Search

This article explores the multifaceted concept of cardiovascular disease (CVD) patients' empowerment, emphasizing a shift from compliance-oriented models to active patient participation. In recognizing that cardiovascular disease is a paramount global health challenge, this study illuminates the pressing need for empowering patients, underscoring their role as active participants in their healthcare journey. Grounded in 5P-Medicine principles-Predictive, Preventive, Participatory, Personalized, and Precision Medicine-the importance of empowering CVD patients through analytics, prevention, participatory decision making, and personalized treatments is highlighted. Incorporating a comprehensive overview of patient empowerment strategies, including self-management, health literacy, patient involvement, and shared decision making, the article advocates for tailored approaches aligned with individual needs, cultural contexts, and healthcare systems. Technological integration is examined to enhance patient engagement and personalized healthcare experiences. The critical role of patient-centered design in integrating digital tools for CVD management is emphasized, ensuring successful adoption and meaningful impact on healthcare outcomes. The conclusion proposes vital research questions addressing challenges and opportunities in CVD patient empowerment. These questions stress the importance of medical community research, understanding user expectations, evaluating existing technologies, defining ideal empowerment scenarios, and conducting a literature review for informed advancements. This article lays the foundation for future research, contributing to ongoing patient-centered healthcare evolution, especially in empowering individuals with a 5P-Medicine approach to cardiovascular diseases., Competing Interests: Ivan Miguel Pires is an Academic Editor for PeerJ Computer Science., (© 2024 Denysyuk et al.)

Background: The Endoscopic Healing Index (EHI) analyzes biomarkers in a patient's peripheral blood to assess mucosal healing. We aimed to characterize the effectiveness of the EHI as a predictor of disease activity in a real world clinical setting., Methods: This retrospective study looked at patients treated and followed up at the University of Chicago Medicine IBD center who had EHI tests done as part of routine clinical care. The results of the EHI were compared with radiological imaging or endoscopy performed within 3 months of the EHI in order to determine accuracy at diagnosing active inflammation., Results: Fifty-five patients with CD and with an available EHI were included in this study. Four (50%) patients with an EHI of < 20 (n = 8) had evidence of objective inflammation. A cutoff of ≤ 20 had a sensitivity of 89% and specificity of 23.5% for predicting no evidence of any objective inflammation with an AUROC of 0.69. This score had a negative predictive value (NPV) of 50% and positive predictive value (PPV) of 72.3%. A cutoff EHI of 30 tended to classify patients as either having objective evidence of inflammation or not more often than FCAL (Correctly classifying inflammation: 89% vs 64%, respectively; p = 0.32)., Conclusion: In this real world analysis, the EHI showed poor predictive value for the absence of active inflammation as assessed by imaging or endoscopy, has limited utility in confirming deep remission and should be used with another objective modality., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Nonoperative management (NOM) is the standard of care for the management of blunt liver and spleen injuries (BLSI) in the stable pediatric patient. Angiography with embolization (AE) is used as an adjunctive therapy in the management of adult BLSI patients, but it is rarely used in the pediatric population. In this planned secondary analysis, we describe the current utilization patterns of AE in the management of pediatric BLSI., Methods: After obtaining IRB approval at each center, cohort data was collected prospectively for children admitted with BLSI confirmed on CT at 10 Level I pediatric trauma centers (PTCs) throughout the United States from April 2013 to January 2016. All patients who underwent angiography with or without embolization for a BLSI were included in this analysis. Data collected included patient demographics, injury details, organ injured and grade of injury, CT finding specifics such as contrast blush, complications, failure of NOM, time to angiography and techniques for embolization., Results: Data were collected for 1004 pediatric patients treated for BLSI over the study period, 30 (3.0%) of which underwent angiography with or without embolization for BLSI. Ten of the patients who underwent angiography for BLSI failed NOM. For patients with embolized splenic injuries, splenic salvage was 100%. Four of the nine patients undergoing embolization of the liver ultimately required an operative intervention, but only one patient required hepatorrhaphy and no patient required hepatectomy after AE. Few angiography studies were obtained early during hospitalization for BLSI, with only one patient undergoing angiography within 1 hour of arrival at the PTC, and 7 within 3 hours., Conclusion: Angioembolization is rarely used in the management of BLSI in pediatric trauma patients with blunt abdominal trauma and is generally used in a delayed fashion. However, when implemented, angioembolization is associated with 100% splenic salvage for splenic injuries., Level of Evidence: Therapeutic/Care Management; Level IV., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Ozanimod regulates lymphocyte egress from the spleen and lymph nodes into the systemic circulation. The histological changes which occur in the lymph nodes of patients on ozanimod is unknown., Materials and Methods: This retrospective study included patients with ulcerative colitis [UC] undergoing total colectomy for treatment-refractory disease who received ozanimod, and a cohort of patients with UC undergoing colectomy who did not have ozanimod exposure. Histology of the lymph nodes from the mesentery of colectomy specimens was reviewed and multiple features were scored by experienced pathologists., Results: Six [13%] ozanimod-treated patients with UC required surgery for treatment-refractory disease. Colectomy specimen data were available for five patients [one patient had surgery at an outside centre]. Lymph node specimens from six control patients with UC who had colectomy were examined. Histological examination of lymph nodes showed that patients treated with ozanimod had significantly greater extent of dilated sinuses [p = 0.03] and greater degrees of sinus histiocytosis [p = 0.03] compared with control patients. In addition, there was a trend towards more Castleman-like angiotrophic hyperplasia, plasma cell infiltration, and subcortical interfollicular expansion in ozanimod-treated patients., Conclusion: This study identifies unique histological changes in the lymph nodes of patients with UC treated with ozanimod. The presence of sinus histiocytosis and dilated sinuses is in keeping with the known mechanism of action of ozanimod, and suggests that blocking lymphocyte egression from lymph nodes was insufficient to ameliorate disease severity in these patients. The possibility of Castleman-like features, identified in several of the cases, needs to be further investigated., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Wearable sensor devices represent a noninvasive technology to continuously track biomarkers linked to inflammatory bowel disease (IBD). We assessed the inflammatory markers associated with IBD in human perspiration., Methods: Participants with IBD were monitored for 40 to 130 minutes with a proprietary wearable sensor device used to measure C-reactive protein, interleukin-6, and calprotectin. Sensor response using electrochemical impedance spectroscopy and serum samples were measured on the same day. The Mann-Whitney test was used to analyze the relationship between active and remission IBD in serum and perspiration, classified according to endoscopic reports and serum biomarker levels. Asynchronously collected fecal calprotectin from a subset of the population was similarly analyzed., Results: A total of 33 subjects were enrolled. Expression of calprotectin was significantly elevated in the active cohort compared with the remission cohort in perspiration (P < .05; median = 906.69 ng/mL; active 95% confidence interval [CI], 466.0-1833 ng/mL; remission 95% CI, 328.4-950.8 ng/mL), serum (median = 1860.82 ng/mL; active 95% CI, 1705-2985 ng/mL; remission 95% CI, 870.2-1786 ng/mL), and stool (P < .05; median = 126.74 µg/g; active 95% CI, 77.08-347.1 µg/g; remission 95% CI, 5.038-190.4 µg/g). Expression of CRP in perspiration and serum was comparable between the active and remission cohorts (perspiration: P > .05; median = 970.83 pg/mL; active 95% CI, 908.7-992 pg/mL; remission 95% CI, 903.3-991.9 pg/mL; serum: median = 2.34 µg/mL; active 95% CI, 1.267-4.492 µg/mL; remission 95% CI, 1.648-4.287 µg/mL). Expression of interleukin-6 in perspiration was nonsignificant in the active cohort compared with the remission cohort and was significantly elevated in serum (perspiration: P < .05; median = 2.13 pg/mL; active 95% CI, 2.124-2.44 pg/mL; remission 95% CI, 1.661-2.451 pg/mL; serum: median = 1.15 pg/mL; active 95% CI, 1.549-3.964 pg/mL; remission 95% CI, 0.4301-1.257 pg/mL). Analysis of the linear relationship between perspiration and serum calprotectin (R2 = 0.7195), C-reactive protein (R2 = 0.615), and interleukin-6 (R2 = 0.5411) demonstrated a strong to moderate relationship across mediums., Conclusions: We demonstrate the clinical utility of perspiration as a noninvasive medium for continuous measurement of inflammatory markers in IBD and find that the measures correlate with serum and stool markers across a range of disease activity., (© The Author(s) 2024. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals' nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal's structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model's accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.

Purpose: Despite limited genetic and histologic heterogeneity, Ewing sarcoma (EwS) tumor cells are transcriptionally heterogeneous and display varying degrees of mesenchymal lineage specification in vitro. In this study, we investigated if and how transcriptional heterogeneity of EwS cells contributes to heterogeneity of tumor phenotypes in vivo., Experimental Design: Single-cell proteogenomic-sequencing of EwS cell lines was performed and integrated with patient tumor transcriptomic data. Cell subpopulations were isolated by FACS for assessment of gene expression and phenotype. Digital spatial profiling and human whole transcriptome analysis interrogated transcriptomic heterogeneity in EwS xenografts. Tumor cell subpopulations and matrix protein deposition were evaluated in xenografts and patient tumors using multiplex immunofluorescence staining., Results: We identified CD73 as a biomarker of highly mesenchymal EwS cell subpopulations in tumor models and patient biopsies. CD73+ tumor cells displayed distinct transcriptional and phenotypic properties, including selective upregulation of genes that are repressed by EWS::FLI1, and increased migratory potential. CD73+ cells were distinguished in vitro and in vivo by increased expression of matrisomal genes and abundant deposition of extracellular matrix (ECM) proteins. In epithelial-derived malignancies, ECM is largely deposited by cancer-associated fibroblasts (CAF), and we thus labeled CD73+ EwS cells, CAF-like tumor cells. Marked heterogeneity of CD73+ EwS cell frequency and distribution was detected in tumors in situ, and CAF-like tumor cells and associated ECM were observed in peri-necrotic regions and invasive foci., Conclusions: EwS tumor cells can adopt CAF-like properties, and these distinct cell subpopulations contribute to tumor heterogeneity by remodeling the tumor microenvironment. See related commentary by Kuo and Amatruda, p. 5002., (©2023 American Association for Cancer Research.)

This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status., (© 2023 The Author(s).)

The neuroinflammatory response after traumatic brain injury (TBI) is implicated as a key mediator of secondary injury in both the acute and chronic periods after primary injury. Microglia are the key innate immune cell in the central nervous system, responding to injury with the release of cytokines and chemokines. In this context, we aimed to characterize the downstream cytokine response of human induced pluripotent stem cell (iPSC)-derived microglia when stimulated with five separate cytokines identified after human TBI. The iPSC-derived microglia were exposed to interleukin (IL)-1β, IL-4, IL-6, IL-10, and tumor necrosis factor (TNF) in the concentration ranges identified in clinical TBI studies. The downstream cytokine response was measured against a panel of 37 separate cytokines over a 72h time-course. The secretome revealed concentration-, time- and combined concentration and time-dependent downstream responses. TNF appeared to be the strongest inducer of downstream cytokine changes (51), followed by IL-1β (26) and IL-4 (19). IL-10 (11) and IL-6 (10) produced fewer responses. We also compare these responses with our previous studies of iPSC-derived neuronal and astrocyte cultures and the in vivo human TBI cytokine response. Notably, we found microglial culture to induce both a wider range of downstream cytokine responses and a greater fold change in concentration for those downstream responses, compared with astrocyte and neuronal cultures. In summary, we present a dataset for human microglial cytokine responses specific to the secretome found in the clinical context of TBI. This reductionist approach complements our previous datasets for astrocyte and neuronal responses and will provide a platform to enable future studies to unravel the complex neuroinflammatory network activated after TBI.

Blue copper proteins are models for illustrating how proteins tune metal properties. Nevertheless, the mechanisms by which the protein controls the metal site remain to be fully elucidated. A hindrance is that the closed shell Cu(I) site is inaccessible to most spectroscopic analyses. Carbon deuterium (C-D) bonds used as vibrational probes afford nonperturbative, selective characterization of the key cysteine and methionine copper ligands in both redox states. The structural integrity of Nostoc plastocyanin was perturbed by disrupting potential hydrogen bonds between loops of the cupredoxin fold via mutagenesis (S9A, N33A, N34A), variably raising the midpoint potential. The C-D vibrations show little change to suggest substantial alteration to the Cu(II) coordination in the oxidized state or in the Cu(I) interaction with the cysteine ligand. They rather indicate, along with visible and NMR spectroscopy, that the methionine ligand distinctly interacts more strongly with the Cu(I) ion, in line with the increases in midpoint potential. Here we show that the protein structure determines the redox properties by restricting the interaction between the methionine ligand and Cu(I) in the reduced state., (© 2023. Springer Nature Limited.)

Background & Aims: Upadacitinib is a novel selective Janus kinase 1 inhibitor that has shown efficacy in the treatment of moderate to severe ulcerative colitis (UC) and Crohn's disease (CD), and has received Food and Drug Administration approval for UC. We report a large real-world experience with upadacitinib in UC and CD., Methods: We performed a prospective analysis of clinical outcomes on upadacitinib in patients with UC and CD using predetermined intervals at weeks 0, 2, 4, and 8 as part of a formalized treatment protocol at our institution. We used the Simple Clinical Colitis Activity Index and the Harvey-Bradshaw index, as well as C-reactive protein and fecal calprotectin to assess efficacy, and also recorded treatment-related adverse events and serious adverse events., Results: A total of 105 patients were followed up for 8 weeks on upadacitinib, 84 of whom (44 UC patients, 40 CD patients) were initiated because of active luminal or perianal disease and included in the analysis. One hundred percent previously received anti-tumor necrosis factor therapy, and 89.3% had received 2 or more advanced therapies. At 4 and 8 weeks of treatment for UC, 19 of 25 (76.0%) and 23 of 27 (85.2%) achieved clinical response and 18 of 26 (69.2%) and 22 of 27 (81.5%) achieved clinical remission, respectively. Of those who previously were tofacitinib-exposed, 7 of 9 (77.8%) achieved clinical remission by 8 weeks. In CD, 13 of 17 (76.5.%) achieved clinical response and 12 of 17 (70.6%) achieved clinical remission by 8 weeks. Of those with increased fecal calprotectin and C-reactive protein levels, 62% and 64% normalized by week 8, respectively. Results were seen as early as week 2 in both UC and CD, with clinical remission rates of 36% and 56.3.%, respectively. Acne was the most commonly reported adverse event, occurring in 24 of 105 patients (22.9%)., Conclusions: In this large real-world experience in medically resistant patients with UC or CD, we report that upadacitinib is rapidly effective and safe, including in those who had prior tofacitinib exposure. This study was approved by the Institutional Review Board at the University of Chicago (IRB20-1979)., (Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Physical issues started to receive more attention due to the sedentary lifestyle prevalent in modern culture. The Ten Meter Walk Test allows measuring the person's capacity to walk along 10 m and analyzing the advancement of various medical procedures for ailments, including stroke. This systematic review is related to the use of mobile or wearable devices to measure physical parameters while administering the Ten Meter Walk Test for the analysis of the performance of the test. We applied the PRISMA methodology for searching the papers related to the Ten Meter Walk Test. Natural Language Processing (NLP) algorithms were used to automate the screening process. Various papers published in two decades from multiple scientific databases, including IEEE Xplore, Elsevier, Springer, EMBASE, SCOPUS, Multidisciplinary Digital Publishing Institute (MDPI), and PubMed Central were analyzed, focusing on various diseases, devices, features, and methods. The study reveals that chronometer and accelerometer sensors measuring spatiotemporal features are the most pertinent in the Gait characterization of most diseases. Likewise, all studies emphasized the close relation between the quality of the sensor's data obtained and the system's ultimate accuracy. In other words, calibration procedures are needed because of the body part where the sensor is worn and the type of sensor. In addition, using ambient sensors providing kinematic and kinetic features in conjunction with wearable sensors and consistently acquiring walking signals can enhance the system's performance. The most common weaknesses in the analyzed studies are the sample size and the unavailability of continuous monitoring devices for measuring the Ten Meter Walk Test., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (© 2023 The Authors.)

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

It is increasingly possible to acquire Electrocardiographic data with featured low-cost devices. The proposed dataset will help map different signals for various diseases related to Electrocardiography data. The dataset presented in this paper is related to the acquisition of electrocardiography data during the standing up and seated positions. The data was collected from 219 individuals (112 men, 106 women, and one other) in different environments, but they are in the Covilhã municipality. The dataset includes the 219 recordings and corresponds to the sensors' recordings of a 30 s sitting and a 30 s standing test, which checks to approximately 1 min for each one. This dataset includes 3.7 h (approximately) of recordings for further analysis with data processing techniques and machine learning methods. It will be helpful for the complementary creation of a robust method for identifying the characteristics of individuals related to Electrocardiography signals., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Background: Fecal calprotectin (Fcal) is a noninvasive, inexpensive biomarker of disease activity. However, patient compliance with this test is variable and incompletely described. We assessed compliance rates with Fcal tests and identified factors associated with noncompliance., Methods: A retrospective chart review of patients with inflammatory bowel disease (IBD) who had a Fcal test ordered through our center between August 2021 and December 2021 was conducted. Demographic, clinical, disease, and test-related information were recorded. Patients with incomplete Fcal orders were sent a survey to better understand their reasons for noncompliance. Simple statistical analysis and and multivariable logistic regression modeling were performed., Results: Of 303 patients, 165 (54.4%) had an order for Fcal. Of the Fcal tests ordered, 55 (33.3%) were not completed. Remission of IBD, no prior Fcal completion, and tests ordered at a distant site were all associated with test noncompletion. A multivariable logistic regression revealed that history of a prior completed Fcal test is associated with subsequent test completion (odds ratio = 2.1, 95% confidence interval 1.9-35.5, P = .004). Patients who did not complete the test described the pandemic and third-party testing center issues as the most common reasons for noncompliance., Conclusions: In this single center experience with Fcal testing in patients with IBD, we identified that a history of incomplete Fcal testing and distant location of lab testing were significantly associated with noncompletion of the test. We provide practical guidance for future utilization and compliance, including the impact of home-based testing., (© The Author(s) 2022. Published by Oxford University Press on behalf of Crohn's & Colitis Foundation.)

Crohn's disease (CD) is a chronic immune-mediated inflammatory condition which can negatively impact a patient's quality of life. The traditional management strategy for CD has focused on symptomatic control, however, this approach fails to prevent organ damage and to change the progressive course of this disease. Thus, the field has moved towards a treat-to-target strategy that includes identifying individualized objective targets, choosing a therapy based on individual factors that include disease severity and risk, closely monitoring disease activity at predefined time points, and optimizing therapies as needed. Due to the increasing number of therapies approved for CD, this review explores the various factors which should be considered in the sequencing of treatment options together with using the treat-to-target framework to control disease activity early in its course and provide holistic patient care., (© 2022 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

Objective: Posttraumatic stress disorder (PTSD) is associated with high comorbidity rates across the full range of psychiatric disorders. However, little is known about how psychiatric comorbidity manifests among people with PTSD, particularly with regard to concurrent diagnoses., Method: Latent class analysis (LCA) was used to characterize discrete classes of PTSD comorbidity using past year DSM-5 diagnostic standards among a large nationally representative epidemiologic sample of U.S. adults. Follow-up analyses compared participant characteristics across latent classes., Results: The LCA was best characterized by five classes: low comorbidity, distress-fear, distress-externalizing, mania-fear-externalizing, and mania-externalizing. Excluding the low comorbidity class, proportions of borderline and schizotypal personality disorder were high across classes., Conclusion: Participant characteristics across classes of past year PTSD comorbidity are explored through the lens of case conceptualization and treatment planning utility., (© 2022 Wiley Periodicals LLC.)

Objective: To investigate the influence of a passive mobilization session on endothelial function in patients with sepsis., Methods: This was a quasi-experimental double-blind and single-arm study with a pre- and postintervention design. Twenty-five patients with a diagnosis of sepsis who were hospitalized in the intensive care unit were included. Endothelial function was assessed at baseline (preintervention) and immediately postintervention by brachial artery ultrasonography. Flow mediated dilatation, peak blood flow velocity and peak shear rate were obtained. Passive mobilization consisted of bilateral mobilization (ankles, knees, hips, wrists, elbows and shoulders), with three sets of ten repetitions each, totaling 15 minutes., Results: After mobilization, we found increased vascular reactivity function compared to preintervention: absolute flow-mediated dilatation (0.57mm ± 0.22 versus 0.17mm ± 0.31; p < 0.001) and relative flow-mediated dilatation (17.1% ± 8.25 versus 5.08% ± 9.16; p < 0.001). Reactive hyperemia peak flow (71.8cm/s ± 29.3 versus 95.3cm/s ± 32.2; p < 0.001) and shear rate (211s ± 113 versus 288s ± 144; p < 0.001) were also increased., Conclusion: A passive mobilization session increases endothelial function in critical patients with sepsis. Future studies should investigate whether a mobilization program can be applied as a beneficial intervention for clinical improvement of endothelial function in patients hospitalized due to sepsis.

Proteins tune the reactivity of metal sites; less understood is the impact of association with a redox partner. We demonstrate the utility of carbon-deuterium labels for selective analysis of delicate metal sites. Introduced into plastocyanin, they reveal substantial strengthening of the key Cu-Cys89 bond upon association with cytochrome f .

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.

Amyotrophic lateral sclerosis is a rapidly progressive and fatal disease. Although astrocytes are increasingly recognized contributors to the underlying pathogenesis, the cellular autonomy and uniformity of astrocyte reactive transformation in different genetic forms of amyotrophic lateral sclerosis remain unresolved. Here we systematically examine these issues by using highly enriched and human induced pluripotent stem cell-derived astrocytes from patients with VCP and SOD1 mutations. We show that VCP mutant astrocytes undergo cell-autonomous reactive transformation characterized by increased expression of complement component 3 (C3) in addition to several characteristic gene expression changes. We then demonstrate that isochronic SOD1 mutant astrocytes also undergo a cell-autonomous reactive transformation, but that this is molecularly distinct from VCP mutant astrocytes. This is shown through transcriptome-wide analyses, identifying divergent gene expression profiles and activation of different key transcription factors in SOD1 and VCP mutant human induced pluripotent stem cell-derived astrocytes. Finally, we show functional differences in the basal cytokine secretome between VCP and SOD1 mutant human induced pluripotent stem cell-derived astrocytes. Our data therefore reveal that reactive transformation can occur cell autonomously in human amyotrophic lateral sclerosis astrocytes and with a striking degree of early molecular and functional heterogeneity when comparing different disease-causing mutations. These insights may be important when considering astrocyte reactivity as a putative therapeutic target in familial amyotrophic lateral sclerosis., (© The Author(s) (2022). Published by Oxford University Press on behalf of the Guarantors of Brain.)

The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including the pants pockets, in a wristband, over the bedside table, on a table, inside the car, or on other furniture, for the acquisition of accelerometer, magnetometer, gyroscope, GPS, and microphone data. The data was collected by 25 individuals (15 men and 10 women) in different environments in Covilhã and Fundão municipalities (Portugal). The dataset includes the sensors' captures related to a minimum of 2000 captures for each motionless activity, which corresponds to 2.8 h (approximately) for each one. This dataset includes 8.4 h (approximately) of captures for further analysis with data processing techniques, and machine learning methods. It will be useful for the complementary creation of a robust method for the identification of these type of activities., (© 2022. The Author(s).)

An increasing number of patients are on anticoagulation for a variety of indications. Patients on anticoagulation who present to the hospital with life-threatening hemorrhage, whether trauma related or not, must be assessed for the reversal of anticoagulation. Identification of the type of anticoagulation, the timing of the most recent usage of anticoagulation, and the efficacy of the anticoagulation all have an impact on whether reversal agents should be used. There are a variety of reversal agents, both nonspecific and specific, that could be used for reversal; however, not all reversal agents work for all anticoagulation medication. As more anticoagulation medications are used and indications expand, providers must be aware of the reversal agents available and the efficacy and indications for these reversal agents., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Sepsis is a serious organ dysfunction leading to endothelial damage in critical patients. Physiologically, there is an augment of vascular diameter in response to increased vascular blood flow and shear stress stimulus. However, the pattern of vascular response in face of passive mobilization (PM), an early mobilization physical strategy, has not yet been explored in patients with sepsis. To explore patterns of vascular response to PM and associations with clinical and cardiovascular profile in patients with sepsis. Cross-sectional, single-arm study. Thirty-two patients diagnosed with sepsis were enrolled. Vascular response was assessed by flow-mediated dilation (FMD) using brachial artery ultrasound, before and after PM. The PM (to assess the response pattern) and SR (shear rate) were also calculated. PM protocol consisted of knees, hips, wrists, elbows, shoulders, dorsiflexion/plantar flexion movements 3 × 10 repetitions each (15 min). Arterial stiffness was assessed by Sphygmocor®, by analyzing the morphology and pulse wave velocity. Cardiac autonomic modulation (CAM) was assessed by analyzing heart rate variability indexes (mean HR, RMSSD, LF, HF, ApEn, SampEn, DFA). Different vascular responses were observed after PM: (1) increased vascular diameter (responders) (n = 13, %FMD = 11.89 ± 5.64) and (2) reduced vascular diameter (non-responders) (n = 19, %FMD= -7.42 ± 6.44). Responders presented a higher non-linear DFA2 index (p = 0.02). There was a positive association between FMD and DFA (r = 0.529; p = 0.03); FMD and SampEn (r = 0.633; p < 0.01). A negative association was identified between FMD and LF (Hz) (r= -0.680; p < 0.01) and IL-6 (r= -0.469; p = 0.037) and SR and CRP (r= -0.427; p = 0.03)., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Following traumatic brain injury (TBI), raised cerebral lactate/pyruvate ratio (LPR) reflects impaired energy metabolism. Raised LPR correlates with poor outcome and mortality following TBI. We prospectively recruited patients with TBI requiring neurocritical care and multimodal monitoring, and utilised a tiered management protocol targeting LPR. We identified patients with persistent raised LPR despite adequate cerebral glucose and oxygen provision, which we clinically classified as cerebral 'mitochondrial dysfunction' (MD). In patients with TBI and MD, we administered disodium 2,3- 13 C 2 succinate (12 mmol/L) by retrodialysis into the monitored region of the brain. We recovered 13 C-labelled metabolites by microdialysis and utilised nuclear magnetic resonance spectroscopy (NMR) for identification and quantification.Of 33 patients with complete monitoring, 73% had MD at some point during monitoring. In 5 patients with multimodality-defined MD, succinate administration resulted in reduced LPR(-12%) and raised brain glucose(+17%). NMR of microdialysates demonstrated that the exogenous 13 C-labelled succinate was metabolised intracellularly via the tricarboxylic acid cycle. By targeting LPR using a tiered clinical algorithm incorporating intracranial pressure, brain tissue oxygenation and microdialysis parameters, we identified MD in TBI patients requiring neurointensive care. In these, focal succinate administration improved energy metabolism, evidenced by reduction in LPR. Succinate merits further investigation for TBI therapy.

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach's feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.

Human activity recognition (HAR) is a significant research area due to its wide range of applications in intelligent health systems, security, and entertainment games. Over the past few years, many studies have recognized human daily living activities using different machine learning approaches. However, the performance of a machine learning algorithm varies based on the sensing device type, the number of sensors in that device, and the position of the underlying sensing device. Moreover, the incomplete activities (i.e., data captures) in a dataset also play a crucial role in the performance of machine learning algorithms. Therefore, we perform a comparative analysis of eight commonly used machine learning algorithms in different sensor combinations in this work. We used a publicly available mobile sensors dataset and applied the k-Nearest Neighbors (KNN) data imputation technique for extrapolating the missing samples. Afterward, we performed a couple of experiments to figure out which algorithm performs best at which sensors' data combination. The experimental analysis reveals that the AdaBoost algorithm outperformed all machine learning algorithms for recognizing five different human daily living activities with both single and multi-sensor combinations. Furthermore, the experimental results show that AdaBoost is capable to correctly identify all the activities presented in the dataset with 100% classification accuracy., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background and Aim: Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction., Methods: This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed., Results: Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results., Conclusions: The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers' well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients' sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices' security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

All mobile devices include a microphone that can be used for acoustic data acquisition. This article presents a dataset of acoustic signals related to nine environments, captured with a microphone embedded on off-the-shelf mobile devices. The mobile phone can be placed in the pants pockets, in a wristband, over the bedside table, on a table, or on other furniture. Data collection environments are bar, classroom, gym, kitchen, library, street, hall, living room, and bedroom. The data was collected by 25 individuals (15 men and 10 women) in different environments around Covilhã and Fundão municipalities (Portugal). The microphone data was sampled with 44,100 Hz into an array with 16-bit unsigned integer values in the range [0, 255] with a 128 offset for zero. The dataset presented in this paper presents at least 2000 samples of 5 s of data for each environment, corresponding to around 2.8 h for each environment into text files. In total, it includes at least 25.2 h of acoustic data for the implementation of data processing techniques, e.g., Fast Fourier Transform (FFT), and other machine learning methods for the different analysis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2021 The Author(s). Published by Elsevier Inc.)

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application's validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92-0.97; CV = 1.85-3.03) and very strong relationships inter-devices for the stand-up time ( r = 0.94) and the total time ( r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.

Background: No prior studies have examined the outcomes of early vasopressor use in children sustaining blunt liver or spleen injury (BLSI)., Methods: A planned secondary analysis of vasopressor use from a 10-center, prospective study of 1004 children with BLSI. Inverse probability of treatment weighting (IPTW) was used to compare patients given vasopressors <48 h after injury to controls based on pretreatment factors. A logistic regression was utilized to assess survival associated with vasopressor initiation factors on mortality and nonoperative management (NOM) failure., Results: Of 1004 patients with BLSI, 128 patients were hypotensive in the Pediatric Trauma Center Emergency Department (ED); 65 total patients received vasopressors. Hypotension treated with vasopressors was associated with a sevenfold increase in mortality (AOR = 7.6 [p < 0.01]). When excluding patients first given vasopressors for cardiac arrest, the risk of mortality increased to 11-fold (AOR = 11.4 [p = 0.01]). All deaths in patients receiving vasopressors occurred when started within the first 12 h after injury. Vasopressor administration at any time was not associated with NOM failure., Conclusion: After propensity matching, early vasopressor use for hypotension in the ED was associated with an increased risk of death, but did not increase the risk of failure of NOM., Level of Evidence: Level III prognostic and epidemiological, prospective., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objectives: Mouse models of ovarian cancer commonly transfer large numbers of tumor cells into the peritoneal cavity to establish experimental metastatic disease, which may not adequately model early metastatic spread from a primary tumor site. We hypothesized we could develop an ovarian cancer model that predictably represents micro-metastatic disease., Methods: Murine ID8 VEGF ovarian cancer cells were transduced to express enhanced luciferase (eLuc) to enable intravital detection of microscopic disease burden and injected beneath the ovarian bursa of C57Bl/6 mice. At 6 or 10 weeks after orthotopic injection, when mice had detectable metastases, hysterectomy and bilateral salpingo-oophorectomy was performed to remove all macroscopic disease, and survival monitored. Immunohistochemistry and gene expression profiling were performed on primary and metastatic tumors., Results: eLuc-transduced ID8 VEGF cells were brighter than cells transduced with standard luciferase, enabling in vivo visualization of microscopic intra-abdominal metastases developing after orthotopic injection. Primary surgical cytoreduction removed the primary tumor mass but left minimal residual disease in all mice. Metastatic sites that developed following orthotopic injection were similar to metastatic human ovarian cancer sites. Gene expression and immune infiltration were similar between primary and metastatic mouse tumors. Surgical cytoreduction prolonged survival compared to no surgery, with earlier cytoreduction more beneficial than delayed, despite micro-metastatic disease in both settings., Conclusions: Mice with primary ovarian tumors established through orthotopic injection develop progressively fatal metastatic ovarian cancer, and benefit from surgical cytoreduction to remove bulky disease. This model enables the analysis of therapeutic regimens designed to target and potentially eradicate established minimal residual disease., Competing Interests: Declaration of Competing Interest P.D.G. has patents with, received funding from, and was a scientific consultant to Juno Therapeutics. R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene, Merck and has received research support from Janssen Pharmaceuticals and Juno Therapeutics, and declares ownership in CellSpace Biosciences., (Copyright © 2020 Elsevier Inc. All rights reserved.)

We use the Positions and Covering methodology to obtain exact solutions for the two-dimensional, non-guillotine restricted, strip packing problem. In this classical NP-hard problem, a given set of rectangular items has to be packed into a strip of fixed weight and infinite height. The objective consists in determining the minimum height of the strip. The Positions and Covering methodology is based on a two-stage procedure. First, it is generated, in a pseudo-polynomial way, a set of valid positions in which an item can be packed into the strip. Then, by using a set-covering formulation, the best configuration of items into the strip is selected. Based on the literature benchmark, experimental results validate the quality of the solutions and method's effectiveness for small and medium-size instances. To the best of our knowledge, this is the first approach that generates optimal solutions for some literature instances for which the optimal solution was unknown before this study., Competing Interests: The authors have declared that no competing interests exist.

Mathematics discussions are important for helping students to develop conceptual understanding and to learn disciplinary norms and practices. In recent years, there has been increased attention to teaching prospective teachers to lead discussions with students. This paper examines the possibilities of designing a formative assessment that gathers information about prospective elementary teachers' skills with leading problem-based mathematics discussions and makes sense of such information. A decomposition of the practice of leading discussions was developed and used to design the assessment. Nine first-year teachers who graduated from a range of different teacher education programs participated in the study. The findings reveal that our formative assessment works to gather information about teachers' capabilities with leading discussions and that the associated tools support making sense of the information gathered. This suggests that such tools could be useful to support the formative assessment of the developing capabilities of prospective teachers., (© The Author(s) 2021.)

Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008-2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries' search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement.

The dataset presented in this paper is related to the performance of five Activities of Daily Living (ADL) with motion, such as walking, running, standing, walking upstairs, and walking downstairs. These activities were performed with a mobile device in a waistband, containing the data acquired from accelerometer, magnetometer, and gyroscope sensors. These data include the motion data, which allow the characterization of the different types of movement. The data acquisition was performed in open environments by 25 individuals (15 man, and 10 woman) in the Covilhã, and Fundão municipalities (Portugal). The data related to the different sensors was acquired with a sampling rate of 100 Hz by the accelerometer, 50 Hz by the magnetometer, and 100 Hz by the gyroscope sensors. It includes the captures related to a minimum of 2000 captures for each ADL, which corresponds to 2.8 h (approximately) for each ADL. In total, this dataset includes 13.9 h (approximately) of captures. These data can be reused for the implementation of data processing techniques, and artificial intelligence methods., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Author(s).)

Adoptive T cell therapy (ACT) with genetically modified T cells has shown impressive results against some hematologic cancers, but efficacy in solid tumors can be limited by restrictive tumor microenvironments (TMEs). For example, Fas ligand is commonly overexpressed in TMEs and induces apoptosis in tumor-infiltrating, Fas receptor-positive lymphocytes. We engineered immunomodulatory fusion proteins (IFPs) to enhance ACT efficacy, combining an inhibitory receptor ectodomain with a costimulatory endodomain to convert negative into positive signals. We developed a Fas-4-1BB IFP that replaces the Fas intracellular tail with costimulatory 4-1BB. Fas-4-1BB IFP-engineered murine T cells exhibited increased pro-survival signaling, proliferation, antitumor function, and altered metabolism in vitro. In vivo, Fas-4-1BB ACT eradicated leukemia and significantly improved survival in the aggressive KPC pancreatic cancer model. Fas-4-1BB IFP expression also enhanced primary human T cell function in vitro. Thus, Fas-4-1BB IFP expression is a novel strategy to improve multiple T cell functions and enhance ACT against solid tumors and hematologic malignancies., Competing Interests: Disclosures: S.K. Oda reported grants from Juno Therapeutics during the conduct of the study. P.D. Greenberg reported grants from Juno Therapeutics and personal fees from Juno Therapeutics during the conduct of the study. P.D. Greenberg reported personal fees from Rapt Therapeutics, personal fees from Elpiscience, personal fees from Celsius, and grants from Lonza outside the submitted work. P.D. Greenberg reported a patent to chimeric receptors pending with Juno Therapeutics. No other disclosures were reported., (© 2020 Oda et al.)

Background: Assessment of the immediate need for specific blood product transfusions in acutely bleeding patients is challenging. Clinical assessment and commonly used coagulation tests are inaccurate and time-consuming. The goal of this practice management guideline was to evaluate the role of the viscoelasticity tests, which are thromboelastography (TEG) and rotational thromboelastometry (ROTEM), in the management of acutely bleeding trauma, surgical, and critically ill patients., Methods: Systematic review and meta-analyses of manuscripts comparing TEG/ROTEM with non-TEG/ROTEM-guided blood products transfusions strategies were performed. The Grading of Recommendations Assessment, Development and Evaluation methodology was applied to assess the level of evidence and create recommendations for TEG/ROTEM-guided blood product transfusions in adult trauma, surgical, and critically ill patients., Results: Using TEG/ROTEM-guided blood transfusions in acutely bleeding trauma, surgical, and critically ill patients was associated with a tendency to fewer blood product transfusions in all populations. Thromboelastography/ROTEM-guided transfusions were associated with a reduced number of additional invasive hemostatic interventions (angioembolic, endoscopic, or surgical) in surgical patients. Thromboelastography/ROTEM-guided transfusions were associated with a reduction in mortality in trauma patients., Conclusion: In patients with ongoing hemorrhage and concern for coagulopathy, we conditionally recommend using TEG/ROTEM-guided transfusions, compared with traditional coagulation parameters, to guide blood component transfusions in each of the following three groups: adult trauma patients, adult surgical patients, and adult patients with critical illness., Level of Evidence: Systematic Review/Meta-Analysis, level III.

Demographic data suggest a rapid aging trend in the active workforce. The concept of aging at work comes from the urgent requirement to help the aging workforce of the contemporary industries to maintain productivity while achieving a work and private life balance. While there is plenty of research focusing on the aging population, current research activities on policies covering the concept of aging at work are limited and conceptually different. This paper aims to review publications on aging at work, which could lead to the creation of a framework that targets governmental decision-makers, the non-governmental sector, the private sector, and all of those who are responsible for the formulation of policies on aging at work. In August 2019 we searched for peer-reviewed articles in English that were indexed in PubMed, IEEE Xplore, and Springer and published between 2008 and 2019. The keywords included the following phrases: "successful aging at work", "active aging at work", "healthy aging at work", "productive aging at work", and "older adults at work". A total of 47,330 publications were found through database searching, and 25,187 publications were screened. Afterwards, 7756 screened publications were excluded from the further analysis, and a total of 17,431 article abstracts were evaluated for inclusion. Finally, further qualitative analysis included 1375 articles, of which about 24 are discussed in this article. The most prominent works suggest policies that encourage life-long learning, and a workforce that comprises both younger and older workers, as well as gradual retirement.

We present a dataset related to the acquisition of different sensors data during the performance of the Timed-Up and Go test with the mobile device positioned in a waistband for the acquisition of accelerometer and magnetometer data, and a BITalino device positioned in a chest band for the acquisition of Electrocardiography and Electroencephalography for further processing. The data acquired from the BITalino device is acquired simultaneously by a Bluetooth connection with the same mobile application. The data was acquired in five institutions, including Centro Comunitário das Lameiras, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia da Santa Casa da Misericórdia do Fundão, and Lar da Aldeia de Joanes da Santa Casa da Misericórdia do Fundão from Fundão and Covilhã municipalities (Portugal). This article describes the data acquired from a several subjects from the different institutions for the acquisition of accelerometer and magnetometer data, where each person performed the Timed-Up and Go test three times, where each output from the sensors was acquired with a sampling rate of 100 Hz. Related to the data acquired by the sensors connected to the BITalino device, 31 persons performed the different experiments related to the Timed-Up and Go Test. Following the data acquired from Electroencephalography and Electrocardiography sensors, only the data acquired from 14 individuals was considered valid. The data acquired by a BITalino device has a sampling rate of 100 Hz. These data can be reused for testing machine learning methods for the evaluation of the performance of the Timed-Up and Go test with older adults., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Author(s).)