Layered double hydroxide-based nanomaterials for biomedical applications

Chemical Society Reviews, Journal Year: 2022, Volume and Issue: 51(14), P. 6126 - 6176

Published: Jan. 1, 2022

Against the backdrop of increased public health awareness, inorganic nanomaterials have been widely explored as promising nanoagents for various kinds biomedical applications. Layered double hydroxides (LDHs), with versatile physicochemical advantages including excellent biocompatibility, pH-sensitive biodegradability, highly tunable chemical composition and structure, ease composite formation other materials, shown great promise in In this review, we comprehensively summarize recent advances LDH-based Firstly, material categories are discussed. The preparation surface modification nanomaterials, pristine LDHs, nanocomposites LDH-derived then described. Thereafter, systematically describe potential LDHs applications drug/gene delivery, bioimaging diagnosis, cancer therapy, biosensing, tissue engineering, anti-bacteria. Finally, on basis current state art, conclude insights remaining challenges future prospects rapidly emerging field.

Language: Английский

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Fluorescent chemosensors facilitate the visualization of plant health and their living environment in sustainable agriculture

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(13), P. 6992 - 7090

Published: Jan. 1, 2024

Globally, 91% of plant production encounters diverse environmental stresses. Fluorescent chemosensors are effective for monitoring health and environment that promotes the development sustainable agriculture.

Language: Английский

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The IBEX Imaging Knowledge-Base: A Community Resource Enabling Adoption and Development of Immunofluoresence Imaging Methods

Published: April 2, 2025

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D 3D immunofluorescence imaging methods. design of the modeled after efforts in open-source software community includes three facets: development platform (GitHub), static website, service data archiving. facilitates practice open science throughout research life cycle by providing validation recommended non-recommended reagents, e.g., primary secondary antibodies. In addition to reporting negative data, empowers method adoption evolution venue sharing protocols, videos, datasets, software, publications. A dedicated discussion forum fosters sense among while addressing questions not covered published manuscripts. Together, scientists from around world are advancing scientific discovery at faster pace, reducing wasted time effort, instilling greater confidence resulting data.

Language: Английский

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An overview of deep learning in medical imaging

Informatics in Medicine Unlocked, Journal Year: 2021, Volume and Issue: 26, P. 100723 - 100723

Published: Jan. 1, 2021

Deep learning (DL) is one of the branches artificial intelligence that has seen exponential growth in recent years. The scientific community focused its attention on DL due to versatility, high performance, generalization capacity, and multidisciplinary uses, among many other qualities. In addition, a large amount medical data development more powerful computers also fostered an interest this area. This paper presents overview current deep methods, starting from most straightforward concept but accompanied by mathematical models are behind functionality type intelligence. first instance, fundamental neural networks introduced, progressively covering convolutional structures, recurrent networks, models, up structure known as Transformer. Secondly, all basic concepts involved training common elements design architectures introduced. Thirdly, some key modern for image classification segmentation shown. Subsequently, review applications realized last years shown, where main features related highlighted. Finally, perspectives future expectations presented.

Language: Английский

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Small-molecule fluorogenic probes for mitochondrial nanoscale imaging

Chemical Society Reviews, Journal Year: 2022, Volume and Issue: 52(3), P. 942 - 972

Published: Dec. 14, 2022

Mitochondria are inextricably linked to the development of diseases and cell metabolism disorders. Super-resolution imaging (SRI) is crucial in enhancing our understanding mitochondrial ultrafine structures functions. In addition high-precision instruments, super-resolution microscopy relies heavily on fluorescent materials with unique photophysical properties. Small-molecule fluorogenic probes (SMFPs) have excellent properties that make them ideal for SRI. This paper summarizes recent advances field SMFPs, a focus chemical spectroscopic required Finally, we discuss future challenges this field, including design principles SMFPs nanoscopic techniques.

Language: Английский

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Recent trends of machine learning applied to multi-source data of medicinal plants

Journal of Pharmaceutical Analysis, Journal Year: 2023, Volume and Issue: 13(12), P. 1388 - 1407

Published: July 25, 2023

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. particular, remarkable curative effect of Chinese during Corona Virus Disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal have, therefore, become increasingly popular among public. However, with increasing demand profit plants, commercial fraudulent events such adulteration or counterfeits sometimes occur, which poses a serious threat to clinical outcomes interests consumers. With rapid advances artificial intelligence, machine learning can be used mine information on various establish an ideal resource database. We herein present review that mainly introduces common algorithms discusses their application multi-source data analysis plants. The combination facilitates comprehensive aids effective evaluation quality findings this provide new possibilities promoting development utilization

Language: Английский

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A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Sensors, Journal Year: 2022, Volume and Issue: 22(13), P. 4938 - 4938

Published: June 30, 2022

One of the most promising research areas in healthcare industry and scientific community is focusing on AI-based applications for real medical challenges such as building computer-aided diagnosis (CAD) systems breast cancer. Transfer learning one recent emerging techniques that allow rapid progress improve imaging performance. Although deep classification cancer has been widely covered, certain obstacles still remain to investigate independency among extracted high-level features. This work tackles two exist when designing effective CAD lesion from mammograms. The first challenge enrich input information models by generating pseudo-colored images instead only using original grayscale images. To achieve this goal different image preprocessing are parallel used: contrast-limited adaptive histogram equalization (CLAHE) Pixel-wise intensity adjustment. preserved channel, while other channels receive processed images, respectively. generated three-channel fed directly into layer backbone CNNs generate more powerful second overcome multicollinearity problem occurs high correlated features models. A new hybrid processing technique based Logistic Regression (LR) well Principal Components Analysis (PCA) presented called LR-PCA. Such a process helps select significant principal components (PCs) further use them purpose. proposed system examined public benchmark datasets which INbreast mini-MAIS. could highest performance accuracies 98.60% 98.80% mini-MAIS datasets, seems be useful reliable diagnosis.

Language: Английский

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Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 89, P. 102920 - 102920

Published: Aug. 6, 2023

Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D 3D techniques. Due to the laborious time-consuming nature manual segmentation large-scale EM datasets, automated approaches are crucial. This review focuses progress deep learning-based techniques in cellular throughout last six years, during which significant has been made both semantic instance segmentation. A detailed account is given for key datasets that contributed proliferation learning The covers supervised, unsupervised, self-supervised methods examines how these algorithms were adapted task segmenting sub-cellular structures images. special challenges posed by such images, like heterogeneity spatial complexity, network architectures overcame some them described. Moreover, an overview evaluation measures used benchmark various tasks provided. Finally, outlook current trends future prospects given, especially with models unlabeled images learn generic features across datasets.

Language: Английский

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Does nano basic building-block of C-S-H exist? – A review of direct morphological observations

Materials & Design, Journal Year: 2024, Volume and Issue: 238, P. 112699 - 112699

Published: Feb. 1, 2024

Despite significant advancements in microstructural characterization methods, the interconnections between nanostructure and morphological diversity of calcium-silicate-hydrate (C-S-H), primary binding phase modern concrete, remain unclear. This review delves into state-of-the-art experimental findings morphology C-S-H comprehensively analyses various influencing factors. The focus here is to address long-standing debate: whether there are fundamental structural units, either fractural globule or nano sheet, that assemble form diverse microstructures. We critically assess formation involves structured assembly layered with an approximate size 4–10 nm, rather than occurring randomly. Such may have substantial heterogeneity deformability, often blurring distinction globular sheet models. Finally, this paper offers perspectives on future research directions aimed at further unravelling intricate structure C-S-H.

Language: Английский

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Image processing tools for petabyte-scale light sheet microscopy data

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Light sheet microscopy is a powerful technique for high-speed 3D imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds gigabytes to petabytes in size single experiment. Conventional computational tools process such images far slower than the time acquire them fail outright due memory limitations. To address these challenges, we present PetaKit5D, scalable software solution efficient petabyte-scale light image processing. This incorporates suite commonly used processing that are performance-optimized. Notable advancements include rapid readers writers, fast memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution, Zarr-based stitching. These features outperform state-of-the-art methods by over one order magnitude, enabling data at full teravoxel rates modern cameras. The opens new avenues discoveries through large-scale experiments.

Language: Английский

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