Browse Articles

The relationship between lactate/albumin ratio and prognosis in children with acute kidney injury

Acute kidney injury (AKI) is a prevalent and critical complication in pediatric patients, severely influencing both survival outcomes and quality of life. This research seeks to evaluate the prognostic value of the lactate/albumin ratio (LAR) in predicting short-term outcomes for children diagnosed with AKI. A retrospective analysis was conducted using data from the Pediatric Intensive Care database, covering the period from 2010 to 2018. The relationship between the LAR and in-hospital mortality was explored using smoothing curve fitting, multivariate logistic regression, and Kaplan-Meier survival analysis. Receiver operating characteristic (ROC) analysis was performed to evaluate the predictive capability of LAR for in-hospital mortality. A total of 631 pediatric patients with AKI were included in this study. Smoothing curve fitting revealed a nearly linear positive association between the LAR and in-hospital mortality. Multivariate logistic regression analysis identified LAR as an independent predictor of in-hospital mortality (OR 2.58, 95% CI 1.85–3.59). Kaplan-Meier survival analysis showed that patients with a higher LAR had a significantly greater 30-day mortality rate compared to those with lower LAR values. ROC curve analysis demonstrated an area under the curve of 0.85 (95% CI 0.80–0.90). At the optimal cutoff value of 0.80, the sensitivity was 80.42%, specificity was 77.97%, and accuracy was 80.19%. These results suggest that LAR is a promising prognostic indicator of mortality in pediatric patients with AKI, and could serve as an early indicator of risk stratification.

The unexplored mechanism of antitumoral effect of pirfenidone in melanoma cells

Dairy cow performance is associated with longitudinal microRNA profiles

Modern high producing dairy cows are still affected by poor fertility and disease, despite improvements achieved through genetic selection programs. Additional biomarkers of health and performance traits in cattle could enhance animal welfare and profitability by allowing farmers to cull animals before problems occur. We performed pilot investigations of plasma microRNA (miRNA) profiles during early life as potential biomarkers associated with future performance in dairy cows. The latter included survival to two years of age, age at first calving, yield of milk, fat and protein, mastitis and lameness traits, conception rate, number of services per conception, and calving interval. Using qPCR, we obtained longitudinal measurements and ratios involving nine miRNAs (miR-126-3p, miR-127, miR-142-5p, miR-154b, miR-27b, miR-30c-5p, miR-34a, miR-363, miR-425-3p) in plasma samples from three age groups: calves (<1 month), heifers (14–23 months), and first lactation cows (29–35 months). Changes in miR-126-3p from calf to first lactation cow were associated with first lactation milk yield and second lactation milk somatic cell count (an udder health indicator). Moreover, the miR-127 to miR-30c-5p ratio in cows was associated with milk fat and protein yield in the first two lactations, whereas miR-142-5p levels and several miRNA ratios involving this miRNA, were associated with second calving interval (a cow fertility trait). Our results identified novel early life biomarkers that warrant further investigation to determine whether they may predict dairy cattle performance.

Medication adherence among children with heart failure at the University of Gondar Comprehensive Specialized Hospital Gondar Northwest Ethiopia

An optimized stacking-based TinyML model for attack detection in IoT networks

With the expansion of Internet of Things (IoT) devices, security is an important issue as attacks are constantly gaining more complex. Traditional attack detection methods in IoT systems have difficulty being able to process real-time and access limitations. To address these challenges, a stacking-based Tiny Machine Learning (TinyML) models has been proposed for attack detection in IoT networks. This ensures detection efficiently and without additional computational overhead. The experiments have been conducted using the publicly available ToN-IoT dataset, comprising a total of 461,008 labeled instances with 10 types of attacks categories. Some amount of data preprocessing has been done applying methods such as label encoding, feature selection, and data standardization. A stacking ensemble learning technique uses multiple models combining lightweight Decision Tree (DT) and small Neural Network (NN) to aggregate power of the system and generalize. The performance of the model is evaluated by accuracy, precision, recall, F1-score, specificity, and false positive rate (FPR). Experimental results demonstrate that the stacked TinyML model is superior to traditional ML methods in terms of efficiency and detection performance, and its accuracy rate is 99.98%. It has an average inference latency of 0.12 ms and an estimated power consumption of 0.01 mW.

The aryl hydrocarbon receptor: a rehabilitated target for therapeutic immune modulation

Association between autonomic dysfunction and arterial stiffness in hypertensive patients

Leaf disease detection and classification in food crops with efficient feature dimensionality reduction

Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. Bidirectional LSTM (BiLSTM) networks store data in both forward and backward directions, while traditional Long Short-Term Memory (LSTM) networks handle data in a specific order. This study proposes a computer vision system that integrates BiLSTM with CNN features for image categorization tasks. The system effectively reduces feature dimensionality using learned features, addressing the high dimensionality problem in leaf image data and enabling early, accurate disease identification. Utilizing CNNs for feature extraction and BiLSTM networks for temporal dependency capture, the method incorporates label information as constraints, leading to more discriminative features for disease classification. Tested on datasets of pepper and maize leaf images, the method achieved a 99.37% classification accuracy, outperforming existing dimensionality reduction techniques. This cost-effective approach can be integrated into precision agriculture systems, facilitating automated disease detection and monitoring, thereby enhancing crop yields and promoting sustainable farming practices. The proposed Efficient Labelled Feature Dimensionality Reduction utilizing CNN-BiLSTM (ELFDR-LDC-CNN-BiLSTM) model is compared to current models to show its effectiveness in reducing extracted features for leaf detection and classification tasks.

Transparent brain tumor detection using DenseNet169 and LIME

Abstract A crucial area of research in the field of medical imaging is that of brain tumor classification, which greatly aids diagnosis and facilitates treatment planning. This paper proposes DenseNet169-LIME-TumorNet, a model based on deep learning and an integrated combination of DenseNet169 with LIME to boost the performance of brain tumor classification and its interpretability. The model was trained and evaluated on the publicly available Brain Tumor MRI Dataset containing 2,870 images spanning three tumor types. Dense169-LIME-TumorNet achieves a classification accuracy of 98.78%, outperforming widely used architectures including Inception V3, ResNet50, MobileNet V2, EfficientNet variants, and other DenseNet configurations. The integration of LIME provides visual explanations that enhance transparency and reliability in clinical decision-making. Furthermore, the model demonstrates minimal computational overhead, enabling faster inference and deployment in resource-constrained clinical environments, thereby highlighting its practical utility for real-time diagnostic support. Work in the future should run towards creating generalization through the adoption of a multi-modal learning approach, hybrid deep learning development, and real-time application development for AI-assisted diagnosis.

Signed log-likelihood ratio test for the scale parameter of Poisson Inverse Weibull distribution with the development of PIW4LIFETIME web application

The three-parameter Poisson Inverse Weibull (PIW) distribution offers enhanced flexibility for modeling system failure times. This study introduces the signed log-likelihood ratio test (SLRT) for hypothesis testing of the scale parameter (ω) in the PIW distribution and compares its performance with the test based on the asymptotic normality of maximum likelihood estimators (ANMLE). Simulation studies show that the SLRT consistently maintains type I error rates within the acceptable range of 0.04 to 0.06 at a significance level of 0.05, satisfying Cochran’s criterion across various sample sizes and parameter configurations. In contrast, the ANMLE method tends to be conservative, often underestimating the nominal significance level. In terms of empirical power, the SLRT outperforms the ANMLE, particularly in small-sample scenarios (n = 10, 15), and maintains superior power across all tested configurations. For example, when testing H0:ω=0.25 against H1:ω=0.5 with β=0.5,λ=1, and n = 10, the SLRT achieves a power of 0.6621, compared to 0.4181 for the ANMLE, demonstrating the SLRT’s robustness and reliability in limited-data. Moreover, the ANMLE generally exhibits low power in most cases, indicating reduced sensitivity to detecting true effects in small samples. However, with medium and large sample sizes (n = 30, 50, 80 and 100), the power of the ANMLE begins to approach that of the SLRT. Despite this, the ANMLE never outperforms the SLRT, highlighting a fundamental limitation of this method. Additionally, varying the shape parameter β while fixing λ=1 showed a negligible impact on power, further confirming the robustness of the SLRT. Sensitivity analyses also validate the reliability of the SLRT under extreme values of ω and across different sample sizes. To support practical application, the PIW4LIFETIME web application (accessible at https://jularatchumnaul.shinyapps.io/PIW4LIFETIME/) was developed to enable users to assess whether data fit the PIW distribution, estimate model parameters using maximum likelihood, and perform two-sided test for the scale parameter using SLRT. The performance of the proposed method and the PIW4LIFETIME web application was demonstrated through a real-world example.

Village and age based precision mapping of schistosomiasis and soil-transmitted helminths in Chevakadzi ward of Shamva district in Zimbabwe

Memory distrust and imagination inflation: A registered report

Imagination inflation occurs when the subjective confidence of a person that an event has occurred increases after they imagine it occurring. In this project, our primary aim was to test whether memory distrust is related to the imagination inflation effect in people who are aware of the discrepancies between their own memories and what they have imagined. Our secondary purpose was to investigate whether the influence of memory distrust on imagination inflation is moderated by traits that are described as disengagement from reality and to test whether memory distrust mediates the relationship between self-esteem and imagination inflation. In a three-step procedure, participants (N = 279) assessed their confidence that a list of childhood events occurred to them; then they imagined three of these events and reassessed their confidence. Half of the participants were subjected to a memory distrust induction procedure. To sensitize participants to discrepancies between actual childhood memories and imagined ones, some of them received cues about the source and/or perspective of the imagined events. Memory distrust as an individual trait was found to be unrelated to the imagination inflation effect. Furthermore, the expected effect of memory distrust as a state on susceptibility to the imagination inflation effect in groups sensitized to discrepancies was not confirmed. Therefore, it seems that people who we consider to be distrustful of their memory are no more susceptible to this type of memory distortion than memory trusters.

Study on an interpretable prediction model for pile bearing capacity based on SHAP and BP neural networks

Prevalence and factors associated with probable anxiety disorders among elderly persons living with HIV at Mulago ISS clinic: A cross-sectional study

Introduction Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major public health concern globally. Due to advancements in Anti-Retroviral Treatment (ART) therapy, more people with HIV are living longer with about 1.4 million infected people in Uganda. Anxiety disorders are often unrecognized and undetected in older persons living with HIV (PLWH) yet they impair an elderly person’s physical health and decrease the ability to perform daily activities. Objective To determine the prevalence and factors associated with probable anxiety disorders among elderly PLWH at Mulago Immune Suppression Syndrome (ISS) clinic. Methods A cross-sectional study was conducted at Mulago ISS clinic among 273 systematically selected participants living with HIV/AIDS on antiretroviral therapy for at least 6 months between April and May 2024. Interviews were conducted using the Generalized Anxiety Disorder 7-item (GAD-7) screening tool to help identify individuals who may be at risk for anxiety disorders and structured questionnaires for socio-demographics, and psychological factors. Drug and clinical factors data were extracted from records, entered into Epidata, and later to STATA version 17 for analysis. Prevalence was reported as a percentage and modified Poisson regression analysis was used to determine the factors associated with anxiety disorders. Results We enrolled 273 participants with a median age (Interquartile range) was 56 (52, 61.5) years. 54.9% were females, 56.8% didn’t have a partner and 53.8% were employed. The prevalence of probable anxiety disorders was 16.8% (95% CI 12.5–21.6). Employment status (aPR- 2.113, 95% CI 1.252–3.567), family history of mental health disorder (aPR-2.041, 95% CI 1.228–3.394), stigma (aPR-2.564, 95% CI 1.544–4.257) and family support (aPR-2.169, 95% CI 1.272–3.699) were significantly associated with having probable anxiety disorders. Conclusion One in every six elderly persons living with HIV may have a probable anxiety disorder. Being unemployed, having a family history of mental health disorders, having stigma and having inadequate family support were significantly associated with having a probable anxiety disorder. Healthcare workers should provide comprehensive anxiety screening and patient-centered care for elderly persons with HIV. At the same time, the government develops financial empowerment strategies and supports mental health through family groups, and public campaigns to reduce HIV stigma and educate families on effective support.

Chemotherapy‐induced taste changes affect nutrition, quality of life

Biomarker-guided decision making in clinical drug development for neurodegenerative disorders

Trait resilience protects against social anxiety in college students through emotion regulation and coping strategies

Research on high-quality development path of strategic emerging enterprises enabled by innovation

Promoting the high-quality development of strategic emerging enterprises is an inevitable approach to building a strong nation, and innovation serves as the critical engine providing core momentum for such development. From the perspective of complex causal effect analysis, this article selects 176 A-share listed companies in strategic emerging industries from 2012 to 2023 as samples and employs a combination of methods including NCA, multi-period fsQCA, and empirical regression analysis to distill practical pathways for innovation-driven high-quality development in these enterprises. The research findings are summarized as follows: (1) There is no single necessary condition for achieving high-quality development in strategic emerging enterprises; rather, it is the result of the synergistic interaction among technological innovation, talent innovation, and policy innovation. (2) Technological innovation has consistently played a pivotal role across all periods, with R&D investment identified as a key factor driving high-quality development. Other contributing factors also exhibit heterogeneous effects within the configurations of each period. (3) Four distinct configuration paths for high-quality development emerge across the three periods: the technology-dominant type, the “technology and talent” dual-driven type, the “technology and policy” dual-driven type, and the comprehensive innovation type. This study leverages complex causal effect analysis to offer scientific insights to relevant policymakers and enterprise managers, thereby facilitating the high-quality development of strategic emerging enterprises.

Is active surveillance an alternative to surgery for some patients with esophageal cancer?

Short-term plasticity influences episodic memory recall: an interplay of synaptic traces in a spiking neural network model

Abstract We investigated the interaction of episodic memory processes with the short-term dynamics of recency effects. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats. In the experimental task, rats were presented with odor pairs in two arenas serving as old or new contexts for specific odor items. Rats were rewarded for selecting the odor that was new to the current context. These new-in-context odor items were deliberately presented with higher recency relative to old-in-context items, so that episodic memory was put in conflict with a short-term recency effect. To study our hypothesis about the major role of synaptic interplay of plasticity phenomena on different time-scales in explaining rats’ performance in such episodic memory tasks, we built a computational spiking neural network model consisting of two reciprocally connected networks that stored contextual and odor information as stable distributed memory patterns. We simulated the experimental task resulting in a dynamic context-item coupling between the two networks by means of Bayesian–Hebbian plasticity with eligibility traces to account for reward-based learning. We first reproduced quantitatively and explained mechanistically the findings of the experimental study, and then to further differentiate the impact of short-term plasticity we simulated an alternative task with old-in-context items presented with higher recency, thus synergistically confounding episodic memory with effects of recency. Our model predicted that higher recency of old-in-context items enhances episodic memory by boosting the activations of old-in-context items. We argue that the model offers a computational framework for studying behavioral implications of the synaptic underpinning of different memory effects in experimental episodic memory paradigms.