A uropathogenic E. coli UTI89 model of prostatic inflammation and collagen accumulation for use in studying aberrant collagen production in the prostate.

Bacterial infection is one known etiology of prostatic inflammation. Prostatic inflammation is associated with prostatic collagen accumulation and both are linked to progressive lower urinary tract symptoms in men. We characterized a model of prostatic inflammation using transurethral instillations of Escherichia coli UTI89 in C57BL/6J male mice with the goal of determining the optimal instillation conditions, understanding the impact of instillation conditions on urinary physiology, and identifying ideal prostatic lobes and collagen 1a1 prostatic cell types for further analysis. The smallest instillation volume tested (50 µL) distributed exclusively to the bladder, 100- and 200-µL volumes distributed to the bladder and prostate, and a 500-µL volume distributed to the bladder, prostate, and ureter. A threshold optical density of 0.4 E. coli UTI89 in the instillation fluid was necessary for significant (P < 0.05) prostate colonization. E. coli UTI89 infection resulted in a low frequency, high volume spontaneous voiding pattern. This phenotype was due to exposure to E. coli UTI89, not catheterization alone, and was minimally altered by a 50-µL increase in instillation volume and doubling of E. coli concentration. Prostate inflammation was isolated to the dorsal prostate and was accompanied by increased collagen density. This was partnered with increased density of protein tyrosine phosphatase receptor type C+, procollagen type I-α1+ copositive cells and decreased density of α2-smooth muscle actin+, procollagen type I-α1+ copositive cells. Overall, we determined that this model is effective in altering urinary phenotype and producing prostatic inflammation and collagen accumulation in mice.

Conspiracy vs science: A large-scale analysis of online discussion cascades.

With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments from an online social media platform, we investigate the propagation of two distinct narratives-(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable. We find that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, and is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are much more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives using the constructed features. We further investigate user's role during the growth of cascades. In contrast with previous assumption that misinformation is primarily driven by a small set of users, we find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information; Instead, we find that conspiracy information from media outlets with left or right orientation triggers smaller cascades and is less viral than information from online social media platforms (e.g., Twitter and Imgur) whose political orientations are unclear. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online.

Coupling dynamics of epidemic spreading and information diffusion on complex networks.

The interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases (H7N9 and Dengue fever) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.

GRAS1 non-coding RNA protects against DNA damage and cell death by binding and stabilizing NKAP.

Non-coding RNA (ncRNA) gene products are involved in diverse biological processes including splicing, epigenetic regulation, gene expression, proliferation, and metabolism. The biological mechanisms by which ncRNAs contribute to cell survival remain poorly understood. We found that the Growth Regulator Antisense 1 (GRAS1) long non-coding RNA (lncRNA) transcript promotes growth in multiple human cell types by protecting against DNA damage. Knockdown of GRAS1 induced DNA damage and cell death, along with significant expression changes in DNA damage response, intrinsic apoptotic signaling, and cellular response to environmental stimulus genes. Extensive DNA damage occurred after GRAS1 knockdown, with numerous double strand breaks occurring in each cell. The number of cells undergoing apoptosis and with fragmented nuclei increased significantly after GRAS1 knockdown. We used RNA antisense purification and mass spectrometry (RAP-MS) to identify the NF-κB activating protein (NKAP) as a direct protein interaction partner of GRAS1 lncRNA. NKAP protein was degraded after GRAS1 knockdown, in a proteasome-dependent manner. Overexpression of GRAS1 or NKAP mitigated the DNA damage effects of GRAS1 knockdown. In summary, GRAS1 and NKAP directly interact to protect against DNA damage and cell death in multiple human cell lines.

Combating antimicrobial resistance: the silent war.

Once hailed as miraculous solutions, antibiotics no longer hold that status. The excessive use of antibiotics across human healthcare, agriculture, and animal husbandry has given rise to a broad array of multidrug-resistant (MDR) pathogens, posing formidable treatment challenges. Antimicrobial resistance (AMR) has evolved into a pressing global health crisis, linked to elevated mortality rates in the modern medical era. Additionally, the absence of effective antibiotics introduces substantial risks to medical and surgical procedures. The dwindling interest of pharmaceutical industries in developing new antibiotics against MDR pathogens has aggravated the scarcity issue, resulting in an exceedingly limited pipeline of new antibiotics. Given these circumstances, the imperative to devise novel strategies to combat perilous MDR pathogens has become paramount. Contemporary research has unveiled several promising avenues for addressing this challenge. The article provides a comprehensive overview of these innovative therapeutic approaches, highlighting their mechanisms of action, benefits, and drawbacks.

The impact of short term, long term and intermittent E. coli infection on male C57BL/6J mouse prostate histology and urinary physiology.

Prostatic inflammation and prostatic fibrosis are associated with lower urinary tract dysfunction in men. Prostatic inflammation arising from a transurethral uropathogenic E. coli infection is sufficient to increase prostatic collagen content in male mice. It is not known whether and how the sequence, duration and chronology of prostatic infection influence urinary function, prostatic inflammation and collagen content. We placed a transurethral catheter into adult male C57BL/6J mice to deliver uropathogenic E. coli UTI189 two-weeks prior to study endpoint (to evaluate the short-term impact of infection), 10-weeks prior to study endpoint (to evaluate the long-term impact of infection), or two-, six-, and ten-weeks prior to endpoint (to evaluate the impact of repeated intermittent infection). Mice were catheterized the same number of times across all experimental groups and instilled with sterile saline when not instilled with E. coli to control for the variable of catheterization. We measured bacterial load in free catch urine, body weight and weight of bladder and dorsal prostate; prostatic density of leukocytes, collagen and procollagen 1A1 producing cells, and urinary function. Transurethral E. coli instillation caused more severe and persistent bacteriuria in mice with a history of one or more transurethral instillations of sterile saline or E. coli. Repeated intermittent infections resulted in a greater relative bladder wet weight than single infections. However, voiding function, as measured by the void spot assay, and the density of collagen and ProCOL1A1+ cells in dorsal prostate tissue sections did not significantly differ among infection groups. The density of CD45+ leukocytes was greater in the dorsal prostate of mice infected two weeks prior to study endpoint but not in other infection groups compared to uninfected controls.

Patient Experience of Photobiomodulation Therapy in Head and Neck Chronic Lymphedema.

Purpose: Lymphedema is a common late effect of head and neck cancer treatment that causes various symptoms, functional impairment, and poor quality of life. We completed a pilot, prospective, single-arm clinical trial to determine the feasibility and potential efficacy of the use of photobiomodulation (PBM) therapy for head and neck lymphedema. In this study, we report patients' perceived treatment experience of PBM therapy and provide suggestions to better understand head and neck cancer survivors' experience of PBM therapy. Methods: Head and neck cancer patients who underwent PBM therapy completed face-to-face semi-structured interviews. Interviews were audio-recorded and then transcribed verbatim. Qualitative content analysis was used to analyze the transcriptions from the interviews. Results: Among 12 participants who consented for the study, 11 (91.7%) completed the PBM therapy. Participants described positive experiences and unique benefits about the PBM therapy, for example, decreased swelling, reduced tightness, increased range of motion, increased saliva production, and improved ability to swallow. Some participants (n = 5, 45.5%) delineated challenges related to traffic, travel time, and distance from study location. Many participants proposed suggestions for future research on PBM therapy, for example, research on internal edema and its relationship with swallowing, and indicated patients with severe lymphedema and fibrosis may be more likely to benefit. Conclusions: Findings from this study suggested the potential benefits of PBM therapy in treatment of chronic head and neck lymphedema. Rigorously designed clinical trials are needed to evaluate the effect of PBM therapy for head and neck cancer-related lymphedema. Trial Registration Number and Date of Registration: ClinicalTrials.gov Identifier: NCT03738332; date of registration: November 13, 2018.

Mechanisms of action of islet neogenesis-associated protein: comparison of the full-length recombinant protein and a bioactive peptide.

Islet neogenesis-associated protein (INGAP) was discovered in the partially duct-obstructed hamster pancreas as a factor inducing formation of new duct-associated islets. A bioactive portion of INGAP, INGAP(104-118) peptide (INGAP-P), has been shown to have neogenic and insulin-potentiating activity in numerous studies, including recent phase 2 clinical trials that demonstrated improved glucose homeostasis in both type 1 and type 2 diabetic patients. Aiming to improve INGAP-P efficacy and to understand its mechanism of action, we cloned the full-length protein (rINGAP) and compared the signaling events induced by the protein and the peptide in RIN-m5F cells that respond to INGAP with an increase in proliferation. Here, we show that, although both rINGAP and INGAP-P signal via the Ras/Raf/ERK pathway, rINGAP is at least 100 times more efficient on a molar basis than INGAP-P. For either ligand, ERK1/2 activation appears to be pertussis toxin sensitive, suggesting involvement of a G protein-coupled receptor(s). However, there are clear differences between the peptide and the protein in interactions with the cell surface and in the downstream signaling. We demonstrate that fluorescent-labeled rINGAP is characterized by clustering on the membrane and by slow internalization (≤5 h), whereas INGAP-P does not cluster and is internalized within minutes. Signaling by rINGAP appears to involve Src, in contrast to INGAP-P, which appears to activate Akt in addition to the Ras/Raf/ERK1/2 pathway. Thus our data suggest that interactions of INGAP with the cell surface are important to consider for further development of INGAP as a pharmacotherapy for diabetes.

How online health groups help you lose weight: The role of group composition and social contact.

Online health-specific goal challenge groups gather people with the same health concerns and provide an environment to keep on track, exchange experiences, and compete for achievements. We examine how group health status composition influences social contact, and how both group health status composition and social contact affect individual health achievement. Using longitudinal data from 2479 online health challenge group users and their corresponding group information, we found that (a) compared with individual health status, group conditions (average weight status and member affiliation) are more strongly connected with social contact; (b) different mechanisms affect whether people participate in social contact and increase their participation, and weight loss; and (c) average group condition influences individuals' weight loss through social contact. We emphasize the effect of group composition and the social contact process in health enhancement.

The spatial dissemination of COVID-19 and associated socio-economic consequences.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.

Census of Twitter users: Scraping and describing the national network of South Korea.

Population-level national networks on social media are precious and essential for network science and behavioural science. This study collected a population-level Twitter network, based on both language and geolocation tags. We proposed a set of validation approaches to evaluate the validity of our datasets. Finally, we re-examined classical network and communication propositions (e.g., 80/20 rule, six degrees of separation) on the national network. Our dataset and strategy would flourish the data collection pool of population-level social networks and further develop the research of network analysis in digital media environment.

The Strength of Structural Diversity in Online Social Networks.

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one's online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one's contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.

Pilot-Scale Production of Cellulosic Nanowhiskers With Similar Morphology to Cellulose Nanocrystals.

This study describes a class of cellulosic nanomaterials, cellulosic nanowhiskers (CNWs), and demonstrates scaled-up production with acid recovery using less expensive equipment made of common stainless steel rather than glass-lined steel. CNWs produced using concentrated maleic acid (MA) hydrolysis followed by mechanical fibrillation have morphology similar to MA-produced cellulose nanocrystals (CNCs) and sulfuric-acid-produced CNCs (S-CNCs) but differ in crystallinity. Applications of CNWs as a substitute for CNCs for which morphology and surface charge, rather than crystallinity, are the pertinent characteristics are presented. The tested CNW suspensions have a wider viscosity range of 0.001 to 1000 Pa.s over a variety of shear rates of 0.01 to 1000 1/s compared to S-CNCs of 0.001 to 0.1 Pa.s and are better suited for applications such as rheology modification and 3D printing. This study proposes CNWs as a less expensive and sustainable replacement for CNCs in applications that do not require crystalline properties.

Adjuvant Radiochemotherapy versus Chemotherapy Alone for Gastric Cancer: Implications for Target Definition.

The INT0116 trial was a milestone study and laid the foundation for the adjuvant radiotherapy (RT) associated to concurrent chemotherapy (CT) for the treatment of gastric cancer (GC) after gastrectomy. However, it is still controversial whether adding RT to CT could further benefit D2-dissected GC patients. The ARTIST trial indicated that the addition of RT to CT did not have a positive impact on disease-free survival (DFS). Nevertheless, in a subgroup of 396 patients with positive pathological lymph nodes, combined treatment with RT was superior to CT alone. A similar randomized Chinese trial confirmed the superiority of adding RT to CT in terms of DFS for patients with D2 lymphadenectomy. However, several previous randomized studies provided inconsistent results with the benefits of combined treatment of RT and CT. The inconsistent results of several studies may be due to the differences between tumor epidemiology, treatment policies, and treatment outcomes. During the past decade, major progress in accurate target delineation utilizing RT technology has been observed. However, even though the use of adjuvant RT doubled after the INT-0116 trial results became public, the fraction of patients receiving adjuvant RT was still low according to the SEER database. The low rate of adjuvant RT can partially be explained by concern over toxicity while undergoing RT. Several studies have also defined the specific location of locoregional recurrence for postoperative RT in GC, but these studies are still limited. A number of retrospective studies demonstrated that the most prevalent nodal recurrence was outside the D2 dissection field. In order to overcome the restricted nature of a retrospective study and provide more individual radiation field determination, additional large-scale prospective multicenter studies are required to evaluate the optimal RT target.

Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong.

BACKGROUND: Health apps on mobile devices provide an unprecedented opportunity for ordinary people to develop social connections revolving around health issues. With increasing penetration of mobile devices and well-recorded behavioral data on such devices, it is desirable to employ digital traces on mobile devices rather than self-reported measures to capture the behavioral patterns underlying the use of mobile health (mHealth) apps in a more direct and valid way. OBJECTIVE: The objectives of this study were to (1) assess the demographic predictors of the adoption of mHealth apps; (2) investigate the temporal pattern underlying the use of mHealth apps; and (3) explore the impacts of demographic variables, temporal features, and app genres on the use of mHealth apps. METHODS: Logfile data of mobile devices were collected from a representative panel of about 2500 users in Hong Kong. Users' mHealth app activities were analyzed. We first conducted a binary logistic regression analysis to uncover demographic predictors of users' adoption status. Then we utilized a multilevel negative binomial regression to examine the impacts of demographic characteristics, temporal features, and app genres on mHealth app use. RESULTS: It was found that 27.5% of mobile device users in Hong Kong adopt at least one genre of mHealth app. Adopters of mHealth apps tend to be female and better educated. However, demographic characteristics did not showcase the predictive powers on the use of mHealth apps, except for the gender effect (Bfemale vs Bmale=-0.18; P=.006). The use of mHealth apps demonstrates a significant temporal pattern, which is found to be moderately active during daytime and intensifying at weekends and at night. Such temporal patterns in mHealth apps use are moderated by individuals' demographic characteristics. Finally, demographic characteristics were also found to condition the use of different genres of mHealth apps. CONCLUSIONS: Our findings suggest the importance of dynamic perspective in understanding users' mHealth app activities. mHealth app developers should consider more the demographic differences in temporal patterns of mHealth apps in the development of mHealth apps. Furthermore, our research also contributes to the promotion of mHealth apps by emphasizing the differences of usage needs for various groups of users.

Cohort trends in perceived Internet influence on political efficacy in Hong Kong.

The study explored overall and cohort-specific trends in Internet political efficacy from an age-period-cohort approach with a cross-sequential design. Perceived Internet influence on political efficacy is found to increase with age. Significant difference between Internet users and nonusers is also found in some cohorts. Online news reading and online chats/discussions have a positive impact in some cohorts.

An Examination of the Relationship between Lipid Levels and Associated Genetic Markers across Racial/Ethnic Populations in the Multi-Ethnic Study of Atherosclerosis.

Large genome-wide association studies have reported hundreds of genetic markers associated with lipid levels. However, the discovery and estimated effect of variants at these loci, derived from samples of exclusively European descent, may not generalize to the majority of the world populations. We examined the collective strength of association among these loci in a diverse set of U.S. populations from the Multi-Ethnic Study of Atherosclerosis. We constructed a genetic risk score for each lipid outcome based on previously identified lipid-associated genetic markers, and examined the relationship between the genetic risk scores and corresponding outcomes. We discover this relationship was often moderated by race/ethnicity. Our findings provide insight into the generalizability and predictive utility of large sample size meta-analyses results when leveraging data from a single population. We hope these findings will encourage researchers to investigate genetic susceptibility in more diverse populations and explore the source of such discrepancies. Until then, we caution clinicians, genetic counselors, and genetic testing consumers when interpreting genetic data on complex traits.

The divided communities of shared concerns: mapping the intellectual structure of e-Health research in social science journals.

PURPOSE: Social scientific approach has become an important approach in e-Health studies over the past decade. However, there has been little systematical examination of what aspects of e-Health social scientists have studied and how relevant and informative knowledge has been produced and diffused by this line of inquiry. This study performed a systematic review of the body of e-Health literature in mainstream social science journals over the past decade by testing the applicability of a 5A categorization (i.e., access, availability, appropriateness, acceptability, and applicability), proposed by the U.S. Department of Health and Human Services, as a framework for understanding social scientific research in e-Health. METHODS: This study used a quantitative, bottom-up approach to review the e-Health literature in social sciences published from 2000 to 2009. A total of 3005 e-Health studies identified from two social sciences databases (i.e., Social Sciences Citation Index and Arts & Humanities Citation Index) were analyzed with text topic modeling and structural analysis of co-word network, co-citation network, and scientific food web. RESULTS: There have been dramatic increases in the scale of e-Health studies in social sciences over the past decade in terms of the numbers of publications, journal outlets and participating disciplines. The results empirically confirm the presence of the 5A clusters in e-Health research, with the cluster of applicability as the dominant research area and the cluster of availability as the major knowledge producer for other clusters. The network analysis also reveals that the five distinctive clusters share much more in common in research concerns than what e-Health scholars appear to recognize. CONCLUSIONS: It is time to explicate and, more importantly, tap into the shared concerns cutting across the seemingly divided scholarly communities. In particular, more synergy exercises are needed to promote adherence of the field.

EvoRiver: Visual Analysis of Topic Coopetition on Social Media.

Cooperation and competition (jointly called "coopetition") are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., "topic leaders") affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).