jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shih-Peng Tai | Hooi Leong Loo | Athirah Bakhtiar | Paul Chi-Lui Ho | Lay‐Hong Chuah
Source: Drug Delivery and Translational Research (article, 2025-08-20)
Field: Materials Science — Biomaterials

Antibody-conjugated polymer nanoparticles for brain cancer Brain cancer remains a significant global health challenge, affecting over a million individuals worldwide. It includes primary tumors such as gliomas, particularly glioblastoma multiforme (GBM) being one of the most aggressive forms, and secondary brain tumors resulting from metastases of breast or lung cancer. A major obstacle in treating these malignancies is the blood-brain barrier (BBB), which limits effective drug delivery to tumor sites. Nanoparticles have long been explored as versatile drug delivery vehicles due to their potential to improve tumor specificity, penetrate the BBB, and enhance drug bioavailability. To further refine these delivery systems, antibodies have been conjugated to nanoparticles to target specific tumor biomarkers, thereby enhancing therapeutic precision and efficacy. In preclinical studies, antibody-conjugated polymer nanoparticles have demonstrated promising capabilities in achieving both targeted delivery and BBB penetration. This review first outlines the pathophysiology and mechanisms underlying brain cancer. It then discusses how polymer nanoparticles address current therapeutic limitations, particularly in overcoming the BBB and improving pharmacokinetics. Various polymer nanoparticles are explored, including polymeric micelles, dendrimers, nanocapsules, nanospheres, nanogels, and polymersomes, each offering distinct therapeutic advantages. We further examine the impact of surface modification with antibodies to enhance tumor targeting, discussing their physiochemical properties, as well as in vitro and in vivo findings. Finally, the review critically evaluates the current challenges in the development of antibody-conjugated polymer nanoparticles, identifies research gaps, and future directions of this promising field. Conjugated system ; Antibody ; Nanoparticle ; Polymer ; Cancer ; Brain cancer ; Chemistry ; Cancer research ; Nanotechnology ; Biophysics ; Medicine ; Materials science ; Immunology ; Internal medicine ; Biology ; Organic chemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Hanane Zaki | Mohamed Ouabane | Soumaya Aissaoui | Marwa Alaqarbeh | Mohammed Bouachrıne
Source: Current Chemistry Letters (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

1,2,4-triazole-chalcone and derivatives as antiproliferative agents: Quantum chemical studies, molecular docking, ADME-Tox and MD simulation The investigation of 1,2,4-triazole-chalcone has sparked immense interest due to their promising biological activities. These compounds, labeled 10C-10S, were synthesized and characterized by Jinjing et al., specifically focusing on their potential applications in biological settings, particularly their antiproliferative properties. Strategic exploration by computational chemistry techniques such as DFT calculations, molecular docking, and molecular dynamics with empirical findings proved pivotal in unraveling the multifaceted properties of these organic molecules. Additionally, molecular docking studies were conducted to elucidate the antiproliferative effects and analyze the potential binding modes of the compounds with specific amino acid residues in proteins. Rigorous comparisons between theoretical and experimental results yielded comprehensive insights into the properties of these compounds. We chose two molecules, C (the most active) and E (the least active), which have affinities of -7.689 and -7.526 kcal/mol, respectively, to test how stable they are with the EGFR receptor (PDB entry code: 6Z4B). Molecular dynamics simulations over 100 ns revealed more stable energies, with ΔG_Bind = -25.135 Kcal/mol and ΔG_Bind_vdW = -30.644 Kcal/mol. Molecular dynamics ; Binding affinities ; Affinities ; Quantum chemical ; Molecular model ; Amino acid residue ; Molecular mechanics ; Docking (animal) Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Biological Evaluation
Authors: Emad Abada | Mukul Sharma | Asmaa A. Alharbi | Shifaa O. Alshammari | Amani Alhejely | Yosra Modafer | Wail Alsolami | Ibrahim Y. Y. Sumaily | Mari Sumayli
Source: Inorganics (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

An Integrative Biosynthetic Approach to Silver Nanoparticles: Optimization Modeling, and Antimicrobial Assessment Silver nanoparticles (AgNPs) are valued for their antimicrobial properties, but conventional synthesis often involves toxic chemicals. Eco-friendly biosynthesis using silver-tolerant microbes from contaminated sites offers a sustainable alternative. This study biosynthesized and characterized AgNPs using a native Bacillus sp. from contaminated soil in the Jazan region, Saudi Arabia, and developed predictive models for optimizing synthesis and antimicrobial activity. AgNPs were synthesized under optimized conditions (1.0 mM AgNO3, 4.0 mL supernatant, pH 8, 85 °C). Characterization using UV–Vis, SEM, TEM, XRD, and FTIR assessed size, shape, structure, and chemistry. Gaussian and second models evaluated yield and inhibition zones based on AgNP concentration, microorganism type, and MIC. The AgNPs were spherical with diameters of 5–10 nm. The optimal nanoparticle yield occurs when the parameters are at their optimal values; C0 = 1.0 mM, V0 = 4.0 mL, pH0 = 8, T0 = 85 °C. XRD confirmed their crystalline nature, and FTIR showed biomolecular capping agents for stabilization. The Gaussian model accurately predicted synthesis efficiency, validated by 3D plots matching experimental data. The AgNPs showed strong antimicrobial activity against Gram-positive (Bacillus subtilis) (ATCC6051), Staphylococcus aureus (ATCC12600), Gram-negative bacteria Escherichia coli (ATCC11775) and fungi Candida albicans (ATCC10231); with E. coli having the lowest MIC (1.87 μg/mL). The inhibition zone model closely matched observed data. Biosynthesized AgNPs using silver-tolerant Bacillus sp. demonstrated potent antimicrobial effects and provide a green alternative to chemical synthesis. Integrating modeling optimizes biosynthesis and predicts biological performance, supporting future nanobiotechnology and antimicrobial applications. Antimicrobial ; Nanobiotechnology ; Candida albicans ; Escherichia coli ; Silver nanoparticle ; Yield (engineering) ; Bacillus subtilis Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering
Authors: Enrico Vezzali | Michael W. Becker | F. Romero-Palomo | Marjolein van Heerden | Caroline Chipeaux | Gregory Hamm | Dinesh S. Bangari | Thomas Lemarchand | Barbara Lenz | Bogdan Munteanu | Bhanu Singh | Céline Thuilliez | Seong‐Wook Yun | Andrew Smith | Rob J. Vreeken
Source: Toxicologic Pathology (review, 2025-01-01)
Field: Chemistry — Spectroscopy

European Society of Toxicologic Pathology—Pathology 2.0 Mass Spectrometry Imaging Special Interest Group: Mass Spectrometry Imaging in Diagnostic and Toxicologic Pathology for Label-Free Detection of Molecules—From Basics to Practical Applications Mass Spectrometry Imaging (MSI) is a powerful tool to understand molecular pathophysiology and therapeutic and toxicity mechanisms, as well as for patient stratification and precision medicine. MSI, a label-free technique offering detailed spatial information on a large number of molecules in different tissues, encompasses various techniques including Matrix-Assisted Laser Desorption Ionization (MALDI), Desorption Electrospray Ionization (DESI), and Secondary Ion Mass Spectrometry (SIMS) that can be applied in diagnostic and toxicologic pathology. Given the utmost importance of high-quality samples, pathologists play a pivotal role in providing comprehensive pathobiology and histopathology knowledge, as well as information on tissue sampling, orientation, morphology, endogenous biomarkers, and pathogenesis, which are crucial for the correct interpretation of targeted experiments. This article introduces MSI and its fundamentals, and reports on case examples, determining the best suited technology to address research questions. High-level principles and characteristics of the most used modalities for spatial metabolomics, lipidomics and proteomics, sensitivity and specific requirements for sample procurement and preparation are discussed. MSI applications for projects focused on drug metabolism, nonclinical safety assessment, and pharmacokinetics/pharmacodynamics and various diagnostic pathology cases from nonclinical and clinical settings are showcased. Mass spectrometry imaging ; MALDI imaging ; Lipidomics ; Molecular pathology ; Medicine ; Pathology ; Desorption electrospray ionization ; Mass spectrometry ; Medical physics ; Bioinformatics ; Chemistry ; Chemical ionization ; Biology ; Ionization ; Matrix-assisted laser desorption/ionization Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Jihyun Kim | Hyunbin Kim | Gang Jin Cheon | Do‐Hyun Lee | In S. Kim | Uiyeon Won | Jongseok Lee | Sang Min Won | Joohoon Kang
Source: Advanced Functional Materials (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Multicolor Optoelectronic Synapse Enabled by Photon‐Modulated Remote Doping in Solution‐Processed Van Der Waals Heterostructures Abstract Optoelectronic synapses have attracted considerable attention for emulating biological visual perception by enabling the recognition of complex visual stimuli, including spatial patterns and multicolor information. Despite significant progress, the realization of multicolor classification with simple and scalable device architecture remains a fundamental challenge, requiring strategies that overcome the limitations of conventional device configurations. Here, a scalable van der Waals heterostructure device is presented that enables multicolor optoelectronic processing by vertically integrating solution‐processed molybdenum disulfide (MoS 2 ) as a light‐absorbing layer and single‐walled carbon nanotubes (SWCNTs) as a semiconducting channel. Unlike conventional complex architectures, such as serially connected synaptic devices and optical‐sensors, the approach employs an electronically disconnected but interactive MoS 2 layer to facilitate photon‐modulated carrier doping in the adjacent SWCNTs channel. This configuration enables bi‐directional photocurrent behavior under different wavelength illumination, essential for emulating the retina's chromatic adaptation while maintaining a structurally simple and scalable design. Leveraging this unique bi‐directional photoresponse, an optical neural network framework is integrated that independently processes distinct spectral information, enabling highly efficient multicolor pattern classification. This synergistic strategy is essential for realizing scalable optoelectronic synapses, as evidenced by a classification accuracy of 92.0%, offering a promising platform for next‐generation vision systems. Scalability ; Heterojunction ; van der Waals force ; Photocurrent ; Molybdenum disulfide ; Plasmon ; Doping ; Layer (electronics) 2D Materials and Applications ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Cheng Wu | Yingjie Feng | Jiarui Cui | Yao Zhang | Hailong Xu | Songlei Wang
Source: Food Science & Nutrition (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Detection of Quality Deterioration of Packaged Raw Beef Based on Hyperspectral Technology It is an important measure to ensure food quality and safety that real-time monitoring of the key quality indicators of fresh meat after packaging in the process of storage and transportation. The feasibility of combining hyperspectral imaging (HSI) technology with chemometrics and deep learning to detect the quality deterioration of polyethylene (PE)-packaged raw beef, especially for a key lipid oxidation indicator of malondialdehyde (MDA) content, was explored in this study. The feasibility of filtering to overcome the interference of packaging film on the spectral data was further investigated. Near-infrared HSI (400-1000 nm) was used to collect spectral and spatial data of beef samples during short-term storage. A least squares regression (PLSR) and echo-neural network optimized by vulture optimization algorithms (BES-ESN) models were developed by multivariate data processing methods. The results showed that the performance of models established by PE-packed beef samples was usually inferior to that established by unpacked beef samples. The changes of MDA content in beef were visualized according to the optimal model. In addition, Gaussian filtering was applied to reduce the interference effect caused by the packaging film. The results have demonstrated that HSI combined with Gaussian filter preprocessing and multivariate data processing provided an efficient and reliable tool for detecting the freshness of beef in PE packaging. The best model had a coefficient of determination (<i>R</i> <sup>2</sup> <sub>P</sub>) of 0.8309 and a root mean squared error of prediction (RMSEP) of 0.2180, demonstrating the potential of hyperspectral techniques for real-time monitoring of packaged raw meat quality. The findings can provide some references for the meat industry to develop advanced non-invasive quality assurance systems in the meat industry. Hyperspectral imaging ; Quality (philosophy) ; Business ; Environmental science ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Samar E. Mahmoud | Ahmed A. Fadda | Ehab Abdel‐Latif | Mohamed R. Elmorsy
Source: Scientific Reports (article, 2025-07-03)
Field: Chemistry — Organic Chemistry

Discovery and molecular characterization of a potent thiazolyl-pyrazole hybrid targeting EGFR for breast cancer therapy Herein, a novel compound 2-(3-methyl-5-oxo-1-phenyl-1,5-dihydro-4H-pyrazolylidene)-3-phenylthiazolidin-5-one )2(was synthesized and reacted with different aromatic aldehydes 3a-f via Knoevenagel condensation reaction to give the corresponding 4-arylidene-2-(5-oxo-1,5-dihydro-4H-pyrazol-4-ylidene)thiazolidin-5-one hybrids 4a-f. The chemical structures were described by spectroscopic tools, IR, <sup>1</sup>H NMR, <sup>13</sup>C NMR, and MS. Their frontier molecular orbitals configuration and electron distribution were estimated to utilize DFT. The cytotoxicity of thiazolyl-pyrazole analogues 2 and 4a-f demonstrated in vitro antitumor activity toward breast cells; MCF-7 and MDA-MB231. Among the prepared analogues, the thiazolyl-pyrazole 2 revealed potent inhibitory toward the two cancer cells, particularly MDA-MB231 (IC<sub>50</sub> = 22.84 µM). SwissADME studies showed the pharmacokinetic parameters, drug-like qualities, and bioavailability of these derivatives, revealing their potential in anticancer applications. Additionally, disease and drug target predictions and construction of the protein-protein interaction (PPI) network identified PPARG, EGFR, and PPARA as major targets. Moreover, other studies were carried out on the most potent conjugate 2 to evaluate the potential interactions against PPARG, EGFR, and PPARA proteins for molecular docking and against EGFR only for molecular dynamic simulation. The mechanism of the most effective analogue 2 was proven experimentally by inhibiting wound healing and EGFR expression in MDA-MB231 culture media. The findings provide more credence to compound 2's potential in current medication development initiatives. Pyrazole ; Chemistry ; Cytotoxicity ; In vitro ; Stereochemistry ; Breast cancer ; Docking (animal) ; Combinatorial chemistry ; Cancer research ; Pharmacology ; Cancer ; Biochemistry ; Medicine ; Internal medicine Synthesis and biological activity ; Click Chemistry and Applications ; Quinazolinone synthesis and applications
Authors: Subhajit Maity | Susanta Sadhukhan | Sudakshina Ghosh | Madhusudan Das
Source: Discover Chemistry. (review, 2025-10-28)
Field: Materials Science — Materials Chemistry

A review on the applications of graphene quantum dots (GQDs) in diabetic complications Graphene quantum dots (GQDs) are tiny carbon nanomaterials composed of a few layers of carbon atoms arranged in a honeycomb structure. They possess unique electrochemical, electrical, and optical properties due to their quantum confinement and edge effects. These properties, along with low toxicity, high biocompatibility, and surface functionalization, make GQDs ideal for biomedical applications. This review aims to explore and critically evaluate the diagnostic and therapeutic roles of GQDs in the management of diabetes mellitus—a metabolic disorder with growing global prevalence. We highlight the development of sensitive and selective GQD-based glucose biosensors, wearable diagnostic devices, and novel drug delivery systems using GQDs for anti-diabetic agents such as metformin and vanadium complexes. Additionally, we discuss the role of GQDs in diabetic wound healing and their emerging application in bioimaging and insulin detection. The review also covers toxicity, biocompatibility, synthesis methods, and future prospects of GQD integration with AI and personalized medicine. A comprehensive toxicity analysis of published data illustrates that most human and animal cell lines typically show high cell viability (> 80%) at doses up to 500 µg/ml. Further in vivo studies show tolerance in zebrafish embryos, mice, and Caenorhabditis elegans, with no appreciable adverse effects at tested dosages. For a complete achievement of their therapeutic potential, future investigations must rely on reproducing expandable, repeatable synthesis techniques, concentrating surface functionality for targeted distribution, and conducting exhaustive longitudinal toxicity evaluations. Therefore, to bring GQDs closer to safe and effective therapeutic applications in diabetes and other diseases, these strategies are critical. Hence, this review provides an overview of the potential of GQDs in revolutionizing diabetes diagnosis and treatment. Quantum dot ; Graphene ; Nanomedicine ; Carbon quantum dots ; Drug delivery ; Diabetes mellitus ; Nanomaterials Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: José-Luis Morales-Reyes | Elia Nora Aquino-Bolaños | Héctor‐Gabriel Acosta‐Mesa | Nancy Pérez-Castro | José Luis Chávez‐Servia
Source: Mathematical and Computational Applications (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Classification of Common Bean Landraces of Three Species Using a Neuroevolutionary Approach with Probabilistic Color Characterization The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and seed mixtures as part of agricultural practices. In this work, we propose a methodology for classifying bean landrace samples using three two-dimensional histograms with data in the CIE L*a*b* color space while additionally integrating chroma (C*) and hue (h°) to develop a new proposal from histograms, employing deep learning for the classification task. The results indicate that utilizing three histograms based on L*, C*, and h° brings an average accuracy of 85.74 ± 2.37 compared to three histograms using L*, a*, and b*, which reported an average accuracy of 82.22 ± 2.84. In conclusion, the new color characterization approach presents a viable solution for classifying common bean landraces of both homogeneous and heterogeneous colors. Hue ; Histogram ; Homogeneous ; Artificial intelligence ; Characterization (materials science) ; Color space ; Mathematics ; Pattern recognition (psychology) ; Domestication ; Probabilistic logic ; Computer science ; Geography ; Biology ; Image (mathematics) ; Combinatorics ; Ecology ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Van Thai Nguyen | Jan Furch | Jan Koláček
Source: Scientific Reports (article, 2025-09-29)
Field: Chemistry — Analytical Chemistry

Using multiple linear regression to predict engine oil life This paper deals with the use of multiple linear regression to predict the viscosity of engine oil at 100 °C based on the analysis of selected parameters obtained by Fourier transform infrared spectroscopy (FTIR). The spectral range (4000-650 cm⁻¹), resolution (4 cm⁻¹), and key pre-processing steps such as baseline correction, normalization, and noise filtering applied prior to modeling. A standardized laboratory method was used to analyze 221 samples of used motor oils. The prediction model was built based on the values of Total Base Number (TBN), fuel content, oxidation, sulphation and Anti-wear Particles (APP). Given the large number of potential predictors, stepwise regression was first used to select relevant variables, followed by Bayesian Model Averaging (BMA) to optimize model selection. Based on these methods, a regression relationship was developed for the prediction of viscosity at 100 °C. The calibration model was subsequently validated, and its accuracy was determined using the Root Mean Squared Error (RMSE) metric, it was 0.287. Finally, the obtained model was used to predict the lifetime of engine oil in diesel engines operating under severe conditions. Linear regression ; Calibration ; Regression analysis ; Range (aeronautics) ; Regression ; Mean squared error ; Stepwise regression ; Diesel engine ; Bayesian linear regression ; Linear model Spectroscopy and Chemometric Analyses ; Lubricants and Their Additives ; Fault Detection and Control Systems
Authors: Helong Yu | Jiaping Zhao | Chun Guang Bi | Lei Shi | Huiling Chen
Source: CAAI Transactions on Intelligence Technology (article, 2025-06-28)
Field: Chemistry — Analytical Chemistry

A Lightweight YOLOv5 Target Detection Model and Its Application to the Measurement of 100‐Kernel Weight of Corn Seeds ABSTRACT The 100‐kernel weight of corn seed is a crucial metric for assessing corn quality, and the current measurement means mostly involve manual counting of kernels followed by weighing on a balance, which is labour‐intensive and time‐consuming. Aiming to address the problem of low efficiency in measuring the 100‐kernel weight of corn seeds, this study proposes a measurement method based on deep learning and machine vision. In this study, high‐contrast camera technology was utilised to capture image data of corn seeds. And improvements were made to the feature extraction network of the YOLOv5 model by incorporating the MobileNetV3 network structure. The novel model employs deep separable convolution to decrease parameters and computational load. It incorporates a linear bottleneck and inverted residual structure to enhance efficiency. It introduces an SE attention mechanism for direct learning of channel number features and updates the activation function. Algorithms and experiments were subsequently designed to calculate the 100‐grain weight in conjunction with the output of the model. The outcomes revealed that the enhanced model in this study achieved an accuracy of 90.1%, a recall rate of 91.3%, and a mAP (mean average precision) value of 92.2%. While meeting production requirements, this model significantly reduces the number of parameters compared to alternative models—50% of the original model. In an applied study focused on measuring the 100‐kernel weight of corn seeds, the counting accuracy yielded a remarkable 97.18%, while the accuracy for weight measurement results reached 94.2%. This study achieves both efficient and precise measurement of the 100‐kernel weight of maize seeds, presenting a novel perspective in the exploration of maize seed weight. Kernel (algebra) ; Agricultural engineering ; Environmental science ; Computer science ; Horticulture ; Mathematics ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI