Browse Articles

A structured multi-day experimental framework integrating green chemistry for the extraction and characterization of Berberine hydrochloride in undergraduate education

Apical and basolateral plasma membranes in epithelial cells have distinct lipidomes and biophysical properties

Epithelial cell polarization is essential for many physiological processes, including tissue morphogenesis, nutrient absorption, barrier integrity, and directional secretion. A defining feature of such polarization is the separation of plasma membrane (PM) lipids and proteins into distinct apical and basolateral compartments. It has long been suggested that the apical compartment is rich in glycolipids and cholesterol and that this composition arises through trafficking of self-assembled membrane domains (termed lipid rafts). However, neither the detailed composition nor the mechanisms of molecular sorting between epithelial cell PM compartments have been fully resolved. Particularly, the lipid profile of the basolateral membrane and consequently the lipid disparity between the apical and basolateral membrane remain undefined. We developed a method to separately isolate the apical and basolateral PM and used lipidomics and biophysical profiling to characterize the changes in membrane composition and properties between these compartments in polarized Madin–Darby canine kidney cells. We find that the apical membrane is enriched in cholesterol, saturated lipids, and glycolipids relative to the basolateral membrane and that its biophysical properties reflect a more ordered environment. Further, we evaluate the longstanding hypothesis that lipid rafts contribute to apical protein trafficking by assessing the relationship between transmembrane domain raft affinity and apical localization and find that lipid raft affinity only modestly influences apical versus basolateral sorting. These findings define the distinct compositional and biophysical features of apical and basolateral compartments of epithelial cells and suggest mechanistic evidence for their biogenesis.

Radiomic features and carotid stenosis in periodontitis a two stage bootstrap and multimodal machine learning study

Abstract This study aims to develop and validate a deep learning model based on Cone Beam Computed Tomography (CBCT) radiomic features to achieve early detection of potential carotid atherosclerosis in periodontitis patients. The study utilised data from 279 observations, each with 206 features, to distinguish between periodontitis patients with and without concomitant carotid atherosclerosis. To address class imbalance, Synthetic Minority Over-sampling Technique(SMOTE) oversampling was applied (dup_size = 1), increasing the sample size to 390 observations. A bootstrap method (n_bootstrap = 1000) was employed for feature selection. In each iteration, a dataset was created by resampling with replacement. Features were first filtered using Spearman’s rank correlation to remove redundant variables (correlation coefficient > 0.8), followed by Lasso regression with ten-fold cross-validation to select predictive variables based on non-zero coefficients. High-frequency features identified through 1000 iterations underwent a second round of bootstrap analysis, where Logistic Regression combined with the Akaike Information Criterion (AIC) was used to determine the final variable set. This rigorous process ensured optimal feature selection for developing an effective early detection model for carotid atherosclerosis in periodontitis patients. The study analyzed data from 279 observations, with each observation characterized by 206 features, to differentiate between periodontitis patients with concurrent carotid atherosclerosis and those without. After SMOTE oversampling, the dataset was increased to 390 observations. As stated in the Methods, SMOTE was applied after baseline analysis to augment the dataset for model development. Feature selection through bootstrap methods identified 26 high-frequency features (> 500 times), which were further refined to a final set of 20 features using Logistic Regression combined with AIC. Three machine learning models—Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF)—were developed and evaluated using five-fold cross-validation. The best-performing model was the RF model, achieving an Area Under the Curve(AUC) of 0.892, sensitivity of 0.957, specificity of 0.710, and accuracy of 0.859. Receiver Operating Characteristic(ROC) curves and calibration plots demonstrated good predictive performance and model calibration across all three models. Decision curve analysis showed that the RF model provided the highest net benefit across a range of risk thresholds, indicating its potential for clinical utility in early detection of carotid atherosclerosis in periodontitis patients. This study developed a random forest model using CBCT radiomics to detect carotid atherosclerosis in periodontitis patients early. After rigorous feature selection and five-fold cross-validation, it achieved an AUC of 0.892, with sensitivity of 0.957 and specificity of 0.710. The model shows high predictive performance and clinical utility, offering an effective tool for early detection.

Spatial transcriptomics reveals tumor microenvironment–driven subtypes of invasive lobular carcinoma

Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer and displays distinct clinical and biological behavior compared to breast cancer of no special type. However, current molecular classifications largely overlook its complex spatial organization and tumor microenvironment (TME). Here, we performed spatial transcriptomics on 43 hormone receptor-positive, HER2-negative (HR+/HER2−) ILC tumors with detailed morphological annotation and long-term clinical follow-up. By integrating spatial gene expression with histology and single-cell deconvolution, we characterized the composition and architecture of the TME and revealed high inter- and intratumor heterogeneity. Spatial clustering uncovered cell populations and pathways linked to clinical outcome. We then developed a multimodal classification of ILC by integrating gene expression, morphology, and spatial metrics, identifying four distinct subtypes: normal/stroma-enriched (NSE), proliferative (P), androgen receptor-enriched (ARE), and metabolic/immune-enriched (MIE). These subtypes, collectively termed ILC4TME, reflect the interplay between tumor and microenvironmental features. Gene signatures derived from the spatial data enabled subtype assignment in external bulk RNA-seq and microarray datasets (SCAN-B, METABRIC), revealing reproducible biology and significant associations with survival. In multivariable models, ILC4TME retained prognostic value beyond established gene signatures and clinicopathological variables. Notably, the P subtype was linked to poor prognosis, even in patients treated with endocrine therapy alone, while the NSE subtype was associated with favorable outcomes. Our findings uncover spatial and cellular heterogeneity in ILC that is not captured by existing classification approaches, offering a refined framework for risk stratification and therapeutic targeting based on tumor microenvironment architecture.

Parental mediation of smart device use and its impact on language development in early childhood

Abstract This study investigated the mediation strategies that parents use regarding the use of smart devices and how those strategies can subsequently affect the enhancement of language in children in Amman. The study applied a stratified sampling technique in its recruitment of 82 families from Amman. The theoretical underpinning of the study was laid by Vygotsky’s Zone of Proximal Development Theory, proposing that active mediation on the part of the parents would create the use of smart devices for interaction and thus help in the acquisition of a language. Instead, the discovery revealed that parents were using passive mediation, that is, rules of when and how screens should be used, rather than active control and guidance. The screen time duration rule, which in many cases involves no direct interaction, can be a difficult to the development of optimum language skills. The research found another very remarkable point: there was no agreement by parents on whether content held educational benefits for using the smart device to foster early reading and language skills, even under their supervision. This doubt is also attached to the value of the parents’ involvement in the promotion of linguistic learning and understanding the context in which language operates. The study based on these results strongly recommends that future efforts in Jordan concentrate on developing and implementing target educational programs for parents. This program should highlight the critical role of active parental mediation and emphasize more on practices such as co-viewing and interaction during smart device use to stimulate early language development effectively.

High-sensitivity, protein-independent detection of dsDNA sequences

Current methodologies for detecting the sequence of double-stranded DNA (dsDNA) require amplifying and denaturing the target into single-stranded DNA (ssDNA) to enable sequence detection through Watson–Crick base pairing. However, these approaches are limited by the risks of nonspecific amplification, reliance on complex, temperature-sensitive protein enzymes, and harsh reaction conditions, such as in strong base or acidic environments. Here, we introduce a dsDNA detection platform that integrates a peptide nucleic acid (PNA) as the dsDNA denaturation agent, with multicomponent deoxyribozyme as the ssDNA detection tool, in a droplet-based system. This protein- and amplification-free method offers single-nucleotide resolution, detects down to a single dsDNA molecule, and delivers results within 1 h at room temperature. This work introduces a conceptually unique approach, that may be useful for both diagnostics and therapeutics.

PLOD2 promotes proliferation, migration and invasion of colorectal cancer cells via PI3K-AKT-GSK3β signaling pathway

Abstract Colorectal cancer (CRC) progression critically depends on the tumor microenvironment. PLOD2, an enzyme involved in collagen biosynthesis, is highly expressed in many cancers. While it promotes CRC growth via the USP15–AKT/mTOR pathway, its role in enhancing tumor cell migration and invasion remains unclear. Our study identified a significant upregulation of PLOD2 in colorectal cancer. This upregulation was closely associated with clinical stage, lymph node metastasis, and nerve invasion in CRC. Functional assays, including CCK-8, colony formation, wound healing, and Transwell migration and invasion assays, showed that PLOD2 overexpression enhanced CRC cell proliferation, migration, and invasion, while PLOD2 silencing exerted the opposite effects. Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that PLOD2 may influence CRC progression via the PI3K-AKT signaling pathway. Co-immunoprecipitation assays demonstrated that PLOD2 was co-precipitated with PI3K, confirming their interaction. Additionally, rescue experiments showed that the PI3K inhibitor LY294002 and the agonist 740Y-P could reverse PLOD2-mediated effects on CRC cell proliferation, migration, and invasion. This study demonstrates that PLOD2 promotes the proliferation, migration, and invasion of CRC cells by interacting with PI3K to activate the PI3K-AKT-GSK3β signaling pathway.

Mapping of the viral shunt across widespread coccolithophore blooms using metabolic biomarkers

The viral shunt is a fundamental ecosystem process which diverts the flux of organic carbon fixed through photosynthesis during algal bloom events from heterotrophic grazers to bacteria. Through the extracellular release of metabolites, lytic viral infections supply 2 to 10% of photosynthetically fixed carbon in the ocean for bacterial respiration. Despite its significance for the carbon cycle, we lack tools to detect the viral shunt in the natural environment and assess its ecological impact. Here, we investigated the use of exometabolites as biomarkers for the viral shunt by applying molecular, metabolomics, and oceanographic tools to study bloom dynamics of the cosmopolitan microalga Gephyrocapsa huxleyi (formerly Emiliania huxleyi ) across the Atlantic Ocean, spanning four biogeochemical provinces between Iceland and Patagonia. We mapped the distinct metabolic footprint of its viral infections using exo- and endometabolomics and detected nineteen organohalogen metabolites across the blooms, showing their global distribution. A time-resolved comparison of particulate and dissolved metabolite pools during an induced mesocosm bloom revealed that virocells—actively infected host cells—were the source of the halogenated metabolites. Three trichloro-iodo metabolites were present during the demise of all virus-infected oceanic blooms, highlighting them as suitable metabolic biomarkers for the viral shunt. The environmental stability of these halometabolites in the dissolved organic matter pool over a few days can recapitulate viral infections at earlier stages of phytoplankton bloom succession. The chloro-iodo metabolites thereby expand the existing repertoire of metabolic biomarkers for viral infections at sea and may advance efforts to trace the biogeochemical impact of alga–virus interactions in the ocean.

Psychological distress and problematic social media use among Moroccan youth mediated by fear of missing out and social media engagement

Metabolite control of enzyme activity links stress to biosynthetic regulation

Cells must continuously adjust metabolic output to maintain homeostasis under changing environmental conditions, yet the mechanisms that enable rapid and reversible control of pathway activity remain largely unknown. The methylerythritol phosphate (MEP) pathway, of bacterial origin and conserved in plastid-bearing eukaryotes, including plants and apicomplexan parasites, produces isoprenoid precursors essential for growth and stress adaptation. Here, we identify methylerythritol cyclodiphosphate (MEcPP) as a dual-function metabolite that serves both as a biosynthetic intermediate and a direct modulator of enzyme activity. Genetic perturbations and high light stress revealed step-specific MEcPP accumulation independent of transcriptional regulation. Biochemical and protease-protection assays showed that MEcPP destabilizes and inhibits methylerythritol cytidylyltransferase (MCT) while modestly stabilizing hydroxymethylbutenyl diphosphate synthase (HDS). Molecular docking analyses indicate that MEcPP interacts directly with the MCT catalytic site, displacing the natural substrate and thereby attenuating enzyme activity, suggesting a competitive, feedback-like mechanism of metabolic control. These results define MEcPP as a metabolic feedback signal that translates stress-induced changes into targeted enzymatic control. This mechanism illustrates how pathway intermediates dynamically coordinate biosynthetic activity with environmental cues, representing a broadly conserved strategy for metabolite-driven control of cellular metabolism.

RETRACTED ARTICLE: Mathematical Modeling and Computation of NM-Polynomial Indices for Physicochemical Properties Prediction

Amplified warming in tropical and subtropical cities under 2 °C climate change

Cities are often warmer than rural surroundings due to a phenomenon known as the urban heat island, which can be influenced by various factors, such as regional climate and land surface types. Under climate change, cities face not only the challenge of increasing temperatures in their surrounding hinterland but also the challenge of potential changes in their heat islands. However, even high-resolution global Earth system models (ESMs) with “urban tiles” can only properly resolve the largest urban areas or megacities. Here, we address these limitations by applying a process-based statistical learning model to ESM outputs to provide projections of changes in land surface temperature (LST) for 104 medium-sized cities of population 300 K to 1 M in the subtropics and tropics. Under a 2 °C global warming scenario, annual mean LST in 81% of these cities is projected to increase faster than the surrounding area. In 16% of these cities, mostly in India and China, mean LST is projected to increase by an additional 50-112% above ESM projections of the surrounding area. Our findings underscore the importance of investigating the specific effects of climate change on urban heat exposure.

Distribution of artificial radionuclides in particle-size soil fractions

Pleistocene demographic histories dominate contemporary genomic diversity in a continental radiation of Himalayan–Hengduan songbirds

Genetic diversity, the fundamental substrate for evolutionary potential, is declining globally at unprecedented rates. Yet the mechanisms governing its distribution across species remain poorly understood, with competing hypotheses emphasizing either historical demography or contemporary ecological constraints. This debate limits our capacity to predict species' responses to environmental change. Here, based on systematic sampling and a unified analytical pipeline, we de novo assembled genomes for 120 songbird species breeding in the Himalayas–Hengduan Mountains (HHMs) and conducted population genomic analysis to examine the drivers of their genomic diversity. We observed a 6.5-fold variation in genome-wide heterogeneity and a 16.4-fold variation nucleotide diversity across species. Notably, these measures of genomic diversity showed no correlation with recent population dynamics, current population size, or other contemporary factors—such as elevational distribution, or life-history traits. Instead, historical demography strongly predicted genetic diversity, with ancestral population size during the late Pleistocene emerging as the sole correlate: Larger ancestral sizes consistently coincided with higher diversity. These findings underscore the critical influence of historical demography on contemporary genetic diversity in natural populations—an insight essential for designing effective conservation strategies.

Error characterization and error correction approaches in combinatorial DNA-based storage

A factor integrating transcription and repression of surface antigen genes in African trypanosomes

Antigenic variation in Trypanosoma brucei ( T. brucei ) requires monoallelic expression of one variant surface glycoprotein (VSG) from one of the subtelomeric bloodstream form (BSF) expression sites (BESs). This transcription is unusually mediated by RNA polymerase I (RNA Pol I) and occurs in a specialized nuclear body, the expression site body (ESB). While factors promoting active BES transcription and silencing inactive BESs are known, how these opposing activities are integrated remains unknown. Here, we identify ESBX (Tb927.3.1660) as a BSF-specific ESB protein necessary for this coordination. We show that ESBX RNAi knockdown prevents RNA Pol I localizing to the ESB and reduces active BES transcription, while also derepressing inactive BESs with low processivity transcription. Conversely, ESBX overexpression weakly activates inactive BESs in a distinct manner from ESBX knockdown, leading to processive transcription, without disrupting the active BES or forming supernumerary ESBs. ESBX knockdown causes a similar transcriptomic defect to ESB1 and VEX2 knockdown combined, establishing ESBX as a key factor linking transcriptional activation of the active BES with inactive BES silencing through the VSG exclusion (VEX) phenomenon. This allows us to suggest models for understanding the establishment and maintenance of monoallelic expression critical for parasite immune evasion.

Automatic classification method of e-commerce commodity raw materials through the introduction of self-supervised concepts and the construction of domain ontology

Reply to Cecchi and Palminteri: On the need to model temporal variation in learning rates

Organic mulching enhances Morpho-Physiological performance and yield of Sesame (Sesamum indicum L.) under water deficit conditions

Molecular tuning of DNA framework–programmed silicification by cationic silica cluster attachment

The organizational complexity of biominerals has long fascinated scientists seeking to understand biological programming and implement new developments in biomimetic materials chemistry. Nonclassical crystallization pathways have been observed and analyzed in typical crystalline biominerals, such as calcium phosphate, calcium carbonate, and ferric oxide, involving the controlled attachment and reconfiguration of nanoparticles and clusters on organic templates. However, the understanding of templated amorphous silica mineralization remains limited, hindering the rational design of complex silica-based materials. Here, we report the finding of ultrastable and monodispersed cationic silica cluster (CSC) and their assembly using DNA nanostructures as programmable attachment templates. Cryo-EM imaging reveal that a typical CSC with a diameter of gyration of ~3.9 nm and an average molecular weight of ~8, 262 Da is characteristic of a branched hierarchical structure. We demonstrate high-fidelity silicification by tuning the composition and structure of CSC, providing a unified model of silicification by cluster attachment. Our findings pave the way toward the molecular tuning of pre- and postnucleation stages of sol–gel reactions and provide insights for the design of silica-based materials with controlled organization and functionality.