Strategies for anti-fibrotic therapies.

The fibrotic diseases encompass a wide spectrum of entities including such multisystemic diseases as systemic sclerosis, nephrogenic systemic fibrosis and sclerodermatous graft versus host disease, as well as organ-specific disorders such as pulmonary, liver, and kidney fibrosis. Collectively, given the wide variety of affected organs, the chronic nature of the fibrotic processes, and the large number of individuals suffering their devastating effects, these diseases pose one of the most serious health problems in current medicine and a serious economic burden to society. Despite these considerations there is currently no accepted effective treatment. However, remarkable progress has been achieved in the elucidation of their pathogenesis including the identification of the critical role of myofibroblasts and the determination of molecular mechanisms that result in the transcriptional activation of the genes responsible for the fibrotic process. Here we review the origin of the myofibroblast and discuss the crucial regulatory pathways involving multiple growth factors and cytokines that participate in the pathogenesis of the fibrotic process. Potentially effective therapeutic strategies based upon this new information are considered in detail and the major challenges that remain and their possible solutions are presented. It is expected that translational efforts devoted to convert this new knowledge into novel and effective anti-fibrotic drugs will be forthcoming in the near future. This article is part of a Special Issue entitled: Fibrosis: Translation of basic research to human disease

Navigating the Ocean with DRL: Path following for marine vessels

Human error is a substantial factor in marine accidents, accounting for 85% of all reported incidents. By reducing the need for human intervention in vessel navigation, AI-based methods can potentially reduce the risk of accidents. AI techniques, such as Deep Reinforcement Learning (DRL), have the potential to improve vessel navigation in challenging conditions, such as in restricted waterways and in the presence of obstacles. This is because DRL algorithms can optimize multiple objectives, such as path following and collision avoidance, while being more efficient to implement compared to traditional methods. In this study, a DRL agent is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm for path following and waypoint tracking. Furthermore, the trained agent is evaluated against a traditional PD controller with an Integral Line of Sight (ILOS) guidance system for the same. This study uses the Kriso Container Ship (KCS) as a test case for evaluating the performance of different controllers. The ship's dynamics are modeled using the maneuvering Modelling Group (MMG) model. This mathematical simulation is used to train a DRL-based controller and to tune the gains of a traditional PD controller. The simulation environment is also used to assess the controller's effectiveness in the presence of wind.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023

The New York City Trans-Fat Ban: Agenda-Setting, Policy Diffusion, and Public Health

New York City has often prided itself on being at the forefront of public health policy and reform. This is certainly the case in its recent action to ban trans fats in food sold in the food service industry. The purpose of this master's paper is to analyze the politics of the enactment of the trans-fat ban. I hypothesize that symbolic politics trumped scientific evidence in the course of legitimating a ban the measurable public health outcomes of which would not be straightforward to measure. I used a triangulation of methods, including review of primary documents, media coverage, and in-depth telephone and e-mail interviews with key stakeholders and observers to elucidate the critical shaping factors influencing the ban's passage. I found that significant political factors came into play in the passage of the ban, although the scientific evidence also weighed heavily. For example, the direction of the Office of the Mayor and the Health Commissioner, as well as the overall political environment of New York City itself, made such a ban much easier to pass than would have been true in many other environments, even with a stronger evidence base. Schattschneider's classic observation that only losers broaden the scope of conflict is illustrated by food service interests' argument that they did not have an adequate opportunity to raise objections to the ban. Stakeholders acknowledged that measuring health outcomes post-ban would likely be impossible, but nonetheless felt that the ban would be in the best interests of New Yorkers' health. My secondary hypothesis, that New York City would be interested in influencing the national agenda, was partially supported: stakeholders felt that trans-fat bans were probably on the national horizon in any case, and were not averse to taking credit for what was going to happen anyway, but also seemed sincerely interested in improving the general health of New Yorkers.Master of Public Healt

Periprosthetic joint infection increases the risk of one-year mortality.

BACKGROUND: Periprosthetic joint infection continues to potentially complicate an otherwise successful joint replacement. The treatment of this infection often requires multiple surgical procedures associated with increased complications and morbidity. This study examined the relationship between periprosthetic joint infection and mortality and aimed to determine the effect of periprosthetic joint infection on mortality and any predictors of mortality in patients with periprosthetic joint infection. METHODS: Four hundred and thirty-six patients with at least one surgical intervention secondary to confirmed periprosthetic joint infection were compared with 2342 patients undergoing revision arthroplasty for aseptic failure. The incidence of mortality at thirty days, ninety days, one year, two years, and five years after surgery was assessed. Multivariate analysis was used to assess periprosthetic joint infection as an independent predictor of mortality. In the periprosthetic joint infection population, variables investigated as potential risk factors for mortality were evaluated. RESULTS: Mortality was significantly greater (p \u3c 0.001) in patients with periprosthetic joint infection compared with those undergoing aseptic revision arthroplasty at ninety days (3.7% versus 0.8%), one year (10.6% versus 2.0%), two years (13.6% versus 3.9%), and five years (25.9% versus 12.9%). After controlling for age, sex, ethnicity, number of procedures, involved joint, body mass index, and Charlson Comorbidity Index, revision arthroplasty for periprosthetic joint infection was associated with a fivefold increase in mortality compared with revision arthroplasty for aseptic failures. In the periprosthetic joint infection population, independent predictors of mortality included increasing age, higher Charlson Comorbidity Index, history of stroke, polymicrobial infections, and cardiac disease. CONCLUSIONS: Although it is well known that periprosthetic joint infection is a devastating complication that severely limits joint function and is consistently difficult to eradicate, surgeons must also be cognizant of the systemic impact of periprosthetic joint infection and its major influence on fatal outcome in patients

Animal bite infections.

Animal bites have become alarmingly common and may represent a quiet epidemic. It is estimated that between 1 and 3.5 million animal bites occur annually in the United States. The highest incidence has consistently been in 5-to-14-year-old schoolchildren, who have greater contact with animals, especially house pets, on a daily basis. This article discusses the epidemiology, microbiology, clinical findings and management of animal bites infections

Modeling Fission Gas Release at the Mesoscale using Multiscale DenseNet Regression with Attention Mechanism and Inception Blocks

Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D nuclear fuel microstructure images. Four convolutional neural network (CNN) architectures with multiscale regression are trained and evaluated on simulated FGR data generated using a hybrid phase field/cluster dynamics model. All four networks show high predictive power, with $R^{2}$ values above 98%. The best performing network combine a Convolutional Block Attention Module (CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute percentage error of 4.4%), training stability, and robustness on very low instantaneous FGR flux values.Comment: Submitted at Journal of Nuclear Materials, 20 pages, 10 figures, 3 table