Anthracycline-induced hypertension in pediatric cancer survivors: unveiling the long-term cardiovascular risks.

BACKGROUND: Long-term cardiovascular complications are common among pediatric cancer survivors, and anthracycline-induced hypertension has become an essential reason for concern. Compared to non-cancer controls, survivors have a higher prevalence of hypertension, and as they age, their incidence rises, offering significant dangers to cardiovascular health. MAIN BODY: Research demonstrates that exposure to anthracyclines is a major factor in the development of hypertension in children who have survived cancer. Research emphasizes the frequency and risk factors of anthracycline-induced hypertension, highlighting the significance of routine measurement and management of blood pressure. Furthermore, cardiovascular toxicities, such as hypertension, after anthracycline-based therapy are a crucial be concerned, especially for young adults and adolescents. Childhood cancer survivors deal with a variety of cardiovascular diseases, such as coronary artery disease and cardiomyopathy, which are made worse by high blood pressure. In order to prevent long-term complications, it is essential to screen for and monitor for anthracycline-induced hypertension. Echocardiography and cardiac biomarkers serve as essential tools for early detection and treatment. In order to lower cardiovascular risks in pediatric cancer survivors, comprehensive management strategies must include lifestyle and medication interventions in addition to survivor-centered care programs. SHORT CONCLUSION: Proactive screening, monitoring, and management measures are necessary for juvenile cancer survivors due to the substantial issue of anthracycline-induced hypertension in their long-term care. To properly include these strategies into survivor-ship programs, oncologists, cardiologists, and primary care physicians need to collaborate together. The quality of life for pediatric cancer survivors can be enhanced by reducing the cardiovascular risks linked to anthracycline therapy and promoting survivor-centered care and research.

Review of Image Quality Assessment Methods for Compressed Images.

The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard. This paper delves into essential research queries to achieve visually lossless images. The paper describes the influence of compression on image quality, appropriate objective image quality metrics (IQMs), and the effectiveness of subjective assessment methods. It also provides an overview of the existing literature, surveys, and subjective and objective image quality assessment (IQA) methods. Our aim is to offer insights, identify challenges in existing methodologies, and assist researchers in selecting the most effective assessment approach for their needs.

Statistical Models for High-Risk Intestinal Metaplasia with DNA Methylation Profiling.

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12×10-4 and 6.94×10-5. Based on our selected model, the significance of TNSC's effect in predicting the risk of IM is justified with a p-value less than 10-6. We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM.

Development of a community severance index for urban areas in the United States: A case study in New York City.

BACKGROUND AND AIMS: Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC). METHODS: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level. RESULTS: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC. CONCLUSION: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.

Riverscape genetics of the orangethroat darter complex.

Freshwater darters belonging to the orangethroat darter species complex, or Ceasia, are widely distributed in the Central and Southern United States, with ranges that span both glaciated and unglaciated regions. Up to 15 species have been recognized in the complex, with one, Etheostoma spectabile, having a widespread northern distribution and another, Etheostoma pulchellum, having a sizeable southern distribution. The other species in the complex have much more restricted distributions in unglaciated regions of the Central Highlands. We sampled 384 darters from 52 sites covering much of the range of Ceasia and evaluated patterns of genetic diversity, genetic structure, and pre- and post-glacial patterns of range contraction and expansion. We anticipated finding much stronger signals of genetic differentiation and diversification in unglaciated regions, given the higher species diversity and levels of endemism reported there. Surprisingly, microsatellite genotyping revealed two well-differentiated genetic clusters of E. spectabile in samples from glaciated regions, one confined to the Illinois River basin and another found in the Wabash drainage and Great Lakes tributaries. This suggests that there was expansion from two isolated glacial refugia, with little subsequent post-glacial gene flow. Fish collected from throughout the unglaciated region were less genetically differentiated. Fish assigned to Etheostoma burri and Etheostoma uniporum based on collection sites and morphological characters were not genetically differentiated from E. spectabile samples from the region. Hybridization and introgression occurring in the Central Highlands may confound genetic delineation of species in this region of high endemism and diversity.

Modeling the interplay between albumin-globulin metabolism and HIV infection.

Human immunodeficiency virus (HIV) infection is a major public health concern with 1.2 million people living with HIV in the United States. The role of nutrition in general, and albumin/globulin in particular in HIV progression has long been recognized. However, no mathematical models exist to describe the interplay between HIV and albumin/globulin. In this paper, we present a family of models of HIV and the two protein components albumin and globulin. We use albumin, globulin, viral load and target cell data from simian immunodeficiency virus (SIV)-infected monkeys to perform model selection on the family of models. We discover that the simplest model accurately and uniquely describes the data. The selection of the simplest model leads to the observation that albumin and globulin do not impact the infection rate of target cells by the virus and the clearance of the infected target cells by the immune system. Moreover, the recruitment of target cells and immune cells are modeled independently of globulin in the selected model. Mathematical analysis of the selected model reveals that the model has an infection-free equilibrium and a unique infected equilibrium when the immunological reproduction number is above one. The infection-free equilibrium is locally stable when the immunological reproduction number is below one, and unstable when the immunological reproduction number is greater than one. The infection equilibrium is locally stable whenever it exists. To determine the parameters of the best fitted model we perform structural and practical identifiability analysis. The structural identifiability analysis reveals that the model is identifiable when the immune cell infection rate is fixed at a value obtained from the literature. Practical identifiability reveals that only seven of the sixteen parameters are practically identifiable with the given data. Practical identifiability of parameters performed with synthetic data sampled a lot more frequently reveals that only two parameters are practically unidentifiable. We conclude that experiments that will improve the quality of the data can help improve the parameter estimates and lead to better understanding of the interplay of HIV and albumin-globulin metabolism.

Variable screening methods in spatial infectious disease transmission models.

Data-driven mathematical modelling can enrich our understanding of infectious disease spread enormously. Individual-level models of infectious disease transmission allow the incorporation of different individual-level covariates, such as spatial location, vaccination status, etc. This study aims to explore and develop methods for fitting such models when we have many potential covariates to include in the model. The aim is to enhance the performance and interpretability of models and ease the computational burden of fitting these models to data. We have applied and compared multiple variable selection methods in the context of spatial epidemic data. These include a Bayesian two-stage least absolute shrinkage and selection operator (Lasso), forward and backward stepwise selection based on the Akaike information criterion (AIC), spike-and-slab priors, and random variable selection (boosting) methods. We discuss and compare the performance of these methods via simulated datasets and UK 2001 foot-and-mouth disease data. While comparing the variable selection methods all performed consistently well except the two-stage Lasso. We conclude that the spike-and-slab prior method is to be recommended, consistently resulting in high accuracy and short computational time.

Alternative immune checkpoints in immunoregulatory profile of cancer stem cells.

Tumor-mediated bypass of immune checkpoint inhibitor (ICI) therapy with anti-programmed death-1 (PD-1), anti-programmed death-ligand 1 (PD-L1, also called B7-H1 or CD274) or anti-cytotoxic T lymphocyte associated antigen-4 (CTLA-4) is a challenge of current years in the area of cancer immunotherapy. Alternative immune checkpoints (AICs) are molecules beyond the common PD-1, PD-L1 or CTLA-4, and are upregulated in patients who show low/no ICI responses. These are members of B7 family including B7-H2 (ICOS-L), B7-H3 (CD276), B7-H4 (B7x), V-domain immunoglobulin suppressor of T cell activation (VISTA), B7-H6, HHLA2 (B7-H5/B7-H7) and catabolic enzymes like indoleamine 2,3-dioxygenase 1 (IDO1), and others that are also contributed to the regulation of tumor immune microenvironment (TIME). There is also strong evidence supporting the implication of AICs in regulation of cancer stemness and expanding the population of cancer stem cells (CSCs). CSCs display immunoregulatory capacity and represent multiple immune checkpoints either on their surface or inside. Besides, they are active promoters of resistance to the common ICIs. The aim of this review is to investigate interrelations between AICs with stemness and differentiation profile of cancer. The key message of this paper is that targeted checkpoints can be selected based on their impact on CSCs along with their effect on immune cells. Studies published so far mainly focused on immune cells as a target for anti-checkpoints. Ex vivo engineering of extracellular vesicles (EVs) equipped with CSC-targeted anti-checkpoint antibodies is without a doubt a key therapeutic target that can be under consideration in future research.

An F-ratio-based method for estimating the number of active sources in MEG.

Introduction: Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. Methods: To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Results: Our results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Discussion: Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.

Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes.

The underground pressure disaster caused by the exploitation of deep mineral resources has become a major hidden danger restricting the safe production of mines. Microseismic monitoring technology is a universally recognized means of underground pressure monitoring and early warning. In this paper, the wavelet coefficient threshold denoising method in the time-frequency domain, STA/LTA method, AIC method, and skew and kurtosis method are studied, and the automatic P-phase-onset-time-picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized, the reliability of the P-phase-onset-time-picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity in the underground mine, which is of great significance to the data processing and analysis of microseismic monitoring.

Impact of mucus modulation by N-acetylcysteine on nanoparticle toxicity.

Human respiratory mucus is a biological hydrogel that forms a protective barrier for the underlying epithelium. Modulation of the mucus layer has been employed as a strategy to enhance transmucosal drug carrier transport. However, a drawback of this strategy is a potential reduction of the mucus barrier properties, in particular in situations with an increased exposure to particles. In this study, we investigated the impact of mucus modulation on its protective role. In vitro mucus was produced by Calu-3 cells, cultivated at the air-liquid interface for 21 days and used for further testing as formed on top of the cells. Analysis of confocal 3D imaging data revealed that after 21 days Calu-3 cells secrete a mucus layer with a thickness of 24 ± 6 µm. Mucus appeared to restrict penetration of 500 nm carboxyl-modified polystyrene particles to the upper 5-10 µm of the layer. Furthermore, a mucus modulation protocol using aerosolized N-acetylcysteine (NAC) was developed. This treatment enhanced the penetration of particles through the mucus down to deeper layers by means of the mucolytic action of NAC. These findings were supported by cytotoxicity data, indicating that intact mucus protects the underlying epithelium from particle-induced effects on membrane integrity. The impact of NAC treatment on the protective properties of mucus was probed by using 50 and 100 nm amine-modified and 50 nm carboxyl-modified polystyrene nanoparticles, respectively. Cytotoxicity was only induced by the amine-modified particles in combination with NAC treatment, implying a reduced protective function of modulated mucus. Overall, our data emphasize the importance of integrating an assessment of the protective function of mucus into the development of therapy approaches involving mucus modulation.

Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization.

INTRODUCTION: The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. METHODS: In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. RESULTS: The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R 2 = 0.168 and adjusted R 2 = 0.165) performed better than BIC (R 2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R 2 = 0.174. The GBDT method with the value of R 2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. CONCLUSION: The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models.

Liquid Hydrogen: A Mirage or Potent Solution for Aviation's Climate Woes?

The aviation industry faces a formidable challenge to cap its climate impact in the face of continued growth in passengers and freight. Liquid hydrogen (LH2) is one of the alternative jet fuels under consideration as it does not produce carbon dioxide upon combustion. We conducted a well-to-wake life cycle assessment of CO2 emissions and non-CO2 climate change impacts per passenger-distance for 17 different hydrogen production routes, as well as conventional jet fuel and biofuels. Six other environmental and health impact categories were also considered. The Boeing 787-800 was used as the reference aircraft, and a range of flight distances were explored. Contrail cirrus contributes around 81 ± 31% of the combustion climate impacts for LH2, compared to 32 ± 7% for conventional jet fuel, showing that research is needed to reduce uncertainty in the case of LH2. The life cycle impacts of the two dominant commercial LH2 pathways are on average 8 and 121% larger than conventional jet fuel. Some novel LH2 pathways do show considerable potential for life cycle climate impact reductions versus conventional fuel (up to -205 ± 78%). LH2 from renewable energy is not climate neutral, though, at best -67 ± 10% compared to conventional over the life cycle.

Forecasting road accidental deaths in India: an explicit comparison between ARIMA and exponential smoothing method.

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India.

Optimized Traffic Light System with AIC and Application to the 2021 M6.7 Yangbi Earthquake Sequence.

One important question in earthquake prediction is whether a moderate or large earthquake will be followed by an even bigger one. Through temporal b-value evolution analysis, the traffic light system can be used to estimate if an earthquake is a foreshock. However, the traffic light system does not take into account the uncertainty of b-values when they constitute a criterion. In this study, we propose an optimization of the traffic light system with the Akaike Information Criterion (AIC) and bootstrap. The traffic light signals are controlled by the significance level of the difference in b-value between the sample and the background rather than an arbitrary constant. We applied the optimized traffic light system to the 2021 Yangbi earthquake sequence, which could be explicitly recognized as foreshock-mainshock-aftershock using the temporal and spatial variations in b-values. In addition, we used a new statistical parameter related to the distance between earthquakes to track earthquake nucleation features. We also confirmed that the optimized traffic light system works on a high-resolution catalog that includes small-magnitude earthquakes. The comprehensive consideration of b-value, significance probability, and seismic clustering might improve the reliability of earthquake risk judgment.

Subsampling based variable selection for generalized linear models.

A novel variable selection method for low-dimensional generalized linear models is introduced. The new approach called AIC OPTimization via STABility Selection (OPT-STABS) repeatedly subsamples the data, minimizes Akaike's Information Criterion (AIC) over a sequence of nested models for each subsample, and includes in the final model those predictors selected in the minimum AIC model in a large fraction of the subsamples. New methods are also introduced to establish an optimal variable selection cutoff over repeated subsamples. An extensive simulation study examining a variety of proposec variable selection methods shows that, although no single method uniformly outperforms the others in all the scenarios considered, OPT-STABS is consistently among the best-performing methods in most settings while it performs competitively for the rest. This is in contrast to other candidate methods which either have poor performance across the board or exhibit good performance in some settings, but very poor in others. In addition, the asymptotic properties of the OPT-STABS estimator are derived, and its root-n consistency and asymptotic normality are proved. The methods are applied to two datasets involving logistic and Poisson regressions.

Modeling and maximum likelihood based inference of interval-censored data with unknown upper limits and time-dependent covariates.

Due to the nature of study design or other reasons, the upper limits of the interval-censored data with multiple visits are unknown. A naïve approach is to treat the last observed time as the exact event time, which may induce biased estimators of the model parameters. In this paper, we first develop a Cox model with time-dependent covariates for the event time and a proportional hazards model with frailty for the gap time. We then construct the upper limits using the latent gap times to resolve the issue of interval-censored event time data with unknown upper limits. A data-augmentation technique and a Monte Carlo EM (MCEM) algorithm are developed to facilitate computation. Theoretical properties of the computational algorithm are also investigated. Additionally, new model comparison criteria are developed to assess the fit of the gap time data as well as the fit of the event time data conditional on the gap time data. Our proposed method compares favorably with competing methods in both simulation study and real data analysis.

Improving the Performance and Stability of TIC and ICE.

Takeuchi's Information Criterion (TIC) was introduced as a generalization of Akaike's Information Criterion (AIC) in 1976. Though TIC avoids many of AIC's strict requirements and assumptions, it is only rarely used. One of the reasons for this is that the trace term introduced in TIC is numerically unstable and computationally expensive to compute. An extension of TIC called ICE was published in 2021, which allows this trace term to be used for model fitting (where it was primarily compared to L2 regularization) instead of just model selection. That paper also examined numerically stable and computationally efficient approximations that could be applied to TIC or ICE, but these approximations were only examined on small synthetic models. This paper applies and extends these approximations to larger models on real datasets for both TIC and ICE. This work shows the practical models may use TIC and ICE in a numerically stable way to achieve superior results at a reasonable computational cost.

A snapshot of climate drivers and temporal variation of Ixodes ovatus abundance from a giant panda living in the wild.

Ticks and tick-borne diseases have negative impacts on the health of wild animals including endangered and vulnerable species. The giant panda (Ailuropoda melanoleuca), a vulnerable and iconic flagship species, is threatened by tick infestation as well. Not only can ticks cause anemia and immunosuppression in the giant panda, but also bacterial and viral diseases. However, previous studies regarding tick infestation on giant pandas were limited in scope as case reports from sick or dead animals. In this study, an investigation focusing on the tick infestation of a reintroduced giant panda at the Daxiangling Reintroduction Base in Sichuan, China was conducted. Ticks were routinely collected and identified from the ears of the giant panda from March to September in 2021. A linear model was used to test the correlation between tick abundance and climate factors. All ticks were identified as Ixodes ovatus. Tick abundance was significantly different among months. Results from the linear model showed temperature positively correlated to tick abundance, while air pressure had a negative correlation with tick abundance. To the best of our knowledge, this study is the first reported investigation of tick species and abundance on a healthy giant panda living in the natural environment, and provides important information for the conservation of giant pandas and other species sharing the same habitat.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.