bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic analysis of acute deterioration in chronic liver disease patients using modified quick sequential organ failure assessment
Authors: Song Do Seon , Kim Hee Yeon , Jung Young Kul , Kim Tae Hyung , Yim Hyung Joon , Yoon Eileen L , Suk Ki Tae , Yoo Jeong-ju , Kim Sang Gyune , Kim Moon Young , Chang Young , Jeong Soung Won , Jang Jae Young , Kim Sung-Eun , Kim Jung-Hee , Park Jung Gil , Kim Won , Yang Jin Mo , Kim Dong Joon ,   Korean Acute-on-Chronic Liver Failure (KACLiF) study group, Choudhury Ashok Kumar , Arora Vinod , Sarin Shiv Kumar ,   APASL ACLF Research Consortium (AARC) for APASL ACLF working party
Published: April 2024

Background/Aims: Quick sequential organ failure assessment (qSOFA) is believed to identify patients at risk of poor outcomes in those with suspected infection. We aimed to evaluate the ability of modified qSOFA (m-qSOFA) to identify high-risk patients among those with acutely deteriorated chronic liver disease (CLD), especially those with acute-onchronic liver failure (ACLF).Methods: We used data from both the Korean Acute-on-Chronic Liver Failure (KACLiF) and the Asian Pacific Association for the Study of the Liver ACLF Research Consortium (AARC) cohorts. qSOFA was modified by replacing the Glasgow Coma Scale with hepatic encephalopathy, and an m-qSOFA ≥2 was considered high.Results: Patients with high m-qSOFA had a significantly lower 1-month transplant-free survival (TFS) in both cohorts and higher organ failure development in KACLiF than those with low m-qSOFA (Ps<0.05). Subgroup analysis by ACLF showed that patients with high m-qSOFA had lower TFS than those with low m-qSOFA. m-qSOFA was an independent prognostic factor (hazard ratios, HR=2.604, 95% confidence interval, CI 1.353–5.013, P=0.004 in KACLiF and HR=1.904, 95% CI 1.484– 2.442, P<0.001 in AARC). The patients with low m-qSOFA at baseline but high m-qSOFA on day 7 had a significantly lower 1-month TFS than those with high m-qSOFA at baseline but low m-qSOFA on day 7 (52.6% vs. 89.4%, P<0.001 in KACLiF and 26.9% vs. 61.5%, P<0.001 in AARC).Conclusions: Baseline and dynamic changes in m-qSOFA may identify patients with a high risk of developing organ failure and short-term mortality among CLD patients with acute deterioration.
From complex data to clear insights: visualizing molecular dynamics trajectories
Authors: Belghit Hayet , Spivak Mariano , Dauchez Manuel , Baaden Marc , Jonquet-Prevoteau Jessica
Published: April 2024

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.
Integrated micro/nano drug delivery system based on magnetically responsive phase-change droplets for ultrasound theranostics
Authors: Chen Jieying , Zhao Chan , Liu Hao , Wang Zhangchao , Ma Luyao , Zhang Jiamin , Xu Ning , Hu Ke , Duan Lei
Published: April 2024

Phase-change droplets (PCDs) are intelligent responsive micro and nanomaterials developed based on micro/nano bubbles. Subject to external energy inputs such as temperature and ultrasound, the core substance, perfluorocarbon (PFC), undergoes a phase transition from liquid to gas. This transformation precipitates alterations in the PCDs’ structure, size, ultrasound imaging capabilities, drug delivery efficiency, and other pertinent characteristics. This gives them the ability to exhibit “intelligent responses”. This study utilized lipids as the membrane shell material and perfluorohexane (PFH) as the core to prepare lipid phase-change droplets. Superparamagnetic nanoparticles (PEG-functionalized Fe3O4 nanoparticles) and the anti-tumor drug curcumin (Cur) were loaded into the membrane shell, forming magnetic drug-loaded phase-change droplets (Fe-Cur-NDs). These nanoscale phase-change droplets exhibited excellent magnetic resonance/ultrasound imaging capabilities and thermal/ultrasound-mediated drug release. The Fe-Cur-NDs showed excellent anti-tumor efficacy for the MCF-7 cells under low-intensity focused ultrasound (LIFU) guidance in vitro. Therefore, Fe-Cur-NDs represent a promising smart responsive theranostic integrated micro/nano drug delivery system.
Machine learning model of the catalytic efficiency and substrate specificity of acyl-ACP thioesterase variants generated from natural and in vitro directed evolution
Authors: Jing Fuyuan , Chen Keting , Yandeau-Nelson Marna D. , Nikolau Basil J.
Published: April 2024

Modulating the catalytic activity of acyl-ACP thioesterase (TE) is an important biotechnological target for effectively increasing flux and diversifying products of the fatty acid biosynthesis pathway. In this study, a directed evolution approach was developed to improve the fatty acid titer and fatty acid diversity produced by E. coli strains expressing variant acyl-ACP TEs. A single round of in vitro directed evolution, coupled with a high-throughput colorimetric screen, identified 26 novel acyl-ACP TE variants that convey up to a 10-fold increase in fatty acid titer, and generate altered fatty acid profiles when expressed in a bacterial host strain. These in vitro-generated variant acyl-ACP TEs, in combination with 31 previously characterized natural variants isolated from diverse phylogenetic origins, were analyzed with a random forest classifier machine learning tool. The resulting quantitative model identified 22 amino acid residues, which define important structural features that determine the catalytic efficiency and substrate specificity of acyl-ACP TE.
A unique choline nitrate-based organo-aqueous electrolyte enables carbon/carbon supercapacitor operation in a wide temperature window (−40°C to 60°C)
Authors: Supiyeva Zhazira , Mansurov Zulkhair , Azat Seitkhan , Abbas Qamar
Published: April 2024

Some drawbacks of aqueous electrolytes, such as freezing at low temperatures and extensive evaporation at high temperatures, restrict their industrial viability. This article introduces a stabilized neutral aqueous choline nitrate electrolyte with a 10 vol.% methanol additive that improves the temperature stability of the electrolyte via enhanced hydrogen bonding with the choline cation and water and maintains the good state of health of the supercapacitor cells under extreme operating conditions. The symmetric carbon/carbon supercapacitor in 5 mol/kg choline nitrate + 10 vol.% methanol (σ = 76 ms/cm at 25°C) exhibits 103 F/g at room temperature during galvanostatic charge/discharge up to 1.5 V, which decreases to 78 F/g at −40°C due to the suppressed Faradaic reactions occurring at the carbon electrode. However, under similar charge/discharge conditions, the capacitance increases to 112 F/g when the supercapacitor operates at 60°C. This capacitance increase at high temperatures is due to the Faradaic reactions related to enhanced hydrogen adsorption and desorption. The most remarkable aspect of the proposed supercapacitor is its ability to maintain capacitance and power performance during high voltage floating at 1.5 V at three tested temperatures (−40°C, 24°C, and 60°C).
Interferons and interferon-related pathways in heart disease
Authors: Tran Duc Tin , Batchu Sri Nagarjun , Advani Andrew
Published: April 2024

Interferons (IFNs) and IFN-related pathways play key roles in the defence against microbial infection. However, these processes may also be activated during the pathogenesis of non-infectious diseases, where they may contribute to organ injury, or function in a compensatory manner. In this review, we explore the roles of IFNs and IFN-related pathways in heart disease. We consider the cardiac effects of type I IFNs and IFN-stimulated genes (ISGs); the emerging role of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway; the seemingly paradoxical effects of the type II IFN, IFN-γ; and the varied actions of the interferon regulatory factor (IRF) family of transcription factors. Recombinant IFNs and small molecule inhibitors of mediators of IFN receptor signaling are already employed in the clinic for the treatment of some autoimmune diseases, infections, and cancers. There has also been renewed interest in IFNs and IFN-related pathways because of their involvement in SARS-CoV-2 infection, and because of the relatively recent emergence of cGAS-STING as a pattern recognition receptor-activated pathway. Whether these advances will ultimately result in improvements in the care of those experiencing heart disease remains to be determined.
A review on dynamics of permeability-glycoprotein in efflux of chemotherapeutic drugs
Authors: Rani Priyanka , Mandal Pranabesh , Rajak Bikash Kumar , Singh Durg Vijay
Published: April 2024

Permeability-glycoprotein (P-gp) belongs to the ABS transporter protein family, with a high expression rate in cancerous cells. The substrate/inhibitors of the protein are structurally diverse, with no lucid mechanism of inhibition. There are two schools of thought on the inhibition mechanism: (i) P-gp inhibitors bind to the huge hydrophobic cavity between two Trans-Membrane Domains (TMDs), supported by ample literary proof and (ii) P-gp inhibitors bind to the vicinity of Nucleotide-Binding Sites (NBSs). Structural biologists have presented several experimental and theoretical structures of P-gp with bound nucleotides and inhibitors to explain the same. However, the available experimental P-gp structures are insufficient to address the catalytic transition path of mammalian P-gp in detail, thus the dynamics and mechanism by which drugs are effluxed is still unknown. Targeted Molecular Dynamics (targeted MD) could be used to minutely analyse and explore the catalytic transition inward open (IO) to outward open (OO) and relaxation path (OO to IO). Finally, analysis of targeted MD trajectory may help to explore different conformational states of Pg-p (reaction coordinate of catalytic transition/relaxation), efflux of compounds aided by the dynamics of Nucleotide Binding Domains/NBDs (ATP coupled process) and TMDs (peristalsis-like movement pushes the bound molecule). This review presents an understanding of the catalytic transition and dynamics of protein which provides insights at the efflux of chemotherapeutic drug using in cancer treatment.
Early cognitive predictors of spelling and reading in German-speaking children
Authors: Sigmund Jan Luis , Mehlhase Heike , Schulte-Körne Gerd , Moll Kristina
Published: April 2024

Theoretical background

While reading and spelling skills often are interconnected in models of literacy development, recent research suggests that the two skills can dissociate and that reading and spelling are associated with at least partly different cognitive predictors. However, previous research on dissociations between reading and spelling skills focused on children who have already mastered the first phases of literacy development. These findings suggest that dissociations are due to distinct deficits in orthographic processing (i.e., unprecise orthographic representations vs. inefficient serial processing). It is therefore unclear whether dissociations already become apparent during the initial stages, or rather emerge later in development. This study aims to enhance the understanding of the predictors of early spelling and reading skills, investigating potential variations, by considering various cognitive factors beyond well-established ones.

Methods

Data were collected at two time points: cognitive predictors and early reading and spelling skills were assessed at the end of kindergarten (T1) before formal literacy instruction started, and reading and spelling skills were again assessed in Grade 1 (T2). The data analysis included 353 first-grade participants. Linear regression analyses assessed predictive patterns, while logistic regression analyses explained children's likelihood of belonging to different proficiency groups (at-risk or typical skills).

Results

Results revealed phonological processing, letter knowledge, and intelligence, as significant predictors for Spelling in grade 1 (T2), even after adding the autoregressor (Spelling in kindergarten at T1) and the respective other literacy skill (Reading T2). For Reading in grade 1 (T2), phonological processing, and rapid automatized naming (RAN) surfaced as significant predictors after adding the autoregressor (Reading T1). However, only RAN surfaced as a significant predictor for Reading T2 after adding the respective other literacy skill (Spelling T2). In line with these findings, logistic regression analyses revealed that phonological processing predicted group allocation for Spelling T2 and RAN predicted group allocation for Reading T2.

Conclusions

Overall, the study underscores the importance of phonological processing and letter knowledge as early predictors of spelling and reading skills in Grade 1. Moreover, intelligence is identified as a predictor for early spelling, while rapid automatized naming (RAN) emerges as a predictor for early reading.
A review of air pollution as a driver of cardiovascular disease risk across the diabetes spectrum
Authors: Bonanni Luke J. , Wittkopp Sharine , Long Clarine , Aleman José O. , Newman Jonathan D.
Published: April 2024

The prevalence of diabetes is estimated to reach almost 630 million cases worldwide by the year 2045; of current and projected cases, over 90% are type 2 diabetes. Air pollution exposure has been implicated in the onset and progression of diabetes. Increased exposure to fine particulate matter air pollution (PM2.5) is associated with increases in blood glucose and glycated hemoglobin (HbA1c) across the glycemic spectrum, including normoglycemia, prediabetes, and all forms of diabetes. Air pollution exposure is a driver of cardiovascular disease onset and exacerbation and can increase cardiovascular risk among those with diabetes. In this review, we summarize the literature describing the relationships between air pollution exposure, diabetes and cardiovascular disease, highlighting how airborne pollutants can disrupt glucose homeostasis. We discuss how air pollution and diabetes, via shared mechanisms leading to endothelial dysfunction, drive increased cardiovascular disease risk. We identify portable air cleaners as potentially useful tools to prevent adverse cardiovascular outcomes due to air pollution exposure across the diabetes spectrum, while emphasizing the need for further study in this particular population. Given the enormity of the health and financial impacts of air pollution exposure on patients with diabetes, a greater understanding of the interventions to reduce cardiovascular risk in this population is needed.
Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning
Authors: Huang Nantian , Wang Xinran , Wang Hao , Wang Hefei
Published: April 2024

To address the challenges posed by the randomness and volatility of multi-energy loads in integrated energy systems for ultra-short-term accurate load forecasting, this paper proposes an ultra-short-term multi-energy load forecasting method based on multi-dimensional coupling feature mining and multi-task learning. Firstly, a method for mining multi-dimensional coupling characteristics of multi-energy loads is proposed, integrating multiple correlation analysis methods. By constructing coupling features of multi-energy loads and using them as input features of the model, the complex coupling relationships between multi-energy loads are effectively quantified. Secondly, an ultra-short-term multi-energy load forecasting model based on multi-task learning and a temporal convolutional network is constructed. In the prediction model construction phase, the potential complex coupling characteristics between multiple loads can be fully explored, and the potential temporal associations and long-term dependencies within data can be extracted. Then, the multi-task learning loss function weight optimization method based on homoscedastic uncertainty is used to optimize the forecasting model, realizing automatic tuning of the loss function weight parameters and further improving the prediction performance of the model. Finally, an experimental analysis is conducted using the integrated energy system of Arizona State University in the United States as an example. The results show that the proposed forecasting method has higher prediction accuracy than other prediction methods.
Assessment of tradeoffs between ecosystem services in large spatially constrained forest management planning problems
Authors: Abate Dagm , Marques Susete , Bushenkov Vladimir , Riffo Jose , Weintraub Andres , Constantino Miguel , Lagoa Constantino , Borges Jose G.
Published: April 2024

Forests provide multiple ecosystem services, some of which are competitive, while others are complementary. Pareto frontier approaches are often used to assess the trade-offs among these ecosystem services. However, when dealing with spatial optimization problems, one is faced with problems that are computationally complex. In this paper, we study the sources of this complexity and propose an approach to address adjacency conflicts while analyzing trade-offs among wood production, cork, carbon stock, erosion, fire resistance and biodiversity. This approach starts by sub-dividing a large landscape-level problem into four smaller sub-problems that do not share border stands. Then, it uses a Pareto frontier method to get a solution to each. A fifth sub-problem included all remaining stands. The solution of the latter by the Pareto frontier method is constrained by the solutions of the four sub-problems. This approach is applied to a large forested landscape in Northwestern Portugal. The results obtained show the effectiveness of using Pareto frontier approaches to analyze the trade-offs between ecosystem services in large spatial optimization problems. They highlight the existence of important trade-offs, notably between carbon stock and wood production, alongside erosion, biodiversity and wildfire resistance. These trade-offs were particularly clear at higher levels of these optimized services, while spatial constraints primarily affected the magnitude of the services rather than the underlying trade-off patterns. Moreover, in this paper, we study the impact of the size and complexity of the spatial optimization problem on the accuracy of the Pareto frontiers. Results suggest that the number of stands, and the number of adjacency conflicts do not affect accuracy. They show that accuracy decreases in the case of spatial optimization problems but it is within an acceptable range of discrepancy, thus showing that our approach can effectively support the analysis of trade-offs between ecosystem services.
Targeted acidosis mediated delivery of antigenic MHC-binding peptides
Authors: Kelly Joey J. , Ankrom Emily T. , Newkirk Sarah E. , Thévenin Damien , Pires Marcos M.
Published: April 2024

Cytotoxic T lymphocytes are the primary effector immune cells responsible for protection against cancer, as they target peptide neoantigens presented through the major histocompatibility complex (MHC) on cancer cells, leading to cell death. Targeting peptide-MHC (pMHC) complex offers a promising strategy for immunotherapy due to their specificity and effectiveness against cancer. In this work, we exploit the acidic tumor micro-environment to selectively deliver antigenic peptides to cancer using pH(low) insertion peptides (pHLIP). We demonstrated the delivery of MHC binding peptides directly to the cytoplasm of melanoma cells resulted in the presentation of antigenic peptides on MHC, and activation of T cells. This work highlights the potential of pHLIP as a vehicle for the targeted delivery of antigenic peptides and its presentation via MHC-bound complexes on cancer cell surface for activation of T cells with implications for enhancing anti-cancer immunotherapy.
The emerging importance of skull-brain interactions in traumatic brain injury
Authors: Goodman Grant W. , Devlin Patrick , West Bryce E. , Ritzel Rodney M.
Published: April 2024

The recent identification of skull bone marrow as a reactive hematopoietic niche that can contribute to and direct leukocyte trafficking into the meninges and brain has transformed our view of this bone structure from a solid, protective casing to a living, dynamic tissue poised to modulate brain homeostasis and neuroinflammation. This emerging concept may be highly relevant to injuries that directly impact the skull such as in traumatic brain injury (TBI). From mild concussion to severe contusion with skull fracturing, the bone marrow response of this local myeloid cell reservoir has the potential to impact not just the acute inflammatory response in the brain, but also the remodeling of the calvarium itself, influencing its response to future head impacts. If we borrow understanding from recent discoveries in other CNS immunological niches and extend them to this nascent, but growing, subfield of neuroimmunology, it is not unreasonable to consider the hematopoietic compartment in the skull may similarly play an important role in health, aging, and neurodegenerative disease following TBI. This literature review briefly summarizes the traditional role of the skull in TBI and offers some additional insights into skull-brain interactions and their potential role in affecting secondary neuroinflammation and injury outcomes.
Influence of glacial influx on the hydrodynamics of Admiralty Bay, Antarctica - study based on combined hydrographic measurements and numerical modeling
Authors: Osińska Maria , Herman Agnieszka
Published: April 2024

This study investigates the impact of glacial water discharges on the hydrodynamics of a glacial bay in Antarctica, comparing it to well-studied northern hemisphere fjords. The research was carried out in Admiralty Bay (AB) in the South Shetland Islands, a wide bay adjacent to twenty marine-terminating glaciers. From December 2018 until February 2023, AB water properties were measured on 136 days. This dataset showed that a maximally two-layered stratification occurs in AB and that glacial water is always the most buoyant water mass. Using the Delft3D Flow, a three-dimensional hydrodynamical model of AB was developed. During tests, the vertical position and initial velocity of glacial discharges have been shown to be insignificant for the overall bay circulation. Fourteen model scenarios have been calculated with an increasing glacial influx added. The AB general circulation pattern consists of two cyclonic cells. Even in scenarios with significant glacial input, water level shifts and circulation are predominantly controlled by the ocean. Glacial freshwater is carried out of AB along its eastern boundary in a surface layer. Freshwater thickness in this outflow current is maximally 0.27-0.35 m. Within the inner AB inlets, significant glacial influx produces buoyancy-driven vertical circulation. Using an approach combining hydrographic and modeling data, a four-year timeseries of glacial influx volumes into AB has been produced. On average, glacial influx in summer is 10 times greater than in spring and winter and 3 times higher than in autumn. The annual glacial influx into AB was estimated at 0.434-0.632 Gt. Overall, the study demonstrated the unique characteristics of the topography and forcings that influence the hydrodynamics of an Antarctic glacial bay.
Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network
Authors: Liu Tong , Zhu Kaiyan , Wang Xinyi , Song Wenbo , Wang Han
Published: April 2024

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies.
Layer-by-layer structured nanocomposite deposits from plasma-synthesized organosilicon nanoparticles and organosilicon nanoparticles decorated with Ag nanoparticles by taking advantage of cyclic nanoparticle formation in Ar/HMDSO reactive plasmas
Authors: Bérard Rémi , Garofano Vincent , Joblin Christine , Stafford Luc , Makasheva Kremena
Published: April 2024

Rational engineering of thin nanocomposite layers, deposited in reactive plasmas, requires knowledge on the plasma behavior in order to produce multifunctional deposits with tailored properties (structural, optical, electrical, etc.) This work presents an experimental study of nanoparticles synthesized in the plasma gas-phase and their subsequent use as building-blocks to form layer-by-layer nanostructures. The experiment is performed in a plasma process that successfully combines plasma polymerization of an organosilicon molecular precursor (hexamethyldisiloxane, HMDSO) and sputtering of a metallic (silver) target. Pulsed injection of the precursor is found to promote cyclic nanoparticle formation in Ar/HMDSO reactive plasmas. The plasma electron temperature is found to vary in the range 1.6—2.2 eV as derived from time-resolved optical emission spectroscopy of the plasma energetic conditions. This diagnostic method is also shown to provide a reliable tool for online monitoring of the nanoparticle synthesis process. Two types of layer-by-layer structured nanocomposites can be obtained depending on the type of nanoparticles synthesized: (i) organosilicon nanoparticles of size less than 100 nm in all studied plasma conditions for a large quantity of injected HMDSO and (ii) raspberry-like nanoparticles of size less than 150 nm when the quantity of injected HMDSO is reduced. The organosilicon nanoparticle growth follows a polydimethylsiloxane (PDMS)-like oligomerization scheme in which the R2-Si(-O)2 silicon bond tends towards the formation of polymeric structure in a R3-Si(-O)1 silicon chemical environment, containing Si-(CH2)-Si type bridges that are involved in cross-linking. The elemental composition of the raspberry-like nanoparticles is similar to that of the organosilicon nanoparticles, supplemented by the Ag component. The decorating silver nanoparticles are ∼15 nm of size, round in shape and polycrystalline. There is no evidence for silver oxides in the nanostructures. The Si-O-Ag bridges, revealed by infrared spectroscopy, suggest the presence of junction sites between the metallic and the organosilicon parts of the raspberry-like nanoparticles. The silver nanoparticles are found to decorate the organosilicon nanoparticles to form the raspberry-like nanoparticles in the plasma gas-phase, before being deposited. This reveals a very interesting phenomenon of simultaneous growth of the silver- and organosilicon-parts in the plasma without mixing during the nucleation phase.
The impact of enriched environments on cerebral oxidative balance in rodents: a systematic review of environmental variability effects
Authors: Ramos Tiago Lacerda , de Sousa Fernandes Matheus Santos , da Silva Fidélis Débora Eduarda , Jurema Santos Gabriela Carvalho , Albuquerque Renata B. , Ferreira Diorginis José Soares , de Souza Raphael Fabrício , Badicu Georgian , Yagin Fatma Hilal , Yagin Burak , Alwhaibi Reem M. , Souto Fabrício Oliveira , Lagranha Claúdia Jacques
Published: April 2024

Introduction

The present review aimed to systematically summarize the impacts of environmental enrichment (EE) on cerebral oxidative balance in rodents exposed to normal and unfavorable environmental conditions.

Methods

In this systematic review, four databases were used: PubMed (830 articles), Scopus (126 articles), Embase (127 articles), and Science Direct (794 articles). Eligibility criteria were applied based on the Population, Intervention, Comparison, Outcomes, and Study (PICOS) strategy to reduce the risk of bias. The searches were carried out by two independent researchers; in case of disagreement, a third participant was requested. After the selection and inclusion of articles, data related to sample characteristics and the EE protocol (time of exposure to EE, number of animals, and size of the environment) were extracted, as well as data related to brain tissues and biomarkers of oxidative balance, including carbonyls, malondialdehyde, nitrotyrosine, oxygen-reactive species, and glutathione (reduced/oxidized).

Results

A total of 1,877 articles were found in the four databases, of which 16 studies were included in this systematic review. The results showed that different EE protocols were able to produce a global increase in antioxidant capacity, both enzymatic and non-enzymatic, which are the main factors for the neuroprotective effects in the central nervous system (CNS) subjected to unfavorable conditions. Furthermore, it was possible to notice a slowdown in neural dysfunction associated with oxidative damage, especially in the prefrontal structure in mice.

Discussion

In conclusion, EE protocols were determined to be valid tools for improving oxidative balance in the CNS. The global decrease in oxidative stress biomarkers indicates refinement in reactive oxygen species detoxification, triggering an improvement in the antioxidant network.
Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice
Authors: Kuttner Samuel , Luppino Luigi T. , Convert Laurence , Sarrhini Otman , Lecomte Roger , Kampffmeyer Michael C. , Sundset Rune , Jenssen Robert
Published: April 2024

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.
Evaluation of the safety and quality of Brazil nuts (Bertholletia excelsa) using the tools of dna sequencing technology and aflatoxin profile
Authors: Esperança Victor J. R. , Moreira Paula I. O. , Chávez Davy W. H. , Freitas-Silva Otniel
Published: April 2024

Introduction

Brazil nuts (BNs) result from sustainable extraction and are widely exploited in the Amazon region. Due to the production characteristics in the forest and the nutritional characteristics of these nuts, the occurrence of fungal contamination and the presence of aflatoxins are extensively discussed in the literature as a great aspect of interest and concern. This study aims to evaluate the microbial profile through DNA sequencing and amplification of 16S and ITS genes for bacterial and fungal analysis, respectively, and the presence of mycotoxins using high-performance liquid chromatography with fluorescence detection (HPLC-FD) from different fractions of the nuts processed.

Methods

The BN samples, harvest A (HA) and harvest B (HB), from two different harvests were collected in an extractive cooperative in the Amazon region for microbiological analysis (from DNA extraction and amplification of 16S genes, bacteria analysis, and ITS for fungi) and mycotoxins (aflatoxins AFB1, AFB2, AFG1, and AFG2) using HPLC-FD/KobraCell®.

Results and discussion

The samples showed a very different microbiome and aflatoxin profile. Genera such as Rothia (HA) and Cronobacter (HB) were abundant during the analysis of bacteria; as for fungi, the genera Aspergillus, Fusarium, Penicillium, and Alternaria were also considered prevalent in these samples. Soil microorganisms, including those pathogenic and related to inadequate hygienic-sanitary production practices, as well as aflatoxins, were found in the samples. However, they were within the established limits permitted by Brazilian legislation. Nuts have a diverse microbiota and are not restricted to fungi of the genus Aspergillus. The microbiological and toxicological profile can vary significantly within the same nut in the same extraction region and can be exacerbated by global climate changes. Therefore, it is necessary to advance sanitary educational actions by applying good production practices and inspection programs to ensure the sustainability and quality of the BN production chain.
Identification of differentially expressed genes and splicing events in early-onset colorectal cancer
Authors: Marx Olivia M. , Mankarious Marc M. , Koltun Walter A. , Yochum Gregory S.
Published: April 2024

Background

The incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC.

Methods

We performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens.

Results

We identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens.

Conclusion

Our transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.
Effects of micro/nano-ozone bubble nutrient solutions on growth promotion and rhizosphere microbial community diversity in soilless cultivated lettuces
Authors: Zhao Qinsong , Dong Jingjing , Li Shibiao , Lei Wenxin , Liu Ake
Published: April 2024

Due to its high efficacy as a wide-spectrum disinfectant and its potential for the degradation of pollutants and pesticides, ozone has broad application prospects in agricultural production. In this study, micro/nano bubble technology was applied to achieve a saturation state of bubble nutrient solution, including micro-nano oxygen (O2 group) and micro-nano ozone (O3 group) bubble nutrient solutions. The effects of these solutions on lettuce physiological indices as well as changes in the microbial community within the rhizosphere substrate were studied. The application of micro/nano (O2 and O3) bubble nutrient solutions to substrate-cultured lettuce plants increased the amount of dissolved oxygen in the nutrient solution, increased the lettuce yield, and elevated the net photosynthetic rate, conductance of H2O and intercellular carbon dioxide concentration of lettuce plants. Diversity analysis of the rhizosphere microbial community revealed that both the abundance and diversity of bacterial and fungal communities in the substrate increased after plant cultivation and decreased following treatment with micro/nanobubble nutrient solutions. RDA results showed that the microbial community in the S group was positively associated with EC, that in the CK and O2 groups exhibited a positive correlation with SC, and that in the O3 group displayed a positive correlation with CAT and POD. Overall, the implementation of micro/nanobubble generation technology in soilless substrates can effectively increase the lettuce growth and yield, and O3 had a more pronounced effect on lettuce yield and quality and the microbial community structure in the substrate than O2. Our study would provide a reference and theoretical basis for developing sustainable and green technology for promoting lettuce production and can be a promising alternative to conventional methods for improving crop yields.
Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening
Authors: Choo Myounglee , Park Doeun , Cho Minseo , Bae Sujin , Kim Jinwoo , Han Doug Hyun
Published: April 2024

Background

Depression and anxiety are prevalent mental health concerns among children and adolescents. The application of conventional assessment methods, such as survey questionnaires to children, may lead to self-reporting issues. Digital biomarkers provide extensive data, reducing bias in mental health self-reporting, and significantly influence patient screening. Our primary objectives were to accurately assess children’s mental health and to investigate the feasibility of using various digital biomarkers.

Methods

This study included a total of 54 boys and girls aged between 7 to 11 years. Each participant’s mental state was assessed using the Depression, Anxiety, and Stress Scale. Subsequently, the subjects participated in digital biomarker collection tasks. Heart rate variability (HRV) data were collected using a camera sensor. Eye-tracking data were collected through tasks displaying emotion-face stimuli. Voice data were obtained by recording the participants’ voices while they engaged in free speech and description tasks.

Results

Depressive symptoms were positively correlated with low frequency (LF, 0.04–0.15 Hz of HRV) in HRV and negatively associated with eye-tracking variables. Anxiety symptoms had a negative correlation with high frequency (HF, 0.15–0.40 Hz of HRV) in HRV and a positive association with LF/HF. Regarding stress, eye-tracking variables indicated a positive correlation, while pNN50, which represents the proportion of NN50 (the number of pairs of successive R-R intervals differing by more than 50 milliseconds) divided by the total number of NN (R-R) intervals, exhibited a negative association. Variables identified for childhood depression included LF and the total time spent looking at a sad face. Those variables recognized for anxiety were LF/HF, heart rate (HR), and pNN50. For childhood stress, HF, LF, and Jitter showed different correlation patterns between the two grade groups.

Discussion

We examined the potential of multimodal biomarkers in children, identifying features linked to childhood depression, particularly LF and the Sad.TF:time. Anxiety was most effectively explained by HRV features. To explore reasons for non-replication of previous studies, we categorized participants by elementary school grades into lower grades (1st, 2nd, 3rd) and upper grades (4th, 5th, 6th).

Conclusion

This study confirmed the potential use of multimodal digital biomarkers for children’s mental health screening, serving as foundational research.
Empathy and psychopathology in children and adolescents: the role of parental mental illness and emotion regulation
Authors: Luczejko Arleta A. , Hagelweide Klara , Stark Rudolf , Weigelt Sarah , Christiansen Hanna , Kieser Meinhard , Otto Kathleen , Reck Corinna , Steinmayr Ricarda , Wirthwein Linda , Zietlow Anna-Lena , Schwenck Christina ,   the COMPARE-family research group
Published: April 2024

Objective

Although empathy is known to be a strength, recent studies suggest that empathy can be a risk factor for psychopathology under certain conditions in children. This study examines parental mental illness as such a condition. Further, it aims to investigate whether maladaptive emotion regulation (ER) mediates the relationship between empathy and psychopathological symptoms of children.

Methods

Participants were 100 children of parents with a mental illness (55% female) and 87 children of parents without a mental illness (50% female) aged 6 - 16 years and their parents.

Results

Greater cognitive empathy was related to more psychopathological symptoms in COPMI, but not in COPWMI. In addition, in COPMI maladaptive ER mediated this relationship. In contrast, greater affective empathy was associated with more psychopathological symptoms regardless of whether parents had a mental illness.

Conclusion

Our findings highlight the importance of implementing preventive programs for COPMI that specifically target the reduction of maladaptive ER.
Ultra-processed food consumption and quality of life: a cross-sectional study in Iranian women
Authors: Hosseininasab Dorsa , Shiraseb Farideh , Bahrampour Niki , da Silva Alessandra , Hajinasab Mohammad Mahdi , Bressan Josefina , Mirzaei Khadijeh
Published: April 2024

Background

Ultra-processed foods (UPFs) have been associated with a higher intake of added sugars, sodium, and unhealthy fats; however, the relationship between UPFs and quality of life (QoL) is not well understood.

Methods

The present cross-sectional study included 193 Iranian women aged 18–48 years with a body mass index (BMI) of ≥25 kg/m2. UPFs were identified using the NOVA classification. QoL was evaluated using the SF-36 questionnaire. Anthropometric measurements and body composition were assessed using an appropriate method.

Results

The mean BMI and fat-free mass (FFM) of the subjects were 30.90 kg/m2 and 46.69 kg, respectively. At first, a significant difference was observed in the height of women across tertiles of UPF consumption. The mean score of the total QoL scale was 66.90. Women who were in the tertile 3 of UPFs intake had 23.59 units lower the scale of limitation in physical capabilities and activity (score of role-physical) (β = −23.59, 95% CI: −37.77–9.40, p = 0.001). Among those with the highest adherence to UPF intake, there was an 8.76 unit reduction in addressing feelings of energy and fatigue (vitality domain) in model 2 (β = −8.76, 95% CI: −16.42–1.11, p = 0.02). Finally, a reduction of 15.78 units was observed in the mental health scale, specifically in the mental states of anxiety and depression, among participants in the third tertile of UPF intake (β = −15.78, 95% CI: −24.11–7.45, p < 0.001).

Conclusion

Increased UPF consumption was associated with lower QoL in Iranian women. Further studies are needed to confirm these findings and develop effective strategies to promote healthy food choices.
Comprehending Health of the Transgender Population in India Through Bibliometric Analysis
Authors: Raj Papia , Dubey Ashwani
Published: April 2024

Objective: In India research on health issues of transgender populations are very recent and limited though transgenders are an important sub-group of the population. Hence, this study attempts to understand the state of transgender health research in India through a systematic review of literature.

Methods: A systematic literature review was conducted using bibliometric analysis. Initially, 132 studies were identified, and only 37 articles meeting selection criteria were subsequently selected for review using PRISMA 2020 guidelines. The research landscape was examined with tools such as Biblioshiny, Arc-GIS (10.1), and Vos-Viewer.

Results: The review highlights that existing literature on transgender health in India mainly focuses on sexual health while neglecting their overall health status. It also emphasises the skewed geographical coverage of these studies. Based on the analysis, the interdisciplinary nature of the subject is illustrated in a three-field plot and through term co-occurrence. These indicate the need for culture-specific gender-affirmative services promoting a holistic approach to comprehend the health of transgender populations in India.

Conclusion: In India research on transgender health is lopsided and at an initial stage. There is a need to develop diverse research focus on various health issues of transgenders that should also be geographically representative. Future in-depth research on this subject will enable optimizing resource allocation, developing effective gender-inclusive policies, and support holistic planning for better health status of transgender people in India, and other countries with similar socio-cultural background.