Browse Articles

The introduction and evaluation of novel decellularized extracellular matrix/gellan gum bioprinting scaffolds for cartilage tissue engineering

Abstract Cartilage demonstrated limited self-regeneration; and there is a need for developing new compounds. Here, gellan gum was selected due to its hydrophilicity, biocompatibility, and native cartilage environmental resemblance, and cartilage decellularized extracellular matrix (dECM) was added (GG/dECMb) to improve the cellular interactions. The decellularization was performed using freeze-thaw cycles and sodium dodecyl sulfate and a hematoxylin-eosin and Bradford assays showed successful decellularization with low extracellular matrix damage. The GG/dECMb compound was formulated, and the gellan gum was considered as the control (GGb). The rheological evaluations demonstrated the shear-thinning and bioprinting capability, while the GG/dECMb had a lower cross-linking degree (5.04 ± 0.79%) in comparison to GGb (6.65 ± 0.48%). Both bioinks were successfully bioprinted. The mechanical test demonstrated the GG/dECMb had a damping feature, which is essential for cartilage regeneration. Furthermore, it has a higher hydrophilic nature (44.27 ± 6.0° contact angle), swelling ratio, and biodegradation ratio in comparison to GGb. The cellular tests confirmed the high capability of GG/dECMb dried scaffolds in cell viability based on the cell viability test (97.41 ± 1.02%) and live/dead assays. The Alcian blue staining proved the glycosaminoglycans deposition and cartilage differentiation of GG/dECMb. Therefore, it seems that GG/dECMb can be effective in cartilage regeneration, although needs further in-vivo studies in the future.

An evaluation of social and emotional learning-based program for kindergarten and primary schools in Poland

Age is an intrinsic driver of inflammatory responses to malaria

Abstract Age is a critical factor in immune responses to infection. In malaria, severe disease risk increases with age in non-immune individuals. Malaria severity is in part driven by inflammation, but mechanisms contributing to age-dependent disease risk are incompletely understood. We assessed inflammatory cytokines during malaria in non-immune children and adults, and innate cell responses in vitro to malaria parasites in naive children and adults. We show during malaria age is associated with increased inflammatory chemokines CCL2, CCL3, CXCL8, CXCL9, along with CRP, and IDO, which associate with symptoms. In naive individuals, classical monocyte and Vδ2+ γδ T cells from adults have higher inflammatory cytokine production, and transcriptional activation following stimulation with parasites. Classical monocyte responses in adults are dominated by CCL2, while in children increased IL10 and enrichment of IL10 signaling pathways is detected. Findings identify age-dependent cellular mechanisms that play crucial roles in driving inflammatory responses in malaria.

Digital innovation and corporate carbon emissions from the perspective of asymmetric supply chain relations

Synergistic potency and GC-MS analysis of Persea americana extracts against Aedes aegypti

HFIP-assisted Brønsted acid-catalyzed ring opening of 1-azabicyclo[1.1.0]butane to access diverse C3-quaternary aza-azetidines and indole-azetidines

Prediction of treatment-resistant depression using the 23andMe survey data

Physicochemical and antioxidant properties of honey across bee species from North Eastern Hill region of India

A predictive approach to enhance time-series forecasting

Abstract Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to data distribution shifts over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding. Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on current data. When discrepancies occur between the forecasting and detection models, a more significant update is applied to the forecasting model, effectively minimizing surprise, allowing the forecasting model to dynamically adjust its parameters. We validate our approach on a variety of tasks, demonstrating a 44.8% increase in AUC-ROC for seizure prediction using EEG data, and a 23.4% reduction in MSE for forecasting in nonlinear dynamical systems (outlier excluded). By incorporating a predictive feedback mechanism, Future-Guided Learning advances how deep learning is applied to time-series forecasting.

A new family for Cephalotrema elasticum (Digenea) based on molecular data, with notes on the status of the definitive host population

Affordable ultrasensitive electrochemical detection of PCA3 for early prostate cancer diagnosis

SARST2 high-throughput and resource-efficient protein structure alignment against massive databases

Determinants and governance of unused rural residential bases in the context of Rural Revitalisation in China

Effect of tocotrienol-rich fraction (TRF) on lipid profile in hyperlipidemic experimental animal model: a systematic review and meta-analysis

Single molecule spectrum dynamics imaging with 3D target-locking tracking

Abstract Fluorescence spectra offer rich physicochemical insights into molecular environments and interactions. However, imaging the dynamic fluorescence spectrum of rapidly moving biomolecules, along with their positional dynamics, remains a significant challenge. Here, we report a three-dimensional target-locking-based single-molecule fluorescence Spectrum Dynamics Imaging Microscopy (3D-SpecDIM), a method capable of simultaneously capturing both rapid 3D positional dynamics and physicochemical parameter changing dynamics of the biomolecules with enhanced spectral accuracy, high spectral acquisition speed, single-molecule sensitivity, and high 3D spatiotemporal localization precision. As a demonstration, 3D-SpecDIM is applied to real-time spectral imaging of the mitophagy process, highlighting its enhanced ratiometric fluorescence imaging capability. Additionally, 3D-SpecDIM is used for multi-resolution imaging, providing valuable contextual information on the mitophagy process. Furthermore, we demonstrated the quantitative imaging capability of 3D-SpecDIM by imaging the cellular blebbing process. By continuously monitoring the physicochemical parameter dynamics of biomolecular environments through spectral information, coupled with 3D positional dynamics imaging, 3D-SpecDIM offers a versatile platform for concurrently acquiring multiparameter dynamics, providing comprehensive insights unattainable through conventional imaging techniques. This work represents a substantial advancement in single-molecule spectral dynamics imaging techniques.

On the oscillation criteria for neutral differential equations with several delays

Synthesis of silver nanoparticles from Vicia faba aqueous extract with cytotoxic activity against human acute T cell leukemia

Abstract Silver nanoparticles (AgNPs) are among the most extensively utilized nanomaterials in commercial and biomedical applications due to their potent cytotoxic properties. Leukemia remains one of the most prevalent cancers worldwide, driving the search for novel therapeutic approaches. In this study, aqueous extracts from Vicia faba seed coats were employed for the green synthesis of AgNPs, and their anticancer activity was evaluated in vitro. The biosynthesized AgNPs exhibited an average hydrodynamic diameter of 23.14 ± 0.20 nm, with all particles measuring under 40 nm, and showed a characteristic surface plasmon resonance (SPR) peak at 430 nm. X-ray diffraction (XRD) analysis confirmed the crystalline nature of the nanoparticles, revealing distinct peaks corresponding to the (111), (200), (220), and (311) planes of metallic silver. Fourier-transform infrared spectroscopy (FTIR) indicated the presence of phenolic functional groups from the extract, likely involved in nanoparticle formation and stabilization. Cell viability assays demonstrated a dose-dependent cytotoxic effect on leukemia cells, with an IC₅₀ of 2.27 mg/mL. Apoptosis induction was confirmed by flow cytometry using the Annexin V assay, which revealed a significant increase in apoptotic cell populations with increasing AgNPs concentrations. Additionally, an increased percentage of cells in the sub-G1 phase was observed, further supporting apoptotic activity. At a concentration of 3 mg/mL, the AgNPs significantly reduced cell viability. These findings suggest that the proposed one-step, cost-effective biosynthesis method yields AgNPs with promising anticancer properties, highlighting their potential as a therapeutic tool for leukemia treatment.

Concordance between male- and female-specific GWAS results helps define underlying genetic architecture of complex traits

Understanding gender variation in the risk factors of hypertension through cross sectional analysis

Radiomics-enhanced modelling approach for predicting the need for ECMO in ARDS patients: a retrospective cohort study

Abstract Decisions regarding veno-venous extracorporeal membrane oxygenation (vv-ECMO) in patients with acute respiratory distress syndrome (ARDS) are often based solely on clinical and physiological parameters, which may insufficiently reflect severity and heterogeneity of lung injury. This study aimed to develop a predictive model integrating machine learning-derived quantitative features from admission chest computed tomography (CT) with selected clinical variables to support early individualized decision-making regarding vv-ECMO therapy. In this retrospective single-center cohort study, 375 consecutive patients with COVID-19-associated ARDS admitted to the ICU between March 2020 and April 2022 were included. Lung segmentation from initial CTs was performed using a convolutional neural network (CNN) to generate high-resolution, anatomically accurate masks of the lungs. Subsequently, 592 radiomic features, quantifying lung aeration, density and morphology, were extracted. Four clinical parameters – age, mean airway pressure, lactate, and C-reactive protein, were selected on the basis of clinical relevance. Three logistic regression models were developed: (1) Imaging Model, (2) Clinical Model, and (3) Combined Model integrating different features. Predictive performance was assessed via the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. A total of 375 patients were included: 172 in the training and 203 in the validation cohort. In the training cohort, the AUROCs were 0.743 (Imaging), 0.828 (Clinical), and 0.842 (Combined). In the validation cohort, the Combined Model achieved the highest AUROC (0.705), outperforming the Clinical (0.674) and Imaging (0.639) Models. Overall accuracy in the validation cohort was 64.0% (Combined), 66.5% (Clinical), and 59.1% (Imaging). The Combined Model showed 68.1% sensitivity and 58.9% specificity. Kaplan-Meier analysis confirmed a significantly greater cumulative incidence of ECMO therapy in patients predicted as high risk ( p  < 0.001), underscoring its potential to support individualized, timely ECMO decisions in ARDS by providing clinicians with objective data-driven risk estimates. Quantitative CT features based on machine learning-derived lung segmentation allow early individualized prediction of the need for vv-ECMO in ARDS. While clinical data remain essential, radiomic markers enhance prognostic accuracy. The Combined Model demonstrates considerable potential to support timely and evidence-based ECMO initiation, facilitating individualized critical care in both specialized and general ICU environments. Trial registration : The study is registered with the German Clinical Trials Register under the number DRKS00027856. Registered 18.01.2022, retrospectively registered due to retrospective design of the study.