Fluorescence Microscopy Images Research Articles

Quantifying Morphologies of Developing Neuronal Cells using Deep Learning with Imperfect Annotations

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons’ length and number of branches.

Mucin-Inspired Polymeric Fibers for Herpes Simplex Virus Type 1 Inhibition.

Mucus lines the epithelial cells at the biological interface and is the first line of defense against multiple viral infection. Mucins, the gel-forming components of mucus, are high molecular weight glycoproteins and crucial for preventing infections by binding pathogens. Consequently, mimicking mucins is a promising strategy for new synthetic virus inhibitors. In this work, we investigated synthetic mucin-inspired polymers (MIPs) as potential inhibitors of herpes simplex virus 1 (HSV-1). By using a telechelic RAFT polymerization technique, we synthesized a new dendronized polysulfatep(G1AAm-OSO3)PDSwith an amide-backbone similar to the native mucin glycoproteins.p(G1AAm-OSO3)PDSshows mucin-like elongated fiber structure, as revealed in cryo-electron microscopy (cryo-EM) imaging, and we tested its HSV-1 inhibition activity together with its previously reported methacrylate analoguep(G1MA-OSO3)PDS. Both of the sulfated MIPs show strong HSV-1 inhibition in plaque reduction assays with IC50 values in lower nanomolar range (< 3nM) and demonstrate a high cell compatibility (CC50>1.0mgmL-1) with lower anticoagulant activity than heparin. In addition, the prophylactic and therapeutic activity of both MIPs was assessed in pre- and post-infection inhibition assays and clearly visualized their high potential for application using fluorescent microscopy imaging of infected cells. This article is protected by copyright. All rights reserved.

Automated segmentation and recognition of C. elegans whole-body cells.

Motivation: Accurate segmentation and recognition of Caenorhabditis elegans cells are critical for various biological studies, including gene expression, cell lineages and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C. elegans whole-body cells is highly desirable. We report a new pipeline, called Automated Segmentation and Recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C. elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C. elegans whole-body cells. The ASR segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C. elegans image stacks with 64728 cells (Accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. The code is available at https://github.com/reaneyli/ASR. Supplementary data are available at Bioinformatics online.

Supported Biomembrane Systems Incorporating Multiarm Polymers and Bioorthogonal Tethering.

To functionalize interfaces with supported biomembranes and membrane proteins, the challenge is to build stabilized and supported systems that mimic the native lipid microenvironment. Our objective is to control substrate-to-biomembrane spacing and the tethering chemistry so proteoliposomes can be fused and conjugated without perturbation of membrane protein function. Furthermore, the substrates need to exhibit low protein and antibody nonspecific binding to use these systems in assays. We have employed protein orthogonal coupling schemes in concert with multiarm poly(ethylene glycol) (PEG) technology to build supported biomembranes on microspheres. The lipid bilayer structures and tailored substrates of the microsphere-supported biomembranes were analyzed via flow cytometry, confocal fluorescence, and super-resolution imaging microscopy, and the lateral fluidity was quantified using fluorescence recovery after photobleaching (FRAP) techniques. Under these conditions, the 4-arm-PEG20,000-NH2 based configuration gave the most desirable tethering system based on lateral diffusivity and coverage.

Far-field superresolution of thermal sources by double homodyne or double array homodyne detection

We propose two schemes for estimating the separation of two thermal sources via double homodyne and double array homodyne detection considering the joint measurement of conjugate quadratures of the image plane field.By using the Cramér–Rao bound, we demonstrate that the two schemes can estimate the separation well below the Rayleigh limit and have an advantage over direct imaging when the average photon number per source exceeds five.For arbitrary source strengths, double homodyne detection is superior to homodyne detection when the separation is above 25/4σ/N s , σ is the beam width, N s is the average photon number per source.A larger separation can be estimated better via double array homodyne detection with the superiority of flexible operation compared with other schemes. High-speed and high-efficiency detection enables the measurement schemes with potential practical applications in fluorescence microscopy, astronomy and quantum imaging.

Performance evolution and mechanism of asphalt crack sealant under UV aging: A continuity study

Asphalt crack repair materials, known as crack sealants, are directly exposed to ultraviolet (UV) radiation in asphalt pavements. This exposure causes the gradual aging and deterioration of both their surface and internal properties. To study this phenomenon, the sealant was subjected to UV irradiation for a range of 0–385 hours, with the basic properties of the aged sealant tested every 55 hours. Several techniques were employed to investigate the changes in the internal chemical composition of the sealant, including Fluorescence microscopy (FM), gel permeation chromatography (GPC) and Fourier transform infrared spectroscopy (FT-IR). The results revealed that the UV aging of the sealant mainly occurs in the thinner surface layer, which becomes progressively lighter in color. Notably, obvious surface cracks form after aging process. The cone penetration and softening point of the sealant exhibit an exponential or logarithmic change corresponding to the aging time. The most pronounced changes occur during the early stages of UV aging, with the properties tending to stabilize after approximately 220 h of aging. In addition, the FM images showed that the dispersed polymer particles within the sealant gradually transform into a continuous network structure after aging over time. The GPC spectrum of the aged sealant shifts to the left, indicating an increase in molecular weight, particularly in the polymer modifier. This is consistent with the transformation observed in the FT-IR analysis, which demonstrates that UV irradiation leads to the breaking and recombination of polymer chemical bonds in the sealant. The FT-IR analysis reveals the conversion of monosubstituted benzene to polysubstituted benzene in the polymer modifier, which also correlates with the formation of the network structure observed in the FM images and the increase in molecular weight detected in the GPC spectrum of the polymer modifier.

3D Poissonian image deblurring via patch-based tensor logarithmic Schatten-p minimization

In medical and biological image processing, multi-dimensional images are often corrupted by blur and Poisson noise. In this paper, we first propose a new tensor logarithmic Schatten-p (t-log-Sp ) low-rank measure and a tensor iteratively reweighted Schatten-p minimization algorithm for minimizing such measure. Furthermore, we adopt this low-rank measure to regularize the non-local tensors formed by similar 3D image patches and develop a patch-based non-local low-rank model. The data fidelity term of the model characterizes the Poisson noise distribution and blur operator. The optimization model is further solved by an alternating minimization technique combined with variable splitting. Experimental results tested on 3D fluorescence microscope images show that the proposed patch-based tensor logarithmic Schatten-p minimization method outperforms state-of-the-art methods in terms of image evaluation metrics and visual quality.

Different effects of pea protein on the properties and structures of starch gel at low and high solid concentrations

In the context of starch–protein composite gels, the influence of protein on gel formation significantly shapes the textural attributes of starch gels, leading to distinct outcomes. This study aimed to evaluate how different ratios of pea protein (PP) affect the properties and structures of starch–protein composite gels at low (10 wt%) and high (40 wt%) solid concentrations. The addition of PP had opposite effects on the two gels. Compared to the pure starch gel, the low-concentration composite gel (LCG) with 20 % PP experienced a 48.90 ± 0.33 % reduction in hardness, and the storage modulus (G′) decreased from 14,100 Pa to 5250 Pa, indicating a softening effect of PP on LCG. Conversely, the hardness of the high-concentration composite gel (HCG) with 20 % PP exhibited a 62.19 ± 0.03 % increase in hardness, and G′ increased from 12,100 Pa to 41,700 Pa, highlighting the enhancing effect of PP on HCG. SEM and fluorescence microscopy images showed that PP induced uneven network sizes in LCG, while HCG with a PP content of 20 %, PP, together with starch, formed a three-dimensional network. This study provides valuable insights and guidance for the design and production of protein-enriched starch gel products with different textural properties.

Using Poly(N‐isopropylacrylamide) and Poly(ethyleneglycol) Grafted Polycarbodiimides as Unique Nanocarriers for Hydrophobic Form of the Doxorubicin (DOX)

AbstractAmphiphilic helical polycarbodiimides bearing side chains with terminal alkyne groups provide a unique platform for construction of polymeric micelles. Incorporation of propargyl pendants into polyguanidine backbone allowed post‐polymerization modification with azide terminated, random coil, poly(N‐isopropylacrylamide), PNIPAM15K and poly(ethyleneglycol), PEG2K moieties using copper‐catalyzed click reaction (CuAAC). Thus, four new amphiphilic brush copolymers with varying grafting density were synthesized and their self‐assembly behavior in aqueous medium was studied by combination of TEM, SEM, AFM, and optical microscopy techniques. It was shown the formation of micellar structures at critical micelle concentration (CMC) ranging from 1.58 × 10−5 to 1.05 × 10−3mg mL−1. A hydrophobic drug doxorubicin (DOX) was then encapsulated successfully into these micellar structures with encapsulation efficiency (EE) falling in the range from 16% to 27%. Further, the cellular uptake studies were carried out by using HeLa cells and the cytotoxic activity of DOX‐loaded micelles was determined to be less than 65% of cell viability at a concentration of 0.250 mg mL−1. Fluorescence microscopy imaging revealed importing the micelles into the cells by endocytosis and internalization of the DOX into nucleus of the HeLa cells.

Long-Term Intrauterine Hypoxia Disrupts Calcium Sparks in Middle Cerebral Arterial Myocytes of Fetal Sheep

Local activation of ryanodine receptors (RyRs) on the sarcoplasmic reticulum in vascular smooth muscle cells (VSMCs) generates rapid subcellular changes in cellular Ca2+ that are commonly known as Ca2+ sparks. These Ca2+ sparks are well regarded for their role in the autoregulation of vascular contractility and cerebral blood flow through activation of potassium channels and feedback modulation of the membrane potential. Gestational hypoxia disrupts cerebrovascular development, increasing the risk of intracranial hemorrhage and stroke in the newborn. Due to the role of RyR’s in regulating neonatal cerebrovascular blood flow, understanding intracellular Ca2+ release patterns in VSMCs can offer insight into hypoxia-related perinatal cerebrovascular disease. The central aim of this study is to determine the extent to which gestational hypoxia disrupts RyR activity. This was achieved through examination of RyR activity in VSMCs of fetal ovine middle cerebral arteries (MCAs). MCAs were isolated from term fetal sheep (~140 days of gestation) delivered by cesarean section from ewes held at low- (700 m) and high-altitude conditions (3801 m) for 100+ days of gestation. Arteries were bathed in a physiological buffer with 5 mm K+ (control) or were treated with either 30 mM K+ (30K), to depolarize myocytes and increase Ca2+ spark events, in the presence or absence of 10 μM ryanodine, to block RyR mediated Ca2+ release. Ca2+ spark activity was measured in situ using line scan confocal microscopy fluorescence imaging techniques. Automated analysis of Ca2+ sparks in the records was conducted using SparkLab 5.8. Membrane depolarization with 30K increased Ca2+ spark activity. Long term hypoxia, however, reduced spark activity in the 30K group. Ryanodine attenuated Ca2+ spark activity in both low- and high-altitude groups. Hypoxia’s influence on the spatial and temporal aspects of Ca2+ sparks in fetal myocytes varied. These findings corroborate previous evidence demonstrating that long-term hypoxia attenuates Ca2+ spark activity. The disruption in Ca2+ spark activity could impact brain blood flow in the neonate, potentially impacting brain development and the likelihood of a traumatic event. R01HL155295, R01HL149608, Advanced Imaging and Microscopy Core Loma Linda University School of Medicine. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Self-Supervised Joint Learning for pCLE Image Denoising.

Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements in deep learning offer promise in image denoising, but the acquisition of clean-noisy image pairs for training networks across all potential scenarios can be prohibitively costly. Few studies have explored training denoising networks on such pairs. Here, we propose an innovative self-supervised denoising method. Our approach integrates noise prediction networks, image quality assessment networks, and denoising networks in a collaborative, jointly trained manner. Compared to prior self-supervised denoising methods, our approach yields superior results on pCLE images and fluorescence microscopy images. In summary, our novel self-supervised denoising technique enhances image quality in pCLE diagnosis by leveraging the synergy of noise prediction, image quality assessment, and denoising networks, surpassing previous methods on both pCLE and fluorescence microscopy images.

Chlorin Conjugates in Photodynamic Chemotherapy for Triple-Negative Breast Cancer.

Breast cancer (BC) is the most common type of cancer in women and the number of new cases in the US is still increasing each year. Triple-negative breast cancer (TNBC), which comprises 15-20% of all breast cancer, is a heterogeneous disease and is considered the most aggressive type of breast cancer due to the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expressions for treatments. Traditional chemotherapy is the standard protocol for the treatment of TNBC. Toxicity and multidrug resistance are major drawbacks to chemotherapy. The lack of molecular targets and poor prognosis for TNBC prompts an urgent need to discover novel therapeutic strategies to improve clinical outcomes and quality of life for patients. Photodynamic therapy (PDT) or light treatment is a binary anti-cancer procedure that uses a photosensitizer (PS) that, upon light activation, produces cytotoxic oxygen species, destroying tumor cells. PDT is minimally invasive and can be repeated a few times without accumulating significant toxicity in the surrounding tissues. The primary goal of this study was to investigate in vitro photodynamic chemotherapy as a ternary combination therapy using our synthesized photosensitizers (chlorin-vitamin conjugates and their corresponding indium complexes) co-treated with known chemotherapeutic agents (taxol, doxorubicin, cisplatin, fluorouracil, or methotrexate) in the presence of light and determine the optimum conditions as a pre-clinical study of an enhanced tumoricidal effect against TNBC. Our results indicated that the best combination for an effective chemophotodynamic effect involves a ternary treatment of the indium complex of the chlorin-lipoic acid conjugate (InCLA) co-treated with taxol, which exhibited strong synergism at the nanomolar concentration when combined in the presence of visible light irradiation. Other ternary combinations containing taxol with a synergistic anti-tumor effect against TNBC include chlorin-pantothenic acid (CPA) and chlorin-biotin (CBTN) conjugates. Several other ternary combinations containing InCLA, CBTN, and CPA with either cisplatin, fluorouracil, or methotrexate were identified to generate a synergistic or additive effect. The light dosage remained constant, but the dosages of photosensitizers and chemotherapy drugs were varied to obtain the lowest possible concentration for the desired effect. The synergistic, additive or antagonistic effects of the drug combinations were determined based on the Chou-Talalay method, with InCLA-taxol having the lowest combination index (CI) of 0.25. Fluorescence and transmission electron microscopy (TEM) images provided evidence of apoptosis as the preferred mode of cell death. Our study demonstrated the combination of PDT and chemotherapy as a potential treatment option for TNBC patients.

Alkyl anchor–modified artificial viral capsid budding outside-to-inside and inside-to-outside giant vesicles

Alkyl anchor–modified artificial viral capsid budding outside-to-inside and inside-to-outside giant vesicles

Detection of Active SARS-CoV-2 3CL Protease in Infected Cells Using Activity-Based Probes with a 2,6-Dichlorobenzoyloxymethyl Ketone Reactive Warhead.

The 3CL protease (3CLpro) is a viral cysteine protease of SARS-CoV-2 and is responsible for the main processing of the viral polyproteins involved in viral replication and proliferation. Despite the importance of 3CLpro as a drug target, the intracellular dynamics of active 3CLpro, including its expression and subcellular localization in SARS-CoV-2-infected cells, are poorly understood. Herein, we report an activity-based probe (ABP) with a clickable alkyne and an irreversible warhead for the SARS-CoV-2 3CL protease. We designed and synthesized two ABPs that contain a chloromethyl ketone (probe 2) or 2,6-dichlorobenzoyloxymethyl ketone (probe 3) reactive group at the P1' site. Labeling of recombinant 3CLpro by the ABPs in the purified and proteome systems revealed that probe 3 displayed ligand-directed and selective labeling against 3CLpro. Labeling of transiently expressed active 3CLpro in COS-7 cells also validated the good target selectivity of probe 3 for 3CLpro. We finally demonstrated that endogenously expressed 3CLpro in SARS-CoV-2-infected cells can be detected by fluorescence microscopy imaging using probe 3, suggesting that active 3CLpro at 5 h postinfection is localized in the juxtanuclear region. To the best of our knowledge, this is the first report investigating the subcellular localization of active 3CLpro by using ABPs. We believe that probe 3 will be a useful chemical tool for acquiring important biological knowledge of active 3CLpro in SARS-CoV-2-infected cells.

AIE-labeled fluorescent polystyrene nanoplastics for quantitative analysis in macrophages uptake

Nanoplastics have attracted significant attention due to their harmful effects on the environment and organisms. However, accurately detecting and quantifying nanoplastics that are ingested by organisms poses a challenge. Conventional organic fluorescent-labeled nanoplastics have reduced labeling efficiency due to aggregation-caused quenching (ACQ), leading to artifacts caused by leakage of fluorescent molecules. To address these limitations, this study utilized AIE fluorescence for internally labeling polystyrene nanoparticles. AIE-active molecules do not emit fluorescence in dilute solutions, thus preventing accuracy issues associated with fluorescence leakage. Three model plastics (TPE@PS) of varying particle sizes were synthesized via the swelling method. The preparation parameters were optimized, followed by stability tests conducted on TPE@PS. Fluorescence microscopy imaging revealed significant uptake of TPE@PS by RAW246.7 cells. A successful standard curve of fluorescence intensity-concentration was established successfully and utilized to accurately quantify the amount of TPE@PS that was ingested by RAW246.7 cells. The results indicate a consistent increase with increasing concentration in TPE@PS uptake across all particle sizes during the fixed exposure duration of 48 h. Furthermore, RAW264.7 cells exhibited a higher propensity to uptake smaller TPE@PS particles. Flow cytometry was also employed to validated the suitability of TPE@PS as a model nanoplastic for tracing purposes.

How Does the Antibiotic Amphotericin B Enter Membranes and What Does It Do There?

Amphotericin B is a popular antifungal antibiotic, but the exact way it works is still a matter of debate. Here, we used monolayers composed of phosphatidylcholine with ergosterol as a model of fungal lipid membranes to study drug incorporation from the aqueous phase and analyze the molecular reorganization of membranes underlying the biological activity of the antibiotic. The results show that the internalization of antibiotic molecules into membranes occurs only in the presence of ergosterol in the lipid phase. Comparison of images of solid-supported monolayers obtained by atomic force microscopy and lifetime imaging fluorescence microscopy shows the formation of intramembrane clusters of various sizes in the lipid phase, consisting mainly of antibiotic dimers and relatively large membrane pores (∼15 nm in diameter). The results reveal multiple modes of action of amphotericin B, acting simultaneously, each of which adversely affects the structural properties of the lipid membranes and their physiological functionality.

Multi-scale perceptual YOLO for automatic detection of clue cells and trichomonas in fluorescence microscopic images

Vaginitis is a common disease among women and has a high recurrence rate. The primary diagnosis method is fluorescence microscopic inspection, but manual inspection is inefficient and can lead to false detection or missed detection. Automatic cell identification and localization in microscopic images are necessary. For vaginitis diagnosis, clue cells and trichomonas are two important indicators and are difficult to be detected because of the different scales and image characteristics. This study proposes a Multi-Scale Perceptual YOLO (MSP-YOLO) with super-resolution reconstruction branch to meet the detection requirements of clue cells and trichomonas. Based on the scales and image characteristics of clue cells and trichomonas, we employed a super-resolution reconstruction branch to the detection network. This branch guides the detection branch to focus on subtle feature differences. Simultaneously, we proposed an attention-based feature fusion module that is injected with dilated convolutional group. This module makes the network pay attention to the non-centered features of the large target clue cells, which contributes to the enhancement of detection sensitivity. Experimental results show that the proposed detection network MSP-YOLO can improve sensitivity without compromising specificity. For clue cell and trichomoniasis detection, the proposed network achieved sensitivities of 0.706 and 0.910, respectively, which were 0.218 and 0.051 higher than those of the baseline model. In this study, the characteristics of the super-resolution reconstruction task are used to guide the network to effectively extract and process image features. The novel proposed network has an increased sensitivity, which makes it possible to detect vaginitis automatically.

Dimensionally stable and durable wood by lignin impregnation

Cracking, warping, and decaying stemming from wood's poor dimensional stability and durability are the most annoying issues of natural wood. There is an urgent need to address these issues, of which, sustainable and green chemical treatments are favorably welcomed. Herein, we developed a facile method through the incorporation of environmentally friendly biopolymer lignin into wood cells for wood dimensional stability and durability enhancement. Enzymatic hydrolysis lignin (EHL) was dissolved into various solvents followed by impregnation and drying to incorporate lignin into wood cells. Impregnation treatment was developed to incorporate into wood to improve its dimensional stability, durability, and micromechanics. The anti-swelling efficiency reached up to 99.4 %, the moisture absorption decreased down to 0.55 %, the mass loss after brown rot decay decreased to 7.22 %, and the cell wall elasticity as well as hardness increased 8.7 % and 10.3 %, respectively. Analyses acquired from scanning electron microscopy, fluorescent microscopy, and Raman imaging revealed that the EHL was successfully colonized in cell lumen as well as in cell walls, thus improved wood dimensional stability and durability. Moreover, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy confirmed EHL interaction with the cell wall components, thus the wood mechanical property was not impaired significantly, whereas nanoindentation data indicated even slight mechanical enhancement on the cell walls. This facile approach can improve the wood properties in multiple aspects and remarkably enhance the outdoor performance of modified wood products. In addition, using lignin as a natural modifying agent to improve wood performance will have a great positive impact on the environment.

Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging

Malaria, a significant global health challenge, is caused by Plasmodium parasites. The Plasmodium liver stage plays a pivotal role in the establishment of the infection. This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis. Convolutional neural networks have been recently proposed as a viable option for tasks such as malaria detection, prediction of host-pathogen interactions, or drug discovery. Our research aimed to predict the transition of Plasmodium-infected liver cells to the merozoite stage, a key development phase, 15 hours in advance. We collected and analyzed hourly imaging data over a span of at least 38 hours from 400 sequences, encompassing 502 parasites. Our method was compared to human annotations to validate its efficacy. Performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were evaluated on an independent test dataset. The outcomes revealed an AUC of 0.873, a sensitivity of 84.6%, and a specificity of 83.3%, underscoring the potential of our CNN-based framework to predict liver stage development of P. berghei. These findings not only demonstrate the feasibility of our methodology but also could potentially contribute to the broader understanding of parasite biology.

Ultrabright quantum dots assisted in vivo NIR-II fluorescence microscopic imaging for brain metastases in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive and often fatal disease, especially since the brain metastasis of TNBC has been a particularly severe manifestation. However, brain metastasis in TNBC at early stages often lacks noticeable symptoms, making it challenging to detect. Near-infrared II (NIR-II) fluorescence microscopic imaging obtains long wavelength, which enables reduced scattering, high spatial resolution and minimal autofluorescence, it is also a favorable imaging method for tumor diagnosis. PbS@CdS quantum dots (QDs) are one of the popular NIR-II fluorescence nanoprobes for well brightness. In this study, NIR-II emissive PbS@CdS QDs were utilized and further encapsulated with thiol-terminated poly(ethylene oxide) (SH-PEG, MW = 5000) to form PbS@CdS@PEG QDs nanoparticles (NPs). The obtained PbS@CdS@PEG QDs NPs were then characterized and further studied in detail. The PbS@CdS@PEG QDs NPs had large absorption spectra, exhibited strong NIR-II fluorescence emission at approximately 1300[Formula: see text]nm, and possessed good NIR-II fluorescence properties. Then, the mice model of early-stage brain metastases of TNBC was established, and the PbS@CdS@PEG QDs NPs were injected into the tumor-bearing mice for NIR-II fluorescence microscopic bioimaging. The brain vessels and tumors of the living mice were detected with high spatial resolution under the NIR-II fluorescence microscopic imaging system with irradiation of 808[Formula: see text]nm laser. The tumor tissues were further restricted and prepared as thin slices. The NIR-II fluorescence signals were collected from the tumor slices with high spatial resolution and signal-to-background ratio (SBR). Thus, the PbS@CdS@PEG QDs NPs-assisted NIR-II fluorescence microscopic system can effectively achieve targeting brain metastases of TNBC imaging, offering a novel and promising approach for TNBC-specific diagnosis.