Browse Articles

The relationship between triglyceride-glucose index and serum neurofilament light chain: Findings from NHANES 2013–2014

Background The Triglyceride-Glucose (TyG) index has become a reliable indicator for evaluating the level of insulin resistance, a pivotal factor in both metabolic and neurodegenerative disorders. Serum neurofilament light chain (sNfL) serves as a responsive biomarker for detecting neuroaxonal injury. Despite this, the interplay between the TyG index and sNfL levels has not been sufficiently investigated. The aim of this research is to scrutinize the correlation between TyG index and sNfL levels across a substantial, population-based cohort. Methods Our study involved an examination of the dataset from the 2013–2014 round of the National Health and Nutrition Examination Survey (NHANES), encompassing a total of 2029 enrolled subjects. The TyG index was calculated using fasting triglycerides and glucose levels. Multivariable linear regression models were conducted to evaluate the relationship between TyG index and sNfL levels, adjusting for potential confounders such as age, sex, race, BMI, hypertension, stroke, congestive heart failure, alcohol consumption and NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio). Nonlinear associations were investigated using regression models based on restricted cubic splines (RCS). Results Both the unadjusted and adjusted regression analyses revealed a substantial positive correlation between the TyG index and ln-sNfL levels. After accounting for all covariates, each unit increase in the TyG index was associated with a 0.15 (95% CI: 0.02–0.27, p =  0.04) increase in ln-sNfL levels. RCS analysis revealed a nonlinear relationship, with a threshold around a TyG index value of 9.63, beyond which ln-sNfL levels increased more rapidly. The association was consistent across subgroups. Conclusion Our study links higher TyG index with increased sNfL levels, indicating insulin resistance’s role in neuroaxonal injury. The nonlinear relationship implies a heightened risk of neurodegeneration beyond a certain insulin resistance threshold. This underscores the need for early metabolic interventions to prevent neurodegenerative processes.

Skin wound healing measured remotely through molecular flux

High-fidelity sub-microsecond single-shot electron spin readout above 3.5 K

Understanding street protests: from a mathematical model to protest management

Street protests have been a common feature of human society for many centuries. They often act as a driver of social changes but they may also disrupt everyday life and lead to considerable economic losses. Understanding of factors that may affect the duration of street protests and the number of participants is a problem of pivotal importance. Mathematical modelling is an efficient research approach to study this problem. Here we present a novel modelling framework that takes into account heterogeneity of protesters behaviour and the effect of policing. Using the 2018–2019 Yellow Vest Movement in France as a case study, we show that our model is in a very good agreement with data. We also show that a moderate increase in the efficiency of police actions on particular days may have a significant effect on protest’s intensity and duration. Our findings open a possibility for a more efficient protests management.

Equilibration of topological defects near the deconfined quantum multicritical point

Comparative Impact of Integrated Palliative Care vs. Standard Care on the Quality of Life in Cancer Patients: A Global Systematic Review and Meta-Analysis of Randomized Controlled Trials

IntroductionCancer is a leading cause of global morbidity and mortality, significantly impairing patients’ quality of life (QoL). Integrated Palliative Care (IPC) has been proposed as a holistic approach to enhance quality of life by addressing patients’ physical, emotional, and psychosocial needs. While some studies suggest Integrated Palliative Care improves quality of life more than standard care, the evidence remains inconclusive. This systematic review and meta-analysis aim to evaluate the comparative impact of Integrated Palliative Care versus standard care on the quality of life in cancer patients. MethodsA comprehensive search of databases including PubMed, Cochrane Library, and Embase was conducted. We selected randomized controlled trials (RCTs) comparing Integrated Palliative Care and standard care for cancer patients, focusing on the quality of life as measured by validated tools such as the EORTC QLQ-C30 and FACT-G. Data were pooled using a random-effects model to account for study heterogeneity. Subgroup and sensitivity analyses were also performed. ResultsNine randomized controlled trials involving 1,794 patients met the inclusion criteria. Meta-analysis showed that Integrated Palliative Care significantly improved quality of life compared to standard care (SMD = 3.25; 95% CI: 1.20–5.30; p < 0.001). Studies conducted in Asia showed the highest standardized mean difference (SMD = 6.15; 95% CI: 3.07–9.23; p < 0.001), followed closely by studies from Africa (SMD = 6.0; 95% CI: 5.13–6.87; p < 0.001), compared to those from other regions. Similarly, research focusing on lung cancer patients showed the greatest standardized mean difference of (SMD = 6.15; 95% CI: 3.07–9.23; p < 0.001) relative to other cancer types. Furthermore, studies involving newly diagnosed cancer patients recorded the highest standardized mean difference of (SMD = 5.69; 95% CI: 4.57–6.80; p < 0.001). ConclusionIntegrated Palliative Care significantly enhances the quality of life in cancer patients compared to standard care. These findings support integrating Integrated Palliative Care into oncology practices to provide comprehensive, patient-centered care that addresses both physical and emotional needs. Further research should explore long-term benefits across diverse populations.

Current pattern of antibiotic resistance and molecular characterization of virulence genes in Klebsiella pneumoniae obtained from urinary tract infection (UTIs) patients, Peshawar

The current study investigates the prevalence of virulence genes obtained from clinical isolates of multidrug-resistant (MDR) Klebsiella pneumoniae at Khyber Teaching Hospital Peshawar, from October 2021 to January 2023. Upon proper consent, clinical samples of suspected UTIs patients were collected and inoculated on the nutrients agar media, McConkey agar media, and Cysteine Lysine Electrolyte Deficient (CLED) agar media followed by incubation at 37°C for 24 hrs. The phenotypic and genotypic identification were employed for the bacterial isolates. The phenotypic identification includes gram staining followed by the Analytical Profile Index (API 20E). A total of 215 (3.85%) positive isolates were found with the highest prevalence observed among the female patients (4.35%) followed by male (3.26%). The highest prevalence, constituting 52.55% (n = 113), was detected in the age group of 21-40 years, followed by 31.62% (n = 68) in the 41-60 age group. Additionally, 10.23% (n = 22), 3.25% (n = 7), and 2.32% (n = 5) of cases were identified in the age groups of 01-10 years, 11-20 years, and above 60 years, respectively. Among the total positive samples, 44.65% (n = 96) were collected from the Outpatient department (OPD), while inpatient department (IPD) cases contributed 55.35% (n = 119). The antibiotic susceptibility profile of K. pneumoniae showed significant resistance to trimethoprim/Sulfamethoxazole (93%) and Colistin (79.07%). Tigecycline emerged as the most effective antibiotic with a sensitivity rate of 90%, along with Cefepime at the same level. Minimum Inhibitory Concentration (MIC) values indicated higher resistance for CTX, MEM, CN, AK, DO, CIP, and SXT in K. pneumoniae-causing UTIs from KTH, Peshawar. Molecular characterization of virulence genes reveals the highest prevalence of fimH (80%) followed by SAT (65%), papEF (49%), afa (29%), and VAT (16%). The sequencing data of the virulence genes reveals mutations in fimH and papEF, while sat, afa and vat virulence genes showed no mutations. The Chi-square test indicated a significant association between the types of bacteria, supporting our null hypothesis with a significance level of p ≤  0.05. The current study’s finding is to evaluate the rise of antibiotic resistance in hospital settings, which highly demands the focus of health authorities and clinicians to manage the burden of the disease effectively.

Prevalence and factors associated with adverse drug reactions among patients on highly active antiretroviral therapy at a tertiary hospital in south western Uganda: A cross-sectional study

Background HIV/AIDS remains a global health challenge, with significant prevalence in sub-Saharan Africa. Highly active antiretroviral therapy (HAART) is the mainstay treatment for HIV, and the number of people living with HIV (PLWHIV) on HAART has considerably increased worldwide. The use of HAART has led to improved patient outcomes; however, it is associated with adverse drug reactions (ADRs) and drug-drug interactions (DDIs), which pose serious concerns in the management of patients with HIV. The aim of the study was to determine the prevalence and factors associated with ADRs among patients on HAART. Methodology This was a hospital-based cross-sectional study carried out among 312 HIV patients on HAART attending HIV clinics at Mbarara Regional Hospital. Data was collected using an interviewer-administered, semi-structured questionnaire and a review of patient charts. ADRs were assessed for causality and categorized using Naranjo ADR assessment scale into probable, possible and definite, for severity using the modified Hartwig and Siegel criteria into mild, moderate and Severe, and for preventability using Schumock and Thornton criteria into definite, probable and non-preventable. Lexicomp® Drug Interaction Checker software was used to identify and rate clinically significant drug-drug interactions. The prevalence of ADRs and potential DDI was analyzed using descriptive statistics while logistic regression analysis was used to establish the association of variables. Results 312 patients were interviewed and their records reviewed. The prevalence of ADRs during this study was 76.0%. On assessment, 78.3% of the ADRs were mild and 76.6% of ADRs were definitely preventable. CD4 count below 200 cells/mm3 (AOR = 1.00, 95% CI: 1.00–1.02; p value = 0.04), primary education level (AOR = 3.27, 95% CI: 1.34–7.95; p value = 0.009), and secondary education level (AOR = 3.64, 95% CI: 1.39–9.52; p value = 0.009) were identified as independent risk factors. Patients who experienced a significant DDI were 5.66 times more likely to experience an ADR (p value = 0.02, 95% CI: 1.32–24.18). Conclusion There is a high prevalence of adverse drug reactions among patients with HIV on HAART. Low CD4 count and lower education levels are risk factors for ADRs in this population; therefore, tailored interventions to these subgroups should be implemented for early ADR identification and management. Significant drug-drug interactions are highly associated with the occurrence of ADRs among HIV patients on HAART, which calls for intensified pharmacovigilance and pharmaceutical care in this population.

An integrated large-scale photonic accelerator with ultralow latency

A framework for visualizing and describing city image promotion short video data based on microcube model

Background The rapid development of media technology and media environment provides rich resources and convenient ways to shape the image of the city. Short video has become an important help to shape the image of the city and build the city brand. How to use short video to shape the image of the city is the key link of urban construction. This study focuses on six primary variables for analyzing city image short videos: unexpected events, emotional resonance, scene transition, elemental amplification, element interaction, and screen style. These variables were selected based on their demonstrated impact on short video engagement and dissemination efficiency. How In order to realize comprehensive analysis of short video content, This study collected 20,668 video screenshots from the Douyin platform as data samples. Data collection spanned July 2019 to December 2019, and analysis was conducted using the Python programming language with the Pandas and Matplotlib libraries for data processing and visualization. Objective To reveal the relationship between video content features and popularity by quantitative and visual methods, and to provide reference for optimizing urban brand promotion strategies. Conclusion (1) The microcube model shows strong flexibility and applicability in short video content analysis, and can help reveal the complex relationship between short video content characteristics and communication effect. (2) Unexpected events and emotional resonance are key factors in the attractiveness and communication effect of short videos. Reasonable design of scene switching and element interaction significantly enhances the visual impact of short videos.

Comparative transcriptome analysis of Labeo calbasu (Hamilton, 1822) from polluted and non-polluted rivers in India

Labeo calbasu (L. calbasu) is an important detrivore fish in an ecosystem. So, the present transcriptome study was undertaken in relation to polluted and non-polluted water sources from a natural perennial river system. The Illumina NovaSeq 6000 platform was used to perform transcriptome analysis on liver samples of L. calbasu that were collected from the Ganga and Yamuna rivers. From 8744 differentially expressed genes (DEGs), 2538 were upregulated, and 6206 were downregulated in response to pollution stress. Biologic process (BP), cellular component (CC), molecular function (MF), and Gene Ontology (GO) demonstrated that relevant genes were associated with peptide metabolic process, cytosol, RNA binding, etc. In the Kyoto Encyclopedia of Genes and Genome (KEGG) analysis, ribosomal and metabolic pathways were more important due to the high False discovery rate (FDR) and the involvement of many genes. Transcripts of uncertain coding potential (TUCP) and various RNAs like mRNAs and long noncoding RNAs (lncRNAs) orchestrate fish cellular responses to environmental stressors in polluted waters, where aquatic ecosystems are threatened. FGG mRNA is co-expressed in both up and down-regulation in the liver of L. calbasu. In conclusion, L. calbasu collected from the Yamuna River have highly pollution-induced ribosomal pathways involving genes like Rpl19, rpl23Ae, rps2e, rps10e, rps15e, and rps7e, etc, which is important for pollution biomarker study. RANBP2 and egr1 lncRNA are the most significantly interlinked with ndc1 and fosab lncRNA.

An intelligent stochastic optimization approach for air cargo order allocation under carbon emission constraints

In air cargo transportation, effective order allocation is crucial for improving the efficiency of business operations and reducing environmental impact. In this paper, we study a high-dimensional stochastic order allocation problem that assigns uncertain orders to different types of aircraft for transportation. Considering the carbon emission and the uncertainty of customer order arrivals in the actual transportation environment, a stochastic optimization model considering the cost of carbon emission is established with the objective of maximizing the expected profit from order transportation. A new intelligent optimization method is introduced for addressing the order assignment problem under carbon emission constraints by combining the improved adaptive large-scale neighborhood search algorithm with the scenario generation technique. The method finds the optimal solution through an improved adaptive large-scale neighborhood search algorithm and uses a scenario generation technique to generate the scenarios required for evaluating candidate solutions to the high-dimensional stochastic optimization problem. Experimental results show that this method surpasses the compared optimization methods regarding both optimization capability and optimization efficiency.

Convergent vocal representations in parrot and human forebrain motor networks

Correction: Evaluation of an autonomous acoustic surveying technique for grassland bird communities in Nebraska

A non-contact wearable device for monitoring epidermal molecular flux

A simulation-based framework for modeling and prediction of personalized blood pressure trajectories in hypertensive patients after antihypertensive treatment

Hypertension, a leading global cause of death, poses challenges in stabilizing blood pressure within target values despite various therapeutic options, often necessitating multiple therapy adjustments and delayed impact assessments. Recently, the first wrist-based wearable blood pressure measurement devices were introduced which allow for a continuous assessment of blood pressure trajectories. This enables the development of statistical methodology for prediction of saturated steady-state of blood pressure under treatment—and thus allowing physicians to adjust the therapy earlier. As a prerequisite for the evaluation of such models and algorithms, it is necessary to simulate reliable and realistic hypothetical patient trajectories under treatment with antihypertensive medication. In this paper, we propose a simulation framework for blood pressure profiles through Pharmacokinetic-Pharmacodynamic modeling, which incorporates individual daily rhythms, patient characteristics, and medication effects. We also propose and evaluate two models for steady-state prediction under antihypertensive therapy, a Gaussian process and a non-linear mixed effect model. When only one day of measurements is available, the Gaussian process is preferred, but in real-world situations with more data, the non-linear mixed effect model is favored. It effectively reduces RMSE and bias in noisy data, outperforming the Gaussian process regardless of sample size.

NEURD offers automated proofreading and feature extraction for connectomics

Abstract We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution 1,2 . Dense reconstruction of cellular compartments in these electron microscopy volumes has been enabled by recent advances in machine learning 3–6 . Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post hoc proofreading is still required to generate large connectomes that are free of merge and split errors. The elaborate 3D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Here, building on existing open source software for mesh manipulation, we present Neural Decomposition (NEURD), a software package that decomposes meshed neurons into compact and extensively annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state-of-the-art automated proofreading of merge errors, cell classification, spine detection, axonal-dendritic proximities and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers.

Assessing the cardioprotective effects of exercise in APOE mouse models using deep learning and photon-counting micro-CT

Background The allelic variations of the apolipoprotein E (APOE) gene play a critical role in regulating lipid metabolism and significantly impact cardiovascular disease risk (CVD). This study aimed to evaluate the impact of exercise on cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT) and deep learning-based segmentation. Methods A total of 140 mice were grouped based on APOE genotype (APOE2, APOE3, APOE4), sex, and exercise regimen. All mice were maintained on a controlled diet to isolate the effects of exercise. Low dose cardiac photon counting micro-CT imaging with intrinsic gating was performed using a custom-built micro-PCCT system and data was reconstructed with an iterative algorithm incorporating both temporal and spectral dimensions. A liposomal-iodine nanoparticle contrast agent was intravenously administered to uniformly opacify cardiovascular structures. Cardiac structures were segmented using a 3D U-Net deep learning model that was trained and validated on manually labeled data. Statistical analyses, including ANOVA, post-hoc analysis, and stratified group comparisons, were used to assess the effects of genotype, sex, and exercise on key cardiac metrics, including ejection fraction and cardiac index. Results The PCCT imaging pipeline provided high-resolution images with enhanced contrast between blood compartment and myocardium allowing for precise segmentation of cardiac features. Deep learning-based segmentation achieved high accuracy with an average Dice coefficient of 0.85. Exercise significantly improved cardiac performance, with ejection fraction increasing by up to 18% and cardiac index by 46% in exercised males, who generally benefited more from exercise. Females, particularly those with the APOE4 genotype, also showed improvements, with a 31% higher ejection fraction in exercised versus non-exercised mice. Stratified analyses confirmed that both sexes benefited from exercise, with males showing larger effect sizes. APOE3 and APOE4 genotypes derived the greatest benefit, while APOE2 mice showed no significant improvement. Conclusions This study demonstrates the utility of PCCT combined with deep learning segmentation in assessing the cardioprotective effects of exercise in APOE mouse models. These findings highlight the importance of genotype-specific approaches in understanding and potentially mitigating the impact of CVD through lifestyle interventions such as exercise.

Novel brain biomarkers of obesity in young adult women based on statistical measurements of white matter tracts

Objective Novel brain biomarkers of obesity were sought by studying statistical measurements on fractional anisotropy (FA) images of different white matter (WM) tracts from young adult women. Methods Tract measurements were chosen that showed differences between two groups (normal weight and overweight/obese) and that were correlated with BMI. From these measurements, a simple and novel process was applied to select those that would allow the creation of models to quantify and classify the state of obesity of individuals. The biomarkers were created from the tract measurements used in the models. Results Positive correlations were found between WM integrity and BMI, mainly in tracts involved in motor functions. From these results, two models were built to quantify and classify obesity status, whose regression coefficients formed the novel proposed obesity associated brain biomarkers. Conclusion A process for the selection of tract measurements was proposed, such models were built to determine the obesity status of subjects individually. From these models, novel brain biomarkers associated with obesity were created. These results generate new knowledge in the field, intended to be used in the future in the clinical environment as a prevention and treatment tool for brain changes associated with obesity. Significance After studying young adult women, results opposed some of the previous results reported in literature. These consisted of positive correlations between WM integrity and obesity mainly in tracts involved in motor functions. Novel brain biomarkers of obesity were also proposed, formed by the regression coefficients involved in precise models of quantification and classification of obesity status. All this allows the generation of new knowledge and its probable subsequent clinical application.

Detecting newly installed bat boxes: Bats’ prior familiarity with artificial roosts may play a bigger role than improved echo-reflective properties

Habitat loss in Europe severely affects bats, particularly tree-roosting species, due to the decreasing availability of tree cavities. One common conservation strategy is the installation of artificial roost boxes. However, the occupation of newly installed roost boxes can take up to several years, and the underlying mechanisms for successful roost detection in bats are still poorly understood. This study proposes enhancing the detectability of roost boxes to echolocating bats by incorporating hollow hemispheres that provide highly conspicuous echoes. The hemispheres strongly reflect the echolocation calls of passing bats and are thus well detectable over a broad range of angles. We hypothesized that roost boxes equipped with these hemispheres would attract more bats and exhibit greater bat activity than standard, unmodified boxes. To evaluate this, we placed 30 modified boxes and 30 unmodified boxes across three forest areas in Northern Germany, each differing in proximity to known bat hibernation sites and the prior presence of artificial roosts. We monitored bat activity by measuring light beam interruptions at each box and found that the activity of bats at the boxes varied considerably. Our findings indicate that, contrary to our hypothesis, bat activity was more strongly influenced by their prior experience with artificial roosts than by the increased detectability provided by hollow hemispheres. Furthermore, our study revealed that light beam interruptions indicated bat presence at the boxes earlier than visual checks for bats or feces, showcasing the benefits of non-invasive monitoring techniques. Conservation efforts are complex, and these results imply that for effective bat conservation, increasing bats’ familiarity with artificial roosts may be more important than merely enhancing the detectability of these structures.