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Genetic and computational analysis of AKR1C4 gene rs17134592 polymorphism in breast cancer among the Bangladeshi population

An integrated analysis of miRNA and mRNA expressions in soybean response to boron stress

Boron (B) fertilization significantly increased soybean seed yields and is an indispensable micronutrient in soybean growth. However, it is difficult to absorb in soil, which can seriously affect soybean growth. In this study, soybeans were planted under B deficiency and B sufficiency conditions, then physiological and biochemical indicators of soybean seedlings were measured. The results indicated that the antioxidant enzyme activity (SOD, POD, CAT, APX) and malondialdehyde content increased considerably compared with their controls in the leaves and roots, respectively. Among these, the difference in the roots was more significant than in the leaves, suggesting that the root system was more sensitive. Therefore, soybean roots under B stress for 12 hours and 8 days were selected to construct the miRNA library for sequencing. In the three comparison groups, 55 miRNAs were differentially expressed in response to B stress, 22 known miRNAs, and 33 novel miRNAs were identified. Through mRNA-miRNA meta-analysis, miRNA-objective gene pairs consisting of 21 DEGs and 11 miRNAs were identified. The GO functional annotation indicated that stress response genes were mostly concentrated in the items of enzyme activity, ion transport, and metabolic processes, etc. In KEGG of 8d, the objective genes were drastically enriched cyanoamino acid metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism, etc. We further constructed the pathway for cyanide amino acid metabolism under B stress and found that miR408c-5p targeted the gene controlling β -glucosidase 18, and the expression level was notably decreased. It was speculated that B stress hindered soybean growth by inhibiting amino acid metabolism and affecting other metabolic pathways. This study combined miRNAs and mRNAs to identify DEMs and metabolic pathways correlated to B stress. This study provides information that will help elucidate the complex mechanism of the B stress response in soybeans. Moreover, candidate miRNAs and mRNAs could yield new strategies for the development of B-tolerant soybean breeding.

Inference of mechanical forces through 3D reconstruction of the closing motion in venus flytrap leaves

Environmental relevant concentrations of copper sulphate induce biochemical and molecular toxicity in Labeo rohita

Copper is an important element involved in the catalysis of many vital reactions in the body of an organism. However, excessive copper causes cellular damage by accelerating the production of reactive oxygen species and disrupting the physiological reactions. The present research was conducted to determine the toxicological effects including oxidative stress profile, concentrations of anti-oxidant enzymes and genotoxicity of three different doses (0.28 µg/L, 0.42 µg/L and 0.56 µg/L) of copper sulphate (CuSO4) subjected to Labeo rohita for 36days. Micronucleus test indicated a significant (p < 0.05) increase in the frequency of morphological and nuclear changes in the erythrocytes of the treated fish. A significant (p < 0.05) increase was observed in oxidative stress parameters (ROS, TBARS) whereas the activity of anti-oxidant enzymes (SOD, POD, GSH, CAT) was significantly (p < 0.05) decreased in the gills, brain, liver and kidneys of fish exposed to 0.56 µg/L concentration of CuSO4. Moreover, CuSO4 exhibited significant (p < 0.05) DNA damage in lymphocytes, brain cells, hepatocytes and renal cells, as determined by comet test. Hence, it has been concluded that CuSO4causesevere biochemical and physiological disruptions in different organs of Labeo rohita, hence, considered as hazardous even at very low sub-lethal concentrations.

AI-based prediction of traffic crash severity for improving road safety and transportation efficiency

Pilot study: In vitro reduction of hemoglobin from canine blood with hemoperfusion using the Cytosorb® adsorber

Cell-free hemoglobin (cfHb) can be toxic and lead to kidney injury. This study assessed the in vitro reduction of cfHb from canine hemolyzed blood using hemoperfusion with a Cytosorb® cytokine adsorber. Canine whole blood was processed in linear and circular setups, with three runs each, at 100 mL/min. Hemolysis and osmolarity adjustments were performed with distilled water and hypertonic saline. Anticoagulation was optimized with heparin (10,000 IU/L). A median of 3.38 L of hemoglobin solution was processed in the linear setup. Samples were collected after the adsorber and from the waste bag. In the circular setup, a median of 2.09 L was processed, with samples taken before and after the adsorber. CfHb concentration was measured using the XT-2000iV® hematology analyzer (Sysmex). A control setup without an adsorber was run for 24 hours to assess cfHb stability. In the linear setup, cfHb concentration decreased by a median of 17.8% (14.7–26.8%), from 1.7 mmol/L to a minimum of 0.9 mmol/L, with 12.1 g (11.9–23.5 g) of cfHb removed. The median cfHb concentration after the adsorber displayed a logarithmic increase from 0.9 mmol/L (0.8–1.2 mmol/L) to 1.6 mmol/L (1.4–2.1 mmol/L). After processing 2.4 L, no further reduction occurred. In the circular setup, cfHb was reduced by a median of 41.3% (46.1–45.0%), representing 17.4 g (14.6–19.0 g) removed after 13.0 L (13.0–14.0 L). The reduction plateau was reached after 13 L. The hemoglobin reduction ratio in the circular setup at 3 L processed cfHb-solution was 25.0% (23.1–33.3%) and was not different from the linear setup (p = 0.400). The cfHb clearance decreased in both setups over time. CfHb concentration in the control setup was stable for 24 hours. Hemoperfusion with the Cytosorb® adsorber reduced cfHb in vitro from a canine blood solution.

A prototype of secure telephone communication

Cultural adaptation and validation of the desire to avoid pregnancy scale in Brazil

The development of valid measures of pregnancy intentions has been an important priority in the reproductive health field. A validated measure, the Desire to Avoid Pregnancy (DAP) scale, was developed in the USA to assess preferences regarding future pregnancy and childbearing, but it has not yet been validated in Brazil. This psychometric study aimed to adapt and evaluate the DAP scale in Brazilian Portuguese using both Item Response Theory and Classical Test Theory methods. Reproductive-aged women who had ever reported sexual activity, had not had a hysterectomy, were not sterilized, and had no partner with a vasectomy (n = 1,596) responded to an online survey with the 14 DAP scale items in March and April 2021. The items were comprehensible, even among women with lower education levels. Internal consistency (Cronbach’s α = 0.958) and test-retest reliability (ICC = 0.95) were both excellent. Exploratory factor analysis confirmed a one-factor model. Based on confirmatory factor analysis and the Item Response Model, items 3 (Thinking about becoming pregnant in the next 3 months makes me feel unhappy) and 5 (Becoming pregnant in the next 3 months would bring me closer to my main partner) did not perform well. However, testing a version without these items did not show substantial improvement in the psychometric parameters. The analysis showed significant differences in DAP scores according to age, educational status, and relationship status. As hypothesized, women with higher DAP scores were more likely to use contraception than those with lower scores [OR=2.12; 95%CI = 1.90–2.35]. The DAP scale, validated in Brazil, should be used in its full 14-item version until future studies are available. The scale has the potential to generate more accurate estimates of prospective pregnancy intention in Brazil and can be used to assess the outcomes of unintended pregnancies in maternal and infant health, providing an improvement over previous approaches.

Evaluation of a rare known anatomic formation: Parinaud’s canal

Exploring the influence of path environment factors on walking behavior in urban parks with configuration attribute control

The existing evidence regarding the influence of visual and physical environmental features of park pathways on walking behavior is limited and contentious, partly due to the lack of control over pathway configurational attributes and walking behavior measurement methods. This study addresses this gap by using space syntax and GPS to quantify the structural attributes and pedestrian counts within the pathways in Fengqing Park. Through stratified regression analysis, we examined the impact of environmental factors on walking behavior while controlling for pathway configurational attributes. The findings reveal that even after accounting for pathway configurational attributes, visual (R² = 0.223) and physical (R² = 0.173) environmental factors significantly affect walking behavior. Specifically, pathways with greater choice (β = 0.359, p < 0.01) and depth (β = 0.179, p < 0.05) are more favored. In terms of visual elements, landscape architecture (β = 0.143, p < 0.05) enhances walking behavior, while the presence of water bodies (β = –0.168, p < 0.01) negatively affects it. With respect to the physical environment, path width (β = 0.514, p < 0.01), length (β = 0.163, p < 0.05), and surface smoothness (β = 0.152, p < 0.05) play significant roles in influencing walking behavior. This study contributes to advancing our understanding of the impact of the environmental features of urban park pathways on walking behavior, reconciling research disparities in this domain, and providing guidance for the design and improvement of park pathways.

Infant rat ultrasonic vocalizations in the neurodevelopmental model of schizophrenia

Abstract Schizophrenia is characterized by early brain developmental abnormalities resulting in, among others, compromised communication. Rodent models, such as prenatal exposure to methylazoxymethanol acetate (MAM), help investigate schizophrenia-related deficits. Ultrasonic vocalizations (USVs) in rodent pups are a preclinical tool to study social communication. This study examines how prenatal MAM exposure affects the development, structure, and maternal potentiation of USVs. Pregnant rats received MAM or saline on the 17th gestational day. Offspring USVs were recorded upon maternal separation on the 6th, 9th, and 12th postnatal days (PND), while maternal potentiation was tested on the 10th PND. USV characteristics, temporal organization, clustering, and syntax were analyzed by DeepSqueak’s machine-learning algorithms. Unlike controls, which showed an increasing USV rate associated with proper vocal development, MAM-exposed pups displayed a stable emission rate across days and emitted fewer USVs on the 12th PND. Maternal potentiation was weak or absent in MAM pups, which also exhibited lower call complexity (reduced bandwidth and frequency) and longer duration. Temporal analysis revealed delayed vocal onset, prolonged inter-call intervals, and disrupted call arrangement. Syntax analysis indicated a simplified transition pattern dominated by low-pitched flat calls. Taken together, prenatal MAM exposure disrupts vocal communication, leading to less complex vocalizations with altered timing and structure. These deficits may serve as early markers of negative-like symptoms or cognitive dysfunctions in schizophrenia.

Higher red cell distribution width (RDW) is associated with increased all-cause and cardiovascular mortality in patients with breast cancer: A retrospective analysis of NHANES data (1999–2018)

Background The correlation between red cell distribution width (RDW) and mortality in breast cancer participants is not well-defined. This study investigates the association between RDW and both all-cause and cardiovascular mortality in the US population. Methods A retrospective cohort study was performed using data from 15,806 participants in the NHANES dataset. Multivariable Cox regression models were used to analyze demographic, socioeconomic, clinical, and laboratory factors, with adjustments for potential confounders. Restricted cubic spline (RCS) analysis was utilized to investigate the non-linear associations between RDW and mortality outcomes, and Kaplan-Meier (KM) survival curves were created to illustrate RDW’s effect on survival rates. Subgroup analyses and time-dependent ROC curves were also utilized to further assess the predictive value of RDW across different time intervals and patient subgroups. Results Elevated RDW significantly correlates with a heightened risk of all-cause mortality (adjusted HR 2.13, 95% CI 1.42–3.20) and cardiovascular mortality (adjusted HR 3.94, 95% CI 1.71–9.09) compared to lower RDW in Model 3. The association remained consistent across subgroups, with no significant interaction effects (p > 0.05). The RCS analysis demonstrated a positive linear relationship between RDW and mortality outcomes. Additionally, Kaplan-Meier analysis indicated that individuals with elevated RDW levels exhibited significantly lower survival rates. Time-dependent ROC and AUC analyses demonstrated that RDW was a more robust predictor of short-term mortality, as evidenced by higher AUC values in the initial years following diagnosis. Conclusions Red cell distribution width (RDW) serves as an independent predictor of both all-cause and cardiovascular mortality in breast cancer patients, showing strong predictive power for outcomes in both the short and long term.

Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations

Antioxidant potential of tree bark extracts: Insight from the multi-level output of the Antioxidant Power 1 assay

Tree bark is a complex protective tissue that serves both physiological and defensive functions and is particularly rich in phenolic compounds bearing antioxidant, antimicrobial, anti-inflammatory and wound healing properties. The aim of this study was to investigate the antioxidant activity of aqueous bark extracts from 6 European tree species, namely black alder, common beech, silver birch, bird cherry, oak and scots pine using the antioxidant assay Antioxidant Power 1 (AOP1) on a keratinocyte cell line in the light of dermatological applications. The AOP1 assay relies on light-induced intracellular reactive oxygen species (ROS) production that disrupts efflux transport, enabling the accumulation of fluorescent cyanine dyes which can be quantitatively detected by increased fluorescence. Particular attention was placed on the multi-level output provided by AOP1, which includes information on the intracellular antioxidant as well as prooxidative effects of specific compounds and insight into the ground stress level of cells. The results showed that tree bark extracts exhibit a different antioxidant mechanism compared to the well-known antioxidative substance resveratrol. Bark extracts limit the total amount of ROS produced over the duration of the assay, with oak, beech and pine bark extracts showing the highest antioxidant capacity. In contrast, resveratrol delays ROS production over several illumination cycles before levels reach those of untreated cells. Cellular ground stress level was elevated by alder and birch whereas oak, beech and pine reduced the ground stress level similar to that of resveratrol. Results of AOP1 were linked to the constituents of the tree bark extracts derived by Soxhlet extraction, determined by HPLC-DAD analysis. The results highlight the potential of AOP1 as a screening tool with multi-level output and demonstrate the antioxidative potential of six European tree bark extracts, underscoring their promise as sustainable, value-added resources for the development of dermatological therapies targeting oxidative stress–related skin disorders.

An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal

Abstract The public transportation sector generates large volumes of sensor data that, if analyzed adequately, can help anticipate failures and initiate maintenance actions, thereby enhancing quality and productivity. This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the Metro pt data set from the metro operator of Porto, Portugal. The results are above 98 % for f -measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high f -measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.

AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features

Accurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and police investigations, which can introduce biases and lack objectivity. To address these limitations, we propose the AMFormer(Arithmetic Feature Interaction Transformer) framework—a deep learning model designed for robust and interpretable traffic accident responsibility prediction. By capturing complex interactions among key factors through spatiotemporal feature modeling, this framework facilitates precise multi-label classification of accident responsibility. Furthermore, we employ SHAP (SHapley Additive Interpretation) analysis to improve transparency by identifying the most influential features in attribution of responsibility, and provide an in-depth analysis of key features and how they combine to significantly influence attribution of responsibility. Experiments conducted on real-world datasets demonstrate that AMFormer outperforms both other deep learning models and traditional approaches, achieving an accuracy of 93.46% and an F1-Score of 93%. This framework not only enhances the credibility of traffic accident responsibility attribution but also establishes a foundation for future research into autonomous vehicle responsibility.

Microbial biomass and enzymatic activity in the rhizosphere of prickly-pear cactus genotypes inoculated with Bacillus subtilis and Paenibacillus Sp

Return to theatre for post-tonsillectomy haemorrhage in children has not fallen with increased use of plasma ablation tonsillectomy: a retrospective analysis of 359,241 tonsillectomies in 15 years of United Kingdom Hospital Episode Statistics

Introduction Plasma ablation tonsillectomy has rapidly increased in popularity and is now the most popular technique in children. This study aims to evaluate the impact of plasma ablation tonsillectomy on the incidence of post-operative haemorrhage requiring surgical intervention in children, a complication affecting patient safety and healthcare resource utilisation. Methods 15 years (2009/10–2023/24) of Hospital Episode Statistics for children 14 years or under, capturing all tonsillectomies in NHS England hospitals was analysed. The proportion performed by plasma ablation and rate of surgical intervention for post-tonsillectomy haemorrhage were calculated. Pearson’s Correlation Coefficient was used to statistically analyse the relationship. Results Data from 359,241 tonsillectomies was analysed. The proportion of tonsillectomies performed with plasma ablation has grown yearly from 7% in 2009/10–47% in 2023/24. A change in trend in the rate of return to theatre for haemorrhage control was not identified across the study period. (Pearson Correlation Coefficient −0.15, p = 0.59). Conclusion These findings do not support a superior safety profile of plasma ablation tonsillectomy with regard to post-operative haemorrhage. However, due to dataset limitations it was not possible to analyse intracapsular and extracapsular procedures independently. There remains a need for continued evaluation of tonsillectomy techniques to inform optimal surgical practice.

Enhancing the biochemical potential of holy basil through methyl jasmonate elicitation with insights into physiological responses in a plant factory

Teaching postsecondary students about the ethics of artificial intelligence: A scoping review protocol

The field of AI carries inherent risks such as algorithmic biases, security vulnerabilities, and ethical concerns related to privacy and data protection. Despite these risks, AI holds significant promise for social good, with applications ranging from improved healthcare diagnostics to enhanced education strategies. Teaching AI ethics in postsecondary settings has emerged as one of the strategies to mitigate AI-related harms. The objectives of this review are to (1) synthesize existing research related to teaching postsecondary students about the principles and practice of ethics and AI, and (2) identify how educators are evaluating changes in student knowledge, skills, attitudes, and behaviors. This scoping review will follow the first five steps articulated by Arksey and O’Malley. A structured search strategy developed by an academic librarian incorporates three primary concept groups related to education, AI, and ethics. Database search strategies emphasize sensitivity rather than precision, given that a supervised machine learning tool will be used to assist in the identification of relevant abstracts. Searches will be conducted in the following academic databases: PubMed, Embase, Scopus, ERIC, LISTA, IEEE Xplore, APA PsycInfo, and ProQuest Dissertations and Theses. Results will include an up-to-date synthesis of the current state of AI ethics education in postsecondary curricula, evaluated teaching strategies, and potential outcomes associated with AI ethics education. Search results will be reported according to the PRISMA-ScR checklist. Data charting will focus on AI ethics pedagogy. This review will inform future research, policy development, and teaching practices, offering valuable insights for educators, policymakers, and researchers working towards responsible AI integration. Findings will contribute to enhanced understandings of the complexities of AI ethics education and have the potential to shape the ways trainees in multiple disciplines learn about the ethical dimensions of AI in practice.