Specific Regions Research Articles

A Dual Role of Mesenchymal Stem Cell Derived Small Extracellular Vesicles on TRPC6 Protein and Mitochondria to Promote Diabetic Wound Healing.

Diabetic wounds exhibit delayed and incomplete healing, usually due to vascular and nerve damage. Dysregulation of cellular Ca2+ homeostasis has recently been shown to be closely related to insulin resistance and type 2 diabetes mellitus. However, the involvement of this dysregulation in diabetic wound complications remains unknown. In this study, we found calcium dysregulation in patients with diabetic ulcers via tissue protein profiling. High glucose and glucometabolic toxicant stimulation considerably impaired the function of TRPC6, a pore subunit of transient receptor potential channels mediating Ca2+ influx, and mitochondria, which regulate calcium cycling and metabolism. Furthermore, we found that mesenchymal stem cell (MSC)-derived small extracellular vesicles (MSC-sEVs) could play a dual role in restoring the function of TRPC6 and mitochondria by delivering transcription factor SP2 and deubiquitinating enzyme USP9, respectively. MSC-sEVs could transfer SP2 that activated TRPC6 expression by binding to its specific promoter regions (-1519 to -1725 bp), thus recovering Ca2+ influx and downstream pathways. MSC-sEVs also promoted mitophagy to restore mitochondrial function by transporting USP9 that stabilized the expression of Parkin, a major player in mitophagy, thereby guaranteeing Ca2+ efflux and avoidance of Ca2+ overload. Targeting the regulation of calcium homeostasis provides a perspective for understanding diabetic wound healing, and the corresponding design of MSC-sEVs could be a potential therapeutic strategy.

Risks associated with the technological adoption of industrial symbiosis: A study of its implementation by geographic regions

This research investigates the factors influencing the adoption of industrial symbiosis practices, focusing on their relevance in geographical contexts within developing countries associated with the European Economic Community. The study employs geographical weighting analysis as its primary methodology, allowing for a nuanced examination of variable impacts within specific geographic regions. Data is gathered through surveys distributed to companies operating in various high-energy consumption sectors that have previously engaged in industrial symbiosis activities to varying extents. The study’s findings emphasize the crucial role that financial and performance-related risks played in influencing the decision-making process surrounding the implementation of industrial symbiosis. Furthermore, the research highlights the significance of regional dynamics in influencing waste disposal practices and the overall success of industrial symbiosis initiatives. Additionally, the study underscores the impact of social media and networks in disseminating vital information that is instrumental in advancing innovative industrial symbiosis processes. These findings hold practical implications for stakeholders, including policymakers, businesses, and organizations aiming to foster sustainable economic practices. Understanding the influence of risk factors and regional intricacies enables tailoring strategies that can promote industrial symbiosis adoption. Moreover, recognizing the pivotal role of social media and networks in knowledge dissemination can accelerate the adoption of novel practices, facilitating the transition towards greener economies in developed and developing regions associated with the European Economic Community.

Organellar genomic characterization of Anunuuluaehu liula representing a new genus and species of Phyllophoraceae (Gigartinales, Rhodophyta) from the mesophotic zone of Hawai'i.

Over the last 2 decades, routine collections in the Hawaiian Archipelago have expanded to mesophotic reefs, leading to the discovery of a new red algal genus and species, here described as Anunuuluaehu liula gen. et sp. nov. This study provides a detailed genus and species description and characterizes chloroplast and mitochondrial organellar genomes. The new genus, Anunuuluaehu, shares many characteristics with the family Phyllophoraceae and shows close similarities to Archestennogramma and Stenogramma, including habit morphology, nemathecia forming proliferations at the outer cortex with terminal chains of tetrasporangia, and carposporophytes with multi-layered pericarps. The single species in this genus exhibits distinctive features within the Phyllophoraceae: the presence of single-layer construction of large medullary cells and the development of long, tubular gonimoblastic filaments. Multi-gene phylogenetic analyses confirmed it as a unique, monophyletic lineage within the family. Cis-splicing genes, interrupted by intron-encoded proteins within group II introns, are present in both the chloroplast and mitochondrial genomes of A. liula. Notably, a specific region of the coxI group II intron exhibits similarity to fungal introns. Anunuuluaehu liula is presumed to be endemic to the Hawaiian Archipelago and thus far is known to live solely at mesophotic depths from Hōlanikū to Kaho'olawe ranging from 54 to 201 m, which is the deepest collection record of any representative in the family. Overall, this study enhances our understanding of the genomic and taxonomic complexities of red algae in mesophotic habitats, emphasizing the significance of continued research in this area to uncover further insights into evolutionary processes and biogeographic patterns.

Frozen tissue coring and layered histological analysis improves cell type-specific proteogenomic characterization of pancreatic adenocarcinoma

BackgroundOmics characterization of pancreatic adenocarcinoma tissue is complicated by the highly heterogeneous and mixed populations of cells. We evaluate the feasibility and potential benefit of using a coring method to enrich specific regions from bulk tissue and then perform proteogenomic analyses.MethodsWe used the Biopsy Trifecta Extraction (BioTExt) technique to isolate cores of epithelial-enriched and stroma-enriched tissue from pancreatic tumor and adjacent tissue blocks. Histology was assessed at multiple depths throughout each core. DNA sequencing, RNA sequencing, and proteomics were performed on the cored and bulk tissue samples. Supervised and unsupervised analyses were performed based on integrated molecular and histology data.ResultsTissue cores had mixed cell composition at varying depths throughout. Average cell type percentages assessed by histology throughout the core were better associated with KRAS variant allele frequencies than standard histology assessment of the cut surface. Clustering based on serial histology data separated the cores into three groups with enrichment of neoplastic epithelium, stroma, and acinar cells, respectively. Using this classification, tumor overexpressed proteins identified in bulk tissue analysis were assigned into epithelial- or stroma-specific categories, which revealed novel epithelial-specific tumor overexpressed proteins.ConclusionsOur study demonstrates the feasibility of multi-omics data generation from tissue cores, the necessity of interval H&E stains in serial histology sections, and the utility of coring to improve analysis over bulk tissue data.

Retrospective Cohort Study on Determinants of Mechanical Ventilation Duration of COVID-19 ICU Patients.

Background In the face of the ongoing global health crisis posed by COVID-19, it becomes imperative to understand the disease's dynamics, particularly in specific regions. This study provides a detailed examination of the factors influencing mechanical ventilation (MV) duration among COVID-19 patients in an intensive care setting, focusing on a diverse patient cohort from the Al Hassa region of Saudi Arabia. The primary aim of this study was to identify key demographic factors, clinical outcomes, and comorbidities that affect the duration of MV among ICU patients with COVID-19. This understanding is crucial for enhancing patient care and informing healthcare strategies in the context of the pandemic. Methods A retrospective cohort study was conducted involving patients diagnosed with COVID-19 and admitted to the ICU in the Al Hassa region.The total number of participants was 1,259. Using a systematic sampling method, these participants were chosen to create a representative sample that reflects the prevailing treatment protocols in ICUs across these hospitals. Data encompassed patient demographics, comorbidities, clinical outcomes, and MV duration. Statistical analyses were employed to explore the associations between these variables. Results Our findings reveal a totalof 1,259 participants significant associations between MV duration and various factors, including nationality, legal status, travel history, and comorbidities like heart failure and immunocompromised status. These insights are instrumental in understanding the nuances of COVID-19 management in critical care. Conclusion The study provides valuable insights into the determinants of MV duration in severe COVID-19 cases, emphasizing the need for individualized patient care approaches. It highlights the complexity of managing COVID-19 in ICU settings and underscores the importance of tailored healthcare responses to this global health challenge, particularly in the Al Hassa region.

Using Supervised Machine Learning to Predict Sales in Marketplaces: Case study Predicting Sales of Padimas Bread in Marketplaces in Indonesia

This project intends to apply supervised machine learning to anticipate sales of Padimas bread in marketplaces in Indonesia, with an emphasis on evaluating sales data to gain more profits and predict future income. Data from the Shopee, Tokopedia, and TikTok markets in 2023 was analyzed, employing techniques like exploratory sales data analysis and machine learning. The analysis findings encompass the top-selling products, the highest sales figures, regions with the most substantial sales, overall market sales, sales patterns, and revenue forecasts. The primary discoveries encompass the widespread appeal of chocolate toast, the most substantial sales of banana chocolate toast in West Java, and Shopee as the marketplace with the most significant sales and revenue. Sales trends exhibit a pattern of oscillation around an average value, but income from sales demonstrates a downward tendency until the 30th day. The strategic ramifications of this analysis encompass augmenting the production of sought-after goods, amplifying sales in specific regions, and delving into prospective marketplaces.

MR Spectroscopy Assessment of Daily Variations of GABA Levels within the Parietal Lobe and Anterior Cingulate Gyrus Regions of Healthy Young Adults.

The changes that occur in the gamma-aminobutyric acid (GABA) levels within specific brain regions throughout the day are less clear. To evaluate the daily fluctuations of GABA levels within the parietal lobe (PL) and anterior cingulate gyrus (ACC) regions and explore their association with melatonin (MT) levels, heart rate (HR), and blood pressure. Prospective. 26 healthy young adults (15 males and 11 females aged 22-27 years). 3.0T, T1-weighted imaging, Mescher-Garwood point resolved spectroscopy (MEGA-PRESS) sequence. The acquired GABA signal contained the overlapping signals of macromolecules and homocarnosine, hence expressed as GABA+. The creatine (Cr) signal was applied as an endogenous reference. The GABA+, GABA+/Cr were measured at six different time points (1:00, 5:00, 9:00, 13:00, 17:00, and 21:00 hours) using MEGA-PRESS. The blood pressure, HR and sputum MT levels, were also acquired. The one-way repeated-measures analysis of variance (ANOVA) was used to evaluate the GABA, blood pressure, HR, and MT levels throughout the day. A general linear model was used to find the correlation between GABA and blood pressure, HR, and MT. P < 0.05 was statistically significant. Significant variations in GABA+/Cr and GABA+ levels were observed throughout the day within the PL region. The lowest levels were recorded at 9:00 hour (GABA+/Cr: 0.100 ± 0.003，GABA+:1.877 ± 0.051 i.u) and the highest levels were recorded at 21:00 hour (GABA+/Cr: 0.115 ± 0.003, GABA+:2.122 ± 0.052 i.u). The MT levels were positively correlated with GABA+/Cr (r = 0.301) and GABA+ (r = 0.312) within the ACC region. GABA+/Cr and GABA+ in ACC are positively correlated with MT. GABA levels in the PL have diurnal differences. These findings may indicate that the body's GABA level change in response to the light-dark cycle. 1 TECHNICAL EFFICACY: Stage 2.

Early prediction of dementia using fMRI data with a graph convolutional network approach

Objective. Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs). Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI. Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification. Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at: https://github.com/Shuning-Han/FC-based-GCN.

A feasibility study investigating cortical hemodynamic changes during infinity walk with fNIRS

This study seeks to explore the correlation between cortical activation and the Infinity Walk pattern, examining how the influence of foot overpronation and footwear may impact motor control. Functional near-infrared spectroscopy (fNIRS), a portable and user-friendly neuroimaging technique, was used to measure hemodynamical changes in six individuals with non-critical pronation degrees. Participants perform the Infinity Walk under various footwear conditions while wearing an fNIRS portable imaging device. Results indicate a consistent hemodynamic pattern in both hemispheres during the Infinity Walk, with no significant differences observed across subjects and footwear conditions in the prefrontal cortex (PFC), pre-motor area, the supplementary motor cortex (PMA & SMC), the primary motor cortex (PMC), and Wernicke’s area (WA). The impact of pronation and footwear on motor control remains inconclusive due to inconsistent hemodynamic patterns. Notably, the activation patterns in Broca’s area (BA) and the temporal gyrus (TG) differ significantly from other brain regions. The balanced hemodynamic responses in the bilateral hemispheres may be attributed to the Infinity Walk’s inherent walking pattern. These findings indicate a need for further investigation into the Infinity Walk to examine the similarities and distinctions in activation patterns within specific brain regions. Additionally, the impact of pronation necessitates more substantial experimental research to establish a correlation between pronation and cortical hemodynamics.

Molecular identification and genetic diversity of zoonotic hookworm infections in domestic dogs from northeastern, Thailand.

Hookworm infections remain a significant public health concern in tropical and subtropical regions, including Thailand. This study investigated the species and genetic diversity of hookworm infections in domestic dogs from northeastern Thailand. The molecular analysis focused on amplifying and sequencing specific regions of ribosomal RNA genes (ITS1-5.8S-ITS2 region) and the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene in hookworm larvae recovered from 21 domestic dog stool samples. Among 21 larvae (one larva per infected dog) analyzed, 14 had sequences identical to Ancylostoma caninum, and 7 showed sequences almost identical to Ancylostoma ceylanicum. Phylogenetic analysis of cox1 sequences placed A. caninum and A. ceylanicum in separate clades. The median-joining network of A. caninum cox1 sequences from Thailand showed high haplotype diversity and belonged to the same cluster as sequences from Australia while forming separate clusters from those of A. caninum samples from the USA. The available published A. ceylanicum cox1 sequences (n = 33), in combination with seven sequences in the present study, represented 15 haplotypes distributed among three clusters. Interestingly, A. ceylanicum sequences from dogs and humans shared the same haplotypes. These findings are crucial for recognizing the potential for zoonotic transmission, highlighting the necessity for targeted control measures, and increasing awareness among pet owners and healthcare professionals to mitigate the risk of hookworm transmission to humans.

SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in live cells.

RNA-protein interactions determine the cellular fate of RNA and are central to regulating gene expression outcomes in health and disease. To date, no method exists that is able to identify proteins that interact with specific regions within endogenous RNAs in live cells. Here, we develop SHIFTR (Selective RNase H-mediated interactome framing for target RNA regions), an efficient and scalable approach to identify proteins bound to selected regions within endogenous RNAs using mass spectrometry. Compared to state-of-the-art techniques, SHIFTR is superior in accuracy, captures minimal background interactions and requires orders of magnitude lower input material. We establish SHIFTR workflows for targeting RNA classes of different length and abundance, including short and long non-coding RNAs, as well as mRNAs and demonstrate that SHIFTR is compatible with sequentially mapping interactomes for multiple target RNAs in a single experiment. Using SHIFTR, we comprehensively identify interactions of cis-regulatory elements located at the 5' and 3'-terminal regions of authentic SARS-CoV-2 RNAs in infected cells and accurately recover known and novel interactions linked to the function of these viral RNA elements. SHIFTR enables the systematic mapping of region-resolved RNA interactomes for any RNA in any cell type and has the potential to revolutionize our understanding of transcriptomes and their regulation.

Bridging Smart Nanosystems with Clinically Relevant Models and Advanced Imaging for Precision Drug Delivery.

Intracellular delivery of nano-drug-carriers (NDC) to specific cells, diseased regions, or solid tumors has entered the era of precision medicine that requires systematic knowledge of nano-biological interactions from multidisciplinary perspectives. To this end, this review first provides an overview of membrane-disruption methods such as electroporation, sonoporation, photoporation, microfluidic delivery, and microinjection with the merits of high-throughput and enhanced efficiency for in vitro NDC delivery. The impact of NDC characteristics including particle size, shape, charge, hydrophobicity, and elasticity on cellular uptake are elaborated and several types of NDC systems aiming for hierarchical targeting and delivery in vivo are reviewed. Emerging in vitro or ex vivo human/animal-derived pathophysiological models are further explored and highly recommended for use in NDC studies since they might mimic in vivo delivery features and fill the translational gaps from animals to humans. The exploration of modern microscopy techniques for precise nanoparticle (NP) tracking at the cellular, organ, and organismal levels informs the tailored development of NDCs for in vivo application and clinical translation. Overall, the review integrates the latest insights into smart nanosystem engineering, physiological models, imaging-based validation tools, all directed towards enhancing the precise and efficient intracellular delivery of NDCs.

Interactions of plumbagin with five common antibiotics against Staphylococcus aureus in vitro.

Staphylococcus aureus is the main culprit, causing a variety of severe clinical infections. At the same time, clinics are also facing the severe situation of antibiotic resistance. Therefore, effective strategies to address this problem may include expanding the antimicrobial spectrum by exploring alternative sources of drugs or delaying the development of antibiotic resistance through combination therapy so that existing antibiotics can continue to be used. Plumbagin (PLU) is a phytochemical that exhibits antibacterial activity. In the present study, we investigated the in vitro antibacterial activity of PLU. We selected five antibiotics with different mechanisms and inhibitory activities against S. aureus to explore their interaction with the combination of PLU. The interaction of combinations was evaluated by the Bliss independent model and visualized through response surface analysis. PLU exhibited potent antibacterial activity, with half maximal inhibitory concentration (IC50) and minimum inhibitory concentration (MIC) values against S. aureus of 1.73 μg/mL and 4 μg/mL, respectively. Synergism was observed when PLU was combined with nitrofurantoin (NIT), ciprofloxacin (CPR), mecillinam (MEC), and chloramphenicol (CHL). The indifference of the trimethoprim (TMP)-PLU pairing was demonstrated across the entire dose-response matrix, but significant synergy was observed within a specific dose region. In addition, no antagonistic interactions were indicated. Overall, PLU is not only a promising antimicrobial agent but also has the potential to enhance the growth-inhibitory activity of some antibiotics against S. aureus, and the use of the interaction landscape, along with the dose-response matrix, for analyzing and quantifying combination results represents an improved approach to comprehending antibacterial combinations.

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

DECIPHERING THE BURDEN OF INFLAMMATORY BOWEL DISEASE LANDSCAPE (1990-2019) IN HIGH INCOME NORTH AMERICA: PROJECTIONS, TEMPORAL SHIFTS, AND A GLIMPSE BEYOND TO 2040

Abstract BACKGROUND In High-Income North America, IBD ranks as the 6th leading cause of disability and the 9th primary cause of mortality among all digestive diseases. The increasing prevalence in this region underscores the urgent need to address both the clinical and socio-economic challenges of managing this chronic condition in a developed setting. METHOD Utilizing Global Burden of DIsease tool, we estimated IBD prevalence, incidence, mortality, and Disability Adjusted Life Years (DALYs) of IBD in High-Income North America. Standardized statistical techniques facilitated comparisons by age, sex, year within this specific region. The DisMod-MR 2.1 tool was employed to estimate incidence and prevalence, while mortality rates were discerned using the Cause of Death Ensemble Model (CODEm). Additionally, we projected the deaths and Years of Life Lost (YLLs) up to 2040 using regression analysis. RESULTS From 1990 to 2019, the overall IBD prevalence in High-Income North America showed a decline from 1,100,006 (95%UI: 961,427-1,248,969) to 920,749 (859,032-982,587). However, deaths rose sharply from 2,442 (2,189-2,963) in 1990 to 6,313 (5,075-6,885) in 2019, marking an Annual Percentage Change (APC) surge of 159% (84-182). Meanwhile, DALYs grew by 17% (2-33). Age-Standardized Rates (ASR) indicate a reduction in incidence and DALYs rates by 30% and 22%, respectively, while mortality rates climbed by 42% (1-54). Country-specific trends show Canada and Greenland with incidence APC increases of 28% and 37%, respectively, while the U.S. saw a 12% decline. For mortality, the U.S. led with a 172% APC rise, followed by Greenland at 1,485% and Canada at 49%. In age-specific trends, children under five witnessed a 20% APC surge in incidence per 100,000 population, whereas the under-20 age bracket saw a 13% rise. Notably, those aged under 70 experienced an 89% jump in deaths from 1990-2019. CONCLUSION The marked shift in IBD prevalence and mortality in High-Income North America over three decades underscores the escalating challenge this disease presents to the healthcare landscape. The substantial increase in deaths, especially in countries like the U.S. and Greenland, combined with the age-specific trends, emphasizes the pressing need for targeted interventions and robust healthcare policies. Notably, in 2019, IBD was responsible for 4.43% of all deaths linked to digestive diseases and contributed to 6.13% of the total disabilities arising from digestive conditions. This data accentuates the profound and growing impact of IBD in the region and demands immediate, evidence-based action.

Evaluating noninvasive brain stimulation to treat overactive bladder in individuals with multiple sclerosis: a randomized controlled trial protocol

BackgroundMultiple Sclerosis (MS) is an often debilitating disease affecting the myelin sheath that encompasses neurons. It can be accompanied by a myriad of pathologies and adverse effects such as neurogenic lower urinary tract dysfunction (NLUTD). Current treatment modalities for resolving NLUTD focus mainly on alleviating symptoms while the source of the discomfort emanates from a disruption in brain to bladder neural circuitry. Here, we leverage functional magnetic resonance imaging (fMRI), repetitive transcranial magnetic stimulation (rTMS) protocols and the brains innate neural plasticity to aid in resolving overactive bladder (OAB) symptoms associated with NLUTD.MethodsBy employing an advanced neuro-navigation technique along with processed fMRI and diffusion tensor imaging data to help locate specific targets in each participant brain, we are able to deliver tailored neuromodulation protocols and affect either an excitatory (20 min @ 10 Hz, applied to the lateral and medial pre-frontal cortex) or inhibitory (20 min @ 1 Hz, applied to the pelvic supplemental motor area) signal on neural circuitry fundamental to the micturition cycle in humans to restore or reroute autonomic and sensorimotor activity between the brain and bladder. Through a regimen of questionnaires, bladder diaries, stimulation sessions and analysis, we aim to gauge rTMS effectiveness in women with clinically stable MS.DiscussionSome limitations do exist with this study. In targeting the MS population, the stochastic nature of MS in general highlights difficulties in recruiting enough participants with similar symptomology to make meaningful comparisons. As well, for this neuromodulatory approach to achieve some rate of success, there must be enough intact white matter in specific brain regions to receive effective stimulation. While we understand that our results will represent only a subset of the MS community, we are confident that we will accomplish our goal of increasing the quality of life for those burdened with MS and NLUTD.Trial registrationThis trial is registered at ClinicalTrials.gov (NCT06072703), posted on Oct 10, 2023.

The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower.

Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops' nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent of vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with seemingly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent of vigor, and highlight specific traits and genomic regions that could play a role in enhancing tolerance.

Correlative evaluation of the corrosion resilience and passivation properties of zinc and aluminum alloys in neutral chloride and acid-chloride solutions

Abstract Comparative analysis of the corrosion resilience and passivation characteristics of pure zinc (Zn) and aluminum (Al) alloys in neutral chloride and acid-chloride solutions at 0.5–3% NaCl concentrations was done with potentiodynamic polarization, potentiostatic evaluation, optical characterization, and open circuit potential measurement. Results show Al alloy was more resistant to general corrosion in both solutions with values ranging from 0.031 to 0.082 mm/year, and 0.037 to 0.389 mm/year compared to Zn alloys with values of 0.432–0.691 mm/year and 0.465–5.016 mm/year. Corrosion potential values of Zn alloy were significantly more electronegative than the values for Al alloy. The passivated region of the polarization plots for Al was thermodynamically unstable with visible current transients compared to that of Zn. Passivation of Al occurred at the early onset of anodic polarization in the neutral chloride solution. Zn alloy passivated at specific potentials, coupled with stable passivation behavior. The passivation range values of Al were generally greater than the values for Zn due to delayed stable pitting activity. Optical images for Al showed extensive localized degradation along specific regions and grain boundaries, whereas Zn morphology indicates general surface degradation. Open circuit potential plots indicate significant growth of Al2O3 oxide on Al coupled with active–passive transition behavior of the oxide. This contrasts the observation for Zn where the plot configuration indicates limited oxide formation and growth but significant thermodynamic stability.

Whole-brain mapping of increased manganese levels in welders and its association with exposure and motor function

Although manganese (Mn) is a trace metal essential for humans, chronic exposure to Mn can cause accumulation of this metal ion in the brain leading to an increased risk of neurological and neurobehavioral health effects. This is a concern for welders exposed to Mn through welding fumes. While brain Mn accumulation in occupational settings has mostly been reported in the basal ganglia, several imaging studies also revealed elevated Mn in other brain areas. Since Mn functions as a magnetic resonance imaging (MRI) T1 contrast agent, we developed a whole-brain MRI approach to map in vivo Mn deposition differences in the brains of non-exposed factory controls and exposed welders. This is a cross-sectional analysis of 23 non-exposed factory controls and 36 exposed full-time welders from the same truck manufacturer. We collected high-resolution 3D MRIs of brain anatomy and R1 relaxation maps to identify regional differences using voxel-based quantification (VBQ) and statistical parametric mapping. Furthermore, we investigated the associations between excess Mn deposition and neuropsychological and motor test performance. Our results indicate that: (1) Using whole-brain MRI relaxometry methods we can generate excess Mn deposition maps in vivo, (2) excess Mn accumulation due to occupational exposure occurs beyond the basal ganglia in cortical areas associated with motor and cognitive functions, (3) Mn likely diffuses along white matter tracts in the brain, and (4) Mn deposition in specific brain regions is associated with exposure (cerebellum and frontal cortex) and motor metrics (cerebellum and hippocampus).

Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning models based on structural magnetic resonance imaging

Objective: To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep learning and T1 high-resolution MRI, and try to reveal the specific regions where cirrhosis and HE accelerating brain aging. Methods: A cross-sectional study. A brain age prediction model based on the 3D full convolutional neural network was constructed through T1 high-resolution MRI data from 3 609 healthy individuals across eight global public datasets. The mean absolute error (MAE) between actual age and predicted brain age, Pearson correlation coefficient (r) and determination coefficient (R2) were calculated to evaluate the accuracy of the model's predictions. A test set (n=555) from the Human Connectome Project was used to assess the accuracy of the model. A total of 136 patients with cirrhosis were recruited from Tianjin First Central Hospital as the case group (79 patients with cirrhosis without HE and 57 patients with cirrhosis with HE), and 70 healthy individuals were recruited from the society as the healthy control group during the same period. Brain-predicted age difference (Brain-PAD), digital connection-A (NCT-A) and digital-symbol test (DST) scores of all subjects were calculated for all subjects to assess brain aging and cognitive function in the healthy control group, the cirrhosis without HE group, and the cirrhosis with HE group. The network occlusion sensitivity analysis method was employed to assess the importance of each brain region in predicting brain age. Results: As for the prediction model, in the training set, MAE=2.85, r=0.98, R2=0.96. In the test set, MAE=4.45, r=0.96, R2=0.92. In the local data set of the healthy control group, MAE=3.77, r=0.85, R2=0.73. The time of NCT-A in both cirrhosis groups was longer than healthy control group, while the DST scores were lower than healthy control group, and the differences were statistically significant (both P<0.001); the Brain-PAD of healthy control group was (0.8±4.5) years, the Brain-PAD of no-HE group was (6.9±8.1) years, and the HE group was (10.2±7.7) years. The differences between the three groups were statistically significant (P<0.001), and the differences between any two groups were statistically significant (all P<0.05). The importance ratio of visual network in predicting brain age increased in cirrhosis patients, and the HE group was higher than no-HE group. Conclusions: In patients with cirrhosis, the cognitive function is reduced, brain aging is accelerated, and these changes are more obvious in patients with HE. The importance differences of each brain network in predicting brain aging provide a new direction for identifying the specific regions where cirrhosis and HE accelerate brain aging.