jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kenichi Watanabe | S Takayama | Toichiro Yamada | Masayo Hashimoto | Jun Tadano | Tetsuya Nakagawa | Takao Watanabe | Eiichiro Fukusaki | Izuru Miyawaki | Shuichi Shimma
Source: Analytical and Bioanalytical Chemistry (article, 2024-08-10)
Field: Chemistry — Spectroscopy

Novel mimetic tissue standards for precise quantitative mass spectrometry imaging of drug and neurotransmitter concentrations in rat brain tissues Understanding the relationship between the concentration of a drug and its therapeutic efficacy or side effects is crucial in drug development, especially to understand therapeutic efficacy in central nervous system drug, quantifying drug-induced site-specific changes in the levels of endogenous metabolites, such as neurotransmitters. In recent times, evaluation of quantitative distribution of drugs and endogenous metabolites using matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) has attracted much attention in drug discovery research. However, MALDI-MSI quantification (quantitative mass spectrometry imaging, QMSI) is an emerging technique, and needs to be further developed for practicable and convenient use in drug discovery research. In this study, we developed a reliable QMSI method for quantification of clozapine (antipsychotic drug) and dopamine and its metabolites in the rat brain using MALDI-MSI. An improved mimetic tissue model using powdered frozen tissue for QMSI was established as an alternative method, enabling the accurate quantification of clozapine levels in the rat brain. Furthermore, we used the improved method to evaluate drug-induced fluctuations in the concentrations of dopamine and its metabolites. This method can quantitatively evaluate drug localization in the brain and drug-induced changes in the concentration of endogenous metabolites, demonstrating the usefulness of QMSI. Mass spectrometry imaging ; Chemistry ; Drug ; Pharmacology ; Mass spectrometry ; Endogeny ; Quantitative analysis (chemistry) ; Dopamine ; Drug discovery ; Metabolite ; Chromatography ; Neuroscience ; Medicine ; Biochemistry ; Biology Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Teng Zhang | Rui Cao | Jingying Li | Hanxiao Tang | Hang Su | Weisheng Feng | Zhijuan Zhang
Source: Scientific Reports (article, 2024-05-23)
Field: Chemistry — Inorganic Chemistry

A dual-responsive RhB-doped MOF probe for simultaneous recognition of Cu2+ and Fe3+ Based on the dual response of RhB@UiO-67 (1:6) to Cu<sup>2+</sup> and Fe<sup>3+</sup>, a proportional fluorescent probe with (I<sub>392</sub>/I<sub>581</sub>) as the output signal was developed to recognize Cu<sup>2+</sup> and Fe<sup>3+</sup>. Developing highly sensitive and selective trace metal ions probes is crucial to human health and ecological sustainability. In this work, a series of ratio fluorescent probes (RhB@UiO-67) were successfully synthesized using a one-pot method to enable fluorescence sensing of Cu<sup>2+</sup> and Fe<sup>3+</sup> at low concentrations. The proportional fluorescent probe RhB@UiO-67 (1:6) exhibited simultaneous quenching of Cu<sup>2+</sup> and Fe<sup>3+</sup>, which was found to be of interest. Furthermore, the limits of detection (LODs) for Cu<sup>2+</sup> and Fe<sup>3+</sup> were determined to be 2.76 μM and 0.76 μM, respectively, for RhB@UiO-67 (1:6). These values were significantly superior to those reported for previous sensors, indicating the probe's effectiveness in detecting Cu<sup>2+</sup> and Fe<sup>3+</sup> in an ethanol medium. Additionally, RhB@UiO-67 (1:6) demonstrated exceptional immunity and reproducibility towards Cu<sup>2+</sup> and Fe<sup>3+</sup>. The observed fluorescence quenching of Cu<sup>2+</sup> and Fe<sup>3+</sup> was primarily attributed to the mechanisms of fluorescence resonance energy transfer (FRET), photoinduced electron transfer (PET), and competitive absorption (CA). This work establishes a valuable foundation for the future study and utilization of Cu<sup>2+</sup> and Fe<sup>3+</sup> sensing technologies. Dual (grammatical number) ; Doping ; Dual role ; Computer science ; Chemistry ; Materials science ; Optoelectronics ; Combinatorial chemistry Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Roya Ahmadzadeh | Seyed Alireza Taheri | Neda Mohammadi | Ahmed Hjazi | Soumya V. Menon | Wesam R. Kadhum | Abhinav Kumar | Maha Shakir | Farid Karkon Shayan | Nahal Shirinkami
Source: Journal of Biochemical and Molecular Toxicology (review, 2024-08-01)
Field: Materials Science — Biomaterials

Biologically based strategies for overcoming in vivo barriers with functional nano‐delivery systems Nanomedicine has been developed to reduce or eliminate the side effects and toxicity upon systemic therapy of chemotherapeutic agents and to improve their therapeutic efficacy. However, the translation of non-sized or nano-encapsulated drugs is hampered by the low penetration and accumulation of engineered nanoparticles (NPs) in sites of tumors as well as their poor pharmacokinetics. This may be due to the synthetic structure of NPs and also complicated and unknown characteristics of the solid tumor microenvironment (TME). As a result, the TME is being better identified, and the interactions between NPs and the TME or human body are being discovered or predicted. These findings have led to the development of more biocompatible, intelligent, and controllable bio-based nanoformulations that could overcome current barriers and provide sufficient drug delivery to the TME, as discussed in this paper. These formulations are designed to (i) modify the surface of NPs to improve blood circulation while reducing their off-target accumulation and side effects in vivo, (ii) pass through the tumor vasculature by modulating or targeting angiogenesis, (iii) promote NPs distribution in solid tumor regions by applying biological/physical stimuli or extracellular matrix remodeling, and (iv) overcome the cell membrane barrier and other compartments of the cell by specific cell targeting to release the payload drug into the cytoplasm or nucleoplasm. In vivo ; Drug delivery ; Nanomedicine ; Angiogenesis ; Chemistry ; Tumor microenvironment ; Extracellular matrix ; Cancer research ; Nanotechnology ; Pharmacology ; Nanoparticle ; Materials science ; Medicine ; Tumor cells ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; 3D Printing in Biomedical Research
Authors: Francesca Bonazza | Lucia Monti | Milena Povolo | Andrea Gasparini | Valeria Pelizzola | Giovanni Cabassi
Source: Molecules (article, 2024-11-26)
Field: Chemistry — Analytical Chemistry

Monitoring the Shelf Life of Hemp Seed Oil Stored at Two Temperatures in Different Materials via Near-Infrared (NIR) Spectroscopy Hempseed oil (HSO) is extremely rich in unsaturated fatty acids, especially linoleic (18:2 n-6) and α-linolenic (18:3 n-3) acids, which determine its high sensitivity to oxidative and photo-oxidative degradations that can lead to rancidity despite the presence of antioxidant compounds. The aim of this work was to evaluate which material/temperature/light solutions better preserve HSO quality during its shelf life and to test NIR as a rapid, non-destructive technique for monitoring oxidation phenomena. Futura 75 hemp seeds were cold-pressed; the oil was packed into 20 mL vials of four different materials (polypropylene, clear glass, amber glass, and amber glass coated with aluminum foil) and stored for 270 days at 25 °C under diffused light and at 10 °C in dark conditions., Peroxides and conjugated dienes and trienes were evaluated at intervals to monitor oil stability. Moreover, NIR spectra were measured in transmission, and the sample dataset was analyzed using ASCA to test the significance of the experimental factors: the model showed the significance of all factors and of all the simple interactions. Our results demonstrate that oil stored in amber glass vials with aluminum foils at refrigerated temperatures receive the highest protection from environmental conditions, mitigating oxidative changes, and that the NIR technique could be used to rapidly monitor HSO oxidation parameters. Shelf life ; Chemistry ; Aluminum foil ; FOIL method ; Food science ; Materials science ; Organic chemistry ; Composite material Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Meat and Animal Product Quality
Authors: Zeynal Abidin Oğuz
Source: Physica Scripta (article, 2024-12-30)
Field: Materials Science — General Materials Science

UV aging effect on buckling properties of nanoclay particulated intraply hybrid composites Abstract This work experimentally investigated how ultraviolet (UV) aging affects the buckling properties of polymer-based intraply hybrid composites reinforced with nanoparticles (NPs) and varied in weight ratios. The nanoclay reinforced intraply carbon/aramid/epoxy composite samples with six different weight ratios of nanoparticles (0, 0.5, 1, 1.5, 2, 3%) were subjected to axial and lateral buckling tests after 250 h and 500 h of UV aging. At the end of the study, SEM images were taken for both buckling types and the damage patterns of the composite materials were analyzed. Considering the unaged samples, the critical buckling load (P cr ) of hybrid composites with NP reinforcement reached the highest values for 1% wt, with increases of 22.28 and 25.98% for the axial and lateral buckling test, respectively. The addition of more particles by weight resulted in a gradual decrease in the P cr for both buckling tests. The P cr decreases for axial and lateral buckling tests of unaged samples with 3 wt% nanoparticle reinforcement were found to be 8.18% and 25.43%, respectively. Experimental results showed that the buckling features of intraply samples changed due to both UV exposure and the addition of NPs. Furthermore, as UV exposure time increases, the P cr for each weight ratio increased for both buckling types. Considering the UV effect, the axial P cr increased by 32.07% and 40.89% in 1%wt particle reinforced samples after 250 and 500 h of UV aging, respectively. Similarly, an increase of 32.65% and 42.32% in lateral P cr was observed in 1%wt particle reinforced samples after 250 and 500 h of UV aging, respectively. Finally, SEM images revealed matrix cracks, fiber breakage, debris, microbuckled fiber, and delamination mode failure morphologies. Materials science ; Composite material ; Buckling Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Tribology and Wear Analysis
Authors: Silvia Pezzola | Mariano Venanzi | Valeria Conte | Federica Sabuzi | Pierluca Galloni
Source: ChemPhysChem (article, 2024-05-27)
Field: Chemistry — Organic Chemistry

New Insights in the Computational pKb Determination of Primary Amines and Anilines Extensive research has already provided reliable methods for the in silico prediction of pKa, while a trustworthy strategy for pKb determination is still being sought. Indeed, the approaches previously exploited for computing pKa have shown their weakness in predicting pKb. In the light of the exceptional reliability demonstrated in the pKa calculation of a wide panel of organic acids, in this work, we exploited our "easy to use methodology", based on the direct approach, to predict the pKb of primary amines. Herein, CAM-B3LYP was compared to WB97XD and B3PW91, exploring the solvation model based on density (SMD) and the polarizable continuum model (PCM), in the presence of two explicit water molecules. Noteworthy, CAM-B3LYP and WB97XD returned completely different solvent accessible surfaces (SAS) and electron potential maps (EPM) for the bases and the conjugated acids, independently from the nature of the substituents. Once again, CAM-B3LYP/SMD/2H<sub>2</sub>O method confirmed its remarkable reliability, leading to a minimum average error (MAE) lower than 0.3. This outstanding result strengthens the trustworthiness of our method, already successfully applied to predict the pKa of different substituted phenols and carboxylic acids. Thus, our "easy-to-use" process can predict also the pKb of primary ammines and anilines, always ensuring consistent outputs. Chemistry ; Solvation ; Implicit solvation ; Trustworthiness ; Polarizability ; Conjugated system ; Solvent ; Computational chemistry ; Molecule ; In silico ; Combinatorial chemistry ; Computer science ; Organic chemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Metal complexes synthesis and properties
Authors: Yang Li | Yankun Peng | Yongyu Li | Tianzhen Yin | Bingwei Wang
Source: Foods (article, 2024-03-28)
Field: Chemistry — Analytical Chemistry

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC. Spectral line ; Partial least squares regression ; Fusion ; Biological system ; Wavelength ; Sampling (signal processing) ; Computer science ; Artificial intelligence ; Mathematics ; Optics ; Statistics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xinyue Cheng | Chenxia Li | Bo Fang | Zhi Hong | Yongxing Jin | X. M. Jing
Source: Journal of Applied Physics (article, 2024-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-based wireless communication technology and its applications Metasurfaces, due to their outstanding ability to control electromagnetic waves, have great application prospects in the field of wireless communication. This paper provides a comprehensive review of research work based on metasurface in three aspects: wireless power transfer, wireless information transmission, and novel wireless transceiver architectures. In the domain of wireless power transfer, several focusing metasurfaces and systems with unique performance are presented along with a new formula for calculating wireless power transfer. Concerning wireless information transmission section, the direct digital information transmission based on metasurface and the information transmission based on space-time-coding digital metasurface are introduced. Lastly, a simplified wireless transceiver with metasurfaces was introduced. The paper concludes with a discussion on the future directions of metasurfaces in the wireless communication domain. Wireless ; Computer science ; Nanotechnology ; Materials science ; Telecommunications Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Henry Lee | Dimitri Vanhecke | Sandor Balog | Patricia Taladriz‐Blanco | Alke Petri‐Fink | Barbara Rothen‐Rutishauser
Source: Nanoscale Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The impact of macrophage phenotype and heterogeneity on the total internalized gold nanoparticle counts Multimodal analysis and quantification of gold nanoparticles at the single-cell level using FIB-SEM tomography in human monocyte-derived macrophages revealed phenotype-dependent behaviors influencing distinct kinetics and heterogeneity in uptake. Phenotype ; Colloidal gold ; Monocyte ; Macrophage ; Kinetics ; Nanoparticle ; Biophysics ; Cell biology ; Materials science ; Chemistry ; Nanotechnology ; In vitro ; Biology ; Immunology ; Biochemistry ; Gene Nanoparticles: synthesis and applications ; Immune cells in cancer ; Barrier Structure and Function Studies
Authors: Zhangjun Fei | Yiying Li | Shiyou Yang
Source: Sensors (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Pattern Recognition of Partial Discharge Faults in Switchgear Using a Back Propagation Neural Network Optimized by an Improved Mantis Search Algorithm The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm's efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge. Switchgear ; Partial discharge ; Pattern recognition (psychology) ; Artificial neural network ; Support vector machine ; Artificial intelligence ; Backpropagation ; Algorithm ; Principal component analysis ; Computer science ; Feature (linguistics) ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Power Transformer Diagnostics and Insulation
Authors: Dongling Jia | Zefei Jiang | Minhui Cui | Xianting Ding
Source: Clinical Cancer Bulletin (article, 2024-11-19)
Field: Chemistry — Spectroscopy

Proteomics efforts for hepatocellular carcinoma drug development Abstract Hepatocellular carcinoma (HCC) is a malignant tumor associated with high global incidence and mortality rates. Proteomics, as a platform technology of cellular protein expression, modification, and interaction, has provided innovative perspectives on early diagnosis, treatment, and targeted drug development for HCC. This review summarizes recent progress in proteomics for advancing HCC biomarker discovery, drug target identification, and understanding drug action mechanisms. Proteomic technologies, including mass spectrometry for specific protein signatures identification, protein microarrays for high-throughput analysis, and bioinformatics for data interpretation, have profoundly promoted the identification of liver cancer-specific biomarkers. These advancements not only facilitate early diagnosis but also improve prognostic assessment. Proteomics is pivotal in expediting the discovery and development of new drugs, providing more effective and personalized treatment options for HCC patients. This review offers a comprehensive overview of the applications of proteomics in anti-HCC drug research, serving as a reference to further advance the development of HCC research and treatment domains. Hepatocellular carcinoma ; Drug development ; Drug ; Proteomics ; Medicine ; Oncology ; Internal medicine ; Pharmacology ; Biology Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research
Authors: Haofan Cui | Fengying Gu | Jingjing Qin | Zhenyuan Li | Yu Zhang | Qin Guo | Qiang Wang
Source: Foods (article, 2024-05-31)
Field: Chemistry — Analytical Chemistry

Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires. Partial least squares regression ; Principal component analysis ; Near-infrared spectroscopy ; Residual ; Artificial neural network ; Quality (philosophy) ; Coefficient of determination ; Regression ; Correlation coefficient ; Linear regression ; Computer science ; Principal component regression ; Food science ; Process engineering ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Machine learning ; Chemistry ; Statistics ; Engineering ; Biology ; Algorithm Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Meat and Animal Product Quality