls for cardiac arrest, whereas signs of ineffective palliative support were reported in 5 subjects (12.5%). Noninvasive forms of respiratory assistance were delivered to 40.0% of subjects in the ward subgroup (median 3 d [interquartile range (IQR) 2-5]), to 45.9% of subjects in the care ceiling subgroup (median 5 d [IQR 3-7]), and to 64.7% of subjects in the ICU subgroup (median 2.5 d [IQR 1-3]). Thirty of the 31 ward subjects (96.7%), 26 of the 34 ICU subjects, (76.4%), and 19 of the 61 ceiling of care subjects (31.1%) were discharged. CONCLUSIONS: In the context of a hospital and ICU surge, a multidisciplinary daily plan supported by a dedicated critical care outreach team was associated with a low rate of cardiac arrest calls, no emergency intubations in the ward, and appropriate palliative care support for subjects with a ceiling of care decision. [ABSTRACT FROM AUTHOR]

The ongoing water crises in Detroit and Flint, Michigan, offer dramatic cases of retrogression in realizing the human right to water--particularly striking in a region that enjoys access to one-fifth of the world's freshwater and a country that has historically enjoyed near-universal access to water and sanitation. Efforts to secure safe, sufficient, affordable, acceptable, and accessible water in these cities reveal a troubling inability to protect the human right to water through legal measures. Compounding the challenge is the lack of reliable government data on the scope and impacts of the water crises--a void that residents have organized to fill. Activists have engaged a number of citizen-led research projects to demonstrate the health impacts of unsafe and unaffordable water. This paper discusses the process and potential of such projects to advance the substance of the human right to water in the United States, considering their effects within and outside the law. These research efforts have significant methodological and legal constraints with respect to widespread water insecurity, exposing a serious vulnerability in communities' ability to protect drinking water and public health in the United States through legal means. However, drawing on Amartya Sen's theory of human rights, I elaborate the extra-juridical powers of human rights, emphasizing their power to galvanize action and articulate ethical demands. Citizen science is a powerful mode of engaging residents in the articulation--and quantification--of those human rights demands, as I demonstrate with local cases. [ABSTRACT FROM AUTHOR]

The constructed medical ontologies need to be updated in order to reflect the changes occurred on the medicine such as the clinical findings, treatments and their side effects. Various researchers defined the ontology maintenance as the process of updating the ontology or the evolution of the ontology. Other researches consider the ontology maintenance as a composed process that involves the ontology evolution and pruning. This paper presents a Rheumatoid ontology maintenance system that incorporates both of the ontology evolution and the ontology pruning. The evolution is executed in a way that ensures the ontology consistency and its relevance to the domain of interest. [ABSTRACT FROM AUTHOR]

Nowadays, in clinical medicine diagnosticians usually use DNA microarray datasets for diagnosis and classification of cancer. However, DNA microarray datasets typically have very large number of genes and less number of samples, therefore, before diagnosis and classification of cancer it is quite requisite to select most relevant genes. In this paper, we have developed a two phase classification model in which most relevant genes are selected by integrating ReliefF with Recursive Binary Gravitational Search Algorithm (RBGSA) in the help of a classifier of Multinomial Naive Bayes. The RBGSA recursively transforms a very raw gene space to an optimized one at each iteration while not degrading the accuracy. We evaluate our model by comparing it with 6 other known methods on 6 different microarray datasets of cancer. Comparison results show that our model gets substantial improvements in accuracy over other methods. [ABSTRACT FROM AUTHOR]

This paper revisits the concept of trajectory, as set out by Glaser and Strauss in 1965 and developed by others. Much of the research utilizing this concept has been focused on illness and the organization of work and biographies of participants involved in managing illness trajectories. The emphasis of this paper is not illness, but clinical trial research. It utilizes the concept of trajectory as a lens for this empirically grounded thick description of the occupational personnel and the work activities of clinical trials research. It draws on data collected in an ethnographic field study of informed consent and AIDS clinical trials. Social scientists and clinicians are involved in the implementation of clinical trials and in the transfer and utilization of knowledge and interventions derived therefrom. Therefore, this paper seeks to contribute to scholarship on this complex field of practice. [ABSTRACT FROM AUTHOR]

Summary: Objectives:: Non-epileptic seizures (NES) present a considerable challenge in clinical practice. This paper reviews published evidence for the reliability of a number of procedures for the differential diagnosis of NES and epilepsy. Methods:: Papers identified from MEDLINE and PsychInfo Databases (1980–2001) and additional hand searches were independently reviewed using methods for evaluating evidence in systematic reviews [Liddle, J., Williamson, M. Irwig, L. Method for evaluating research guideline evidence. New South Wales Department of Health; 1996 [State Health publication no. (CEB) 96–204]; SIGN. An introduction to SIGN methodology for the development of evidence based clinical guidelines. Scottish Intercollegiate Network; 1999]. Included studies had to have an NES group and a control group of people with epilepsy (each n ≥10), allocated using EEG linked video-recording of concurrent behaviour, and sensitivity and specificity values had to be stated or be calculable. Results:: Thirty-three papers were identified, of which 13 satisfied criteria. Excluded studies are briefly described. Those retained comprised a range of procedures [seizure induction, MMPI assessment, physiological assessment (prolactin, SPECT), pre-ictal pseudosleep, and ictal/post-ictal characteristics]. No procedure emerged with both high sensitivity and specificity and adequately replicated findings, although high levels of specificity were more commonly reported than high levels of sensitivity. This suggests that procedures were generally better at excluding a possible diagnosis. Conclusions:: No procedure attains reliability equivalent to EEG video-telemetry. Further rigorous evaluation, using standardised and replicable methodologies, is required. The range of symptoms presented in NES suggests that a multi-method approach may be required. This too would require evaluation. [Copyright &y& Elsevier]

The purpose of this paper is to present a new approach for inter-subject non-rigid registration of 3D MR brain images which could assist in automatic labeling of brain structures. A physical dynamic elasticity model (DEM) is developed which tends to represent the complex non-linear deformations as elastic waves. The transformation is governed by elastodynamics wave equation. The registration process ensues in a hierarchical fashion, thus reducing the risk of obtaining a local optimal transformation. Along with the correction of local misalignments, it also removes global shape differences without any prior initialization. The proposed scheme was compared against high ranking registration methods including: DROP, SyN, ART and DRAMMS. The results were quantitatively analyzed by computing and testing the statistical significance of the volume overlap measures and Hausdorff distance for segmented structures with DROP, SyN, ART, DRAMMS and DEM registration methods. Experimental results demonstrate that the proposed DEM registration method leads to very promising results when applied to the problem of inter-subject registration and that favorably compares against classical registration approaches. Since DEM registration method is able to reduce registration errors significantly, hence it could be used to automatically label the anatomical structures for clinical studies. [ABSTRACT FROM AUTHOR]

• A promising deep learning based approach was presented to detect cancer metastasis in lymph node images with higher accuracy. • The proposed deep learning framework uses ResNet architectures, transfer learning and 1cycle policy. • The validity of the proposed approach was evaluated on PCAM dataset consisted of 220,025 lymph node images. • The experiments demonstrated that the presented method outperformed most of the current studies. Lymph node metastases are one of the most indicator of some cancer types such as breast, colon and prostate. Breast cancer mostly spreads to lymph nodes in the armpit and it is one of the most common causes of death in women worldwide. Pathologists need more attention and time to diagnose metastasis in digitized lymph node images and also this process tends to be misinterpreted. In this paper, a promising deep learning based approach was presented to detect cancer metastasis in lymph node images with higher accuracy. The proposed deep learning framework uses ResNet architectures, transfer learning and 1cycle policy which is the method of finding the optimal learning rate. The validity of the proposed approach was evaluated on PCAM dataset consisted of 220,025 lymph node images. The experiments demonstrated that the presented method outperformed most of the current studies, achieving accuracy of 98.60% on the PCAM dataset by using ResNet models and fine-tuning techniques effectively. The burden of diagnosis procedure of metastasis can be lessened with the proposed deep learning framework in the clinical applications. [ABSTRACT FROM AUTHOR]

Automatic multi-organ segmentation in medical images is crucial for many clinical applications. The art methods have reported promising results but rely on massive annotated data. However, such data is hard to obtain due to the need for considerable expertise. In contrast, obtaining a single-organ dataset is relatively easier, and many well-annotated ones are publicly available. To this end, this work raises the partially supervised problem: can we use these single-organ datasets to learn a multi-organ segmentation model? In this paper, we propose the P a r t i al- and M utual- P rior incorporated framework (PRIMP) to learn a robust multi-organ segmentation model by deriving knowledge from single-organ datasets. Unlike existing methods that largely ignore the organs' anatomical prior knowledge, our PRIMP is designed with two key prior shared across different subjects and datasets: (1) partial-prior, each organ has its own character (e.g. , size and shape) and (2) mutual-prior, the relative position between different organs follows the comparatively fixed anatomical structure. Specifically, we propose to incorporate partial-prior of each organ by learning from the single-organ statistics, and inject mutual-prior of organs by learning from the multi-organ statistics. By doing so, the model is encouraged to capture organs' anatomical invariance across different subjects and datasets, thus guaranteeing the anatomical reasonableness of the predictions, narrowing down the problem of domain gaps, capturing spatial information among different slices, thereby improving organs' segmentation performance. Experiments on four publicly available datasets (LiTS, Pancreas, KiTS, BTCV) show that our PRIMP can improve the performance on both the multi-organ and single-organ datasets (17.40% and 3.06% above the baseline model on DSC, respectively) and can surpass the comparative approaches. • We propose PRIMP to learn multi-organ segmentation model from single-organ datasets. • PRIMP can effectively capture anatimical priors of single- and multi-organ. • Experiments on four publicly available datasets show PRIMP's effectiveness. [ABSTRACT FROM AUTHOR]

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Accurate segmentation of the Corpus Callosum (CC) is an important aspect of clinical medicine and is used in the diagnosis of various brain disorders. In this paper, we propose an automated method for two and three dimensional segmentation of the CC using diffusion tensor imaging. It has been demonstrated that Hartigan's K-means is more efficient than the traditional Lloyd algorithm for clustering. We adapt Hartigan's K-means to be applicable for use with the metrics that have a f -mean (e.g. Cholesky, root Euclidean and log Euclidean). Then we applied the adapted Hartigan's K-means, using Euclidean, Cholesky, root Euclidean and log Euclidean metrics along with Procrustes and Riemannian metrics (which need numerical solutions for mean computation), to diffusion tensor images of the brain to provide a segmentation of the CC. The log Euclidean and Riemannian metrics provide more accurate segmentations of the CC than the other metrics as they present the least variation of the shape and size of the tensors in the CC for 2D segmentation. They also yield a full shape of the splenium for the 3D segmentation. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN. [ABSTRACT FROM AUTHOR]

Mammogram breast cancer images have the ability to assist physicians in detecting diseases caused by abnormal growth. Output images from the sensors are more likely not optimal for display or further processing mainly because of noise. In order to prevent these problems, image processors should accompany image sensors. Typically, two separate chips for sensing and processing are integrated together Introduction of such smart sensing in computer aided diagnostic system will assist physicians in their daily work. This paper presents the advantage of smart sensing and also presents the use of current mode CMOS active pixel sensors in smart sensing for low power and fast processing applications. We have used Bayesian Network to diagnose based on symptom. [ABSTRACT FROM AUTHOR]

Background and objective: Used as an integrated tool, mHealth may improve the ability of healthcare providers in rural areas to provide care, improve access to care for underserved populations, and improve biophysical outcomes of care for persons with diabetes in rural, underserved populations. Our objective in this paper is to present an integrated review of the impact of mHealth interventions for community dwelling individuals with type two diabetes. Materials and methods: A literature search was performed using keywords in PubMed to identify research studies which mHealth technology was used as the intervention Results and discussion: Interventions using mHealth have been found to improve outcomes, be cost effective, and culturally relevant. mHealth technology that has been used to improve outcomes include: seeking out health information via the web, access to appointment scheduling and medication refills, secure messaging, computerized interventions to manage a chronic condition, use of a personal health record, use of remote monitoring devices, and seeking support from others with similar health concerns through social networks. Conclusion: Using the validated Chronic Care Model to translate what is known about mHealth technology to clinical practice has the potential to improve the ability of healthcare providers in rural areas to provide care, improve access to care for underserved populations, and improve biophysical outcomes of care for persons with diabetes in rural underserved populations. While these approaches were effective in improving some outcomes, they have not resulted in the establishment of the necessary electronic infrastructure for a sustainable mobile healthcare delivery model. [ABSTRACT FROM AUTHOR]

• Extract inter-slice spatial information in the form of 2.5D. • Proposes a lightweight Inception convolution structure with residual connections to significantly reduce the network's parameters. • Employ a combination of BCE and Dice loss to achieve fast convergence and low fluctuations in network training. • Evaluate the proposed method on publicly available datasets, LiTS17 and 3Dircadb. One critical factor that restricts the clinical application of computer-aided liver and tumor segmentation is the method's high complexity and low accuracy. Overcoming this limitation is what we are concerned about in this study. This paper presented a new 2.5D lightweight network for fast and accurate liver and tumor segmentation from CT images. The method is grounded in the U-Net framework, which leverages the techniques from the residual and Inception theories. We first adopted the 2.5D training mode for CNN networks to improve the utilization of spatial information. Then, we designed an improved U-type architecture to substantially reduce the parameters by introducing residual block and InceptionV3, named RIU-Net. Finally, a hybrid loss function combined BCE and Dice is employed to speed up the convergence and improve accuracy. We evaluated the proposed method on two publicly available databases, LiTS17 and 3DIRCADb. The performance of our approach is compared with five closely related techniques. Our result outperforms the others on both accuracy and time cost. Specifically, the total number of parameters is reduced by 70% compared to U-Net. Both quantitative and qualitative results demonstrated the superior applicability of our method and thus proved to be a promising lightweight tool for computer-aided liver and tumor segmentation.. [ABSTRACT FROM AUTHOR]

• A dual attention mechanism is proposed which can highlight the meaningful local patterns contained in the skin lesion regions, enhancing the feature representation and the interpretability of the proposed network at the same time. • An auxiliary learning mechanism which contains auxiliary supervision and KL divergence based regularization is proposed. The KL regularization can make the two auxiliary supervision branches collaborate with each other during training through mutual knowledge transferring, and guide the network to extract the meaningful local pattern features contained in the skin lesion region in a weakly supervised manner. Besides, it brings in strong regularization which makes our proposed network avoid over-fitting when training on the small training data. • The proposed network gained the state-of-the-art performance for skin lesion classification, regardless of binary- or multi-classification. Besides, the robustness and interpretability of the proposed network are strong which can promote its clinical application. Skin lesion varies greatly in appearance, and its classification task suffers from large inter-class similarity and intra-class variation, thus the subtle differences of local pattern contained in the skin lesion regions are critical for its classification. In this paper, we propose a dual attention based network for skin lesion classification with auxiliary learning. The dual attention mechanism includes the spatial attention (SA) and the channel attention (CA) modules. The SA module can focus on the skin lesion region feature with reduced irrelevant artifacts features. In the subsequent CA module, it first captures the non-local based global feature of the skin lesion region and then generates the feature channels reweighting vector, which is used to further refine the meaningful local pattern feature contained in the skin lesion region. Therefore, the performance and the interpretability of the proposed network are enhanced at the same time. The proposed auxiliary learning contains two auxiliary supervision branches and KL regularization. The KL regularization makes the two auxiliary supervision branches collaborate with each other during training through mutual knowledge transferring. The introduced strong regularization can guide the dual attention mechanism to focus on the meaningful local pattern features in a weakly supervised manner and make the network avoid overfitting on small training data. Without extra training data, our proposed network can outperform current competition winners on several datasets, regardless of binary- or multi-classification. The proposed network is robust enough and owns strong interpretability which promotes its clinical application. [ABSTRACT FROM AUTHOR]

• A two-view and multi-scale supervision network is proposed for pancreas segmentation. • A location branch for localization and a segmentation branch for segmentation. • Multi-scale supervision is used to learn multi-scale features. • Effectiveness verified on two pancreas datasets and one spleen dataset. Automatic organ segmentation systems can accelerate the development of computer-aided diagnosis (CAD) in clinical applications. In this paper, we focus on the challenging pancreas segmentation task. The tiny size, poor contrast, and blurred boundaries of the pancreas make it hard to detect. Current approaches emphasize decomposing this task into subtasks (localization and segmentation) and using the same network to solve different tasks. However, they overestimate the generalization ability of their models. In addition, current methods rely too much on the result of localization. To address these challenges, we propose a novel network by two-view feature learning based on attention mechanism and multi-scale supervision, which we term TVMS-Net. For localization, we adopt Attention Gate (AG) to distinguish appearance features of the pancreas in shallow layers. For segmentation, a simple and effective Residual Multi-Scale Dilated Attention (RMSA) module is designed to extract comprehensive inter-channel relationships and multi-scale spatial information. TVMS-Net is supervised in multi-scale to learn specific-level semantic information. Experimental results on two pancreas datasets show that TVMS-Net obtains remarkable performance. Importantly, TVMS-Net also achieves excellent segmentation accuracy on another tiny organ dataset, i.e., the spleen, which justifies the reliability and robustness of our method. [ABSTRACT FROM AUTHOR]

• A novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. • 3-DES has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. • The encrypted and authenticated ECG features are transmitted to cardiologist for analysis via an IoT platform. • The significant of the proposed technique attains better value of results as compared to the existing methods. Telecardiology offers early detection, consultations and disease management in Golden Hours to patients. Transmitting ECG data to a cardiologist without security may incline to misdiagnosis. In this paper, a novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. Firstly, ECG datasets are taken from the MIT-BIH database and pre-processed. Savitzky-Golay (SG) filter has been used for the removal of low-frequency noise, while high frequency noise is eliminated using maximal overlap wavelet packet transform (MOWPT) followed by features extraction. For security, triple data encryption standard (3-DES) has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. Then encrypted and authenticated ECG features are transmitted to cardiologist for analysis via ThingSpeak, an internet of things (IoT) platform. The performance of the proposed method is calculated viz. avalanche effect is 50.12% and mean square error is 0.463, number of pixels change rate (NPCR) is 100, unified averaged changed intensity (UACI) is 39.698 and execution time is 0.003 msec. The significant of the proposed technique provides minimum error with less processing time, better values of avalanche effect, NCPR and UACI as compared to the existing methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integrated framework, the Vienna Cloud Environment, for building a generic, service-based data infrastructure for biomedical data integration needs. The infrastructure consists of diverse service types, which in an example scenario are used to expose and link several open drug and clinical trials data sources under a unified interface. As a result, the biomedical research community is offered a convenient way to query parameters related to drugs, for example generic compound names or brand names or their molecular targets, and associate these to their effective utilization in clinical trials. We develop and discuss several approaches on setting up such integration infrastructure and discuss implications of this framework exemplarily on an experimental evaluation of the drug-trial integration application. Practical recommendations and theoretical considerations inspired by the implementation and experimental evaluation of the drug-trials integration application are presented. [Copyright &y& Elsevier]

In The Logic of Life (1970), François Jacob (1920- ), Nobel Prize laureate in Physiology or Medicine (1965), proclaimed the end of vitalism based on the concept of life. More than two decades before this capital sentence condemning vitalism was pronounced, Georges Canguilhem (1904-1995), a French philosopher of medicine, already acknowledged that eighteenth-century vitalism was scientifically retrograde and politically reactionary or counter-revolutionary insofar as it was rooted in the animism of Georg Ernst Stahl (1660-1734). The negative preconception of the term 'vitalism' came to be established as an orthodox view, since Claude Bernard (1813-1878) unfairly criticized contemporary vitalism in order to propagate his idea of experimental medicine. An eminent evolutionary biologist like Ernst Mayr (1904-2005) still defended similar views in This is Biology (1997), arguing that if vitalists were decisive and convincing in their rejection of the Cartesian model (negative heuristics), however they were equally indecisive and unconvincing in their own explanatory endeavors (positive heuristics). Historically speaking, vitalists came to the forefront for their outstanding criticism of Cartesian mechanism and physicochemical reductionism, while their innovative concepts and theories were underestimated and received much less attention. Is it true that vitalism was merely a pseudo-science, representing a kind of romanticism or mysticism in biomedical science? Did vitalists lack any positive heuristics in their biomedical research? Above all, what was actually the so-called 'vitalism'? This paper aims to reveal the positive heuristics of vitalism defined by Paul-Joseph Barthez (1734-1806) who was the founder of the vitalist school of Montpellier. To this end, his work and idea are introduced with regard to the vying doctrines in physiology and medicine. At the moment when he taught at the medical school of Montpellier, his colleagues advocated the mechanism of René Descartes (1596-1650), the iatromechanism of Herman Boerhaave (1668-1738), the iatrochemistry of Jan Baptist van Helmont (1579-1644), the animism of Stahl, and the organicism of Théophile de Bordeu (1722-1776). On the contrary, Barthez devoted himself to synthesize diverse doctrines and his vitalism consequently illustrated an eclectic character. Always taking a skeptical standpoint regarding the capacity of biomedical science, he defined his famous concept of 'vital principle (principe vital)' as the 'x (unknown variable)' of physiology. He argued that the hypothetical concept of vital principle referred to the 'experimental cause (cause expérimentale)' verifiable by positive science. Thus, the vital principle was not presupposed as an a priori regulative principle. It was an a posteriori heuristic principle resulting from several experiments. The 'positivist hypothetism' of Barthez demonstrates not only pragmatism but also positivism in his scientific terminology. Furthermore, Barthez established a guideline for clinical practice according to his own methodological principles. It can be characterized as a 'humanist pragmatism' for the reason that all sort of treatments were permitted as far as they were beneficial to the patient. Theoretical incoherence or incommensurability among different treatments did not matter to Barthez. His practical strategy for clinical medicine consisted of three principles: namely, the natural, analytic, and empirical method. This formulation is indebted to the 'analytic method (méthode analytique)' of the French empiricist philosopher Étienne Bonnot de Condillac (1714-1780). In conclusion, the eighteenth-century French vitalism conceived by Barthez pursued pragmatism in general, positivism in methodology, and humanism in clinics. [ABSTRACT FROM AUTHOR]

Abstract: Electroencephalographic (EEG) arousals are seen in EEG recordings as an awakening response of the human brain. Sleep apnea is a serious sleep disorder. Severe sleep apnea brings about EEG arousals and sleep for patients with sleep apnea syndrome (SAS) is thus frequently interrupted. The number of respiratory-related arousals during the whole night on PSG recordings is directly related to the quality of sleep. Detecting EEG arousals in the PSG record is thus a significant task for clinical diagnosis in sleep medicine. In this paper, a method for automatic detection of EEG arousals in SAS patients was proposed. To effectively detect respiratory-related arousals, threshold values were determined according to pathological events as sleep apnea and electromyogram (EMG). If resumption of ventilation (end of the apnea interval) was detected, much lower thresholds were adopted for detecting EEG arousals, including relatively doubtful arousals. Conversely, threshold was maintained high when pathological events were undetected. The proposed method was applied to polysomnographic (PSG) records of eight patients with SAS and accuracy of EEG arousal detection was verified by comparative visual inspection. Effectiveness of the proposed method in clinical diagnosis was also investigated. [Copyright &y& Elsevier]

• A multi-branch network improves the resolution of anisotropic MRI image. • Transforming adjacent slices feature to enhance the resolution of target slice. • Spatial attention adaptively highlights meaningful features. • Hybrid loss encourages the learning of fine contents and structures. Magnetic resonance imaging (MRI) is widely used in clinical applications. However, due to the limitations in signal-to-noise ratio, physical properties of the scanner and scanning time, MRI images are usually acquired in low resolution, which restrains the accuracy of segmentation and recognition tasks. Recently, convolutional neural network (CNN) super-resolution methods have shown great potential in improving the resolution of MRI. Unfortunately, current methods neglect the data continuity and prior information of MRI images. In this paper, we handle the anisotropic 3D brain MRI images SR task as the problem of inserting new slices between adjacent in-plane slices. Then, we propose a novel adjacent slices feature transformer (ASFT) network to utilize the similarity of adjacent slices. Specifically, the backbone of the ASFT network consists of a series of stacked multi-branch features transformation and extraction (MFTE) blocks. In each MFTE block, we construct new spatial attention to focus on features from specific areas in reference branches and use channel attention to enhance the most valuable information. In addition, we propose a hybrid loss function with content and gradient information to refine the pixels and structures of the reconstructed image. We also introduce global residual learning to reduce the difficulty of network training. Experimental results show that the ASFT network gains the PSNR of 43.68 dB, 40.96 dB, and 41.22 dB with the scale factor of × 2 on the public Kirby21, ANVIL-adult, and MSSEG datasets, respectively. When compared with state-of-the-art MRI SR methods, the ASFT network achieves superior quantitative and qualitative performance. [ABSTRACT FROM AUTHOR]

• C-BiLSTM with 1-D kernel is efficient for audio feature extraction. • Boundary regression contributes to more accurate cough detection. • Strong generalization ability is shown facing unknown non-cough events. • High performance of cough detection meets the end of practical clinical application. • The Corp Dataset provides powerful support for further research. Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data. [ABSTRACT FROM AUTHOR]

Background: In the last few years, several studies have focused on describing and understanding how virtual coaches (ie, coaching program or smart device aiming to provide coaching support through a variety of application contexts) could be key drivers for health promotion in home care settings. As there has been enormous technological progress in the field of artificial intelligence and data processing in the past decade, the use of virtual coaches gains an augmented attention in the considerations of medical innovations.Objective: This scoping review aimed at providing an overview of the applications of a virtual coach in the clinical field. In particular, the review focused on the papers that provide tangible information for coaching activities with an active implication for engaging and guiding patients who have an ongoing plan of care.Methods: We aimed to investigate the use of the term virtual coach in the clinical field performing a methodical review of the relevant literature indexed on PubMed, Scopus, and Embase databases to find virtual coach papers focused on specific activities dealing with clinical or medical contexts, excluding those aimed at surgical settings or electronic learning purposes.Results: After a careful revision of the inclusion and exclusion criteria, 46 records were selected for the full-text review. Most of the identified articles directly or indirectly addressed the topic of physical activity. Some papers were focused on the use of virtual coaching (VC) to manage overweight or nutritional issues. Other papers dealt with technological interfaces to facilitate interactions with patients suffering from different chronic clinical conditions such as heart failure, chronic obstructive pulmonary disease, depression, and chronic pain.Conclusions: Although physical activity is a healthy practice that is most encouraged by a virtual coach system, in the current scenario, rehabilitation is the great absentee. This paper gives an overview of the tangible applications of this tool in the medical field and may inspire new ideas for future research on VC. [ABSTRACT FROM AUTHOR]

COVID-19 infection segmentation has essential applications in determining the severity of a COVID-19 patient and can provide a necessary basis for doctors to adopt a treatment scheme. However, in clinical applications, infection segmentation is performed by human beings, which is time-consuming and generally introduces bias. In this paper, we developed a novel evolvable adversarial framework for COVID-19 infection segmentation. Three generator networks compose an evolutionary population to accommodate the current discriminator, i.e., generator networks evolved with different mutations instead of the single adversarial objective to provide sufficient gradient feedback. Compared with the existing work that enforces a Lipschitz constraint by weight clipping, which may lead to gradient exploding or vanishing, the proposed model also incorporates the gradient penalty into the network, penalizing the discriminator's gradient norm input. Experiments on several COVID-19 CT scan datasets verified that the proposed method achieved superior effectiveness and stability for COVID-19 infection segmentation. [ABSTRACT FROM AUTHOR]

Abstract: An outline of the development of a “brave new partnership” in clinical trials is provided in this specific personal account. This paper was one of several delivered in November 2007 by colleagues deemed to have contributed to the distinguished career of Professor Michael Baum, at a Festschrift convened to honour him. This account describes this strange, new doctor–patient relationship in the research process that we developed, both in our dialogues and in the work of the Consumers'' Advisory Group for Clinical Trials (CAG-CT) that we jointly founded. This work formed but one facet of his outstanding contribution to society and academe, in the fields of medicine, ethics and science. [Copyright &y& Elsevier]

Abstract: Over the last 8 years, the National Cancer Institute (NCI) has launched a major effort to integrate molecular and clinical cancer-related information within a unified biomedical informatics framework, with controlled terminology as its foundational layer. The NCI Thesaurus is the reference terminology underpinning these efforts. It is designed to meet the growing need for accurate, comprehensive, and shared terminology, covering topics including: cancers, findings, drugs, therapies, anatomy, genes, pathways, cellular and subcellular processes, proteins, and experimental organisms. The NCI Thesaurus provides a partial model of how these things relate to each other, responding to actual user needs and implemented in a deductive logic framework that can help maintain the integrity and extend the informational power of what is provided. This paper presents the semantic model for cancer diseases and its uses in integrating clinical and molecular knowledge, more briefly examines the models and uses for drug, biochemical pathway, and mouse terminology, and discusses limits of the current approach and directions for future work. [Copyright &y& Elsevier]

This literature review and the recommendations therein were prepared for the American Gastroenterological Association Institute Clinical Practice and Economics Committee. The paper was approved by the Committee on January 19, 2006, and by the AGA Institute Governing Board on April 20, 2006. [Copyright &y& Elsevier]

This literature review and the recommendations therein were prepared for the American Gastroenterological Association Clinical Practice and Economics Committee. The paper was approved by the Committee on April 22, 2005, and by the AGA Governing Board on October 6, 2005. [Copyright &y& Elsevier]

The measurement of bioelectrical impedance in humans is a relatively new technique that is increasing in applications as well as its acceptance by the clinical community.Some of the reasons for this increase are technological as the developments in electronics have increased the number of commercial systems available.Other reasons are the low-cost,ease of use and safety of the technique.This paper presents an over- view of its clinical applications,differentiating them according to the bioelectrical impedance change rate. Although this is a widely used technique,there are still some problems and limitations that in some cases jeopardize its reliability.The future of the measurement of bioelectrical impedance as a clinical and routine diagnostic tool needs to be carefully analyzed and these limitations and their possible effects considered for each specific application. [ABSTRACT FROM AUTHOR]

Background: User-friendly information at the point of care for health care professionals should be well structured, rapidly accessible, comprehensive, and trustworthy. The reliability of information and the associated methodological process must be clear. There is no standard tool to evaluate the trustworthiness of such point-of-care (POC) information.Objective: We aim to develop and validate a new tool for assessment of trustworthiness of evidence-based POC resources to enhance the quality of POC resources and facilitate evidence-based practice.Methods: We designed the Critical Appraisal of Point-of-Care Information (CAPOCI) tool based on the criteria important for assessment of trustworthiness of POC information, reported in a previously published review. A group of health care professionals and methodologists (the authors of this paper) defined criteria for the CAPOCI tool in an iterative process of discussion and pilot testing until consensus was reached. In the next step, all criteria were subject to content validation with a Delphi study. We invited an international panel of 10 experts to rate their agreement with the relevance and wording of the criteria and to give feedback. Consensus was reached when 70% of the experts agreed. When no consensus was reached, we reformulated the criteria based on the experts' comments for a next round of the Delphi study. This process was repeated until consensus was reached for each criterion. In a last step, the interrater reliability of the CAPOCI tool was calculated with a 2-tailed Kendall tau correlation coefficient to quantify the agreement between 2 users who piloted the CAPOCI tool on 5 POC resources. Two scoring systems were tested: a 3-point ordinal scale and a 7-point Likert scale.Results: After validation, the CAPOCI tool was designed with 11 criteria that focused on methodological quality and author-related information. The criteria assess authorship, literature search, use of preappraised evidence, critical appraisal of evidence, expert opinions, peer review, timeliness and updating, conflict of interest, and commercial support. Interrater agreement showed substantial agreement between 2 users for scoring with the 3-point ordinal scale (τ=.621, P<.01) and scoring with the 7-point Likert scale (τ=.677, P<.01).Conclusions: The CAPOCI tool may support validation teams in the assessment of trustworthiness of POC resources. It may also provide guidance for producers of POC resources. [ABSTRACT FROM AUTHOR]

A conference paper on a randomized controlled trial which examined the point-of care referrals of smokers by community-based dental practices to an interactive, tailored patient education website is presented. The paper concluded that the "ReferASmoker" tool is easy to use and offers useful resources to assist with tobacco control services.

Background: Early identification of child emotional and behavioral concerns is essential for the prevention of mental health problems; however, few suitable child-reported screening measures are available. Digital tools offer an exciting opportunity for obtaining clinical information from the child's perspective. Objective: The aim of this study was to describe the initial development and pilot testing of the Interactive Child Distress Screener (ICDS). The ICDS is a Web-based screening instrument for the early identification of emotional and behavioral problems in children aged between 5 and 12 years. Methods: This paper utilized a mixed-methods approach to (1) develop and refine item content using an expert review process (study 1) and (2) develop and refine prototype animations and an app interface using codesign with child users (study 2). Study 1 involved an iterative process that comprised the following four steps: (1) the initial development of target constructs, (2) preliminary content validation (face validity, item importance, and suitability for animation) from an expert panel of researchers and psychologists (N=9), (3) item refinement, and (4) a follow-up validation with the same expert panel. Study 2 also comprised four steps, which are as follows: (1) the development of prototype animations, (2) the development of the app interface and a response format, (3) child interviews to determine feasibility and obtain feedback, and (4) refinement of animations and interface. Cognitive interviews were conducted with 18 children aged between 4 and 12 years who tested 3 prototype animated items. Children were asked to describe the target behavior, how well the animations captured the intended behavior, and provide suggestions for improvement. Their ability to understand the wording of instructions was also assessed, as well as the general acceptability of character and sound design. Results: In study 1, a revised list of 15 constructs was generated from the first and second round of expert feedback. These were rated highly in terms of importance (mean 6.32, SD 0.42) and perceived compatibility of items (mean 6.41, SD 0.45) on a 7-point scale. In study 2, overall feedback regarding the character design and sounds was positive. Children's ability to understand intended behaviors varied according to target items, and feedback highlighted key objectives for improvements such as adding contextual cues or improving character detail. These design changes were incorporated through an iterative process, with examples presented. Conclusions: The ICDS has potential to obtain clinical information from the child's perspective that may otherwise be overlooked. If effective, the ICDS will provide a quick, engaging, and easy-to-use screener that can be utilized in routine care settings. This project highlights the importance of involving an expert review and user codesign in the development of digital assessment tools for children. [ABSTRACT FROM AUTHOR]