Davidsonfink9286
The repertoire of protein expression along the renal tubule depends both on regulation of transcription and regulation of alternative splicing that can generate multiple proteins from a single gene.
A full-length, small-sample RNA-seq protocol profiled transcriptomes for all 14 renal tubule segments microdissected from mouse kidneys.
This study identified >34,000 transcripts, including 3709 that were expressed in a segment-specific manner. All data are provided as an online resource (https//esbl.nhlbi.nih.gov/MRECA/Nephron/). Many of the genes expressed in unique patterns along the renal tubule were solute carriers, transcription factors, or G protein-coupled receptors that account for segment-specific function. Mapping the distribution of transcripts associated with Wnk-SPAK-PKA signaling, renin-angiotensin-aldosterone signaling, and cystic diseases of the kidney illustrated the applications of the online resource. The method allowed full-length mapping of RNA-seq reads, which facilitated comprehensripts. Identification of alternative splicing along the renal tubule may prove critical to understanding renal physiology and pathophysiology.Two papers, one in 1986 and another one in 1988, reported a strong inverse correlation between urinary anion gap (UAG) and urine ammonia excretion (UNH4) in patients with metabolic acidosis and postulated that UAG could be used as an indirect measure of UNH4 This postulation has persisted until now and is widely accepted. In this review, we discuss factors regulating UAG and examine published evidence to uncover errors in the postulate and the design of the original studies. The essential fact is that, in the steady state, UAG reflects intake of Na, K, and Cl. Discrepancy between intake and urinary output of these electrolytes (i.e, UAG) indicates selective extrarenal loss of these electrolytes or nonsteady state. UNH4 excretion, which depends, in the absence of renal dysfunction, mainly on the daily acid load, has no consistent relationship to UAG either theoretically or in reality. Any correlation between UAG and UNH4, when observed, was a fortuitous correlation and cannot be extrapolated to other situations. Furthermore, the normal value of UAG has greatly increased over the past few decades, mainly due to increases in dietary intake of potassium and widespread use of sodium salts with anions other than chloride as food additives. The higher normal values of UAG must be taken into consideration in interpreting UAG.
Proximal tubule cells dominate the kidney parenchyma numerically, although less abundant cell types of the distal nephron have disproportionate roles in water and electrolyte balance.
Coupling of a FACS-based enrichment protocol with single-cell RNA-seq profiled the transcriptomes of 9099 cells from the thick ascending limb (CTAL)/distal convoluted tubule (DCT) region of the mouse nephron.
Unsupervised clustering revealed
/
and
/
cells, identified as DCT1 and DCT2 cells, respectively. DCT1 cells appear to be heterogeneous, with orthogonally variable expression of
,
, and
. An additional DCT1 subcluster showed marked enrichment of cell cycle-/cell proliferation-associated mRNAs (
.,
,
, and
), which fit with the known plasticity of DCT cells. No DCT2-specific transcripts were found. DCT2 cells contrast with DCT1 cells by expression of epithelial sodium channel
- and
-subunits and much stronger expression of transcripts associated with calcium transport (
,
,
, and
). Additionally, scRNA-seq identified three distinct CTAL (
) cell subtypes. One of these expressed
and
, consistent with macula densa cells. The other two CTAL clusters were distinguished by
and
in one and
and
in the other. These two CTAL cell types were also distinguished by expression of alternative Iroquois homeobox transcription factors, with
and
in the
CTAL cells and
in the
CTAL cells.
Single-cell transcriptomics revealed unexpected diversity among the cells of the distal nephron in mouse. Web-based data resources are provided for the single-cell data.
Single-cell transcriptomics revealed unexpected diversity among the cells of the distal nephron in mouse. Web-based data resources are provided for the single-cell data.
Communicating scientific uncertainty about public health threats such as COVID-19 is an ethically desirable task endorsed by expert guidelines on crisis communication. However, the communication of scientific uncertainty is challenging because of its potential to promote ambiguity aversion-a well-described syndrome of negative psychological responses consisting of heightened risk perceptions, emotional distress, and decision avoidance. Communication strategies that can inform the public about scientific uncertainty while mitigating ambiguity aversion are a critical unmet need.
This study aimed to evaluate whether an "uncertainty-normalizing" communication strategy-aimed at reinforcing the expected nature of scientific uncertainty about the COVID-19 pandemic-can reduce ambiguity aversion, and to compare its effectiveness to conventional public communication strategies aimed at promoting hope and prosocial values.
In an online factorial experiment conducted from May to June 2020, a national sample of 1497tainty may be an effective strategy for mitigating ambiguity aversion in crisis communication efforts. More research is needed to test uncertainty-normalizing communication strategies and to elucidate the factors that moderate their effectiveness.
Communicating scientific uncertainty about the COVID-19 pandemic produces ambiguity-averse cognitive and emotional, but not behavioral, responses among the general public, and an uncertainty-normalizing communication strategy reduces these responses. Normalizing uncertainty may be an effective strategy for mitigating ambiguity aversion in crisis communication efforts. More research is needed to test uncertainty-normalizing communication strategies and to elucidate the factors that moderate their effectiveness.
With the approval of two COVID-19 vaccines in Canada, many people feel a sense of relief, as hope is on the horizon. However, only about 75% of people in Canada plan to receive one of the vaccines.
The purpose of this study is to determine the reasons why people in Canada feel hesitant toward receiving a COVID-19 vaccine.
We screened 3915 tweets from public Twitter profiles in Canada by using the search words "vaccine" and "COVID." The tweets that met the inclusion criteria (ie, those about COVID-19 vaccine hesitancy) were coded via content analysis. Codes were then organized into themes and interpreted by using the Theoretical Domains Framework.
Overall, 605 tweets were identified as those about COVID-19 vaccine hesitancy. Vaccine hesitancy stemmed from the following themes concerns over safety, suspicion about political or economic forces driving the COVID-19 pandemic or vaccine development, a lack of knowledge about the vaccine, antivaccine or confusing messages from authority figures, and a lack ostating that one of the worst threats to global health is vaccine hesitancy, it is important to have a comprehensive understanding of the reasons behind this reluctance. By using a behavioral science framework, this study adds to the emerging knowledge about vaccine hesitancy in relation to COVID-19 vaccines by analyzing public discourse in tweets in real time. Health care leaders and clinicians may use this knowledge to develop public health interventions that are responsive to the concerns of people who are hesitant to receive vaccines.
Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers.
During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period.
This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time beloD 13%; P=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; P<.001) than in those with a GMI of <7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; P=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (P=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters.
These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.
These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.
Health care personnel (HCP) are at high risk for exposure to the SARS-CoV-2 virus. While personal protective equipment (PPE) may mitigate this risk, prospective data collection on its use and other risk factors for seroconversion in this population is needed.
The primary objectives of this study are to (1) determine the incidence of, and risk factors for, SARS-CoV-2 infection among HCP at a tertiary care medical center and (2) actively monitor PPE use, interactions between study participants via electronic sensors, secondary cases in households, and participant mental health and well-being.
To achieve these objectives, we designed a prospective, observational study of SARS-CoV-2 infection among HCP and their household contacts at an academic tertiary care medical center in North Carolina, USA. Enrolled HCP completed frequent surveys on symptoms and work activities and provided serum and nasal samples for SARS-CoV-2 testing every 2 weeks. Additionally, interactions between participants and their movement within the clinical environment were captured with a smartphone app and Bluetooth sensors. Finally, a subset of participants' households was randomly selected every 2 weeks for further investigation, and enrolled households provided serum and nasal samples via at-home collection kits.
As of December 31, 2020, 211 HCP and 53 household participants have been enrolled. Recruitment and follow-up are ongoing and expected to continue through September 2021.
Much remains to be learned regarding the risk of SARS-CoV-2 infection among HCP and their household contacts. Through the use of a multifaceted prospective study design and a well-characterized cohort, we will collect critical information regarding SARS-CoV-2 transmission risks in the health care setting and its linkage to the community.
DERR1-10.2196/25410.
DERR1-10.2196/25410.
The COVID-19 pandemic has acted as a catalyst for the development and adoption of a broad range of remote monitoring technologies (RMTs) in health care delivery. It is important to demonstrate how these technologies were implemented during the early stages of this pandemic to identify their application and barriers to adoption, particularly among vulnerable populations.
The purpose of this knowledge synthesis was to present the range of RMTs used in delivering care to patients with COVID-19 and to identify perceived benefits of and barriers to their use. The review placed a special emphasis on health equity considerations.
A rapid review of published research was conducted using Embase, MEDLINE, and QxMD for records published from the inception of COVID-19 (December 2019) to July 6, 2020. Synthesis involved content analysis of reported benefits of and barriers to the use of RMTs when delivering health care to patients with COVID-19, in addition to health equity considerations.
Of 491 records identifieenerating strategies to improve equitable access for marginalized populations).
The COVID-19 pandemic has led to a notable increase in telemedicine adoption. However, the impact of the pandemic on telemedicine use at a population level in rural and remote settings remains unclear.
This study aimed to evaluate changes in the rate of telemedicine use among rural populations and identify patient characteristics associated with telemedicine use prior to and during the pandemic.
We conducted a repeated cross-sectional study on all monthly and quarterly rural telemedicine visits from January 2012 to June 2020, using administrative data from Ontario, Canada. We compared the changes in telemedicine use among residents of rural and urban regions of Ontario prior to and during the pandemic.
Before the pandemic, telemedicine use was steadily low in 2012-2019 for both rural and urban populations but slightly higher overall for rural patients (11 visits per 1000 patients vs 7 visits per 1000 patients in December 2019, P<.001). The rate of telemedicine visits among rural patients significan(n=261,814/290,401, 90.2% vs n=28,587/290,401, 9.8%, respectively, in 2020; P<.001).
Telemedicine adoption increased in rural and remote areas during the COVID-19 pandemic, but its use increased in urban and less rural populations. Future studies should investigate the potential barriers to telemedicine use among rural patients and the impact of rural telemedicine on patient health care utilization and outcomes.
Telemedicine adoption increased in rural and remote areas during the COVID-19 pandemic, but its use increased in urban and less rural populations. Future studies should investigate the potential barriers to telemedicine use among rural patients and the impact of rural telemedicine on patient health care utilization and outcomes.Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched ``oversmoothing problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes' attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.The distributed optimal position control problem, which aims to cooperatively drive the networked uncertain nonlinear Euler-Lagrange (EL) systems to an optimal position that minimizes a global cost function, is investigated in this article. In the case without constraints for the positions, a fully distributed optimal position control protocol is first presented by applying adaptive parameter estimation and gain tuning techniques. As the environmental constraints for the positions are considered, we further provide an enhanced optimal control scheme by applying the ε-exact penalty function method. Different from the existing optimal control schemes of networked EL systems, the proposed adaptive control schemes have two merits. First, they are fully distributed in the sense without requiring any global information. Second, the control schemes are designed under the general unbalanced directed communication graphs. The simulations are performed to verify the obtained results.This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.Heterogeneous palmprint recognition has attracted considerable research attention in recent years because it has the potential to greatly improve the recognition performance for personal authentication. In this article, we propose a simultaneous heterogeneous palmprint feature learning and encoding method for heterogeneous palmprint recognition. Unlike existing hand-crafted palmprint descriptors that usually extract features from raw pixels and require strong prior knowledge to design them, the proposed method automatically learns the discriminant binary codes from the informative direction convolution difference vectors of palmprint images. Differing from most heterogeneous palmprint descriptors that individually extract palmprint features from each modality, our method jointly learns the discriminant features from heterogeneous palmprint images so that the specific discriminant properties of different modalities can be better exploited. Furthermore, we present a general heterogeneous palmprint discriminative feature learning model to make the proposed method suitable for multiple heterogeneous palmprint recognition. Experimental results on the widely used PolyU multispectral palmprint database clearly demonstrate the effectiveness of the proposed method.Recently-emerged haptic guidance systems have a potential to facilitate the acquisition of handwriting skills in both adults and children. In this paper we present a longitudinal experimental study that examined the effects of haptic guidance to improve handwriting skills in children with learning difficulties. A haptic-based handwriting training platform that provides haptic guidance along the trajectory of a handwriting task was utilized. 12 children with mild intellectual difficulty, experiencing challenges in manipulating the visual information to control a pincer grip, participated in the study. Children were divided into two groups, a target group and a control group. The target group completed haptic-guided training and pencil-and-paper test whereas the control group took only the pencil-and-paper test without any training. A total of 32 handwriting tasks was used in the study where 16 tasks were used for training while the entire 32 tasks were completed for evaluation. Results demonstrated that the target group performed significantly better than the control group for handwriting tasks that are visually familiar but haptically difficult (Wilcoxon signed-rank test, p less then 0.01). An improvement was also seen in the performance of untrained tasks as well as trained tasks (Spearman's linear correlation coefficient, 0.667; p=0.05).COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.Researchers have developed a computational field called virtual screening (VS) to aid experimental drug development. These methods utilize experimentally validated biological interaction information to generate datasets and use the physicochemical and structural properties of compounds and target proteins as input information to train computational prediction models. At present, deep learning has been used in the field of biomedicine widely, and the prediction of CPRs based on deep learning has developed rapidly and has achieved good results. The purpose of this study is to investigate and discuss the latest applications of deep learning techniques in CPR prediction. First, we describe the datasets and feature engineering (i.e., compound and protein representations and descriptors) commonly used in CPR prediction methods. Then, we review and classify recent deep learning approaches in CPR prediction. Next, a comprehensive comparison is performed to demonstrate the prediction performance of representative methods on classical datasets. Finally, we discuss the current state of the field, including the existing challenges and our proposed future directions. We believe that this investigation will provide sufficient references and insight for researchers to understand and develop new deep learning methods to enhance CPR predictions.Point clouds are fundamental in the representation of 3D objects. However, they can also be highly unstructured and irregular. This makes it difficult to directly extend 2D generative models to three-dimensional space. In this paper, we cast the problem of point cloud generation as a topological representation learning problem. To infer the representative information of 3D shapes in the latent space, we propose a hierarchical mixture model that integrates self-attention with an inference tree structure for constructing a point cloud generator. Based on this, we design a novel Generative Adversarial Network (GAN) architecture that is capable to generate realistic point clouds in an unsupervised manner. The proposed adversarial framework (SG-GAN) relies on self-attention mechanism and Graph Convolution Network (GCN) to hierarchically infer the latent topology of 3D shapes. Embedding and transferring the global topology information in a tree framework allows our model to capture and enhance the structural connectivity. Furthermore, the proposed architecture endows our model with partially generating 3D structures. Finally, we propose two gradient penalty methods to stabilize the training of SG-GAN and overcome the possible mode collapse of GAN networks. To demonstrate the performance of our model, we present both quantitative and qualitative evaluations and show that SG-GAN is more efficient in training and it exceeds the state-of-the-art in 3D point cloud generation.Cross-domain object detection in images has attracted increasing attention in the past few years, which aims at adapting the detection model learned from existing labeled images (source domain) to newly collected unlabeled ones (target domain). Existing methods usually deal with the cross-domain object detection problem through direct feature alignment between the source and target domains at the image level, the instance level (i.e., region proposals) or both. However, we have observed that directly aligning features of all object instances from the two domains often results in the problem of negative transfer, due to the existence of (1) outlier target instances that contain confusing objects not belonging to any category of the source domain and thus are hard to be captured by detectors and (2) low-relevance source instances that are considerably statistically different from target instances although their contained objects are from the same category. With this in mind, we propose a reinforcement learning based method, coined as sequential instance refinement, where two agents are learned to progressively refine both source and target instances by taking sequential actions to remove both outlier target instances and low-relevance source instances step by step. Extensive experiments on several benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art baselines for cross-domain object detection.Mobile phones offer an excellent low-cost alternative for Virtual Reality. However, the hardware constraints of these devices restrict the displayable visual complexity of graphics.Image-Based Rendering techniques arise as an alternative to solve this problem, but usually, the support of collisions and irregular surfaces (i.e. any surface that is not flat or even) represents a challenge. In this work, we present a technique suitable for both virtual and real-world environments that handle collisions and irregular surfaces for an Image-Based Rendering technique in low-cost virtual reality. We also conducted a user evaluation for finding the distance between images that presents a realistic and natural experience by maximizing the perceived virtual presence and minimizing the cybersickness effects. The results prove the benefits of our technique for both virtual and real-world environments.An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First, we present a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omni-scale features. The basic building block consists of multiple convolutional streams, each detecting features at a certain scale. For omni-scale feature learning, a unified aggregation gate is introduced to dynamically fuse multi-scale features with channel-wise weights. OSNet is lightweight as its building blocks comprise factorised convolutions. Second, to improve generalisable feature learning, we introduce instance normalisation (IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. Extensive experiments show that, in the conventional same-dataset setting, OSNet achieves state-of-the-art performance, despite being much smaller than existing re-ID models. In the more challenging yet practical cross-dataset setting, OSNet beats most recent unsupervised domain adaptation methods without using any target data.This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various conditional learning tasks.Objective An optical imaging system is proposed for quantitatively assessing jugular venous response to altered central venous pressure. Methods The proposed system assesses sub-surface optical absorption changes from jugular venous waveforms with a spatial calibration procedure to normalize incident tissue illumination. Widefield frames of the right lateral neck were captured and calibrated using a novel flexible surface calibration method. A hemodynamic optical model was derived to quantify jugular venous optical attenuation (JVA) signals, and generate a spatial jugular venous pulsatility map. JVA was assessed in three cardiovascular protocols that altered central venous pressure acute central hypovolemia (lower body negative pressure), venous congestion (head-down tilt), and impaired cardiac filling (Valsalva maneuver). Results JVA waveforms exhibited biphasic wave properties consistent with jugular venous pulse dynamics when time-aligned with an electrocardiogram. JVA correlated strongly (median, interquartile range) with invasive central venous pressure during graded central hypovolemia (r=0.85, [0.72, 0.95]), graded venous congestion (r=0.94, [0.84, 0.99]), and impaired cardiac filling (r=0.94, [0.85, 0.99]). Reduced JVA during graded acute hypovolemia was strongly correlated with reductions in stroke volume (SV) (r=0.85, [0.76, 0.92]) from baseline (SV 7915 mL, JVA 0.560.10 a.u.) to -40 mmHg suction (SV 5918 mL, JVA 0.470.05 a.u.; p less then 0.01). Conclusion The proposed non-contact optical imaging system demonstrated jugular venous dynamics consistent with invasive central venous monitoring during three protocols that altered central venous pressure. Significance This system provides non-invasive monitoring of pressure-induced jugular venous dynamics in clinically relevant conditions where catheterization is traditionally required, enabling monitoring in non-surgical environments.
The present study aimed to investigate the intervening role of anxiety symptoms in relations between self-regulation and multiple forms of prosocial behaviors in U.S. Latino/a college students.
The sample is based on data from a cross-sectional study on college students' health and adjustment. Participants were 249 (62% women;
age =20 years; 86% U.S. born) college students who self-identified as Latino/a.
College students self-reported on their self-regulation, anxiety symptoms, and types and targets of prosocial behaviors using online surveys. Path analyses were conducted to test direct and indirect associations among the study variables.
Self-regulation was directly and indirectly associated with several types of prosocial behaviors via anxiety symptoms. The hypothesized associations also differed by the target of helping.
Our findings underscore a strengths-based view of the coping and mental health resources that predict positive well-being among U.S. Latino/a college students.
Our findings underscore a strengths-based view of the coping and mental health resources that predict positive well-being among U.S. Latino/a college students.Objective This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants Participants were 100 students enrolled in 4-year universities. Methods Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.
Locomotive syndrome (LS) is the leading cause of persons needing long-term care in old age and is characterized by locomotive organ impairment including musculoskeletal pain. The aim was to examine the association between musculoskeletal pain and LS in young and middle-aged persons.
A total of 836 participants (male 667, female 169; mean age 44.4 years) were examined in this cross-sectional study. The LS was evaluated by three screening tools the two-step test, the stand-up test, and the 25-question Geriatric Locomotive Function Scale. Musculoskeletal pain, exercise habits, physical function (walkability and muscle strength), and physical activity were also assessed.
The LS was found in 22.8% of participants. The number with musculoskeletal pain was significantly higher in those with the LS. A significant correlation was found between the degree of musculoskeletal pain and exercise habits. Less regular exercise was significantly associated with higher LS prevalence. Physical activity and function were greater in participants with more regular exercise.
Musculoskeletal pain was significantly related to LS even in young and middle-aged persons. The present results suggest that control of musculoskeletal pain and improvement of exercise habits in young and middle-aged persons might help prevent the LS.
Musculoskeletal pain was significantly related to LS even in young and middle-aged persons. The present results suggest that control of musculoskeletal pain and improvement of exercise habits in young and middle-aged persons might help prevent the LS.In this study, researchers aimed to assess the situation of domestic violence against women during the pandemic. 332 women participated in the study. It was found that emotional, verbal and total violence scores of the literate ones were higher. The emotional violence scores of the women who do not work and whose partners do not work due to the pandemic are higher (p less then 0.05). The researchers reached the conclusion that emotional violence is higher during the pandemic process, and that failing to work in an income-generating job triggers this situation.
To evaluate the visual and refractive outcomes of trifocal intraocular lens (IOL) implantation in eyes previously treated with myopic and hyperopic corneal refractive laser surgery.
Clinica Baviera-AIER-Eye group, Spain.
Retrospective comparative case series.
The series was divided into two groups according to the type of corneal laser refraction (myopic and hyperopic). The main visual and refractive outcome measures included corrected and uncorrected distance and near visual acuity (CDVA, UDVA, UNVA), safety, efficacy, and predictability. The secondary outcome measures were percentage of enhancement and NdYAG-capsulotomy, and influence of pre-laser magnitude of myopia and hyperopia on the outcome of trifocal IOL implantation.
The sample comprised 868 eyes (543 patients) myopic, n=319 eyes, 36.7%; and hyperopic, n=549 eyes, 63.2%. Three months postoperatively , visual outcomes were poorer in the hyperopic group than in the myopic group for mean-CDVA (0.06+/-0.05 vs 0.04+/-0.04, p<0.01) and safetyision but worse visual and safety outcomes in the high-hyperopia subgroup.Aside from the first week postnatal, murine heart regeneration is restricted and responses to damage follow classic fibrotic remodeling. Recent transcriptomic analyses have suggested that significant cross talk with the sterile immune response could maintain a more embryonic-like signaling network that promotes acute, transient responses. However, with age, this response-likely mediated by neonatal yolk sac macrophages-then transitions to classical macrophage-mediated, cardiac fibroblast (CF)-based remodeling of the extracellular matrix (ECM) after myocardial infarction (MI). The molecular mechanisms that govern the change with age and drive fibrosis via inflammation are poorly understood. Using multiple ribonucleic acid sequencing (RNA-Seq) datasets, we attempt to resolve the relative contributions of CFs and macrophages in the bulk-healing response of regenerative (postnatal day 1) and nonregenerative hearts (postnatal day 8+). We performed an analysis of bulk RNA-Seq datasets from myocardium and cardiac fiys, and molecules that regulate this behavior are unclear. By comparing RNA-Seq datasets from regenerative mouse hearts and older, nonregenerative hearts, we are able to identify biological processes that are hallmarks of regeneration. We find that sterile inflammatory processes are upregulated in nonregenerative hearts, initiating profibrotic gene programs 3 days after myocardial infarction that can cause myocardial disease.Heart failure (HF) is a multifactorial syndrome that remains a leading cause of worldwide morbidity. Despite its high prevalence, only half of patients with HF respond to guideline-directed medical management, prompting therapeutic efforts to confront the molecular underpinnings of its heterogeneity. In the current study, we examined epigenetics as a yet unexplored source of heterogeneity among patients with end-stage HF. Specifically, a multicohort-based study was designed to quantify cardiac genome-wide cytosine-p-guanine (CpG) methylation of cardiac biopsies from male patients undergoing left ventricular assist device (LVAD) implantation. In both pilot (n = 11) and testing (n = 31) cohorts, unsupervised multidimensional scaling of genome-wide myocardial DNA methylation exhibited a bimodal distribution of CpG methylation found largely to occur in the promoter regions of metabolic genes. Among the available patient attributes, only categorical self-identified patient race could delineate this methylation sigoportionately affect metabolic signaling pathways. Socioeconomic factors are associated with racial differences in the cardiac methylome among men with end-stage heart failure.The detailed physiological consequences of aerobic training, in patients with hypertrophic cardiomyopathy (HCM), are not well understood. In athletes and nonathletes with HCM, there are two hypothetical concerns with respect to exercise exercise-related worsening of the phenotype (e.g., promoting hypertrophy and fibrosis) and/or triggering of arrhythmia. The former concern is unproven and animal studies suggest an opposite effect, where exercise has been shown to be protective. The main reason for exercise restriction in HCM is fear of exercise-induced arrhythmia. Although the safety of sports in HCM has been reviewed, even more recent data suggest a substantially lower risk for sudden cardiac death (SCD) in HCM than previously thought, and there is an ongoing debate about restrictions of exercise imposed on individuals with HCM. This review outlines the pathophysiology of HCM, the impact of acute and chronic exercise (and variations of exercise intensity, modality, and athletic phenotype) in HCM including changes in autonomic function, blood pressure, cardiac dimensions and function, and cardiac output, and the underlying mechanisms that may trigger exercise-induced lethal arrhythmias. It provides a critical evaluation of the evidence regarding risk of SCD in athletes and the potential benefits of targeted exercise prescription in adults with HCM. Finally, it provides considerations for personalized recommendations for sports participation based on the available data.Nicotinic receptors (NRs) play an important role in the cholinergic regulation of heart functions, and converging evidence suggests a diverse repertoire of NR subunits in the heart. A recent hypothesis about the plasticity of β NR subunits suggests that β2-subunits and β4-subunits may substitute for each other. In our study, we assessed the hypothetical β-subunit interchangeability in the heart at the level of mRNA. Using two mutant mice strains lacking β2 or β4 NR subunits, we examined the relative expression of NR subunits and other key cholinergic molecules. We investigated the physiology of isolated hearts perfused by Langendorff's method at basal conditions and after cholinergic and/or adrenergic stimulation. Lack of β2 NR subunit was accompanied with decreased relative expression of β4-subunits and α3-subunits. No other cholinergic changes were observed at the level of mRNA, except for increased M3 and decreased M4 muscarinic receptors. Isolated hearts lacking β2 NR subunit showed different dynamics in to a high dose of acetylcholine upon adrenergic stimulation.In this study, we investigated whether human umbilical cord mesenchymal stem cell (hUCMSC) fibrin patches loaded with nerve growth factor (NGF) poly(lactic-co-glycolic acid) (PLGA) nanoparticles could enhance the therapeutic potency of hUCMSCs for myocardial infarction (MI). In vitro, NGF significantly improved the proliferation of hUCMSCs and mitigated cytotoxicity and apoptosis under hypoxic injury. NGF also promoted the paracrine effects of hUCMSCs on angiogenesis and cardiomyocyte protection. The tyrosine kinase A (TrkA) and phosphoinositide 3-kinase (PI3K)-serine/threonine protein kinase (Akt) signaling pathways in hUCMSCs were involved in the NGF-induced protection. NGF PLGA nanoparticles continued to release NGF for at least 1 mo and also exerted a protective effect on hUCMSCs, the same with free NGF. In vivo, we treated MI mice with nothing (MI group), a cell-free fibrin patch with blank PLGA nanoparticles (MI + OP group), a cell-free fibrin patch with NGF nanoparticles (MI + NGF group), and hUCMSC fiial apoptosis and promote angiogenesis in the mouse heart after MI.There are currently no Food and Drug Administration-approved treatments for heart failure with preserved ejection fraction (HFpEF). Here we compared the effects of exercise with and without α/β-adrenergic blockade with carvedilol in Col4a3-/- Alport mice, a model of the phenogroup 3 subclass of HFpEF with underlying renal dysfunction. Alport mice were assigned to the following groups no treatment control (n = 29), carvedilol (n = 11), voluntary exercise (n = 9), and combination carvedilol and exercise (n = 8). Cardiac function was assessed by echocardiography after 4-wk treatments. Running activity of Alport mice was similar to wild types at 1 mo of age but markedly reduced at 2 mo (1.3 ± 0.40 vs. 4.5 ± 1.02 km/day, P less then 0.05). There was a nonsignificant trend for increased running activity at 2 mo by carvedilol in the combination treatment group. Combination treatments conferred increased body weight of Col4a3-/- mice (22.0 ± 1.18 vs. 17.8 ± 0.29 g in untreated mice, P less then 0.01), suggesting e. Carvedilol alone or in combination with exercise also improved kidney function. Molecular analyses indicate that the observed improvements in cardiorenal functions were mediated at least in part by effects on serum osteopontin and related inflammatory cytokine cascades. The work presents new potential therapeutic targets and approaches for HFpEF.African American (AA) individuals are at a greater risk for the development of cardiovascular complications, such as hypertension, compared with European Americans (EAs). Higher vagally mediated heart rate variability (HRV) is typically associated with lower blood pressure (BP) and total peripheral resistance (TPR). However, research has yet to examine the differential impact of HRV on longitudinal hemodynamic activity between AAs and EAs. We sought to rectify this in a sample of 385 normotensive youths (207 AAs, 178 EAs; mean age 23.16 ± 2.9 yr). Individuals participated in two laboratory evaluations spanning approximately 6 yr. Bioimpedance was used to assess HRV at time 1 and cardiac output at both time 1 and time 2. Mean arterial pressure (MAP) was measured at both time points via an automated BP machine. TPR was calculated as MAP divided by cardiac output. Results showed AAs to have higher BP and higher TPR at time 2 compared with EAs, independent of several important covariates. Also, higher HRV at time 1 significantly predicted both lower TPR and BP at time 2 among EAs only; these associations were attenuated and not significant in AAs. HRV did not significantly predict cardiac output at time 2 in the full sample or split by ethnicity. Our findings highlight that AAs show TPR mediated long-term increases in BP irrespective of resting HRV, providing a physiological pathway linking AAs with a greater risk for mortality and morbidity from hypertension and potentially other cardiovascular disease.NEW & NEWSWORTHY African Americans and European Americans differ in hemodynamics underlying long-term blood pressure regulation. Over 6 yr, African Americans show total peripheral resistance-mediated increases in blood pressure compared with European Americans. Higher heart rate variability predicts lower blood pressure and total peripheral resistance 6 yr later in European Americans but not African Americans.In Brazil, the increasing prevalence of HIV infection in young people makes it critical to know its distribution in university communities. In this cross-sectional study, we evaluated the impact of STI/HIV testing campaigns on university campuses from 2013 to 2017. The participants took part in rapid testing for HIV, syphilis, hepatitis B and C, and counseling sessions. A total of 2691 people participated in the campaigns. Of these, 79.4% were single, and 50.3% were women. The median age was 24 years old, and 77.9% of participants had ≥12 years of formal education. Most reported having unprotected sex in the last year (87.4%). The positivity rates for HIV, syphilis, hepatitis B virus, and hepatitis C virus were 0.56%, 1.20%, 0.19%, and 0.11%, respectively. The characteristics associated with HIV infection were being men who have sex with men (MSM) (aOR = 12.06; 95% CI = 3.83-37.99) and having less then 12 years of schooling (aOR = 3.28; 95% CI = 1.03-10.38). Factors associated with syphilis seropositivity were older age (aOR = 1.06; 95% CI = 1.03-1.09), multiple partners (aOR = 2.44; 95% CI = 1.08-5.50), and being MSM (aOR = 5.40; 95% CI = 2.49-11.72). Positivity for hepatitis B tended to decrease with the years of testing (p = 0.023) and for hepatitis C to increase with age (p = 0.035). Our study observed a high vulnerability to HIV and syphilis infection in a university community, which needs an early prevention strategy, including regular testing, continuing sexual education, easy access to condoms, and pre- and postexposure HIV prophylaxis.Introduction The COVID-19 pandemic originated from the emergence of anovel coronavirus, SARS-CoV-2, which has been intensively studied since its discovery in order to generate the knowledge necessary to accelerate the development of vaccines and antivirals. Of note, many researchers believe there is great potential in systematically identifying host interactors of viral factors already targeted by existing drugs.Areas Covered Herein, the authors discuss in detail the only available large-scale systematic study of the SARS-CoV-2-host protein-protein interaction network. More specifically, the authors review the literature on two key SARS-CoV-2 drug targets, the Spike surface glycoprotein, and the RNA polymerase. The authors also provide the reader with their expert opinion and future perspectives.Expert opinion Interactions made by viral proteins with host factors reveal key functions that are likely usurped by the virus and, as aconsequence, points to known drugs that can be repurposed to fight viral infection and collateral damages that can exacerbate various disease conditions in COVID-19.Background To investigate the risk factors for enterococcal intra-abdominal infections (EIAIs) and the association between EIAIs and outcomes in intensive care unit (ICU) patients. Methods We reviewed retrospectively the records of patients with intra-abdominal infections admitted to the Department of Critical Care Medicine at Nanfang Hospital, Southern Medical University, China, from January 2011 to December 2018. Patients with intra-abdominal infections were divided into enterococcal and non-enterococcal groups based on whether enterococci were isolated from intra-abdominal specimens. Results A total of 431 patients with intra-abdominal infections were included, of whom 119 were infected with enterococci and 312 were infected with non-enterococci. Enterococci were isolated in 27.6% of patients, accounting for 24.5% (129/527) of all clinical bacterial isolates. Post-operative abdominal infection (adjusted odds ratio [OR], 2.361; p = 0.004), intestinal infection (adjusted OR, 2.703; p less then 0.001), Mannndent risk factors for enterococcal infection. Enterococcal infection was associated with reduced short-term survival in ICU patients.Tajikistan, a country of approximately nine million people, has a relatively small but quickly growing HIV epidemic. No peer-reviewed study has assessed factors associated with HIV, or associated risk factors, among female sex workers (FSWs) in Tajikistan. The purpose of the current study is to elucidate the factors associated with HIV status and risk factors in the Tajikistani context and add to the scant literature on risk factors among FSWs in Tajikistan and Central Asia. We used cross-sectional data from an HIV bio-behavioral survey (BBS) conducted among FSWs in the Republic of Tajikistan (n = 2174) in 2017. Using Respondent Driven Sampling Analysis Tool software, we calculated the prevalence of HIV, diagnosed cases, linkage to antiretroviral therapy (ART), and the prevalence of syphilis for FSWs in Tajikistan. Prevalence data were adjusted for network size and any clustering effects in the network. Further, using univariate and multivariable logistic regression, we determined correlates of HIV-positive status.
