Search Results (668)

Mesenchymal stromal cells (MSCs) and their released factors (secretome) are intriguing options for regenerative medicine approaches based on the management of inflammation and tissue restoration, as in joint disorders like osteoarthritis (OA). Production strategy may modulate cells and secretome fingerprints, and for the latter, the effect of serum removal by starvation used in clinical-grade protocols has been underestimated. In this work, the effect of starvation on the molecular profile of interleukin 1 beta (IL1β)-primed adipose-derived MSCs (ASCs) was tested by assessing the expression level of 84 genes related to secreted factors and 84 genes involved in defining stemness potential. After validation at the protein level, the effect of starvation modulation in the secretomes was tested in a model of OA chondrocytes. IL1β priming in vitro led to an increase in inflammatory mediators’ release and reduced anti-inflammatory potential on chondrocytes, features reversed by subsequent starvation. Therefore, when applying serum removal-based clinical-grade protocols for ASCs’ secretome production, the effects of starvation must be carefully considered and investigated. Full article

Indonesia is arguably a major player in worldwide rice production. Though white rice is the most predominantly cultivated, red, brown, and red rice are also very common. These types of rice are known to have different cooking properties that may be related to differences in their starch properties. Investigating the starch properties, especially the fine structure of their amylopectin, can help understand these differences. This study aims to investigate the starch characteristics of some Indonesian rice varieties by evaluating the starch granule morphology and size, molecular characteristics, amylopectin unit and internal chain profiles, and thermal properties. Starches were extracted from white rice (long grain (IR-64) and short grain (IR-42)), brown rice, red rice, and black rice cultivated in Java Island, Indonesia. IR-42 had the highest amylose content of 39.34% whilst the black rice had the least of 1.73%. The enthalpy of gelatinization and onset temperature of the gelatinization of starch granules were between 3.2 and 16.2 J/g and 60.1 to 73.8 °C, respectively. There were significant differences between the relative molar amounts of the internal chains of the samples. The two white rice and black rice had a significantly higher amount of A-chains, but a lower amount of B-chains and fingerprint B-chains (B_fp) than the brown and red rice. The average chain length (CL), short chain length (SCL), and external chain length (ECL) were significantly longer for the red rice and the black rice in comparison to both the white rice amylopectins. The long chain length (LCL) and internal chain length (ICL) of the sample amylopectins were similar. Rice starches were significantly different in the internal structure but not as much in their amylopectin unit chain profile. These results suggest the differences in their amylopectin clusters and building blocks. Full article

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules. Full article

Pulmonary sclerosing pneumocytoma (PSP) is a quite rare tumor outside Eastern countries. This rarity, together with a wide histological appearance, makes its correct identification a diagnostic challenge for pathologists under the microscope. Historically, PSP was considered a vascular-derived neoplasm (sclerosing hemangioma), but its immunohistochemical profile clearly supports its epithelial origin. No specific molecular fingerprint has been detected so far. This short narrative revisits the clinical, histological, immunohistochemical, and molecular aspects of this tumor, paying special attention to some controversial points still not well clarified, i.e., clinical aggressiveness and metastatic spread, multifocality, the supposed development of sarcomatoid change in a subset of cases, and tumor associations with lung adenocarcinoma and/or well-differentiated neuroendocrine hyperplasia/tumors. The specific diagnostic difficulties on fine-needle aspiration cytology/biopsy and perioperative frozen sections are also highlighted. Finally, a teaching case of tumor concurrence of lung adenocarcinoma, neuroendocrine lesions, and PSP, paradigmatic of tumor association in this context, is also presented. Full article

Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds. Full article

Air pollution has been associated with several health problems. Detecting and measuring the concentration of harmful pollutants present in complex air mixtures has been a long-standing challenge, due to the intrinsic difficulty of distinguishing among these substances from interferent species and environmental conditions, both indoor and outdoor. Despite all efforts devoted by the scientific and industrial communities to tackling this challenge, the availability of suitable device technologies able to selectively discriminate these pollutants present in the air at minute, yet dangerous, concentrations and provide a quantitative measure of their concentrations is still an unmet need. Thermal conductivity detectors (TCDs) show promising characteristics that make them ideal gas sensing tools capable of recognising different gas analytes based on their physical fingerprint characteristics at the molecular level, such as their density, thermal conductivity, dynamic viscosity, and others. In this paper, the operation of TCD gas sensors is presented and explored using a finite element simulation of Joule heating in a sensing electrode placed in a gas volume. The results obtained show that the temperature, and hence, the resistance of the individual suspended microbridge sensor device, depends on the surrounding gas and its thermal conductivity, while the sensitivity and power consumption depend on the properties of the constitutive metal. Moreover, the electrode resistance is proven to be linearly dependent on the applied voltage. Full article

Molecular markers play a crucial role in marker-assisted breeding and varietal identification. However, the application of insertion/deletion markers (InDels) in grapevines has been limited by the low throughput and separability of gel electrophoresis. To developed effective InDel markers for grapevines, this study reports a novel, effective and high-throughput pipeline for InDel marker development and identification. After rigorous filtering, 11 polymorphic multi-allelic InDel markers were selected. These markers were then used to perform genetic identification of 123 elite grape cultivars using agarose gel electrophoresis and next-generation sequencing (NGS). The polymorphism rate of the InDel markers identified by gels was 37.92%, while the NGS-based results demonstrated a higher polymorphism rate of 61.12%. Finally, the NGS-based fingerprints successfully distinguished 122 grape varieties (99.19%), surpassing the gels, which could distinguish 116 grape varieties (94.31%). Specifically, we constructed phylogenetic trees based on the genotyping results from both gels and NGS. The population structure revealed by the NGS-based markers displayed three primary clusters, consisting of the patterns of the evolutionary divergence and geographical origin of the grapevines. Our work provides an efficient workflow for multi-allelic InDel marker development and practical tools for the genetic discrimination of grape cultivars. Full article

Drug–target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug–target interactions (DTIs). In this study, a novel drug–target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug–target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug–target interactions. Additionally, we predicted and validated 22 Alzheimer’s disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing. Full article

Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours’ characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer. Full article

The rapid spread of SARS-COV-2 and the millions of worldwide deaths and hospitalizations have prompted an urgent need for the development of screening tests capable of rapidly and accurately detecting the virus, even in asymptomatic people. The easy collection and the biomarker content of saliva, together with the label-free and informative power of surface-enhanced Raman spectroscopy (SERS) analysis have driven the creation of point-of-care platforms capable of identifying people with COVID-19. Indeed, different salivary fingerprints were observed between uninfected and infected people. Hence, we performed a retrospective analysis of SERS spectra from salivary samples of COVID-19-infected and -vaccinated subjects to understand if viral components and/or the immune response are implicated in spectral variations. The high sensitivity of the proposed SERS-based method highlighted the persistence of molecular alterations in saliva up to one month after the first positive swab, even when the subject tested negative for the rapid antigenic test. Nevertheless, no specific spectral patterns attributable to some viral proteins and immunoglobulins involved in COVID-19 infection and its progression were found, even if differences in peak intensity, presence, and position were observed in the salivary SERS fingerprint. Full article

Surface-enhanced Raman spectroscopy (SERS) is a promising and highly sensitive molecular fingerprint detection technology. However, the development of SERS nanocomposites that are label-free, highly sensitive, selective, stable, and reusable for gaseous volatile organic compounds (VOCs) detection remains a challenge. Here, we report a novel TiO₂NTs/AuNPs@ZIF−8 nanocomposite for the ultrasensitive SERS detection of VOCs. The three-dimensional TiO₂ nanotube structure with a large specific surface area provides abundant sites for the loading of Au NPs, which possess excellent local surface plasmon resonance (LSPR) effects, further leading to the formation of a large number of SERS active hotspots. The externally wrapped porous MOF structure adsorbs more gaseous VOC molecules onto the noble metal surface. Under the synergistic mechanism of physical and chemical enhancement, a better SERS enhancement effect can be achieved. By optimizing experimental conditions, the SERS detection limit for acetophenone, a common exhaled VOC, is as low as 10⁻¹¹ M. And the relative standard deviation of SERS signal intensity from different points on the same nanocomposite surface is 4.7%. The acetophenone gas achieves a 1 min response and the signal reaches stability in 4 min. Under UV irradiation, the surface-adsorbed acetophenone can be completely degraded within 40 min. The experimental results demonstrate that this nanocomposite has good detection sensitivity, repeatability, selectivity, response speed, and reusability, making it a promising sensor for gaseous VOCs. Full article

Plant diseases pose a significant threat to plant and crop health, leading to reduced yields and economic losses. The traditional methods for diagnosing plant diseases are often invasive and time-consuming and may not always provide accurate results. In recent years, there has been growing interest in utilizing Raman microscopy as a non-invasive and label-free technique for plant disease diagnosis. Raman microscopy is a powerful analytical tool that can provide detailed molecular information about samples by analyzing the scattered light from a laser beam. This technique has the potential to revolutionize plant disease diagnosis by offering rapid and accurate detection of various plant pathogens, including bacteria and fungi. One of the key advantages of Raman microscopy/spectroscopy is its ability to provide real-time and in situ analyses of plant samples. By analyzing the unique spectral fingerprints of different pathogens, researchers can quickly identify the presence of specific diseases without the need for complex sample preparation or invasive procedures. This article discusses the development of a Raman microspectroscopy system for disease diagnosis that can accurately detect and identify various plant pathogens, such as bacteria and fungi. Full article

Background: Ophiocordyceps sinensis has long been recognized as a mysterious and valuable traditional Chinese medicine but there has been little research on quality markers for O. sinensis. Purpose: This study looked into the potential of using powder X-ray diffractometry (PXRD) to analyze polysaccharides as a quality marker for O. sinensis. Study design: There were 16 different habitats of O. sinensis collected in Qinghai, Gansu, Sichuan, Yunnan, and Tibet. In addition, five different types of Cordyceps species were collected. The characteristic diffraction peaks of O. sinensis were determined and then matched with the characteristic diffraction peaks of intracellular polysaccharides obtained from O. sinensis to determine the attribution relationship of the characteristic diffraction peaks. Methods: O. sinensis powder’s X-ray diffraction pattern is determined by its composition, microcrystalline crystal structure, intramolecular bonding mechanism, and molecular configuration. After fractionation and alcohol precipitation of crude intracellular polysaccharide, mycelium crude intracellular polysaccharide (MCP) and fruiting body crude intracellular polysaccharide (FCP) were obtained and the fingerprint of O. sinensis was identified by the specific characteristic peaks of the X-ray diffraction pattern from intracellular polysaccharide. Results: The results indicated that the PXRD patterns of different populations of O. sinensis were overlaid well with 18 characteristic diffraction peaks obtained by microcrystalline diffraction. Moreover, the powder diffractograms as a fingerprint provided a practical identification of O. sinensis from other Cordyceps species. In addition, we detected that the powder diffractograms of intracellular polysaccharide MCP and MCP75 could be coupled with the PXRD of O. sinensis. Specifically, 18 characteristic diffraction peaks were identified as coming from MCP and MCP75 according to those interplanar crystal spacing, which matched well with those of PXRD of O. sinensis. Conclusions: PXRD spectra combined with an updated multivariable discriminant model were found to be an efficient and sensitive method for O. sinensis quality control. According to the findings of this study, PXRD should be further investigated for quality control assessments and plant extract selection trials. Full article

A terahertz metamaterial microfluidic sensing chip for ultrasensitive detection is proposed to investigate the response of substances to terahertz radiation in liquid environments and enhance the molecular fingerprinting of trace substances. The structure consists of a cover layer, a metal microstructure, a microfluidic channel, a metal reflective layer, and a buffer layer from top to bottom, respectively. The simulation results show that there are three obvious resonance absorption peaks in the range of 1.5–3.0 THz and the absorption intensities are all above 90%. Among them, the absorption intensity at M1 = 1.971 THz is 99.99%, which is close to the perfect absorption, and its refractive index sensitivity and Q-factor are 859 GHz/RIU and 23, respectively, showing excellent sensing characteristics. In addition, impedance matching and equivalent circuit theory are introduced in this paper to further analyze the physical mechanism of the sensor. Finally, we perform numerical simulations using refractive index data of normal and cancer cells, and the results show that the sensor can distinguish different types of cells well. The chip can reduce the sample pretreatment time as well as enhance the interaction between terahertz waves and matter, which can be used for early disease screening and food quality and safety detection in the future. Full article

Durum wheat (Triticum turgidum L. ssp. durum) landraces, traditional local varieties representing an intermediate stage in domestication, are gaining attention due to their high genetic variability and performance in challenging environments. While major kernel metabolites have been examined, limited research has been conducted on minor bioactive components like lipids, despite their nutritional benefits. To address this, we analyzed twenty-two tetraploid accessions, comprising modern elite cultivars and landraces, to (i) verify if the selection process for yield-related traits carried out during the Green Revolution has influenced lipid amount and composition; (ii) uncover the extent of lipid compositional variability, giving evidence that lipid fingerprinting effectively identifies evolutionary signatures; and (iii) identify genotypes interesting for breeding programs to improve yield and nutrition. Interestingly, total fat did not correlate with kernel weight, indicating lipid composition as a promising trait for selection. Tri- and di-acylglycerol were the major lipid components along with free fatty acids, and their relative content varied significantly among genotypes. In particular, landraces belonging to T. turanicum and carthlicum ecotypes differed significantly in total lipid and fatty acid profiles. Our findings provide evidence that landraces can be a genetically relevant source of lipid variability, with potential to be exploited for improving wheat nutritional quality. Full article