A broad range of aspects are needed to be taken into consideration in the design and development of personalized coaching systems based on artificial intelligence methodologies. This research presents the initial phase of joining different professional and stakeholder perspectives on behavior change technologies into a flexible design proposal for a digital coaching system. The diversity and sometimes opposed views on content, behavior, purposes and context were managed using a structured argument-based design approach, which also feed into the behavior of the personalized system. Results include a set of personalization strategies that will be further elaborated with the target user group to manage sensitive issues such as ethics, social norms, privacy, motivation, autonomy and social relatedness.Measuring the center of pressure (CoP) for a subject positioned on a force plate is one of the most commonly used tools to investigate balance. Several studies have proven a significant degradation of the body’s stability after the age of 60. The conclusions, however, are based on a limited number of indicators and without systematic nonlinear analysis methods being used to evaluate the progression of CoP parameter values. Neither the change in CoP movement in subjects over 60 years of age nor the considerations of their body mass index (BMI) has been systematically evaluated by nonlinear methods so far. This study is based on one of the frequent methods for nonlinear evaluation – the Recurrent Quantification analysis. This article discusses the applicability of this method with regards to the evaluation of changes in postural stability of subjects over 60 years of age. Postural stability changes were evaluated using CoP motion and tested by the nonlinear method. For this research purpose, a group of 103 elderly women were selected and divided into age-respective groups of 60-69 years and 70-79 years old. Each age group was further divided into a subgroup of normal and overweight subjects according to their BMI. The following recurrent analysis parameters were employed in the evaluation of CoP motion in medial-lateral and anterior-posterior directions determinism (DET), laminarity (LAM) and trapping time (TT). The results of the Wilcoxon test revealed a statistically significant difference between the values in parameters for the different age groups of overweight subjects almost in all the cases. Conversely, statistically significant differences between age groups rarely occurred in a subgroup of subjects with a normal BMI.The prevalence of Heart Failure is growing exponentially in the last decades, particularly amongst older adults. Heart Failure is a chronic cardiovascular disease that demands self-care management and substantial healthcare resources. For that reason, it is highly associated with hospital readmissions and mortality. Due to increased hospitalization costs, excessive waiting times and lack of specialized healthcare professionals to follow-up this growing population, telemedicine and telemonitoring technologies have become the best solutions to support health providers in the disease management tasks. Telemonitoring technologies offer better and more comfortable care because the elderly do not have to leave the comfort of their home to interact with the doctors, giving and receiving daily feedbacks trough these new applications, wearables, and health care platforms. This paper provides a comprehensive review covering the current progress of research in telemedicine and telemonitoring and their applications to Heart Failure Management services. It presents SmartBEAT, which demonstrated during a pilot phase, a user adherence of 97% for three months. Furthermore, SmartBEAT plus, an improved solution, is described, and the system usability a technology acceptance will be evaluated through a pilot with 40 Heart Failure Patients, involving nurses and cardiologists.The COVID-19 pandemic has posed several challenges on citizens and health systems. Information and Communication Technology (ICT) can be a valuable tool in providing tools for self-assessment and reporting of physical symptoms, early detection of symptom changes, up to date information towards citizen empowerment, personalized recommendations and communication with healthcare providers in case of need. To this direction, this paper reports on the design and implementation of a novel technical infrastructure to support citizens with possible or confirmed COVID-19 disease. The designed platform builds upon an existing personal health record to facilitate symptom tracking, self-management, and personalized recommendations, effective communication channels between patients and clinicians and public health authorities assisting citizens to remain longer safe at home.Falls are a well-known danger for older adults. With the worldwide population aging, there has been an increasing interest in assessing the risk of falling. This work presents a novel algorithm for continuous fall risk assessment, relying on a linear regression model whose inputs consist of both measured and self-reported risk factors. Two models were conceived and compared, following two distinct approaches, a theoretical and an empirical one. The system is pervasive and was tested in free-living unsupervised conditions. The results of our fall risk scoring system unveiled a strong correlation with the output of the clinical functional tests POMA and TUG (90% and 89%, respectively), which was deemed a promising outcome concerning the feasibility of pervasive monitoring for fall risk assessment in daily living.The use of different data formats complicates the standardization and exchange of valuable medical data. Moreover, a big part of medical data is stored as unstructured medical records that are complicated to process. In this work we solve the task of unstructured allergy anamnesis categorization according to categories provided by FHIR. We applied two stage classification model with manually labeled records. On the first stage the model filters records with information about allergies and on the second stage it categorizes each record. The model showed high performance. The development of this approach will ensure secondary use of data and interoperability.The demographic change is no longer a prognosis, but a reality seen in everyday life situations and requires mechanisms to make the public and private space elderly-adequate. These required mechanisms need to consider the varying aging process for each individual as well as adapt to the dynamic daily life of individuals characterized by spatial, temporal and activity variance. Developing assistance systems that are user-adaptive within dynamic environments is a challenging task. AI-based cyber-physical assistance systems enable such adaptive, flexible and individual assistance by processing acquired data from the physical environment using cyber resources and delivering intelligent assistance as well as interfaces to further medical services. This contribution discusses a flexible, reusable, and user-specific concept for AI-based assistance systems. Relying on distributed and heterogeneous data, the user’s context is continuously modeled and reasoned over to infer actionable knowledge within a middleware between the data layer and the application layer. To demonstrate the applicability of the concept, the use case of intelligently supporting patients’ medication adherence is shown.Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals. In this work, we developed a system using the Internet of Things (IoT) and machine learning technologies in order to monitor and assist individuals in their daily life. We compared the data collected using a mobile application and a wearable device with built-in sensors (accelerometer and gyroscope) with the data of a publicly available dataset. By this way, we were able to validate our results and also investigate the functionality and applicability of the wearable device that we choose for the Human Activity Recognition problem. The classification results for the different types of activities presented using our dataset (99%) outperforms the results from the publicly database (97%).The paper compares two approaches to multi-step ahead glycaemia forecasting. While the direct approach uses a different model for each number of steps ahead, the iterative approach applies one one-step ahead model iteratively. Although it is well known that the iterative approach suffers from the error accumulation problem, there are no clear outcomes supporting a proper choice between those two methods. This paper provides such comparison for different ARX models and shows that the iterative approach outperformed the direct method for one-hour ahead (12-steps ahead) forecasting. Moreover, the classical linear ARX model outperformed more complex non-linear versions for training data covering one-month period.In this paper, we follow up on research dealing with body tracking and motor rehabilitation. We describe the current situation in telerehabilitation in the home environment. Existing solutions do not allow wide adoption due to hardware requirements and complicated setup. We come with the possibility of telerehabilitation using only laptop or mobile web camera. Together with physiotherapists, we have compiled a set of complex motor exercises to show that the system can be practically used.Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8 – 84.7 % for the conditional diagnosis of DPN in primary care. Prediction methods that utilize data from personal health records deal with large non-specific datasets with different prediction methods. Li et al. utilized 30 independent variables, which allowed to implement a model with AUC = 0.8863 for a Multilayer perceptron (MLP). Linear regression (LR) based methods produced up to AUC = 0.8 %. This way, modern data mining and computational methods can be effectively adopted in clinical medicine to derive models that use patient-specific information to predict the development of diabetic polyneuropathy, however, there still is a space to improve the efficiency of the predictive models. The goal of this study is the implementation of machine learning methods for early risk identification of diabetes polyneuropathy based on structured electronic medical records. It was demonstrated that the machine learning methods allow to achieve up to 0.7982 precision, 0.8152 recall, 0.8064 f1-score, 0.8261 accuracy, and 0.8988 AUC using the neural network classifier.In this paper, we describe a strategy for the development of a genetic analysis comprehensive representation. The primary intention is to ensure the available utilization of genetic analysis results in clinical practice. The system is called Personnel Genetic Card (PGC), and it is developed in cooperation of CIIRC CTU in Prague and the Mediware company. Nowadays, genetic information is more and more part of medicine and life quality services (e.g. nutritional consulting). Therefore, there is necessary to bind genetic information with the clinical phenotype, such as drug metabolism or intolerance to various substances. We proposed a structured form of the record, where we utilize the LOINC® standard to identify genetic test parameters, and several terminology databases for representing specific genetic information (e.g. HGNC, NCBI RefSeq, NCBI dbNSP, HGVS). Further, there are also several knowledge databases (PharmGKB, SNPedia, ClinVar) that collect interpretation for genetic analysis results. In the results of this paper, we describe our idea in the structure and process perspective. The structural perspective includes the representation of the analysis record and its binding with the interpretations. The process perspective describes roles and activities within the PGC system use.Type 2 diabetes is one of the most common chronic diseases in the world. World Diabetes Federation experts predict that the diabetes patients’ number by 2035 will increase by 205 million to reach 592 million. For health care, this diabetes type is one of the highest priority problems. This disease is associated with many concomitant diseases leading to early disability and high cardiovascular risk. A severity disease indicator is the degree of carbohydrate metabolism compensation. Decompensated and subcompensated carbohydrate metabolism patients have increased cardiovascular risks. Therefore, it is important to be able to select the right therapy to control carbohydrate metabolism. In this study, we propose a new method for selecting the optimal therapy automatically. The method includes creating personal optimal therapies. This kind of therapy has the highest probability of compensating carbohydrate metabolism for a patient within a six-month. The method includes models for predicting the results of different therapies. It is based on data from the previous medical history and current medical indicators of patients. This method provides high-quality predictions and medical recommendations. Therefore, medical professionals can use this method as part of the Support and Decision-Making Systems for working with T2DM patients.

The electronically submitted data from midwives and hospitals to the Netherlands perinatal registry vary significantly in their data definitions, and electronic message versions. The purpose of this article is to describe the semantic cross-mapping tool and execution procedure to prepare the data for statistical analysis.

requirements analysis, design, development and testing.

The tool for governance of versions of datasets, CIMs, data, and value sets is designed, developed, and tested. The test is based on the data-mart of version PRN 1.3 based data from 2019. Data are semantically cross mapped to current version perinatology data 2.2.

The cross-mapping of PRN 1.3 data to perinatology 2.2 data are defined in the tool, testing revealed this mapping is successful.

The cross-mapping of PRN 1.3 data to perinatology 2.2 data are defined in the tool, testing revealed this mapping is successful.Timely identification of risk factors in the early stages of pregnancy, risk management and mitigation, prevention, adherence management can reduce the number of adverse perinatal outcomes and complications for both mother and a child. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov specialized medical center in Saint-Petersburg, Russia. Correlation analysis was performed using Pearson correlation coefficient to select the most relevant predictors. We used APGAR score as a metrics for the childbirth outcomes. Score of 5 and less was considered as a negative outcome. To analyze the influence of the unstructured anamnesis data on the prediction accuracy we have run two prediction experiments for every classification task 1. Without unstructured data and 2. With unstructured data. This study presents implementation of predictive models for adverse childbirth events that provides higher precision than state of the art models. This is due to the use of unstructured medical data in addition to the structured dataset that allowed to reach 0.92 precision. Identification of main risk factors using the results of the features importance analysis can support clinicians in early identification of possible complications and planning and execution preventive measures.Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This is especially valid in the countries with multilevel health care institutions like Russia. In Russia medical organizations are distributed into national, regional and municipal levels. Organizations of each level can provide treatment of different types and quality. For example, pregnancies with low risk of complications are routed to the municipal hospitals, moderate risk pregnancies are routed to the reginal and high risk of complications are routed to the hospitals of the national level. In the situation of resource deficiency especially on the national level it is necessary to plan admission date and a treatment team in advance to provide the best possible care. When pregnancy data is not standardized and semantically interoperable, data driven models. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov perinatal medical center in Saint-Petersburg, Russia. The dataset was exported from the medical information system. It consisted of structured and semi structured data with the total of 73115 lines for 12989 female patients. The proposed due date prediction data-driven model allows a high accuracy prediction to allow proper resource planning. The models are based on the real-world evidence and can be applied with limited amount of predictors.Technological advancements in smart assistive technology enable navigating and manipulating various types of computer-aided devices in the operating room through a contactless gesture interface. Understanding surgeon actions is crucial to natural human-robot interaction in operating room since it means a sort of prediction a human behavior so that the robot can foresee the surgeon’s intention, early choose appropriate action and reduce waiting time. In this paper, we present a new deep network based on Convolution Long Short-Term Memory (ConvLSTM) for gesture prediction configured to provide natural interaction between the surgeon and assistive robot and improve operating-room efficiency. The experimental results prove the capability of reliably recognizing unfinished gestures on videos. We quantitatively demonstrate the latter ability and the fact that GestureConvLSTM improves the baseline system performance on LSA64 dataset.A lower-extremity exoskeleton can facilitate the lower limbs’ rehabilitation by providing additional structural support and strength. This article discusses the design and implementation of a functional prototype of lower extremity brace actuation and its wireless communication control system. The design provides supportive torque and increases the range of motion after complications reducing muscular strength. The control system prototype facilitates elevating a leg, gradually followed by standing and slow walking. The main control modalities are based on an Artificial Neural Network (ANN). The prototype’s functionality was tested by time-angle graphs. The final prototype demonstrates the potential application of the ANN in the control system of exoskeletons for joint impairment therapy.In healthcare settings, questionnaires are used to collect information from a patient. A standard method for this are paper-based questionnaires, but they are often complex to understand or long and frustrating to fill. To increase motivation, we developed a chatbot-based system Ana that asks questions that are normally asked using paper forms or in face-to-face encounters. Ana has been developed for the specific use case of collecting the music biography in the context of music therapy. In this paper, we compare user motivation, relevance of answers and time needed to answer the questions depending on the data entry method (i.e. app Ana versus paper-based questionnaire). A randomised trial was performed with 26 students of music therapy. The results show that the chatbot is more motivating and answers are given faster than on paper. No differences in answer relevance could be determined between the two means. We conclude that a chatbot could become an additional data entry method for collecting personal health information.The value of data models in general and information models in specific has been evaluated by many scientific papers. UML as one modelling notation has documented its value as a foundation for precise specifications. Analyzing implementation guides for data exchange, they rarely include or are based on information models but simple data sets, if at all, as simple technical representation thereof. This paper wants to argue in favor of information models as a basis for creating interoperability specifications using a quite simple example and to include – or at least reference – them when providing implementation guides. The reader is invited to transfer this example to even more complex scenarios.Today’s digital information systems and applications collect every day a huge amount of personal health information (PHI) from sensor and surveillance systems, and every time we use personal computers or mobile phones. Collected data is processed in clouds, platforms and ecosystems by digital algorithms and machine learning. Pervasive technology, insufficient and ineffective privacy legislation, strong ICT industry and low political will to protect data subject’s privacy have together made it almost impossible for a user to know what PHI is collected, how it is used and to whom it is disclosed. Service providers’ and organizations’ privacy policy documents are cumbersome and they do not guarantee that PHI is not misused. Instead, service users are expected to blindly trust in privacy promises made. In spite of that, majority of individuals are concerned of their privacy, and governments’ assurance that they meet the responsibility to protect citizens in real life privacy is actually dead. Because PHI is probably the most sensitive data we have, and the authors claim it cannot be a commodity or public good, they have studied novel privacy approaches to find a way out from the current unsatisfactory situation. Based on findings got, the authors have developed a promising solution for privacy-enabled use of PHI. It is a combination of the concept of information fiduciary duty, Privacy as Trust approach, and privacy by smart contract. This approach shifts the onus of privacy protection onto data collectors and service providers. A specific information fiduciary duty law is needed to harmonize privacy requirements and force the acceptance of proposed solutions. Furthermore, the authors have studied strengths and weaknesses of existing or emerging solutions.The International Patient Summary Standard (EN 17269) normalizes the dataset within the European Guideline on cross-border exchange of a patient summary. This dataset has been widely appreciated and been taken as the basis for projects in both Europe and wider afield, e.g. U.S.A, Canada and more. The dataset is a relatively mature dataset and it is currently in its third iteration (i.e., 2013, 2016, 2020). Even so, to move from a policy-driven guideline to a formal standard was not straight forward. The paper describes how the 'minimal and non-exhaustive’ dataset could be the basis for a reference standard; one that was intended to facilitate both an 'implementable’ and 'sustainable’ solution. In particular, the requirement of 'extensibility’ for the standard dataset had to be addressed.Medical data can be represented in various forms. The most common is visualization, but recent work started to also add sonic representation – sonification. In this study we start with a theoretical background, then focus on medical applications. The discussion synthesizes the authors view about the present state of the domain and tries to foresee future potential developments in medicine. In conclusion we present a set of original recommendations for developing new applications with potential use in medicine and healthcare.The paper describes the concept of the Industry 4.0 and its reflection in health care. Industry 4.0 connects intelligent production concepts with external factors, including those linked with the production and those linked more with human, as for example intelligent homes or social web systems. Communication, data and information play an important role in the whole system. After explaining basic characteristics of the Industry 4.0 concept and its main parts, we show how they can be utilized in the health care sector and what their advantages are. Key technologies and techniques include Internet of Things, big data, artificial intelligence, data integration, robotization, virtual reality, and 3D printing. Finally, we identify the main challenges and research directions. Among the most important ones are interoperability, standardization, reliability, security and privacy, ethical and legal issues.Multidisciplinary and highly dynamic pHealth ecosystems according to the 5P Medicine paradigm require careful consideration of systems integration and interoperability within the domains knowledge space. The paper addresses the different aspects or levels of knowledge representation (KR) and management (KM) from cognitive theories (theories of knowledge) and modeling processes through notation up to processing, tooling and implementation. Thereby, it discusses language and grammar challenges and constraints, but also development process aspects and solutions, so demonstrating the limitation of data level considerations. Finally, it presents the ISO 23903 Interoperability and Integration Reference Architecture to solve the addressed problems and to correctly deploy existing standards and work products at any representational level including data models as well as data model integration and interoperability.Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it has diverse etiologies with multiple mechanisms. The diagnosis of HCC typically occurs at advanced stages when there are limited therapeutic options. Hepatocarcinogenesis is considered a multistep process, and hepatic macrophages play a critical role in the inflammatory process leading to HCC. Emerging evidence has shown that tumor-associated macrophages (TAMs) are crucial components defining the HCC immune microenvironment and represent an appealing option for disrupting the formation and development of HCC. In this review, we summarize the current knowledge of the polarization and function of TAMs in the pathogenesis of HCC, as well as the mechanisms underlying TAM-related anti-HCC therapies. Eventually, novel insights into these important aspects of TAMs and their roles in the HCC microenvironment might lead to promising TAM-focused therapeutic strategies for HCC.MicroRNAs have become pivotal modulators in the pathogenesis of Alzheimer’s disease. MiR-338-5p is associated with neuronal differentiation and neurogenesis, and expressed aberrantly in patients with cognitive dysfunction. However, its role and potential mechanism involved in Alzheimer’s disease remain to be elucidated. Herein, we showed that the expression of miR-338-5p decreased in APP/PS1 mice, accompanied by the elevation in the expression level of amyloid β, which indicated a reverse relationship between Alzheimer’s disease progression and miR-338-5p. In addition, lentiviral overexpression of miR-338-5p through intrahippocampal injection mitigated the amyloid plaque deposition and cognitive dysfunction in APP/PS1 mice, suggesting a protecting role of miR-338-5p against the development of Alzheimer’s disease. Moreover, miR-338-5p decelerated apoptotic loss of neurons in APP/PS1 mice. MiR-338-5p decreased neuronal apoptosis in vitro induced by amyloid β accumulation, which was attributed to the negative regulation of BCL2L11 by miR-338-5p, since the restoration of BCL2L11 eliminated the protective role of miR-338-5p against neuronal apoptosis. Taken together, all of these results may indicate miR-338-5p as an innovative modulator in the pathogenesis of Alzheimer’s disease, and also suggest that the protective effect of miR-338-5p on neuronal apoptosis may underlie its beneficial effect on APP/PS1 mice.The age-dependent decline in stem cell function plays a critical role in aging, although the molecular mechanisms remain unclear. PTRF/Cavin-1 is an essential component in the biogenesis and function of caveolae, which regulates cell proliferation, endocytosis, signal transduction and senescence. This study aimed to analyze the role of PTRF in hematopoietic stem cells (HSCs) senescence using PTRF transgenic mice. Flow cytometry was used to detect the frequency of immune cells and hematopoietic stem/progenitor cells (HSCs and HPCs). The results showed than the HSC compartment was significantly expanded in the bone marrow of PTRF transgenic mice compared to age-matched wild-type (WT) mice, and exhibited the senescent phenotype characterized by G1 cell cycle arrest, increased SA-β-Gal activity and high levels of reactive oxygen species (ROS). The PTRF-overexpressing HSCs also showed significantly lower self-renewal and ability to reconstitute hematopoiesis in vitro and in vivo. Real-time PCR was performed to analyze the expression levels of senescence-related genes. PTRF induced HSCs senescence via the ROS-p38-p16 and caveolin-1-p53-p21 pathways. Furthermore, the PTRF+cav-1-/- mice showed similar HSCs function as WT mice, indicating that PTRF induces senescence in HSCs partly through caveolin-1. Thus PTRF impaired HSCs aging partly via caveolin-1.

We sought to describe the outcomes of patients who underwent caval valve implantation (CAVI) for treatment of severe tricuspid regurgitation (TR) in the United States. Previous studies on CAVI have used a variety of techniques and transcatheter valves. We present our findings from CAVI with inferior vena cava (IVC) implant only using a single valve.

Patients who were determined to be poor candidates for tricuspid valve surgery and underwent CAVI in the United States from March 1, 2013 through March 1, 2018 were included in this study. Data during hospitalizations and interim outpatient follow-up from each individual site were collected and entered into a central password-protected database.

A total of 24 patients were treated. The median age was 79.5 years, 63% were women, and 96% were white. Twenty-three of 24 patients underwent valve implantation with a 29 mm Sapien 3 valve (Edwards Lifesciences). There was a 100% rate of successful valve implantation. There were no cases requiring emergency surgery. Thirty-day mortality rate was 25%. The median survival as of last follow-up of all patients was 350 days. Pre- and postprocedure New York Heart Association (NYHA) class data were available in 11 of 24 patients; of these 11 patients, 72.7% improved at least 1 NYHA class from baseline.

CAVI may be performed safely in a high surgical risk population with severe tricuspid regurgitation. Dedicated studies with longer-term follow-up are needed.

CAVI may be performed safely in a high surgical risk population with severe tricuspid regurgitation. Dedicated studies with longer-term follow-up are needed.

Opiates and benzodiazepines are commonly used during invasive coronary angiography (ICA) to address pain and anxiety. In the United States (US), these medications are used in more than 90% of such cases. The utility of these medications during ICA has not been addressed by the scientific societies. The goals of this study were to evaluate the impact of music on the use of opiates and benzodiazepines and levels of pain and anxiety in patients undergoing ICA.

In this prospective pilot study, a total of 72 subjects undergoing elective ICA were randomized to receive planned pharmacologic standard conscious sedation (SCS), including opiates and/or benzodiazepines pre-ICA vs music plus opiates and/or benzodiazepines as needed. Pain and anxiety levels, as well as use of SCS medications, were monitored during the periprocedural period.

Baseline characteristics, including rates of anxiety, depression, and other psychiatric disorders, were similar between the SCS and music groups. The levels of pain and anxiety were relatively low and similar between the two cohorts during the peri-ICA period. There was a trend toward less frequent use of SCS medications in the music group (62.2% in the SCS group vs 40.0% in the music group; P=.06) and significantly less use of midazolam per case in the music group (0.68 mg in the SCS group vs 0.37 mg in the music group; P=.048). SCS medication use also differed significantly between the two operators.

Listening to patient-selected music during the peri-ICA period may reduce the need for pharmacologic conscious sedation without adversely affecting pain and anxiety levels.

Listening to patient-selected music during the peri-ICA period may reduce the need for pharmacologic conscious sedation without adversely affecting pain and anxiety levels.

To evaluate risks of disease reactivity during pregnancy and postpartum following rituximab (RTX) and natalizumab (NTZ) suspension in women with MS.

An observational cohort study of all women with MS disease onset before childbirth between 2006 and 2017. Women were identified through the Swedish MS Registry, a nationwide clinical register, with substratification into 3 groups women who suspended RTX and NTZ within 6 months before conception and women who were not treated with any disease-modifying treatment (DMT) within 1 year of conception. The primary outcome was the annualized relapse rate (ARR) during pregnancy and 1 year postpartum.

We identified 2,386 women with MS onset before a live birth; of these, 76 women suspended RTX and 53 suspended NTZ, and 457 were untreated within 1 year before conception. In all women, regardless of the treatment type, the ARR declined from 0.05-0.04 prepregnancy to 0.03-0.02 during pregnancy, returning to prepregnancy rates at 3-6 months (0.05) postpartum. In the susp.

In this observational study, we explored cortical structure as function of cortical depth through a laminar analysis of the T1/T2-weighted (T1w/T2w) ratio, which has been related to dendrite density in ex vivo brain tissue specimens of patients with MS.

In 39 patients (22 relapsing-remitting, 13 female, age 41.1 ± 10.6 years; 17 progressive, 11 female, age 54.1 ± 9.9 years) and 21 healthy controls (8 female, , age 41.6 ± 10.6 years), we performed a voxel-wise analysis of T1w/T2w ratio maps from high-resolution 7T images from the subpial surface to the gray matter/white matter boundary. Six layers were sampled to ensure accuracy based on mean cortical thickness and image resolution.

At the voxel-wise comparison (

< 0.05, family wise error rate corrected), the whole MS group showed lower T1w/T2w ratio values than controls, both when considering the entire cortex and each individual layer, with peaks occurring in the fusiform, temporo-occipital, and superior and middle frontal cortex. In relapsing-remts, widespread cortical abnormalities can be observed, not only, as described before, with regard to myelin/iron concentration but, possibly, to other microstructural features.Individual cell migration requires front-to-back polarity manifested by lamellipodial extension. At present, it remains debated whether and how membrane motility mediates this cell morphological change. To gain insights into these processes, we perform live imaging and molecular perturbation of migrating chick neural crest cells in vivo. Our results reveal an endocytic loop formed by circular membrane flow and anterograde movement of lipid vesicles, resulting in cell polarization and locomotion. Rather than clathrin-mediated endocytosis, macropinosomes encapsulate F-actin in the cell body, forming vesicles that translocate via microtubules to deliver actin to the anterior. In addition to previously proposed local conversion of actin monomers to polymers, we demonstrate a surprising role for shuttling of F-actin across cells for lamellipodial expansion. Thus, the membrane and cytoskeleton act in concert in distinct subcellular compartments to drive forward cell migration.Recently developed linker-mediated vitrimers based on metathesis of dioxaborolanes with various commercially available polymers have shown both good processability and outstanding performance, such as mechanical, thermal, and chemical resistance, suggesting new ways of processing cross-linked polymers in industry, of which the design principle remains unknown [M. Röttger et al., Science 356, 62-65 (2017)]. Here we formulate a theoretical framework to elucidate the phase behavior of the linker-mediated vitrimers, in which entropy plays a governing role. We find that, with increasing the linker concentration, vitrimers undergo a reentrant gel-sol transition, which explains a recent experiment [S. Wu, H. Yang, S. Huang, Q. Chen, Macromolecules 53, 1180-1190 (2020)]. More intriguingly, at the low temperature limit, the linker concentration still determines the cross-linking degree of the vitrimers, which originates from the competition between the conformational entropy of polymers and the translational entropy of linkers. Our theoretical predictions agree quantitatively with computer simulations, and offer guidelines in understanding and controlling the properties of this newly developed vitrimer system.Cyclic dinucleotides (CDNs) are secondary messengers used by prokaryotic and eukaryotic cells. In mammalian cells, cytosolic CDNs bind STING (stimulator of IFN gene), resulting in the production of type I IFN. Extracellular CDNs can enter the cytosol through several pathways but how CDNs work from outside eukaryotic cells remains poorly understood. Here, we elucidate a mechanism of action on intestinal epithelial cells for extracellular CDNs. We found that CDNs containing adenosine induced a robust CFTR-mediated chloride secretory response together with cAMP-mediated inhibition of Poly IC-stimulated IFNβ expression. Signal transduction was strictly polarized to the serosal side of the epithelium, dependent on the extracellular and sequential hydrolysis of CDNs to adenosine by the ectonucleosidases ENPP1 and CD73, and occurred via activation of A2B adenosine receptors. These studies highlight a pathway by which microbial and host produced extracellular CDNs can regulate the innate immune response of barrier epithelial cells lining mucosal surfaces.The recently identified ferroptotic cell death is characterized by excessive accumulation of hydroperoxy-arachidonoyl (C204)- or adrenoyl (C224)- phosphatidylethanolamine (Hp-PE). The selenium-dependent glutathione peroxidase 4 (GPX4) inhibits ferroptosis, converting unstable ferroptotic lipid hydroperoxides to nontoxic lipid alcohols in a tissue-specific manner. While placental oxidative stress and lipotoxicity are hallmarks of placental dysfunction, the possible role of ferroptosis in placental dysfunction is largely unknown. We found that spontaneous preterm birth is associated with ferroptosis and that inhibition of GPX4 causes ferroptotic injury in primary human trophoblasts and during mouse pregnancy. Importantly, we uncovered a role for the phospholipase PLA2G6 (PNPLA9, iPLA2beta), known to metabolize Hp-PE to lyso-PE and oxidized fatty acid, in mitigating ferroptosis induced by GPX4 inhibition in vitro or by hypoxia/reoxygenation injury in vivo. Together, we identified ferroptosis signaling in the human and mouse placenta, established a role for PLA2G6 in attenuating trophoblastic ferroptosis, and provided mechanistic insights into the ill-defined placental lipotoxicity that may inspire PLA2G6-targeted therapeutic strategies.Recent single-molecule experiments have observed transition paths, i.e., brief events where molecules (particularly biomolecules) are caught in the act of surmounting activation barriers. Such measurements offer unprecedented mechanistic insights into the dynamics of biomolecular folding and binding, molecular machines, and biological membrane channels. A key challenge to these studies is to infer the complex details of the multidimensional energy landscape traversed by the transition paths from inherently low-dimensional experimental signals. A common minimalist model attempting to do so is that of one-dimensional diffusion along a reaction coordinate, yet its validity has been called into question. Here, we show that the distribution of the transition path time, which is a common experimental observable, can be used to differentiate between the dynamics described by models of one-dimensional diffusion from the dynamics in which multidimensionality is essential. Specifically, we prove that the coefficient of variation obtained from this distribution cannot possibly exceed 1 for any one-dimensional diffusive model, no matter how rugged its underlying free energy landscape is In other words, this distribution cannot be broader than the single-exponential one. Thus, a coefficient of variation exceeding 1 is a fingerprint of multidimensional dynamics. Analysis of transition paths in atomistic simulations of proteins shows that this coefficient often exceeds 1, signifying essential multidimensionality of those systems.The fine balance of growth and division is a fundamental property of the physiology of cells, and one of the least understood. Its study has been thwarted by difficulties in the accurate measurement of cell size and the even greater challenges of measuring growth of a single cell over time. We address these limitations by demonstrating a computationally enhanced methodology for quantitative phase microscopy for adherent cells, using improved image processing algorithms and automated cell-tracking software. Accuracy has been improved more than twofold and this improvement is sufficient to establish the dynamics of cell growth and adherence to simple growth laws. It is also sufficient to reveal unknown features of cell growth, previously unmeasurable. With these methodological and analytical improvements, in several cell lines we document a remarkable oscillation in growth rate, occurring throughout the cell cycle, coupled to cell division or birth yet independent of cell cycle progression. We expect that further exploration with this advanced tool will provide a better understanding of growth rate regulation in mammalian cells.Any defects of sociality in individuals diagnosed with autism spectrum disorder (ASD) are standardly explained in terms of those individuals’ putative impairments in a variety of cognitive functions. Recently, however, the need for a bidirectional approach to social interaction has been emphasized. Such an approach highlights differences in basic ways of acting between ASD and neurotypical individuals which would prevent them from understanding each other. Here we pursue this approach by focusing on basic action features reflecting the agent’s mood and affective states. These are action features Stern named „vitality forms,” and which are widely assumed to substantiate core social interactions [D. N. Stern, The Interpersonal World of the Infant (1985); D. N. Stern, Forms of Vitality Exploring Dynamic Experience in Psychology, Arts, Psychotherapy, and Development (2010)]. Previously we demonstrated that, although ASD and typically developing (TD) children alike differentiate vitality forms when performing actions, ASD children express them in a way that is motorically dissimilar to TD children. To assess whether this motor dissimilarity may have consequences for vitality form recognition, we asked neurotypical participants to identify the vitality form of different types of action performed by ASD or TD children. We found that participants exhibited remarkable inaccuracy in identifying ASD children’s vitality forms. Interestingly, their performance did not benefit from information feedback. This indicates that how people act matters for understanding others and for being understood by them. Because vitality forms pervade every aspect of daily life, our findings promise to open the way to a deeper comprehension of the bidirectional difficulties for both ASD and neurotypical individuals in interacting with one another.We present a comprehensive theoretical study of the phase diagram of a system of many Bose particles interacting with a two-body central potential of the so-called Lennard-Jones form. First-principles path-integral computations are carried out, providing essentially exact numerical results on the thermodynamic properties. The theoretical model used here provides a realistic and remarkably general framework for describing simple Bose systems ranging from crystals to normal fluids to superfluids and gases. The interplay between particle interactions on the one hand and quantum indistinguishability and delocalization on the other hand is characterized by a single quantumness parameter, which can be tuned to engineer and explore different regimes. Taking advantage of the rare combination of the versatility of the many-body Hamiltonian and the possibility for exact computations, we systematically investigate the phases of the systems as a function of pressure (P) and temperature (T), as well as the quantumness parameter. We show how the topology of the phase diagram evolves from the known case of 4He, as the system is made more (and less) quantum, and compare our predictions with available results from mean-field theory. Possible realization and observation of the phases and physical regimes predicted here are discussed in various experimental systems, including hypothetical muonic matter.Chronic neurodegeneration in survivors of traumatic brain injury (TBI) is a major cause of morbidity, with no effective therapies to mitigate this progressive and debilitating form of nerve cell death. Here, we report that pharmacologic restoration of the blood-brain barrier (BBB), 12 mo after murine TBI, is associated with arrested axonal neurodegeneration and cognitive recovery, benefits that persisted for months after treatment cessation. Recovery was achieved by 30 d of once-daily administration of P7C3-A20, a compound that stabilizes cellular energy levels. Four months after P7C3-A20, electron microscopy revealed full repair of TBI-induced breaks in cortical and hippocampal BBB endothelium. Immunohistochemical staining identified additional benefits of P7C3-A20, including restoration of normal BBB endothelium length, increased brain capillary pericyte density, increased expression of BBB tight junction proteins, reduced brain infiltration of immunoglobulin, and attenuated neuroinflammation. These changes were accompanied by cessation of TBI-induced chronic axonal degeneration. Specificity for P7C3-A20 action on the endothelium was confirmed by protection of cultured human brain microvascular endothelial cells from hydrogen peroxide-induced cell death, as well as preservation of BBB integrity in mice after exposure to toxic levels of lipopolysaccharide. P7C3-A20 also protected mice from BBB degradation after acute TBI. Collectively, our results provide insights into the pathophysiologic mechanisms behind chronic neurodegeneration after TBI, along with a putative treatment strategy. Because TBI increases the risks of other forms of neurodegeneration involving BBB deterioration (e.g., Alzheimer’s disease, Parkinson’s disease, vascular dementia, chronic traumatic encephalopathy), P7C3-A20 may have widespread clinical utility in the setting of neurodegenerative conditions.Conventional „bulk” PCR often yields inefficient and nonuniform amplification of complex templates in DNA libraries, introducing unwanted biases. Amplification of single DNA molecules encapsulated in a myriad of emulsion droplets (emulsion PCR, ePCR) allows the mitigation of this problem. Different ePCR regimes were experimentally analyzed to identify the most robust techniques for enhanced amplification of DNA libraries. A phenomenological mathematical model that forms an essential basis for optimal use of ePCR for library amplification was developed. A detailed description by high-throughput sequencing of amplified DNA-encoded libraries highlights the principal advantages of ePCR over bulk PCR. ePCR outperforms PCR, reduces gross DNA errors, and provides a more uniform distribution of the amplified sequences. The quasi single-molecule amplification achieved via ePCR represents the fundamental requirement in case of complex DNA templates being prone to diversity degeneration and provides a way to preserve the quality of DNA libraries.Chikungunya virus (CHIKV) is an emerging viral pathogen that causes both acute and chronic debilitating arthritis. Here, we describe the functional and structural basis as to how two anti-CHIKV monoclonal antibodies, CHK-124 and CHK-263, potently inhibit CHIKV infection in vitro and in vivo. Our in vitro studies show that CHK-124 and CHK-263 block CHIKV at multiple stages of viral infection. CHK-124 aggregates virus particles and blocks attachment. Also, due to antibody-induced virus aggregation, fusion with endosomes and egress are inhibited. CHK-263 neutralizes CHIKV infection mainly by blocking virus attachment and fusion. To determine the structural basis of neutralization, we generated cryogenic electron microscopy reconstructions of FabCHIKV complexes at 4- to 5-Å resolution. CHK-124 binds to the E2 domain B and overlaps with the Mxra8 receptor-binding site. CHK-263 blocks fusion by binding an epitope that spans across E1 and E2 and locks the heterodimer together, likely preventing structural rearrangements required for fusion. These results provide structural insight as to how neutralizing antibody engagement of CHIKV inhibits different stages of the viral life cycle, which could inform vaccine and therapeutic design.The extracellular polysaccharide capsule of Klebsiella pneumoniae resists penetration by antimicrobials and protects the bacteria from the innate immune system. Host antimicrobial peptides are inactivated by the capsule as it impedes their penetration to the bacterial membrane. While the capsule sequesters most peptides, a few antimicrobial peptides have been identified that retain activity against encapsulated K. pneumoniae, suggesting that this bacterial defense can be overcome. However, it is unclear what factors allow peptides to avoid capsule inhibition. To address this, we created a peptide analog with strong antimicrobial activity toward several K. pneumoniae strains from a previously inactive peptide. We characterized the effects of these two peptides on K. pneumoniae, along with their physical interactions with K. pneumoniae capsule. Both peptides disrupted bacterial cell membranes, but only the active peptide displayed this activity against capsulated K. pneumoniae Unexpectedly, the active peptide showed no decrease in capsule binding, but did lose secondary structure in a capsule-dependent fashion compared with the inactive parent peptide. We found that these characteristics are associated with capsule-peptide aggregation, leading to disruption of the K. pneumoniae capsule. Our findings reveal a potential mechanism for disrupting the protective barrier that K. pneumoniae uses to avoid the immune system and last-resort antibiotics.