Browse Articles

Defining synergy for three-phase polymer nanocomposites: a volume-weighted quantitative framework

Correction: Integrated bioinformatics-based identification of proliferative diabetic retinopathy and idiopathic pulmonary fibrosis: Focus on fibrosis and immune infiltration

Seed metabolomic profiling of contrasting mung bean (Vigna radiata) genotypes under heat stress

Transcriptional and functional characterization of terpene synthase genes of the aromatic plant Plectranthus hadiensis

Plectranthus hadiensis (Lamiaceae) is recognized for its rich terpene content and potential applications in agriculture, medicine, and aromatherapy. Terpenes are major constituents of P. hadiensis essential oil, yet its terpene synthase (TPS) genes remain insufficiently characterized. In this study, we assembled a de novo transcriptome from RNA-seq data generated from leaf, stem, and root tissues and identified 26 TPS genes. Phylogenetic analysis classifies these genes into five TPS subfamilies (TPS-a, TPS-b, TPS-c, TPS-e/f, and TPS-g), broadly associated with sesquiterpene, monoterpene, and diterpene biosynthesis. Expression profiling revealed apparent tissue specificity; notably, PhTPS1 showed high transcript abundance in the leaf and stem. BLASTP analysis indicated that PhTPS1 is closely related to Lamiaceae monoterpene synthases, with the top hit being a rosemary ( Salvia rosmarinus ) limonene synthase. Heterologous expression of PhTPS1 in yeast, followed by headspace gas chromatography-mass spectrometry, detected limonene, confirming PhTPS1 as a functional limonene synthase. To our knowledge, this is the first limonene synthase gene functionally characterized in P. hadiensis . Collectively, these findings provide a curated TPS genes catalog with their tissue-specific expression patterns and identify PhTPS1 as a promising target for breeding and metabolic engineering to enhance limonene yield.

Integrated platform for linezolid combinations against rifampicin-resistant Mycobacterium tuberculosis: synergy, macrophage apoptosis, and immune modulation

Multitarget docking and molecular enumeration reveal DdpMPyPEPhU as a potent modulator of cell cycle, glucocorticoid, and estrogen signalling in breast cancer

Breast cancer is one of the most prevalent cancers worldwide, ranked as the second most diagnosed cancer and the fourth leading cause of cancer-related deaths. Despite the availability of FDA-approved therapies, limitations such as drug resistance and off-target effects highlight the need for novel, multitargeted therapeutic agents. In this study, we aimed to identify and design an in-silico promising multitarget drug for breast cancer by simultaneously targeting three critical proteins: Glucocorticoid Receptor, Estrogen Receptor-alpha (ER-alpha), and Cyclin-Dependent Kinase 2 (CDK2). FDA-approved drugs corresponding to these targets were initially subjected to multitarget molecular docking to evaluate their binding affinities. Based on this screening, the 15 highest-ranking ligands were selected and underwent molecular enumeration, resulting in the generation of 14,750 novel derivative compounds. The re-docking identified 1-((R)-2,3-dihydroxypropyl) −3-(3-((R)-1–5-methyl-1H-pyrrolo [2,3-b]pyridin-3-yl)ethyl)phenyl) urea (DdpMPyPEPhU) (Patent No. 202024101028.0) as a promising multitarget candidate. The compound exhibited enhanced binding pocket engagement through numerous stabilising interactions, including hydrogen bonds, π-π stacking, and π-cation interactions, with high docking scores (–14.869 to –4.57 kcal/mol) and favourable Molecular Mechanics Generalised Born Surface Area (MM-GBSA) energies (–72.32 to –11.97 kcal/mol). Comparative docking and pharmacokinetic analyses with standard drugs Lapatinib and Tamoxifen indicated better drug-like properties and pharmacokinetic advantages for DdpMPyPEPhU. Additional validation using Density Functional Theory (DFT) optimisation, 5 ns WaterMap analysis, and 250 ns molecular dynamics simulations under neutralised conditions confirmed structural stability and strong intermolecular interactions, supported by binding free energy calculations. Overall, our computational findings suggest that DdpMPyPEPhU is a promising therapeutic candidate for breast cancer, providing a rational basis for further experimental evaluation.

Quality of life, sleep quality, and psychological wellbeing of patients with chronic kidney disease in Sabaragamuwa Province, Sri Lanka: a descriptive cross-sectional study

Manuscript submission systems and metadata completeness in Crossref: Patterns and associations

The importance of open research information, particularly publication metadata, is widely recognised. Crossref is one of the most important infrastructures for registering open metadata as part of DOI record registration. It is widely known, however, that the metadata of many publications is far from complete, with many publishers making certain metadata openly available, but failing to do so for other metadata elements. Publishers’ ability to register this metadata with Crossref depends on their capacity to capture and retain this data in their production workflows. Manuscript submission systems are an important, yet largely overlooked, factor in the extent to which publishers make metadata available through Crossref. In this paper, we present the results of an analysis investigating the relation between the level of metadata that publishers deposit with Crossref and the submission systems that they deploy for their journals. We have looked at the 153 publishers with the largest amounts of publications in Crossref and concentrate on the four most commonly used systems: Editorial Manager, ScholarOne, Open Journal Systems (OJS) and eJournalPress. We show that some submission systems appear better suited to capturing certain metadata elements. However, there are always cases where publishers using the same system differ widely in the level of metadata they register, suggesting that technology is not the only prohibiting factor and other considerations are at play.

Integrative multi-omics analysis identified FUT9 and MS4A3 as novel immune-phenotype and prognosis biomarkers for colorectal cancer and analyze the role of FUT9 in oncoimmunology

Social inequalities in the misbelief of chloroquine’s protective effect against COVID-19: results from the EPICOVID-19 study in Brazil

Objectives This study aimed to assess the dissemination of anti-science messages regarding COVID-19 in Brazil, specifically examining how social inequalities contributed to the misconception that chloroquine has a protective effect against the virus. Study design Three countrywide population-based studies were conducted in 2020 (May 14–21, June 4–7, and June 21–24), including 133 Brazilian cities (N = 74,077). Methods Participants (≥20 years old) were asked whether they believed in chloroquine’s protective effect against infection with the SARS-CoV-2 virus (no/yes/don’t know). “Yes” and “don’t know” answers were considered misconceptions (effect of denialism). A jeopardy index score was calculated to assess cumulative social deprivation based on sex, race and ethnicity, and socioeconomic variables. Descriptive analysis and inequality measures (Slope Index of Inequality – SII; and Concentration Index) were used to evaluate the association between believing in chloroquine’s protective effect against COVID-19 and the jeopardy index. Multinomial logistic regression was used in the unadjusted and multivariable analysis. Results Overall, 47.6% of participants either believed that chloroquine prevented COVID-19 or stated, “I don’t know.” Marginalized racial and ethnicity groups, those with low education level, and those with low socioeconomic status were more likely to erroneously believe that chloroquine prevented COVID-19. The chance of lack of knowledge (Don’t know) was higher (Odds Ratio: 2.57,CI95% 2.21; 2.99) among women, Black/Brown/East Asian/Indigenous, and among those in the lowest education level and wealth quartiles compared to men, white individuals, and those in the highest education and wealth quartiles. Absolute and relative inequalities were observed according to the jeopardy index. The highest absolute inequality was observed for the category “I don’t know” (SII = −15.1). Conclusions Misbelief in chloroquine’s protective effect against SARS-CoV-2 was high in Brazil. People with greater social vulnerability were more likely to wrongly believe that chloroquine prevented COVID-19.

Spherical fuzzy hypergraph in decision making

A qualitative study of lived experiences of underrepresented electrical workers using creative non-fiction

Objectives Women, Indigenous peoples, racialized individuals, and persons with disabilities remain underrepresented in the electrical industry. This study explored the lived experience of underrepresented electrical workers related to their mental health and workplace integration. Methods A qualitative narrative design was employed. One-on-one interviews were conducted with eleven participants who self-identified as women, Indigenous peoples, racialized individuals, and/or persons with disabilities. Interview data were first analyzed using a narrative thematic approach and informed the development of creative non-fictional stories. Results Three stories were developed. Story 1 – “Asking for a ride: being a women electrician” illustrated the experiences of a woman apprentice who faced inadequate job site accommodations and sexism challenges in the workplace. Story 2 – “The lunch talk: Indigenous people and racialized individuals in the trade” highlighted the experiences of Indigenous and racialized participants who encountered language barriers and discriminatory comments. Story 3 – “Luke’s notes: living and working with disability” demonstrated the impacts of physical disability on the daily work of electrical workers, particularly in managing the physical demands and mental health strains. Conclusions Electrical workers from underrepresented groups experience persistent barriers to mental health and workplace integration, including a lack of accommodations, limited social support, and experiences of discrimination at the workplace. These individuals also reported challenges in seeking workplace support due to a “toughness” culture within the industry. Electrical employers should foster an inclusive organizational culture that prioritizes the health and psychosocial well-being of underrepresented workers.

Organophosphides: A New Class of Luminophore Ligands for Copper(I) Carbene Based TADF Emitters and Photocatalysts

ABSTRACT Luminescent carbene copper(I) charge transfer complexes are promising candidates as molecular materials for photonic applications. Apart from steric and electronic modification of the acceptor carbene, most of the research has been dedicated to amide donor ligands to control the luminescence properties, while the remaining pnictogen group as anionic electron donating ligands is photophysically underrepresented. Herein, we demonstrate that dimesityl phosphide (Mes 2 P–) as a heavier homologue in [Cu(cAAC)(PMes 2 )] (cAAC = cyclic amino(alkyl) carbene) gives rise to orange emission with quantum yields of up to ϕ max   = 0.52 in the solid state that is bathochromically shifted by ∼3000 cm −1 in comparison to related amide complexes due to the lower electronegativity of phosphorus. Time‐resolved variable temperature studies reveals that the µs‐lifetimes and radiative rate constants of ca. k r   =   5 ⋅ 10 4  s −1 are due to thermally activated delayed fluorescence (TADF) as the dominating emission mechanism at room temperature. In polystyrene matrices, the complexes exhibit environment dependent chiroptical properties (up to g lum   =  10 −2 , B CPL   =  1.31 M −1  cm −1 , k CPL   =  41.5 s −1 ) and are efficient blue light photocatalysts for hydrophosphination of alkynes in solution, highlighting the potential of heavier pnictogen ligands for photonic materials.

Path planning for manipulators based on the planar constraint RRT* algorithm

Abstract Path planning is crucial for automatic measurement to ensure a collision-free process for manipulators. However, the more complex the measurement environment, the more complex the path planning scheme is often required in order to meet the above requirements. To overcome this problem, a planar constraint RRT* method (PC-RRT*) is proposed to limit nodes to a specific plane, reduce the blindness of RRT*, and smooth paths. The measurement space model is built according to the position of the manipulator, the obstacle environment, the starting point, and the ending point. A set of equiangular interval collinear planes is built by utilizing the starting and ending line as the central axis. A local path on each plane is planned by extended rapidly exploring random tree strategy. By optimizing the planned local path set, the high-quality measurement path will be solved. Both numerical simulation and experimental analysis are carried out to verify the effectiveness of the PC-RRT* method. The experimental results show that the average path lengths (APL) are 216.03 mm for PC-RRT*, 346.83 mm for RRT, 241.93 mm for RRT*, and 383.6 mm for Q-learning (QL), the average number of nodes is 9.5 for PC-RRT*, 11.12 for RRT, 11.6 for RRT*, and 18.92 for QL. The suggested PC-RRT* has an 37.71% improvement for APL and 14.57% improvement for path nodes compared to the RRT algorithm. PC-RRT* has a 10.71% improvement for APL and 18.1% improvement for path nodes compared to the RRT* algorithm. Additionally, PC-RRT* achieves a 43.68% improvement for APL and a 49.79% improvement for path nodes compared to the QL algorithm. In all, the PC-RRT* method is superior to other traditional methods.

“You Cannot Be Yourself”: Identity disruption, stigma, and the lived experience of anal fistula

Anal fistula is a complex and often prolonged condition that significantly impacts patients’ daily lives and psychological well-being. This qualitative study explored how individuals living with anal fistula experience stigma, disruption, and identity strain in everyday life. Fifteen participants undergoing active treatment were interviewed at two hospitals. Data was analyzed using qualitative content analysis. Findings show that living with anal fistula was marked by shame, uncertainty, and social withdrawal, often contributing to an altered or fractured sense of self. Participants navigated this experience through three identity-shaping mechanisms: Knowledge Uncertainty, Expectations and Experiences, and Quality-of-Life. These mechanisms influenced how participants made sense of their condition, coped with invisibility, and negotiated bodily control in the context of pain and stigma. While many struggled to maintain a coherent identity in the face of chronic symptoms, interactions with empathetic healthcare providers, particularly those offering consistent information and emotional support played a critical role in helping patients feel seen and supported. The study highlights the need for more holistic, person-centered approaches to care that address not only the physical but also the psychosocial dimensions of life with an anal fistula.

Seasonal variation in particulate organic carbon sequestration in subarctic and subtropical gyres of the western North Pacific

Abstract The ocean’s biological carbon pump regulates climate by transferring a portion of surface-fixed CO 2 to the deep ocean through sinking particulate organic carbon (POC). Although this flux is strongly attenuated in the twilight zone, the seasonal controls on attenuation remain poorly understood. We examined seasonal variations in POC flux, the nitrogen isotope ratio of sinking particulate nitrogen (δ 15 N sink ), and mineral composition using sediment traps observations at subarctic (K2) and subtropical (S1) stations in the western North Pacific. POC sequestration efficiency at 500 m [Seq (500)  = POC flux/NPP] was quantified, with net primary productivity (NPP) reconstructed from δ 15 N sink using an empirical framework. Seq (500) remained nearly constant at K2 (7.4–8.1%) but varied substantially at S1 (3.4–6.5%). At K2, CaCO 3 and opal contents exhibited complementary seasonal patterns, whereas S1 showed pronounced variability primarily in CaCO 3 . We propose that mineral composition modulates aggregate settling velocity and adhesive strength, thereby regulating POC attenuation through fragmentation processes. These findings indicate that seasonal shifts in surface ecosystem structure influence the physical properties of sinking aggregates and ultimately control the fate of NPP in the twilight zone.

Exploring the links between social connection and physical functioning among older Adults: A network analysis

Purpose The aims of this study were to 1) characterize and visualize the association between social connection and physical functioning using network analysis, with attention to different types of measures of these constructs, and 2) identify key indicators that link social connection and physical functioning. Methods Data come from the 2014/2016 Health and Retirement Study (N = 7,270, mean age = 67.3). Network analysis was used to depict and explore the relationships between physical functioning and social connection. Both of these constructs were measured using a combination of “objective” (e.g., balance test, number of social connections) and “subjective” (e.g., self-evaluated activities of daily living (ADLs), perceived loneliness) indicators. The network was estimated using a regularized partial correlation network. Features of the network structure were characterized using 1) assortativity, 2) community detection, and 3) centrality indices (i.e., strength and betweenness). Betweenness centrality was used to identify the key nodes linking the two constructs. Results The network structure is determined by constructs (i.e., social connection vs. physical functioning, assortativity = 0.87), instead of types of measures (i.e., objective vs. subjective, assortativity = 0.45). There are five communities identified from the network, three of which include both subjective and objective indicators. The incomplete balance tes t is the key bridging indicator that links social connection and physical functioning. Conclusion The linkages between social connection and physical functioning are multifaceted. Balance impairment is the key bridge between these constructs for older adults.

LM-UNet: Lightweight Mamba-UNet Prostate MRI image segmentation network

Accurate segmentation of lesions in prostate magnetic resonance images (MRI) is important for assessing patient health and personalized treatment in the clinic. However, the traditional UNet segmentation network has low segmentation accuracy because of the fuzzy boundary and low contrast. Therefore, we propose a Lightweight Mamba-UNet (LM-UNet) prostate MRI image segmentation method. Initially, the encoder-decoder backbone structure consists of parallel vision mamba (PV-Mamba) and efficient multi-scale attention (EMA). The number of model parameters is reduced by constructing PV-Mamba while extracting the correlation between features over long distances. The EMA is then used to learn different spatial features in groups and construct cross-spatial information aggregation methods for richer feature aggregation. Subsequently, we construct the edge feature extraction (EFE) and the edge feature fusion (EFF) to achieve different levels of feature fusion in the encoder. Ultimately, we suggest a multi-stage and multi-level skip connections (MMSC) to achieve multi-level fusion between the encoder and decoder, there reducing semantic discrepancies between contextual features and improving segmentation accuracy. Experimental results demonstrate that on the PROMISE12 dataset, LM-UNet outperforms seven comparative segmentation methods in terms of parameter count, computational memory requirements, and precise segmentation of lesion margins.

Enhanced fibrinolytic enzyme production by Oidiodendron maius through green bioprocessing of agro-industrial residue

Thrombosis denotes the formation of blood clots within arteries and veins, representing a primary etiological factor in cardiovascular diseases often culminating in fatal outcomes. Prompt resolution of thrombotic disorders is achieved through expedited fibrinolysis facilitated by the administration of fibrinolytic enzymes, which constitute the optimal therapeutic approach. This research aimed to enhance the production of fibrinolytic enzymes through the cultivation of indigenously isolated strains of Oidiodendron maius using physical and chemical mutagenesis techniques. Fibrinolytic enzyme activity from mutant strains was validated by enzyme assays followed by purification using ammonium sulfate precipitation, desalting, ion exchange chromatography, gel filtration chromatography, and SDS-PAGE. Various kinetic and thermodynamic parameters were systematically optimized to maximize enzyme activity. In O. maius , the ethidium bromide mutant strain showed better results as compared to the other mutants with specific activity of 1642.24 U/mg and 0.5 mg/mL protein content compared to the wild-type strain which 90.20 U/mg specific activity and 3.7 mg/mL protein content. The optimum temperature and pH were 35°C and 7.5, respectively. The findings indicated that treating O. maius with ethidium bromide resulted in the generation of better mutants with enhanced enzyme activities compared to wild-type and other mutant strains. With optimization of multiple parameters, these strains demonstrate significant potential for enhanced fibrinolytic enzyme production by the usage of wheat bran as substrate.

Advancing workpiece dimension measurement: Integrating AI-based edge detection with machine vision and coordinate measuring systems

Image preprocessing and edge detection are critical in industrial machine vision for workpiece dimension measurement. Challenges arise from interference regions on workpiece surfaces, complicating edge detection and roundness assessment. This paper investigates the application of AI-based detection methods within the industrial image analysis framework of coordinate measuring machines. Initially, two models with varying hole sizes and counts were designed in SolidWorks, fabricated using a Prolight 3-axis CNC milling machine, and analyzed. A transfer learning approach mitigated overfitting on the limited dataset of model surface features. The study employed a Convolutional Neural Network (CNN) to identify interference regions and predict circularity, enhancing measurement accuracy. Validated with a testing dataset, the CNN achieved 100% classification accuracy, confirmed by a Confusion Matrix. Fine-tuning of the CNN with specific training data leveraged image preprocessing to enhance features via multi-layer convolution, pooling, and detailed analysis through fully connected layers. Comparative diameter analysis across Models 1–2 showed all methods maintained ≤0.05 mm deviation from actual values, with CNN exhibiting minor variations at Model 1’s points 3,7,9 while matching CMM precision (r = 1.000) and outperforming vision systems in Model 2’s multi-hole measurements, supported by ANOVA-confirmed discrimination (F = 34,514,683, p < .001) and cross-material scalability to Drelin via 200-image retraining. The results underscore the effectiveness of integrating deep learning techniques into industrial inspection, contributing a standardized methodology for precise workpiece dimension measurement. This research highlights the potential of combining machine vision, deep learning, and coordinate measuring systems to advance industrial measurement processes.