-
Cell Jun 2024Gamete formation and subsequent offspring development often involve extended phases of suspended cellular development or even dormancy. How cells adapt to recover and...
Gamete formation and subsequent offspring development often involve extended phases of suspended cellular development or even dormancy. How cells adapt to recover and resume growth remains poorly understood. Here, we visualized budding yeast cells undergoing meiosis by cryo-electron tomography (cryoET) and discovered elaborate filamentous assemblies decorating the nucleus, cytoplasm, and mitochondria. To determine filament composition, we developed a "filament identification" (FilamentID) workflow that combines multiscale cryoET/cryo-electron microscopy (cryoEM) analyses of partially lysed cells or organelles. FilamentID identified the mitochondrial filaments as being composed of the conserved aldehyde dehydrogenase Ald4 and the nucleoplasmic/cytoplasmic filaments as consisting of acetyl-coenzyme A (CoA) synthetase Acs1. Structural characterization further revealed the mechanism underlying polymerization and enabled us to genetically perturb filament formation. Acs1 polymerization facilitates the recovery of chronologically aged spores and, more generally, the cell cycle re-entry of starved cells. FilamentID is broadly applicable to characterize filaments of unknown identity in diverse cellular contexts.
Topics: Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Cryoelectron Microscopy; Mitochondria; Gametogenesis; Meiosis; Aldehyde Dehydrogenase; Electron Microscope Tomography; Coenzyme A Ligases; Spores, Fungal; Cytoplasm; Cell Nucleus
PubMed: 38906101
DOI: 10.1016/j.cell.2024.04.026 -
Ecotoxicology and Environmental Safety Jun 2024Iron-nanoparticles (Fe-NPs) are increasingly been utilized in environmental applications due to their efficacy and strong catalytic activities. The novelty of... (Review)
Review
Harnessing plant extracts for eco-friendly synthesis of iron nanoparticle (Fe-NPs): Characterization and their potential applications for ameliorating environmental pollutants.
Iron-nanoparticles (Fe-NPs) are increasingly been utilized in environmental applications due to their efficacy and strong catalytic activities. The novelty of nanoparticle science had attracted many researchers and especially for their green synthesis, which can effectively reuse biological resources during the polymerization reactions. Thus, the synthesis of Fe-NPs utilizing plant extracts could be considered as the eco-friendly, simple, rapid, energy-efficient, sustainable, and cost-effective. The green synthesis route can be recognized as a practical, valuable, and economically effective alternative for large-scale production. During the production process, some biomolecules present in the extracts undergo metal salts reduction, which can serve as both a capping and reducing mechanism, enhancing the reactivity and stability of green-synthesized Fe-NPs. The diversity of species provided a wide range of potential sources for green synthesis of Fe-NPs. With improved understanding of the specific biomolecules involved in the bioreduction and stabilization processes, it will become easier to identify and utilize new, potential plant materials for Fe-NPs synthesis. Newly synthesized Fe-NPs require different characterization techniques such as transmission electron microscope, ultraviolet-visible spectrophotometry, and X-ray absorption fine structure, etc, for the determination of size, composition, and structure. This review described and assessed the recent advancements in understanding green-synthesized Fe-NPs derived from plant-based material. Detailed information on various plant materials suitable of yielding valuable biomolecules with potential diverse applications in environmental safety. Additionally, this review examined the characterization techniques employed to analyze Fe-NPs, their stability, accumulation, mobility, and fate in the environment. Holistically, the review assessed the applications of Fe-NPs in remediating wastewaters, organic residues, and inorganic contaminants. The toxicity of Fe-NPs was also addressed; emphasizing the need to refine the synthesis of green Fe-NPs to ensure safety and environmental friendliness. Moving forward, the future challenges and opportunities associated with the green synthesis of Fe-NPs would motivate novel research about nanoparticles in new directions.
PubMed: 38905935
DOI: 10.1016/j.ecoenv.2024.116620 -
Indian Journal of Ophthalmology Jul 2024
Topics: Humans; Cataract Extraction; Cataract; Microscopy; Ophthalmology; Equipment Design
PubMed: 38905468
DOI: 10.4103/IJO.IJO_3158_23 -
Science Advances Jun 2024Integral membrane proteins (IMPs) constitute a large fraction of organismal proteomes, playing fundamental roles in physiology and disease. Despite their importance, the...
Integral membrane proteins (IMPs) constitute a large fraction of organismal proteomes, playing fundamental roles in physiology and disease. Despite their importance, the mechanisms underlying dynamic features of IMPs, such as anomalous diffusion, protein-protein interactions, and protein clustering, remain largely unknown due to the high complexity of cell membrane environments. Available methods for in vitro studies are insufficient to study IMP dynamics systematically. This publication introduces the freestanding bilayer microscope (FBM), which combines the advantages of freestanding bilayers with single-particle tracking. The FBM, based on planar lipid bilayers, enables the study of IMP dynamics with single-molecule resolution and unconstrained diffusion. This paper benchmarks the FBM against total internal reflection fluorescence imaging on supported bilayers and is used here to estimate ion channel open probability and to examine the diffusion behavior of an ion channel in phase-separated bilayers. The FBM emerges as a powerful tool to examine membrane protein/lipid organization and dynamics to understand cell membrane processes.
Topics: Lipid Bilayers; Single Molecule Imaging; Membrane Proteins; Ion Channels; Diffusion; Cell Membrane
PubMed: 38905330
DOI: 10.1126/sciadv.ado4722 -
PloS One 2024In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular...
In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system's components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide.
Topics: Microscopy; Humans; Printing, Three-Dimensional; Software; Robotics; Smartphone; Automation; Imaging, Three-Dimensional
PubMed: 38905190
DOI: 10.1371/journal.pone.0304085 -
Biodiversity Data Journal 2024The genus Iredale, 1929 consisting of marine trochids, primarily inhabits the intertidal zone. Globally, eight recent species have been documented, all of which occur...
BACKGROUND
The genus Iredale, 1929 consisting of marine trochids, primarily inhabits the intertidal zone. Globally, eight recent species have been documented, all of which occur in the Pacific Region. The genus has not previously been recorded from Chinese seas.
NEW INFORMATION
This study fills a knowledge gap by reporting, for the first time, the presence of the trochid genus Iredale, 1929 in China. Specifically, (Oyama, 1942) was detailed using morphological characteristics derived from the shell (Fig. 1A-F and H-I), operculum (Fig. 1G) and radula (Fig. 1J-L). Additionally, this study introduces comprehensive scanning electron microscope illustrations and molecular data, contributing valuable taxonomic information for the first time.
PubMed: 38903960
DOI: 10.3897/BDJ.12.e117114 -
Frontiers in Medicine 2024Burkitt Lymphoma (BL) is a highly treatable cancer. However, delayed diagnosis of BL contributes to high mortality in BL endemic regions of Africa. Lack of enough...
Burkitt Lymphoma (BL) is a highly treatable cancer. However, delayed diagnosis of BL contributes to high mortality in BL endemic regions of Africa. Lack of enough pathologists in the region is a major reason for delayed diagnosis. The work described in this paper is a proof-of-concept study to develop a targeted, open access AI tool for screening of histopathology slides in suspected BL cases. Slides were obtained from a total of 90 BL patients. 70 Tonsillectomy samples were used as controls. We fine-tuned 6 pre-trained models and evaluated the performance of all 6 models across different configurations. An ensemble-based consensus approach ensured a balanced and robust classification. The tool applies novel features to BL diagnosis including use of multiple image magnifications, thus enabling use of different magnifications of images based on the microscope/scanner available in remote clinics, composite scoring of multiple models and utilizing MIL with weak labeling and image augmentation, enabling use of relatively low sample size to achieve good performance on the inference set. The open access model allows free access to the AI tool from anywhere with an internet connection. The ultimate aim of this work is making pathology services accessible, efficient and timely in remote clinics in regions where BL is endemic. New generation of low-cost slide scanners/microscopes is expected to make slide images available immediately for the AI tool for screening and thus accelerate diagnosis by pathologists available locally or online.
PubMed: 38903819
DOI: 10.3389/fmed.2024.1345611 -
Frontiers in Microbiology 2024Beibu Gulf is an important semi-enclosed bay located in the northwestern South China Sea, and is famous for its high bio-productivity and rich bio-diversity. The fast...
Beibu Gulf is an important semi-enclosed bay located in the northwestern South China Sea, and is famous for its high bio-productivity and rich bio-diversity. The fast development along the Beibu Gulf Economical Rim has brought pressure to the environment, and algal blooms occurred frequently in the gulf. In this study, surface water samples and micro-plankton samples (20-200 μm) were collected in the northern Beibu Gulf coast. Diversity and distribution of eukaryotic planktonic microalgae were analyzed by both metabarcoding and microscopic analyses. Metabarcoding revealed much higher diversity and species richness of microalgae than morphological observation, especially for dinoflagellates. Metabarcoding detected 144 microalgal genera in 8 phyla, while microscopy only detected 40 genera in 2 phyla. The two methods revealed different microalgal community structures. Dinoflagellates dominated in microalgal community based on metabarcoding due to their high copies of 18 s rRNA gene, and diatoms dominated under microscopy. Altogether 48 algal bloom and/or toxic species were detected in this study, 34 species by metabarcoding and 19 species by microscopy. Our result suggested a high potential risk of HABs in the Beibu Gulf. Microalgal community in the surface water samples demonstrated significantly higher OTU/species richness, alpha diversity, and abundance than those in the micro-plankton samples, although more HAB taxa were detected by microscopic observations in the micro-plankton samples. Furthermore, nano-sized taxa, such as those in chlorophytes, haptophytes, and chrysophyceans, occurred more abundantly in the surface water samples. This study provided a comprehensive morphological and molecular description of microalgal community in the northern Beibu Gulf.
PubMed: 38903786
DOI: 10.3389/fmicb.2024.1403964 -
ArXiv Apr 2024Whole Slide Images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However,...
Whole Slide Images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to AI-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. It is not just that medical diagnostics is recorded at the specimen level, the detection of oncogene mutation is also experimentally obtained, and recorded by initiatives like The Cancer Genome Atlas (TCGA), at the slide level. This configures a dual challenge: a) accurately predicting the overall cancer phenotype and b) finding out what cellular morphologies are associated with it at the tile level. To address these challenges, a weakly supervised Multiple Instance Learning (MIL) approach was explored for two prevalent cancer types, Invasive Breast Carcinoma (TCGA-BRCA) and Lung Squamous Cell Carcinoma (TCGA-LUSC). This approach was explored for tumor detection at low magnification levels and TP53 mutations at various levels. Our results show that a novel additive implementation of MIL matched the performance of reference implementation (AUC 0.96), and was only slightly outperformed by Attention MIL (AUC 0.97). More interestingly from the perspective of the molecular pathologist, these different AI architectures identify distinct sensitivities to morphological features (through the detection of Regions of Interest, RoI) at different amplification levels. Tellingly, TP53 mutation was most sensitive to features at the higher applications where cellular morphology is resolved.
PubMed: 38903738
DOI: No ID Found -
ArXiv Apr 2024Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks...
Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based backbone model from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5 by 5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.
PubMed: 38903737
DOI: No ID Found