Deciphering the interplay of gut microbiota and metabolomics in retinal vein occlusion.

This study aims to explore the link between retinal vein occlusion (RVO), a blinding ocular condition, and alterations in gut microbiota composition, to offer insights into the pathogenesis of RVO. Fecal samples from 25 RVO patients and 11 non-RVO individuals were analyzed using 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC-MS). Significant differences in the abundance of gut microbial species were noted between RVO and non-RVO groups. At the phylum level, the RVO group showed an elevation in the ratio of Firmicutes to Bacteroidetes. At the genus level, the RVO group showed higher abundance in Escherichia_Shigella (P < 0.05) and less abundance in Parabacteroides (P < 0.01) than the non-RVO group. Functional predictions indicated reduced folate synthesis, biotin metabolism, and oxidative phosphorylation, with an increase in butyric acid metabolism in the RVO group. LC-MS analysis showed significant differences in purine metabolism, ABC transporters, and naphthalene degradation pathways, especially purine metabolism. Pearson correlation analysis revealed significant associations between bacterial genera and fecal metabolites. Enrichment analysis highlighted connections between specific metabolites and bacterial genera. The findings showed that the dysregulation of gut microbiota was observed in RVO patients, suggesting the gut microbiota as a potential therapeutic target. Modulating the gut microbiota could be a novel strategy for managing RVO and improving patient outcomes. Furthermore, the study findings suggest the involvement of gut microbial dysbiosis in RVO development, underscoring the significance of understanding its pathogenesis for effective treatment development. IMPORTANCE: Retinal vein occlusion (RVO) is a blinding ocular condition, and understanding its pathogenesis is crucial for developing effective treatments. This study demonstrates significant differences in gut microbiota composition between RVO patients and non-RVO individuals, implicating the involvement of gut microbial dysbiosis in RVO development. Functional predictions and metabolic profiling provide insights into the underlying mechanisms, highlighting potential pathways for therapeutic intervention. These findings suggest that modulating the gut microbiota might be a promising strategy for managing RVO and improving patient outcomes.

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.

A real-time antibody-dependent cellular phagocytosis assay by live cell imaging.

Antibody-dependent cellular phagocytosis (ADCP) is a cellular process by which antibody-opsonized targets (pathogens or cells) activate the Fc receptors on the surface of phagocytes to induce phagocytosis, resulting in internalization and degradation of pathogens or target cells through phagosome acidification. Besides NK cells-mediated antibody-dependent cellular cytotoxicity (ADCC), tumor-infiltrated monocytes and macrophages can directly kill tumor cells in the presence of tumor antigen-specific antibodies through ADCP, representing another attractive strategy for cancer immunotherapy. Even though several methods have been developed to measure ADCP, an automated and high-throughput quantitative assay should offer highly desirable advantages for drug discovery. In this study we established a new ADCP assay to identify therapeutical monoclonal antibodies (mAbs) that facilitate macrophages phagocytosis of live target cells. We used Incucyte, an imaging system for live cell analysis. By labeling the live target cells with a pH sensitive dye (pHrodo), we successfully monitored the ADCP in real time. We demonstrated that our image-based assay is robust and quantitative, suitable for screening and characterization of therapeutical mAbs that directly kill target cells through ADCP. Furthermore, we found different subtypes of macrophages have distinct ADCP activities using both mouse and human primary macrophages differentiated in vitro. By studying various mAbs with mutations in their Fc regions using our assay, we showed that the variants with increased binding to Fc gamma receptors (FcγRs) have enhanced ADCP activities.

Association analyses of apolipoprotein E genotypes and cognitive performance in patients with Parkinson's disease.

BACKGROUND: Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD). The apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer's disease (AD). However, the effect of APOEε4 on cognitive function of PD patients remains unclear. In this study, we aimed to understand whether and how carrying APOEε4 affects cognitive performance in patients with early-stage and advanced PD. METHODS: A total of 119 Chinese early-stage PD patients were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hamilton anxiety scale, Hamilton depression scale, non-motor symptoms scale, Mini-mental State Examination, Montreal Cognitive Assessment, and Fazekas scale were evaluated. APOE genotypes were determined by polymerase chain reactions and direct sequencing. Demographic and clinical information of 521 early-stage and 262 advanced PD patients were obtained from Parkinson's Progression Marker Initiative (PPMI). RESULTS: No significant difference in cognitive performance was found between ApoEε4 carriers and non-carriers in early-stage PD patients from our cohort and PPMI. The cerebrospinal fluid (CSF) Amyloid Beta 42 (Aß42) level was significantly lower in ApoEε4 carrier than non-carriers in early-stage PD patients from PPMI. In advanced PD patients from PPMI, the BJLOT, HVLT retention and SDMT scores seem to be lower in ApoEε4 carriers without reach the statistical significance. CONCLUSIONS: APOEε4 carriage does not affect the cognitive performance of early-stage PD patients. However, it may promote the decline of CSF Aß42 level and the associated amyloidopathy, which is likely to further contribute to the cognitive dysfunction of PD patients in the advanced stage.

Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network.

Aims: Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results: We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion: Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.

Unearthing the soil-borne microbiome of land plants.

Plant-soil biodiversity interactions are fundamental for the functioning of terrestrial ecosystems. Yet, the existence of a set of globally distributed topsoil microbial and small invertebrate organisms consistently associated with land plants (i.e., their consistent soil-borne microbiome), together with the environmental preferences and functional capabilities of these organisms, remains unknown. We conducted a standardized field survey under 150 species of land plants, including 58 species of bryophytes and 92 of vascular plants, across 124 locations from all continents. We found that, despite the immense biodiversity of soil organisms, the land plants evaluated only shared a small fraction (less than 1%) of all microbial and invertebrate taxa that were present across contrasting climatic and soil conditions and vegetation types. These consistent taxa were dominated by generalist decomposers and phagotrophs and their presence was positively correlated with the abundance of functional genes linked to mineralization. Finally, we showed that crossing environmental thresholds in aridity (aridity index of 0.65, i.e., the transition from mesic to dry ecosystems), soil pH (5.5; i.e., the transition from acidic to strongly acidic soils), and carbon (less than 2%, the lower limit of fertile soils) can result in drastic disruptions in the associations between land plants and soil organisms, with potential implications for the delivery of soil ecosystem processes under ongoing global environmental change.

CXCR6 defines therapeutic subtypes of CD4+ cytotoxic T cell lineage for adoptive cell transfer therapy in pediatric B cell acute lymphoblastic leukemia.

The potential of cytotoxic CD4+ T cells and tissue resident memory T cells (Trm) in achieving adult leukemia remission have been highlighted [1,2]. We hypothesized that CXCR6 could serve as a marker for cytotoxic CD4+ Trm cells in the bone marrow (BM) of pediatric B-ALL patients. Flow cytometry (FCM) and published single cell RNA sequencing (scRNA-seq) datasets were employed to characterize CXCR6+CD4+ T cells in the BM and peripheral blood (PB) of pediatric B-ALL patients and healthy donors. FCM, scRNA-seq and co-culture were utilized to explore the cytotoxicity of CXCR6+CD4+ T cells in vitro based on in vitro induction of CXCR6+CD4+ T cells using tumor antigens and peripheral blood mononuclear cells (PBMCs). The ssGSEA based on the cell markers identified according to the in vivo scRNA-seq data, the TARGET-ALL-P2 datasets, and integrated machine learning algorithm were employed to figure out the key cells with prognostic values, followed by simulation of adoptive cell transfer therapy (ACT). Integrated machine learning identified the high-risk cells for disease free survival, and overall survival, while simulation of ACT therapy using CXCR6+CD4+T cells indicated that CXCR6+CD4+ T cells could remodel the bone marrow microenvironments towards anti-tumor. Based on the expression of genes involved in formation of resident memory T cells, CXCR6 is not a marker of resident memory CD4+T cells but defines therapeutic subtypes of CD4+ cytotoxic T cell lineage for pediatric B-ALL.

Readily releasable ß cells with tight Ca2+-exocytosis coupling dictate biphasic glucose-stimulated insulin secretion.

Biphasic glucose-stimulated insulin secretion (GSIS) is essential for blood glucose regulation, but a mechanistic model incorporating the recently identified islet ß cell heterogeneity remains elusive. Here, we show that insulin secretion is spatially and dynamically heterogeneous across the islet. Using a zinc-based fluorophore with spinning-disc confocal microscopy, we reveal that approximately 40% of islet cells, which we call readily releasable ß cells (RRßs), are responsible for 80% of insulin exocytosis events. Although glucose up to 18.2 mM fully mobilized RRßs to release insulin synchronously (first phase), even higher glucose concentrations enhanced the sustained secretion from these cells (second phase). Release-incompetent ß cells show similarities to RRßs in glucose-evoked Ca2+ transients but exhibit Ca2+-exocytosis coupling deficiency. A decreased number of RRßs and their altered secretory ability are associated with impaired GSIS progression in ob/ob mice. Our data reveal functional heterogeneity at the level of exocytosis among ß cells and identify RRßs as a subpopulation of ß cells that make a disproportionally large contribution to biphasic GSIS from mouse islets.

Method of smoldering combustion for the treatment of oil sludge-contaminated soil.

There is an urgent need to globally remediate oil sludge-contaminated soil (OSS). Smoldering combustion is a new low-energy approach for the treatment of organic waste. Therefore, the feasibility of smoldering combustion for the treatment of OSS was investigated in this study using a series of laboratory-scale experiments. The effective remediation of OSS was found to be achievable when the mass ratio of oil sludge in the sample reached 1/12 and above. Experimental results showed that smoldering at peak temperatures above 500 °C was found to completely remove petroleum hydrocarbons from the samples. The mass ratio of oil sludge in the sample had little effect on the distribution of the major elements (Si, Al, and Ca) in the smoldering products, and most of the minerals in the oil sludge adhered to the surface of the soil particles after smoldering. The smoldering heating environment is detrimental to the reusability of the soil, increases soil pH and available phosphorus content, and decreases organic carbon and total nitrogen content. Moreover, the influence of the airflow rate and material height on smoldering characteristics was investigated. Matching the appropriate airflow rate can help maintain optimal smoldering conditions, and smoldering remains stable with increasing material height. The addition of recovered oil to a sample with a low mass ratio of oil sludge can help with smoldering ignition and improve the removal efficiency of petroleum hydrocarbons. This study has confirmed that smoldering can be used to treat OSS within a broad range of oil sludge concentrations without pretreatment.

Learning Interpretable Rules for Scalable Data Representation and Classification.

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. A novel design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on ten small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios.

Livestock grazing modifies soil nematode body size structure in mosaic grassland habitats.

Body size is closely related to the trophic level and abundance of soil fauna, particularly nematodes. Therefore, size-based analyses are increasingly prominent in unveiling soil food web structure and its responses to anthropogenic disturbances, such as livestock grazing. Yet, little is known about the effects of different livestock on the body size structure of soil nematodes, especially in grasslands characterized by local habitat heterogeneity. A four-year field grazing experiment from 2017 to 2020 was conducted in a meadow steppe characterized by typical mosaics of degraded hypersaline patches and undegraded hyposaline patches to assess the impacts of cattle and sheep grazing on the body size structure of soil nematodes within and across trophic groups. Without grazing, the hypersaline patches harbored higher abundance of large-bodied nematodes in the community compared to the hyposaline patches. Livestock grazing decreased large-bodied nematodes within and across trophic groups mainly by reducing soil microbial biomass in the hypersaline patches, with sheep grazing resulting in more substantial reductions compared to cattle grazing. The reduction in large-bodied nematode individuals correspondingly resulted in decreases in nematode community-weighted mean (CWM) body size, nematode biomass, and size spectra slopes. However, both cattle and sheep grazing had minimal impacts on the CWM body size and size spectra of total nematodes in the hyposaline patches. Our findings suggest that livestock grazing, especially sheep grazing, has the potential to simplify soil food webs by reducing large-bodied nematodes in degraded habitats, which may aggravate soil degradation by weakening the bioturbation activities of soil fauna. In light of the widespread land use of grasslands by herbivores of various species and the ongoing global grassland degradation of mosaic patches, the recognition of the trends revealed by our findings is critical for developing appropriate strategies for grassland grazing management.

Translatability of In Vitro Stress for Predicting Deamidation and Oxidation Biotransformation on Biotherapeutics.

Biotransformation leading to single residue modifications (e.g., deamidation, oxidation) can contribute to decreased efficacy/potency, poor pharmacokinetics, and/or toxicity/immunogenicity for protein therapeutics. Identifying and characterizing such liabilities in vivo are emerging needs for biologics drug discovery. In vitro stress assays involving PBS for deamidation or AAPH for oxidation are commonly used for predicting liabilities in manufacturing and storage and are sometimes considered a predictive tool for in vivo liabilities. However, reports discussing their in vivo translatability are limited. Herein, we introduce a mass spectrometry workflow that characterizes in vivo oxidation and deamidation in pharmacokinetically relevant compartments for diverse protein therapeutic modalities. The workflow has low bias of <10% in quantitating degradation in the relevant pharmacokinetic concentration range for monkey and rabbit serum/plasma (1-100 µg/mL) and allows for high sequence coverage (∼85%) for discovery/monitoring of amino acid modifications. For oxidation and deamidation, the assay was precise, with percent coefficient of variation of <8% at 1-100 µg/mL and ≤6% method-induced artifacts. A high degree of in vitro and in vivo correlation was observed for deamidation on the six diverse protein therapeutics (seven liability sites) tested. In vivo translatability for oxidation liabilities were not observed for the 11 molecules tested using in vitro AAPH stress. One of the molecules dosed in eyes resulted in a false positive and a false negative prediction for in vivo oxidation following AAPH stress. Finally, peroxide stress was also tested but resulted in limited success (1 out of 4 molecules) in predicting oxidation liabilities.

Structural basis of antibody inhibition and chemokine activation of the human CC chemokine receptor 8.

The C-C motif chemokine receptor 8 (CCR8) is a class A G-protein coupled receptor that has emerged as a promising therapeutic target in cancer. Targeting CCR8 with an antibody has appeared to be an attractive therapeutic approach, but the molecular basis for chemokine-mediated activation and antibody-mediated inhibition of CCR8 are not fully elucidated. Here, we obtain an antagonist antibody against human CCR8 and determine structures of CCR8 in complex with either the antibody or the endogenous agonist ligand CCL1. Our studies reveal characteristic antibody features allowing recognition of the CCR8 extracellular loops and CCL1-CCR8 interaction modes that are distinct from other chemokine receptor - ligand pairs. Informed by these structural insights, we demonstrate that CCL1 follows a two-step, two-site binding sequence to CCR8 and that antibody-mediated inhibition of CCL1 signaling can occur by preventing the second binding event. Together, our results provide a detailed structural and mechanistic framework of CCR8 activation and inhibition that expands our molecular understanding of chemokine - receptor interactions and offers insight into the development of therapeutic antibodies targeting chemokine GPCRs.

Carpets with visual cues can improve gait in Parkinson's disease patients: may be independent of executive function.

BACKGROUND: Gait impairment is common in Parkinson's disease (PD) patients, which greatly reduces their quality of life. Executive dysfunction is associated with gait impairment. Compensatory strategies, including visual cues, have been shown to be effective in improving PD gait. In this study, we aimed to understand whether carpets with visual cues could improve PD gait, and how the improvement varies across patients with different executive function state. METHODS: We designed carpets with chessboard and stripe cues. A total of 65 Chinese PD patients were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, L-dopa equivalent daily dosage, Hoehn & Yahr stage, Frontal Assessment Battery, Mini Mental State Examination Scale, Hamilton Anxiety Scale, and Hamilton Depression Scale were evaluated. Gait parameters including stride length, gait speed and fall risk were recorded by a wearable electronic device. RESULTS: The stride length and gait speed were significantly improved and the fall risk was significantly mitigated when PD patients walked on carpets with chessboard and stripe patterns. Further analysis showed the amelioration of gait parameters was independent of executive dysfunction. CONCLUSIONS: Our study demonstrates that carpets with visual cues can improve the gait of PD patients even in those with mild executive dysfunction.

A novel, label-free, pre-equilibrium assay to determine the association and dissociation rate constants of therapeutic antibodies on living cells.

BACKGROUND AND PURPOSE: Monoclonal antibodies (Ab) represent the fastest growing drug class. Knowledge of the biophysical parameters (kon , koff and KD ) that dictate Ab:receptor interaction is critical during the drug discovery process. However, with the increasing complexity of Ab formats and their targets, it became apparent that existing technologies present limitations and are not always suitable to determine these parameters. Therefore, novel affinity determination methods represent an unmet assay need. EXPERIMENTAL APPROACH: We developed a pre-equilibrium kinetic exclusion assay using recent mathematical advances to determine the kon , koff and KD of monoclonal Ab:receptor interactions on living cells. The assay is amenable to all human IgG1 and rabbit Abs. KEY RESULTS: Using our novel assay, we demonstrated for several monoclonal Ab:receptor pairs that the calculated kinetic rate constants were comparable with orthogonal methods that were lower throughput or more resource consuming. We ran simulations to predict the critical conditions to improve the performance of the assays. We further showed that this method could successfully be applied to both suspension and adherent cells. Finally, we demonstrated that kon and koff , but not KD , correlate with in vitro potency for a panel of monoclonal Abs. CONCLUSIONS AND IMPLICATIONS: Our novel assay has the potential to systematically probe binding kinetics of monoclonal Abs to cells and can be incorporated in a screening cascade to identify new therapeutic candidates. Wide-spread adoption of pre-equilibrium assays using physiologically relevant systems will lead to a more holistic understanding of how Ab binding kinetics influence their potency.

[Corrigendum] Knockdown of HOXA10 reverses the multidrug resistance of human chronic mylogenous leukemia K562/ADM cells by downregulating P­gp and MRP­1.

Following the publication of the above article, an interested reader drew to the authors' attention that, in Fig. 2 on p. 1408, the microscopic images shown for the light scope images (upper row) and the green fluorescence images (lower row) appeared to be overlapping, such that these images appeared to have been derived from the same original sources even though they were intended to portray the results from differently performed experiments. After having re­examined their figures, the authors realized that this figure was assembled incorrectly. The revised version of Fig. 2, showing the correct data for all four experimental panels, is shown below. Note that the errors made during the assembly of these figures did not affect the overall conclusions reported in the paper. All the authors agree with the publication of this corrigendum, and are grateful to the Editor of International Journal of Molecular Medicine for allowing them the opportunity to publish this. They also apologize to the readership for any inconvenience caused. [International Journal of Molecular Medicine 37: 1405­1411, 2016; DOI: 10.3892/ijmm.2016.2539].

Isolated unilateral oculomotor palsy caused by pure midbrain infarction: a case report.

Purpose: Multiple etiologies may cause oculomotor nerve palsies. Identification of different etiologies is very important for subsequent treatment. Midbrain infarction is a rare cause of oculomotor nerve palsy. Materials and methods: We herein present a case of isolated unilateral oculomotor paresis caused by pure midbrain infarction. Results: Her pupillary sphincter and inferior rectus muscles were selectively spared. The symptoms were completely relieved after two months of antiplatelet therapy. We proposed that fibers from Edinger-Westphal nucleus and inferior rectus nucleus do not course through the paramedian area of the midbrain. Conclusions: Our report adds to the understanding of fascicles arrangement in the midbrain.

Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure.

Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language models (PLMs), the application prospect and promotion potential of machine learning methods in the relevant field have been further inspired. PLMs have recently achieved tremendous success in diverse text processing tasks, whereas limited by the significant semantic gap between the pre-training corpus and the structured electronic health records (EHRs), PLMs cannot converge to anticipated disease diagnosis and prediction results. Unfortunately, establishing connections between PLMs and EHRs typically requires the extraction of curated predictor variables from structured EHR resources, which is tedious and labor-intensive, and even discards vast implicit information.In this work, we propose an Input Prompting and Discriminative language model with the Mixture-of-experts framework (IPDM) by promoting the model's capabilities to learn knowledge from heterogeneous information and facilitating the feature-aware ability of the model. Furthermore, leveraging the prompt-tuning mechanism, IPDM can inherit the impacts of the pre-training in downstream tasks exclusively through minor modifications. IPDM remarkably outperforms existing models, proved by experiments on one disease diagnosis task and two disease prediction tasks. Finally, experiments with few-feature and few-sample demonstrate that IPDM achieves significant stability and impressive performance in predicting chronic diseases with unclear early-onset characteristics or sudden diseases with insufficient data, which verifies the superiority of IPDM over existing mainstream methods, and reveals the IPDM can powerfully address the aforementioned challenges via establishing a stable and low-resource medical diagnostic system for various clinical scenarios.

A series of fluorescent dyes based on 4-phenylacetylene-1,8-naphthalimide: Synthesis, theoretical calculations, photophysical properties and application in two-color imaging and dynamic behavior monitoring of lipid droplets and lysosomes.

A series of fluorescent dyes (NapPAs) based on 4-phenylacetylene-1,8-naphthalimide were synthesized and characterized, whose conjugated structures were extended by the introduction of phenylethynyl. Furthermore, changes in the photophysical properties of the dyes when substituents with varying electron richness were introduced at the p-position of phenylacetylene were studied. The theoretical calculation of the dye molecules was carried out by B3LYP functional and 6-31G(d,p) basis set, and the effects of different substituents at the p-position of phenylacetylene on the electronic structure and photophysical properties of the dyes were studied by theoretical calculation results. Theoretical calculations provided a reliable means of predicting the properties of dyes, which could help in the design of more efficient and novel dyes. To verify the practicability of the dyes, two dyes with excellent photophysical properties (large Stokes shift, high polarity-viscosity sensitivity, good biocompatibility) were selected as fluorescent probes for visualization of LDs and two-color imaging of LDs and lysosomes. Cell imaging showed that NapPA-LDs and NapPA-LDs-Lyso serve as excellent imaging tools to monitor the dynamic changes, movements, and behaviors of LDs and lysosomes in real time. Notably, NapPA-LDs-Lyso held promise as a potential tool to study the interaction between LDs and lysosomes.