Ozone Mortality Burden Changes Driven by Population Aging and Regional Inequity in China in 2013-2050.

Air pollution exposure is closely linked to population age and socioeconomic status. Population aging and imbalance in regional economy are thus anticipated to have important implications on ozone (O3)-related health impacts. Here we provide a driver analysis for O3 mortality burden due to respiratory disease in China over 2013-2050 driven by population aging and regional inequity. Unexpectedly, we find that population aging is estimated to result in dramatic rises in annual O3 mortality burden in China; by 56, 101-137, and 298-485 thousand over the periods 2013-2020, 2020-2030, and 2030-2050, respectively. This reflects the exponential rise in baseline mortality rates with increasing age. The aging-induced mortality burden rise in 2030-2050 is surprisingly large, as it is comparable to the net national mortality burden due to O3 exposure in 2030 (359-399 thousand yr-1). The health impacts of O3 pollution, shown as mortality burden per capita, are inequitably distributed, with more severe effects in less developed provinces than their developed counterparts by 23.1% and 21.5% in 2019 and 2030, respectively. However, the regional inequity in O3 mortality burden is expected to be mitigated in 2050. This temporal variation reflects evolving demographic dividend characterized by a larger proportion of younger individuals in developed regions. These findings are critical for targeted improvement of healthcare services to ensure the sustainability of social development.

Circular RNA circMRPS35 represses malignant progression in osteosarcoma cells via targeting miR-105-5p/FOXO1.

Osteosarcoma is a highly metastatic, aggressive bone cancer that occurs in children and young adults worldwide. Circular RNAs (circRNAs) are crucial molecules for osteosarcoma progression. In this study, we aimed to investigate the impact of circMRPS35 overexpression and its interaction with FOXO1 via evaluating apoptosis, cell cycle, and bioinformatic analyses on the malignant development of osteosarcoma in MG63 and MNNG/HOS cells. We found that circMRPS35 overexpression reduced osteosarcoma cell viability and inhibited tumor growth in vivo. It increased the apoptosis rate and induced cell cycle arrest in osteosarcoma cells. We identified a potential interaction between circMRPS35 and FOXO1 with miR-105-5p using bioinformatics analysis. Overexpression of circMRPS35 decreased miR-105-5p expression, whereas miR-105-5p mimic treatment increased its expression. This mimic also suppressed the luciferase activity of circMRPS35 and FOXO1 and reduced FOXO1 expression. Overexpression of circMRPS35 elevated FOXO1 protein levels, but this effect was reversed by co-treatment with the miR-105-5p mimic. We demonstrated that inhibiting miR-105-5p decreased viability and induced apoptosis. Overexpression of FOXO1 or treatment with a miR-105-5p inhibitor could counteract the effects of circMRPS35 on viability and apoptosis in osteosarcoma cells. Therefore, we concluded that circMRPS35 suppressed the malignant progression of osteosarcoma via targeting the miR-105-5p/FOXO1 axis.

Unveiling the Interfacial Species Synergy in Promoting CO2 Tandem Electrocatalysis in Near-Neutral Electrolyte.

The interfacial species-built local environments on Cu surfaces impact the CO2 electroreduction process significantly in producing value-added multicarbon (C2+) products. However, intricate interfacial dynamics leads to a challenge in understanding how these species affect the process. Herein, with ab initio molecular dynamics (AIMD) and finite element method (FEM) simulations, we reveal that the highly concentrated interfacial species, including the *CO, hydroxide, and K+, could synergistically promote the C-C coupling on the one-dimensional (1D) porous hollow structure regulated interfacial environment. The Cu-Ag tandem catalyst was then synthesized with the as-designed structure, exhibiting a high C2+ Faradaic efficiency of 76.0% with a partial current density of 380.0 mA cm-2 in near-neutral electrolytes. Furthermore, in situ Raman spectra validate that the 1D porous structure regulates the concentration of interfacial CO intermediates and ions to increase *CO coverage, local pH value, and ionic field, promoting the CO2-to-C2+ activity. These results provide insights into the design of practical ECR electrocatalysts by regulating interfacial species-induced local environments.

Wear and corrosion of titanium alloy spinal implants in vivo.

To investigate the wear and corrosion of titanium alloy spinal implants in vivo, we evaluated removed implants and their surrounding scar tissues from 27 patients between May 2019 and April 2021. We performed scanning electron microscopy, energy-dispersive X-ray spectroscopy, and histological analysis. The results revealed metal-like particles in the soft tissues of seven patients, without any considerable increase in inflammatory cell infiltration. Patients with fractures showed lower percentages of wear and corrosion compared with other patients (42% and 17% vs. 59% and 26%). Polyaxial screws exhibited higher wear and corrosion percentages (53% and 23%) compared with uniaxial screws (39% and 3%), although in patients with fracture, the reverse was observed (20% and 0% vs. 39% and 3%). We found that titanium alloy spinal implants experience some degree of wear and corrosion in vivo. The titanium alloy particles formed by wear exhibited good histocompatibility, not causing inflammation, foreign body reactions, or osteolysis. Therefore, spinal implants should be removed cautiously when treating titanium alloy spinal metallosis. The wear and corrosion of the implants increase with the increase in implantation time, although the screw structure does not significantly affect these changes.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

CD4+ T cells display a spectrum of recall dynamics during re-infection with malaria parasites.

Children in malaria-endemic regions can experience repeated Plasmodium infections over short periods of time. Effects of re-infection on multiple co-existing CD4+ T cell subsets remain unresolved. Here, we examine antigen-experienced CD4+ T cells during re-infection in mice, using scRNA-seq/TCR-seq and spatial transcriptomics. TCR transgenic TEM cells initiate rapid Th1/Tr1 recall responses prior to proliferating, while GC Tfh counterparts are refractory, with TCM/Tfh-like cells exhibiting modest non-proliferative responses. Th1-recall is a partial facsimile of primary Th1-responses, with no upregulated effector-associated genes being unique to recall. Polyclonal, TCR-diverse, CD4+ T cells exhibit similar recall dynamics, with individual clones giving rise to multiple effectors including highly proliferative Th1/Tr1 cells, as well as GC Tfh and Tfh-like cells lacking proliferative capacity. Thus, we show substantial diversity in recall responses mounted by multiple co-existing CD4+ T cell subsets in the spleen, and present graphical user interfaces for studying gene expression dynamics and clonal relationships during re-infection.

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification.

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

Exploring the immune characteristions of CRKP pneumonia at single-cell level.

The immune dysregulation associated with carbapenem-resistant Klebsiella pneumoniae (CRKP) severity was investigated through single-cell RNA sequencing (scRNA-seq) of 5 peripheral blood samples from 3 patients with moderate and severe CRKP pneumonia. Additionally, scRNA-seq datasets from two individuals with COVID-19 were included for comparative analysis. The dynamic characterization and functional properties of each immune cell type were examined by delineating the transcriptional profiles of immune cells throughout the transition from moderate to severe conditions. Overall, most immune cells in CRKP patients exhibited a robust interferon-α response and inflammatory reaction compared to healthy controls, mirroring observations in COVID-19 patients. Furthermore, cell signatures associated with NK cells, macrophages, and monocytes were identified in CRKP progression including PTPRCAP for NK cells, C1QB for macrophages, and S100A12 for both macrophages and monocytes. In summary, this study offers a comprehensive scRNA-seq resource for illustrating the dynamic immune response patterns during CRKP progression, thereby shedding light on the associations between CRKP and COVID-19.

Research progress of cGAS-STING signaling pathway in intestinal diseases.

The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signal has drawn much consideration due to its sensitivity to DNA in innate immune mechanisms. Activation of the cGAS-STIN signaling pathway induces the production of interferon and inflammatory cytokines, resulting in immune responses, or inflammatory diseases. The intestinal tract is a vital organ for the body's nutrition absorption, recent studies have had various points of view on the job of cGAS-STING pathway in various intestinal sicknesses. Therefore, understanding its role and mechanism in the intestinal environment can help to develop new strategies for the treatment of intestinal diseases. This article examines the mechanism of the cGAS-STING pathway and its function in inflammatory bowel disease, intestinal cancer, and long-injury ischemia-reperfusion, lists the current medications that target it for the treatment of intestinal diseases, and discusses the impact of intestinal flora on this signaling pathway, to offer a theoretical and scientific foundation for upcoming targeted therapies for intestinal disorders via the cGAS-STING pathway.

The effect of perceived organizational justice on workplace deviant behavior of new nurses: the role of emotional labor and psychological capital.

BACKGROUND: New nurses are prone to workplace deviant behavior in the constrained hospital environment, which will not only directly affect the safety of patients, but also reduce the work efficiency of nurses and bring negative results to the hospital. The purpose of this study was to investigate the relationship between perceived organizational justice, emotional labor, psychological capital, and workplace deviant behavior of new nurses. METHODS: A cross-sectional study was used in this study. A survey was conducted in 5 hospitals in Henan Province, Chain from February to April 2023. The sample size was 546. The questionnaire included general information, perceived organizational justice scale, emotional labor scale, psychological capital scale, and workplace deviant behavior scale. SPSS 26.0 and PROCESS Macro were used for data analysis. PROCESS Model 4 and Model 14 were used to verify the model. RESULTS: This study displays that perceived organizational justice was negatively correlated with emotional labor and workplace deviant behavior, and emotional labor was positively correlated with workplace deviant behavior. Meanwhile, emotional labor plays a partial mediating role between perceived organizational justice and workplace deviant behavior, accounting for 32.7% of the total effect. Moreover, the path of emotional labor on workplace deviant behavior is moderated by psychological capital. CONCLUSION: This study further understood the workplace deviant behavior of new nurses, and provided a new perspective for solving this problem. Nurse managers can reduce workplace deviant behavior by enhancing the perceived organizational justice and psychological capital of new nurses and improving emotional labor.

Health Information Seeking Using Tech and Non-Tech Sources Among a Diverse Sample of Caregivers in the Deep South.

Caregivers of people living with dementia (PLWD) are often tasked with making decisions about their loved one's daily care and healthcare treatment, causing stress and decision-making fatigue. Many caregivers engage in health information seeking to improve their health literacy for optimal decision-making, though there is limited knowledge about the strategies used to increase their health literacy. This study involved a survey of caregivers in Alabama, most of whom were African American and/or living in rural communities that have historically underserved. The findings shed light on caregivers' experiences in seeking out health-related information and their perceptions of various sources of information.

LncRNA XIST modulates miR-328-3p ectopic expression in lung injury induced by tobacco-specific lung carcinogen NNK both in vitro and in vivo.

BACKGROUND AND PURPOSE: It is well acknowledged that tobacco-derived lung carcinogens can induce lung injury and even lung cancer through a complex mechanism. MicroRNAs (MiRNAs) are differentially expressed in tobacco-derived carcinogen nicotine-derived nitrosamine ketone (NNK)-treated A/J mice. EXPERIMENTAL APPROACH: RNA sequencing was used to detect the level of long non-coding RNAs (lncRNAs). Murine and human lung normal and cancer cells were used to evaluate the function of lncRNA XIST and miR-328-3p in vitro, and NNK-treated A/J mice were used to test their function in vivo. In vivo levels of miR-328-3p and lncRNA XIST were analysed, using in situ hybridization. miR-328-3p agomir and lncRNA XIST-specific siRNA were used to manipulate in vivo levels of miR-328-3p and lncRNA XIST in A/J mice. KEY RESULTS: LncRNA XIST was up-regulated in NNK-induced lung injury and dominated the NNK-induced ectopic miRNA expression in NNK-induced lung injury both in vitro and in vivo. Either lncRNA XIST silencing or miR-328-3p overexpression exerted opposing effects in lung normal and cancer cells regarding cell migration. LncRNA XIST down-regulated miR-328-3p levels as a miRNA sponge, and miR-328-3p targeted the 3'-UTR of FZD7 mRNA, which is ectopically overexpressed in lung cancer patients. Both in vivo lncRNA XIST silencing and miR-328 overexpression could rescue NNK-induced lung injury and aberrant overexpression of the lung cancer biomarker CK19 in NNK-treated A/J mice. CONCLUSIONS AND IMPLICATIONS: Our results highlight the promotive effect of lncRNA XIST in NNK-induced lung injury and elucidate its post-transcriptional mechanisms, indicating that targeting lncRNA XIST/miR-328-3p could be a potential therapeutic strategy to prevent tobacco carcinogen-induced lung injury in vivo.

Photothermal, superhydrophobic, conductive, and anti-UV cotton fabric loaded with polydimethylsiloxane-encapsulated copper sulfide nanoflowers.

Multifunctional textiles have attracted widespread attention with the improvement of awareness of health. Especially, the fluorine-free superhydrophobic and conductive cellulose fiber-based fabrics have received intensive interest due to their broad and high-value applications. Herein, the copper sulfide nanoflowers were in-situ deposited on cotton fabric followed by polydimethylsiloxane (PDMS) treatment for encapsulating CuS nanoflowers and obtaining superhydrophobicity, recorded as Cot@PTA@CuS@PDMS. Cot@PTA@CuS@PDMS possesses superhydrophobicity with contact angles of 153.0 ± 0.4°, photothermal effect, excellent UV resistance, good conductivity, and anti-fouling. Interestingly, the resistance of Cot@PTA@CuS@PDMS is significantly reduced from 856.4 to 393.1 Ω under simulated sunlight irradiation with 250 mW/cm2. Notably, the resistance can be slightly recovered after shutting off simulated sunlight. Besides, Cot@PTA@CuS@PDMS has efficient oil-water separation efficiency for corn germ oil and castor oil, respectively. Briefly, this work provides a novel, facile, and promising strategy to fabricate multifunctional fiber-based textiles with the reversible change of resistance under simulated sunlight irradiation, inspiring more scholars to control the resistance change of textiles by light irradiation.

Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection.

We present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1-km grid, using seven ocean color features from the MODIS-Aqua satellite platform (2002-2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth-dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K-nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;20:1432-1446. © 2024 SETAC.

Mechanism of programmed cell death in the posterior silk gland of the silkworm, Bombyx mori, during pupation based on Ca2+ homeostasis.

The silkworm, Bombyx mori, is a complete metamorphosed economic insect, and the silk gland is a significant organ for silk protein synthesis and secretion. The silk gland completely degenerates during pupation, but the regulatory mechanism of programmed cell death (PCD) has not yet been understood. In the present study, we investigated the non-genetic pathway of 20E-induced PCD in the posterior silk gland (PSG) based on intracellular Ca2+ levels. Silk gland morphology and silk gland index indicated rapid degeneration of silk gland during metamorphosis from mature silkworm (MS) to pupal day 1 (P1), and Ca2+ levels within the PSG were found to peak during the pre-pupal day 1 (PP1) stage. Moreover, the results of autophagy and apoptosis levels within the PSG showed that autophagy was significantly increased in MS-PP1 periods, and significantly decreased in PP2 and P1 periods. Apoptosis was almost absent in MS-PP1 periods and significantly increased in PP2 and P1 periods. Additionally, western blotting results showed that autophagy preceded apoptosis, and the autophagy-promoting ATG5 was cleaved by calpain to the autophagy-inhibiting and apoptosis-promoting NtATG5 since PP1 period, while decreased autophagy was accompanied by increased apoptosis. Collectively, these findings suggest that Ca2+ is a key factor in the shift from autophagy to apoptosis.

CO-Tolerant Hydrogen Oxidation Electrocatalysts for Low-Temperature Hydrogen Fuel Cells.

The severe performance degradation of low-temperature hydrogen fuel cells upon exposure to trace amounts of carbon monoxide (CO) impurities in reformate hydrogen fuels is one of the challenges that hinders their commercialization. Despite significant efforts that have been made, the CO-tolerance performance of electrocatalysts for the hydrogen oxidation reaction (HOR) is still unsatisfactory. This Perspective discusses the path forward for the rational design of CO-tolerant HOR electrocatalysts. The fundamentals of the CO-tolerant mechanisms on commercialized platinum group metal (PGM) electrocatalysts via either promoting CO electrooxidation or weakening CO adsorption are provided, and comprehensive discussions based on these strategies are presented with typical examples. Given the recent progress, some emerging strategies, including blocking CO diffusion with a barrier layer and developing non-PGM HOR catalysts, are also discussed. We conclude with a discussion of the strengths and limitations of these strategies along with the perspectives of the major challenges and opportunities for future research on CO-tolerant HOR electrocatalysts.

Flavin mononucleotide regulated photochemical isomerization and degradation of zeatin.

cis-Zeatin (cZ), a cytokinin often overlooked compared to trans-zeatin (tZ), can now be controlled in live cells and plants through a new biocompatible reaction. Using flavin photosensitizers, cZ can be isomerized to tZ or degraded, depending on the presence of a reducing reagent. This breakthrough offers a novel approach for regulating plant growth through chemical molecules.

The Toll/IMD pathways mediate host protection against dipteran parasitoids.

Parasitoids have utilized a variety of strategies to counteract host defense. They are in different taxonomic status and exhibit phenotypic and genetic diversity, and thus are thought to evolve distinct anti-defense mechanisms. In this study, we investigated the performance of two closely related parasitoids, Exorista japonica and Exorista sorbillans (Diptera: Tachinidae) that are biological control agents in agriculture and major insect pests in sericulture, on the host Bombyx mori. We show that the host is more susceptible to E. sorbillans infection while relatively resistant to E. japonica infection. Moreover, the expression levels of host antimicrobial peptides (AMPs) genes are repressed at early infection and induced at late infection of E. japonica, while AMPs are over-expressed at early infection and return to normal levels at late infection of E. sorbillans. In parallel, Toll and IMD pathway genes are generally induced at late infection of E. japonica, whereas these genes are up-regulated at early infection and down-regulated at late infection of E. sorbillans. Activating of host Toll/IMD pathways and AMPs expression by lipopolysaccharide (LPS) represses the larval growth of E. sorbillans. Conversely, inhibiting host Toll/IMD pathways by RNA interference significantly promotes E. japonica development. Therefore, the Toll/IMD pathways are required in the host for defense against infection of dipteran parasitoids. Overall, our study provides the new insight into the diversified host-parasitoid interactions, and offers a theoretical basis for further studies of the adaptive mechanism of dipteran parasitoids.

A parasitoid regulates 20E synthesis and antibacterial activity of the host for development by inducing host nitric oxide production.

Parasitoids are important components of the natural enemy guild in the biological control of insect pests. They depend on host resources to complete the development of a specific stage or whole life cycle and thus have evolved towards optimal host exploitation strategies. In the present study, we report a specific survival strategy of a fly parasitoid Exorista sorbillans (Diptera: Tachinidae), which is a potential biological control agent for agricultural pests and a pest in sericulture. We found that the expression levels of nitric oxide synthase (NOS) and nitric oxide (NO) production in host Bombyx mori (Lepidoptera: Bombycidae) were increased after E. sorbillans infection. Reducing NOS expression and NO production with an NOS inhibitor (NG-nitro-L-arginine methyl ester hydrochloride) in infected B. mori significantly impeded the growth of E. sorbillans larvae. Moreover, the biosynthesis of 20-hydroxyecdysone (20E) in infected hosts was elevated with increasing NO production, and inhibiting NOS expression lowered 20E biosynthesis. More importantly, induced NO synthesis was required to eliminate intracellular bacterial pathogens that presumably competed for shared host resources. Inhibiting NOS expression down-regulated the transcription of antimicrobial peptide genes and increased the number of bacteria in parasitized hosts. Collectively, this study revealed a new perspective on the role of NO in host-parasitoid interactions and a novel mechanism for parasitoid regulation of host physiology to support its development.

Morphological Profiling for Drug Discovery in the Era of Deep Learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.