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Conditional diffusion model for high-accuracy brain tumor segmentation in MRI images

A modular strategy for extracellular vesicle-mediated CRISPR-Cas9 delivery through aptamer-based loading and UV-activated cargo release

Abstract CRISPR-Cas9 gene editing technology offers the potential to permanently repair genes containing pathological mutations. However, efficient intracellular delivery of the Cas9 ribonucleoprotein complex remains a major hurdle in its therapeutic application. Extracellular vesicles (EVs) are biological nanosized membrane vesicles that play an important role in intercellular communication, and have an innate capability of intercellular transfer of biological cargos, including proteins and RNA. Here, we present a versatile, modular strategy for EV-mediated loading and delivery of Cas9. We leverage the high affinity binding of MS2 coat proteins fused to EV-enriched proteins to MS2 aptamers incorporated into guide RNAs, in combination with a UV-activated photocleavable linker domain, PhoCl. Moreover, we demonstrate that Cas9 can readily be exchanged for other variants, including transcriptional activator dCas9-VPR and adenine base editor ABE8e. Taken together, we describe a robust, modular strategy for successful Cas9 delivery, which can be applied for CRISPR-Cas9-based genetic engineering and transcriptional regulation.

Evaluating the reliability of large language models for clinical data extraction in bladder cancer prognosis

Enantiotopic-group-selective coupling for unified access to carbazole atropisomers as versatile chiral chromophores

Abstract Chiral organic chromophores are foundational for advanced optical and electronic devices. Despite the widespread use of N -aryl carbazoles in visible-luminescent materials, chiroptical applications of their atropisomers have remained underdeveloped due to the synthetic challenge of achieving remote atroposelectivity necessitated by extended π-systems. Here, we present a unified strategy for the efficient synthesis of enantioenriched N–C and N–N carbazole atropisomers. By integrating 13 C NMR-based ligand parameterization, we achieve enantiotopic-group-selective coupling reactions that simultaneously incorporate tailored π-functionalities and establish axial chirality (up to >99:1 er) using synthetic pathways established in carbazole chemistry. Through covalent modulation and noncovalent complexation, we investigate novel chiroptical functions of carbazole atropisomers, including circular dichroism (CD), circularly polarized luminescence (CPL), charge-transfer CPL (CT-CPL), and circularly polarized thermally activated delayed fluorescence (CP-TADF). By establishing an electrostatic steering strategy for remote atroposelectivity, our work paves the way for integrating multifunctional carbazoles into advanced optical and optoelectronic technologies.

Study of anomaly registration detection based on multilayer kernel autoencoder extreme learning machine model

3D Integration of functionally diverse 2D materials for optoelectronic reservoir computing

Morpho-physiological parameters, nutritional status and water use efficiency of Zebda mango in relation to biochar and hydrogel application under semi-arid region

Abstract Irrigation water conservation techniques are an effective tool for maximizing irrigation water utilization, especially in coarse sandy soils under semi-arid conditions. Therefore, the objective of this research was to investigate the impacts of soil application of biochar (BC) at 7, 14 kg tree − 1 , hydrogel (HD) at 50–100 g tree − 1 and without application (control) on the morpho-physiological, nutritional status and productivity of Zebda mango trees during two seasons. Results indicated that, soil application of 14 kg BC tree − 1 , followed by 100 g HD tree − 1 was more effective in enhancing growth and fruit yield. BC at rate of 14 kg tree − 1 increased number of leaves by a percentage reached to 22.27 and 32.05%, leaves area by 18.51 and 18.51, shoot length by 21.19 and 17.95% and chlorophyll content by 81 and 51%, while it decreased leaves proline content by 9.15 and 13.78% compared to the control in the first and second seasons, respectively. Moreover, 14 kg BC increased leaf N concentration by 42.98 and 27.2%, leaf P by 75 and 47.62%, leaf K by 5% and 9.64% and leaf Mg by 27 and 6% compared to the control in the first and second seasons, respectively. Also, it improved the percentage of final fruit set by 50% and 38.09%, number of fruit by 29.47% and 22.19%, fruit weight by 20.46% and 12.76%, and increased both fruit yield and water use efficiency by 55.98 and 37.79%, while it decreased fruit drop by 2.79 and 2.16% compared to the control in the first and second seasons, respectively. Furthermore it increased the percentage of TSS by 17.09% and 18.27%, titratable acidity by 21.43% and 12.63%, ascorbic acid by 23.56% and 18%, and total sugars by 27.92% and 3.19% compared to the control in the first and second seasons, respectively.

Tripotent Lgr5 stem cells in the posterior tongue generate lingual, taste, and salivary gland lineages

Effectiveness of an information-motivation-behavioural skills model-based education kit (PREM-Kit) on human immunodeficiency virus knowledge and attitude among Malaysian late adolescents

The phase diagram of quantum chromodynamics in one dimension on a quantum computer

Rasch analysis and application research of the Chinese version of the Skidmore anxiety stigma scale: a cross-sectional study

Epithelial-mesenchymal cell competition coordinates fate transitions across tissue compartments during lung development and fibrosis

Enhanced YOLOv7 with CDP-ELAN and gather-distribute mechanism for robust smoke and flame detection

Preparation and evaluation of Dendrobium formosum extract-loaded microemulsions for anti-aging purpose

Sox9 regulation of hexokinase 1 controls neuroinflammatory astrocyte subtypes in a rat model of neuropathic pain

Multi-attribute monitoring (MAM) methodology for glycosylated subunit vaccines

Abstract Many protein-based vaccines comprise viral surface proteins which are chosen for their ability to stimulate the immune system. These vaccine molecules are often heavily glycosylated, and glycosylation plays critical roles in the immunological and stability properties of vaccines. The structural characterization and product quality attribute monitoring of such complex vaccine therapeutics during process development and manufacturing is very challenging. High throughput monitoring of multiple molecular attributes, particularly glycosylation, of recombinant glycoprotein subunit vaccines are needed to support entire vaccine production processes. Multi-attribute monitoring (MAM) technology involves assessing multiple critical molecular attributes of molecules in one set of analyses in an automated fashion, for product quality attribute requirements. MAM is still in the early development stages and is currently applied to therapeutics with very low levels of glycosylation such as monoclonal antibodies. MAM on glycoproteins with a higher number of glycosylation sites with high glycan heterogeneity such as subunit vaccine molecules is challenging as each glycan site and glycan modification exponentially increases data processing complexity. We developed a MAM workflow to perform detailed structural characterization of subunit protein vaccines, monitoring critical parameters such as intact mass, sequence identity, protein clipping, glycosylation, other post-translational modifications, and host cell proteins (HCP). By using a combination of software tools and product process monitoring strategy, we performed data processing at multiple steps and identified key attributes for each vaccine candidate under the development pipeline. Further, a high-throughput critical attribute monitoring MAM workflow was developed to support the influenza and HIV vaccine development processes including cell line selection, cell clone selection, cell culture optimization, stability study evaluation and final vaccine product characterization.

A locally adaptive regularization of a hybrid variational model for color image diffusion via integration of diffusion with normalized data

Cancer cell-derived IL-1β reverses chemo-immunotherapy resistance in non-small cell lung cancer

Quantifying and mitigating electrical and environmental impacts of corona discharge

Abstract Corona discharge has been recognized for centuries, with sailors reporting the bluish glow of St. Elmo’s fire on ship masts during storms. In the early development of high-voltage engineering, researchers such as Townsend and Peek described the physical basis of this phenomenon as the ionization of air around a conductor when the electric field exceeds the strength of the surrounding medium. The result is a partial discharge that produces visible light, hissing sounds, ozone, and other reactive gases, while also creating radio interference and ultraviolet radiation. In modern transmission systems, these effects appear as wasted power, accelerated wear of insulators, shortened equipment lifetime, and environmental concerns. Although corona has been studied for decades, it continues to challenge the reliable and economical operation of high-voltage networks, particularly under changing weather conditions. This study investigates the phenomenon by analyzing its causes, effects, and mitigation strategies through a combination of theoretical modelling, simulation, and statistical analysis. Using MATLAB Simulink and Python, simulations were conducted under varying environmental conditions—including temperature, humidity, and pressure—as well as electrical parameters such as voltage and conductor design, using observed data to ensure practical relevance. Comparable data sources may be used in other national or regional contexts. Key statistical techniques, including linear and multiple regression, analysis of variance (ANOVA), t-tests, and Monte Carlo simulations, were applied to determine the most influential factors affecting corona discharge losses. Results confirmed that higher voltage levels and unfavorable environmental conditions significantly increase corona loss, while increased conductor spacing and the use of corona rings emerged as the most effective mitigation strategies. An economic analysis based on probabilistic modelling estimated potential annual savings of up to 455 million Egyptian pounds (EGP) for the Egyptian grid, serving as a representative case study. The analytical framework is general and can be applied to other national transmission systems with appropriate data. The findings offer data-driven insights for improving transmission efficiency, minimizing power losses, and enhancing the overall reliability and cost-effectiveness of high-voltage power systems.

Smartphone-integrated portable microfluidic platform for liver biomarker quantification using deep learning

Abstract Accurate and decentralized liver biomarker testing is critical for early diagnosis and monitoring of hepatic dysfunctions, particularly in resource-constrained settings. This work presents a novel smartphone-integrated colorimetric sensing platform that combines microfluidics, deep learning, and mobile health technologies to estimate liver biomarkers quantitatively. A stereolithography (SLA) 3D-printed microfluidic flow cell, optimized for low reagent use and high optical clarity, processes 100 µL of sample-reagent mixture via a peristaltic pump at 50 µL/s. Biomarker-specific chromogenic reactions are imaged within a controlled lighting enclosure using multiple smartphone models and analyzed using a convolutional neural network (CNN) for a regression approach. The system achieves clinically relevant detection ranges of 0.1–20 mg/dL for direct and total bilirubin, and 10–300 U/L for alanine aminotransferase (ALT) and aspartate aminotransferase (AST), with limits of detection of 0.1 mg/dL, 0.05 mg/dL, 2.97 U/L, and 2.5 U/L, respectively. A two-point smartphone adaptability framework ensures robust cross-device performance without retraining. An Android application has been developed, which provides users with disease identification, real-time inference, and visualization of result. This clinical-grade analyzer features an average coefficient of determination (R 2 ) of 0.997 for all biomarkers, and the repeatability is shown by coefficients of variation under 3%. This innovative, cost-effective and portable solution gives precise liver function assessment, making it ideal for rural healthcare and mobile diagnostics.