Disease Research Research Articles

Effect of HPV Oncoprotein on Carbohydrate and Lipid Metabolism in Tumor Cells.

High-risk HPV infection accounts for 99.7% of cervical cancer, over 90% of anal cancer, 50% of head and neck cancers, 40% of vulvar cancer, and some cases of vaginal and penile cancer, contributing to approximately 5% of cancers worldwide. The development of cancer is a complex, multi-step process characterized by dysregulation of signaling pathways and alterations in metabolic pathways. Extensive research has demonstrated that metabolic reprogramming plays a key role in the progression of various cancers, such as cervical, head and neck, bladder, and prostate cancers, providing the material and energy foundation for rapid proliferation and migration of cancer cells. Metabolic reprogramming of tumor cells allows for the rapid generation of ATP, aiding in meeting the high energy demands of HPV-related cancer cell proliferation. The interaction between Human Papillomavirus (HPV) and its associated cancers has become a recent focus of investigation. The impact of HPV on cellular metabolism has emerged as an emerging research topic. A significant body of research has shown that HPV influences relevant metabolic signaling pathways, leading to cellular metabolic alterations. Exploring the underlying mechanisms may facilitate the discovery of biomarkers for diagnosis and treatment of HPV-associated diseases. In this review, we introduced the molecular structure of HPV and its replication process, discussed the diseases associated with HPV infection, described the energy metabolism of normal cells, highlighted the metabolic features of tumor cells, and provided an overview of recent advances in potential therapeutic targets that act on cellular metabolism. We discussed the potential mechanisms underlying these changes. This article aims to elucidate the role of Human Papillomavirus (HPV) in reshaping cellular metabolism and the application of metabolic changes in the research of related diseases. Targeting cancer metabolism may serve as an effective strategy to support traditional cancer treatments, as metabolic reprogramming is crucial for malignant transformation in cancer.

Eight induced pluripotent stem cell lines (iPSCs) derived from two patients with Leukocyte adhesion deficiency Type I (LAD I) with mutations in the ITGB2 gene

Leukocyte Adhesion Deficiency Type I (LAD I) is a rare inborn error of immunity caused by mutations in the ITGB2 gene coding for β2-integrin CD18 on the surface of leukocytes. Affected patients display severe clinical manifestations with life threatening infections and inflammatory complications due to an impaired ability of leukocytes to transmigrate from the blood vessel to the tissue. Here we describe the generation of eight induced pluripotent stem cell lines from two patients with LAD I and mutations in the ITGB2 gene. With this project we contribute patient individualized cell lines for explorative research in a rare disease

CircTMTC1 Mediates Nucleo-cytoplasmic Translocation of DDX3X to Regulate Osteogenic Differentiation of Human Ligamentum Flavum Cells.

This study was aimed to investigate the potential roles of circTMTC1 in the underlying pathophysiological mechanisms of ossification of the ligamentum flavum (OLF). OLF is the primary contributor to thoracic spinal stenosis, which may cause severe neurological symptoms. There is a lack of effective medical therapy for OLF available so far because the exact underlying mechanism of OLF has not been fully elucidated. CircRNAs are a special class of non-coding RNAs and have attracted a growing interest of research in various human diseases recently. Therefore, we explored the potential roles of circRNAs in the underlying pathophysiological mechanisms of OLF. We performed RNA-seq analysis to investigate the differentially expression profile of circRNAs in osteogenic differentiation of human LF cells, and identified a key circular RNA circTMTC1 functioned in OLF. Subsequently, we performed a series of experiments to investigate the exact molecular and cellular mechanisms in osteogenic differentiation of human ligamentum flavum cells. CircTMTC1 is significantly up-regulated during osteogenic differentiation of human LF cells. Mechanistically, we found that circTMTC1 could interact with the RNA binding protein DDX3X and enhance its nucleo-cytoplasmic translocation. An increased cytoplasmic level of DDX3X activated the NLRP3 inflammasome pathway and thus promoted osteogenic differentiation of human ligamentum flavum cells. Our findings suggested the circTMTC1-DDX3X-NLRP3 inflammasome signaling plays a pivotal role in osteogenic differentiation of human ligamentum flavum cells, which may provide novel diagnostic and therapeutic strategies for OLF.

Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data.

The 2024 Mary Tyler Moore Vision Initiative (MTM Vision) Workshop on Data convened to discuss best practices and specific considerations for building a comprehensive, shareable MTM Vision data lake. The workshop aimed to accelerate the development of new indications, therapies, and regulatory pathways for diabetic retinal disease (DRD) by standardizing and harmonizing clinical data and ocular 'omics analyses. Standardization of data collection, the use of common data elements, and data interoperability were emphasized, alongside federated learning approaches to promote data sharing and collaboration while maintaining data privacy and security. The integration of molecular data with other multimodal data types was recognized as a promising strategy for leveraging machine learning and AI approaches to advancing therapeutics development and improving treatment outcomes for DRD patients. Partnerships with entities such as the National Eye Institute, part of the National Institutes of Health, foundations, and industry were deemed vital for the successful implementation of these initiatives.

Designing the First Trials for Parkinson's Prevention.

For decades the greatest goal of Parkinson's disease (PD) research has often been distilled to the discovery of treatments that prevent the disease or its progression. However, until recently only the latter has been realistically pursued through randomized clinical trials of candidate disease-modifying therapy (DMT) conducted on individuals after they received traditional clinical diagnosis of PD (i.e., tertiary prevention trials). Now, in light of major advances in our understanding of the prodromal stages of PD, as well as its genetics and biomarkers, the first secondary prevention trials for PD are beginning. In this review, we take stock of DMT trials to date, summarize the breakthroughs that allow the identification of cohorts at high risk of developing a traditional diagnosis of PD, and describe key design elements of secondary prevention trials and how they depend on the prodromal stage being targeted. These elements address whom to enroll, what interventions to test, and how to measure secondary prevention (i.e., slowed progression during the prodromal stages of PD). Although these design strategies, along with the biological definition, subtype classification, and staging of the disease are evolving, all are driven by continued progress in the underlying science and integrated by a broad motivated community of stakeholders. While considerable methodological challenges remain, opportunities to move clinical trials of DMT to earlier points in the disease process than ever before have begun to unfold, and the prospects for PD prevention are nowtangible.

The African Initiative for Bioinformatics Online Training in Neurodegenerative Diseases (AI‐BOND): Investing in the next generation of African neuroscientists

AbstractNeurodegenerative disorders, including Alzheimer's disease and AD‐related dementias (AD/ADRD), pose significant challenges to health care systems globally, particularly in Africa. With the advances in medical technology and research capabilities, especially in next‐generation sequencing and imaging, vast amounts of data have been generated from AD/ADRD research. Given that the greatest increase in AD/ADRD prevalence is expected to occur in Africa, it is critical to establish comprehensive bioinformatics training programs to help African scientists leverage existing data and collect additional information to untangle AD/ADRD heterogeneity in African populations. The South Texas Alzheimer's Disease Research Center, with efforts from the National Institutes of Health and the Global Brain Health Institute, has partnered with the Brain Research Africa Initiative to develop the African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND). AI‐BOND is a comprehensive and accessible training program, the aim of which is to advance biostatistics and bioinformatics expertise in Africa in studying neurodegenerative diseases. This expertise is essential to enable African scientists to utilize the extensive AD/ADRD data and enhance the continent's ability to contribute to global research efforts in this field. The training addresses the gap in analyzing neurodegenerative disease data by providing skills and knowledge in genetic epidemiology, biostatistics, and bioinformatics to African students and researchers. This innovative online training program will last 6 months and provide training in skill sets R, SAS, and Python programing, genome‐wide association studies, genomics, transcriptomics, proteomics, metabolomics, microbiome analysis, and advanced statistical methods. Additional training will include study design and manuscript and grant writing. The first cohort of the AI‐BOND program will graduate in June 2024. The AI‐BOND program is expected to build research computational capacities in Africa that will improve the ability of graduates to conduct and utilize large‐scale studies, with the goal of curbing the growing incidence of neurodegenerative diseases in Africa.Highlights Alzheimer's disease (AD) and AD‐related dementias (ADRD) pose significant health challenges globally, particularly in Africa. The most significant AD/ADRD prevalence increase is predicted to occur in Africa. It is crucial to establish a bioinformatics training capacity in Africa to leverage the vast number of multi‐omics and imaging biomarkers of AD/ADRD data being generated. The African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND) training addresses the gaps in study design, biostatistics, genetic epidemiology, and bioinformatics related to neurodegenerative diseases in Africa. The success of AI‐BOND is anticipated to help build computational research capacities in Africa.

Lessons Learned from Patient Engagement in the Polycystic Kidney Disease Research Resource Consortium (PKD RRC)

Lessons Learned from Patient Engagement in the Polycystic Kidney Disease Research Resource Consortium (PKD RRC)

Tocilizumab may not be a good option for vascular involvement due to Behçet's syndrome.

Tocilizumab has been increasingly reported as an alternative therapeutic agent in the management of Behçet's syndrome (BS) and it has been mostly tried in BS patients with neurological and eye involvement. As therapeutic responses to each drug may vary across different types of BS involvement, we aimed to report seven patients with large vessel involvement treated with tocilizumab. We enrolled seven BS patients with vascular involvement who were given tocilizumab at the Behçet's Disease Research Centre in Istanbul University-Cerrahpaşa between 2000 and 2022. Demographic information, BS features, types of vascular involvement, previous and concomitant medications, C-reactive protein (CRP) levels, imaging modality results, and outcomes were documented from the patients' medical records. Within a median of 6 months after the initiation of tocilizumab, 5 patients experienced vascular relapses. These relapses included the emergence of new bilateral pulmonary artery aneurysms, a new pulmonary artery thrombus, parenchymal lung involvement, deep vein thrombosis in the lower extremity, and pseudotumor cerebri in one patient each. CRP levels were normal in 4 of the 5 patients at the time of vascular relapse. One of these 5 patients and another patient with aortitis had an exacerbation of mucocutaneous symptoms. In the last patient, venous ulcers did not respond to tocilizumab and were complicated with infection. Tocilizumab could potentially exacerbate vascular manifestations, similar to what is observed with mucocutaneous lesions in BS patients. Furthermore, CRP levels appear to be ineffective in monitoring these patients.

Trade-off between photosymbiosis and innate immunity influences cnidarian's response to pathogenic bacteria.

Mutualistic relationships with photosynthetic organisms are common in cnidarians, which form an intracellular symbiosis with dinoflagellates in the family Symbiodiniaceae. The establishment and maintenance of these symbionts are associated with the suppression of key host immune factors. Because of this, there are potential trade-offs between the nutrition that cnidarian hosts gain from their symbionts and their ability to successfully defend themselves from pathogens. To investigate these potential trade-offs, we utilized the facultatively symbiotic polyps of the upside-down jellyfish Cassiopea xamachana and exposed aposymbiotic and symbiotic polyps to the pathogen Serratia marcescens. Symbiotic polyps had a lower probability of survival following S. marcescens exposure. Gene expression analyses 24 hours following pathogen exposure indicate that symbiotic animals mounted a more damaging immune response, with higher levels of inflammation and oxidative stress likely resulting in more severe disruptions to cellular homeostasis. Underlying this more damaging immune response may be differences in constitutive and pathogen-induced expression of immune transcription factors between aposymbiotic and symbiotic polyps rather than broadscale immune suppression during symbiosis. Our findings indicate that in facultatively symbiotic polyps, hosting symbionts limits C. xamachana's ability to survive pathogen exposure, indicating a trade-off between symbiosis and immunity that has potential implications for coral disease research.

Welcoming new Editorial Advisory Board members.

Summary: We welcome five new Editorial Advisory Board members, who help to expand our expertise in areas of cutting-edge disease research and maintain our journal's high publishing standards.

Potential causal relationships between blood metabolites, inflammatory cytokines, and venous thromboembolism.

Venous thromboembolism (VTE) is the abnormal coagulation of blood in deep veins, which impairs venous return and includes deep vein thrombosis (DVT) and pulmonary embolism (PE). The incidence of VTE is increasing, leading to severe complications and sequelae. Despite the widespread application of multi-omics analyses in vascular disease research, identifying the specific links between various metabolic products, cytokines, and VTE, as well as their potential mediating roles, requires further validation due to confounding factors. Summary statistics for 1,091 metabolites, 309 metabolite ratios (8,299 individuals), and 41 inflammatory cytokines (8,293 individuals) were obtained from the largest genome-wide association studies (GWAS). Summary statistics for VTE (21,021 cases, 391,160 controls), DVT (6,501 cases, 357,111 controls), and PE (10,046 cases, 401,128 controls) were derived from the FinnGen R10 dataset. We initially examined causal relationships using two-sample MR analysis, followed by Two-step Mendelian Randomization (TSMR) and Multivariable Mendelian Randomization (MVMR) to identify potential mediating mechanisms. We identified causal associations for 78 blood metabolites with VTE, 79 with DVT, and 81 with PE. Among all 41 inflammatory cytokines included, only platelet-derived growth factor BB (PDGF-BB) levels showed a causal relationship with increased risks of VTE, DVT, and PE. MVMR analysis revealed that the associations between glycocholate levels and VTE, DVT, and PE were mediated by PDGF-BB, accounting for 14.54% (p=2.84E-04), 17.10% (p=3.64E-05), and 10.44% (p=1.39E-02), respectively. Furthermore, the associations between dodecanedioate (C12:1-DC) levels and VTE and DVT were also mediated by PDGF-BB, accounting for 12.79% (p=6.10E-04) and 12.17% (p=2.13E-04), respectively. This study reveals significant associations between specific blood metabolites and the risks of VTE, DVT, and PE, with some associations potentially mediated by PDGF-BB.

Synthetic data: how could it be used in infectious disease research?

Synthetic data: how could it be used in infectious disease research?

Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data

BackgroundRNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individually, limiting their effectiveness. Our study introduces the Gaussian Self-Benchmarking (GSB) framework, a novel approach that leverages the natural distribution patterns of guanine (G) and cytosine (C) content in RNA to mitigate multiple biases simultaneously. This method is grounded in a theoretical model, organizing k-mers based on their GC content and applying a Gaussian model for alignment to ensure empirical sequencing data closely match their theoretical distribution.ResultsThe GSB framework demonstrated superior performance in mitigating sequencing biases compared to existing methods. Testing with synthetic RNA constructs and real human samples showed that the GSB approach not only addresses individual biases more effectively but also manages co-existing biases jointly. The framework’s reliance on accurately pre-determined parameters like mean and standard deviation of GC content distribution allows for a more precise representation of RNA samples. This results in improved accuracy and reliability of RNA sequencing data, enhancing our understanding of RNA behavior in health and disease.ConclusionsThe GSB framework presents a significant advancement in RNA sequencing analysis by providing a well-validated, multi-bias mitigation strategy. It functions independently from previously identified dataset flaws and sets a new standard for unbiased RNA sequencing results. This development enhances the reliability of RNA studies, broadening the potential for scientific breakthroughs in medicine and biology, particularly in genetic disease research and the development of targeted treatments.

Association of mixed exposure to microplastics with sperm dysfunction: a multi-site study in China

Microplastics are environmental pollutants detected in various human organs and tissues. These particles originate from multiple sources including the degradation of larger plastic items and the intentional inclusion in consumer goods. Potential risks for human health resulting from microplastics exposure have also been reported. However, the distribution in the male reproductive system and its effect remains largely unknown. This study aims to investigate the presence of multiple microplastics in human semen and urine and their association with sperm quality in a multi-site study across China. We conducted a cross-sectional study involving 113 male participants from three regions in China. Semen and urine samples were collected and analysed using Raman microscopy to detect eight types of microplastics: polystyrene (PS), polypropylene (PP), polycarbonate (PC), polyethylene (PE), polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyethylene terephthalate (PET), and acrylonitrile butadiene styrene (ABS). Semen quality parameters, including total sperm count, concentration, motility, and morphology, were assessed. Statistical analyses, including single and multi-variable models, were used to evaluate the relationship between microplastic exposure and semen quality, with a focus on PTFE, after adjusting confounding factors of age, body mass index (BMI), smoking, alcohol drinking, and sites. Microplastics were detected in all semen and urine samples, with participants typically exposed to 3-5 different types. The detection rates of PS, PP and PE were the highest. Notably, PTFE exposure was significantly associated with decreased semen quality. Participants exposed to PTFE showed reductions in total sperm count [188.90±163.71 vs. 207.67±132.36 million, p=0.091], sperm concentration [52.13±47.47 vs. 58.32±37.26 million/mL, p=0.041], and progressive motility [40.29%±19.06 vs. 34.11%±17.02, p=0.083]. The multi-linear regression analysis indicated that each additional type of microplastic exposure was associated with a significant decrease in total sperm number [β=-15.4 (95% CI:-25.6,-5.2)], sperm concentration [β=-7.2 (95% CI:-12.4,-2.0)], and progressive motility [β=-8.3 (95% CI:-13.5,-3.1)]. Latent category analysis further refined these groups by types of microplastic exposure, highlighting specific types more strongly associated with decreased semen quality (OR=3.5, 95% CI: 1.8, 6.9, p<0.001). The nomogram can be used to assess the risk of sperm damage by combining the type of microplastic exposure in urine with age and BMI. Our findings highlight the potential reproductive health risks posed by microplastic contamination, particularly PTFE, a non-stick pan coating material, and raise concerns about the potential of urine testing as an indicator of male reproductive microplastic exposure. Future research is warranted to further elucidate the mechanisms underlying the adverse effects of microplastics on male fertility and cross-generational effects. This study was funded by the Clinical Research Project of Shanghai Municipal Commission of Health and Family planning (20224Y0085), Open Fund Project of Guangdong Academy of Medical Sciences (YKY-KF202202), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-064), Shanghai Clinical Research Center for Gynecological Diseases (22MC1940200), Shanghai Urogenital System Diseases Research Centre (2022ZZ01012), Key Discipline Construction Project (2023-2025) of Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai (GWVI-11.1-35, GWVI-11.2-YQ29) and Shanghai Frontiers Science Research Base of Reproduction and Development.

Potential of quantum machine learning for solving the real-world problem of cancer classification

Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world biological large datasets including ten different cancer types based on gene expression values. By comparing the classification results obtained from the quantum algorithm with those from classical approaches, we disclose that the QML algorithm overall achieves comparable and reliable results. Moreover, we identify novel biomarkers that can contribute to the understanding of cancer biology. Some of these biomarkers are consistent with DNA promoter methylation. Our findings highlight the potential of QML in cancer classification and biomarker discovery, paving the way for future advancements in other disease research and clinical applications.

The Vaccine Godmother: Dr. Gagandeep Kang's Pioneering Journey in Global Health and Vaccine Development.

Dr. Gagandeep Kang is a distinguished Indian microbiologist and virologist known for her pioneering work in the study of gastrointestinal diseases, diarrheal infections, and vaccine development. This article highlights her career, beginning with her medical education at Christian Medical College (CMC) Vellore, where she embarked on her groundbreaking research in enteric diseases, particularly rotavirus, a major cause of child mortality globally. Over the course of her distinguished career, she has led groundbreaking research on rotavirus, contributing to the development of two WHO-approved vaccines tailored for Indian communities: Rotavac (Bharat Biotech, Hyderabad, India) and Rotasiil (Serum Institute of India, Pune, India). Beyond her research, Dr. Kang has held significant advisory roles on national and global platforms, including WHO's Global Advisory Committee on Vaccine Safety and the Coalition for Epidemic Preparedness Innovations (CEPI). She has authored over 300 scientific publications and coauthored the bestselling book "Till We Win,"which details India's fight against the COVID-19 pandemic. Her exceptional contributions have earned her numerous accolades, including the Infosys Prize in Life Sciences, the Fellowship of the Royal Society, and the Canada Gairdner Global Health Award. Dr. Kang's career exemplifies her dedication to advancing public health, vaccine development, and global health initiatives, making her a trailblazer in the field of microbiology and virology.

Mapping the research of nursing in Parkinson's disease: a bibliometric and quantitative analysis.

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disorder. Clinically, the therapeutic strategy of PD could only alleviate the symptoms. Nursing plays a crucial role in providing patient education, symptom management, and psychosocial support. This study aims to analyze the current state and prospects of research in the field of Parkinson's disease (PD) and its associated nursing care through bibliometric methods to explore the trends that May guide its future development. Literature related to Parkinson's disease and nursing care was systematically searched by the Web of Science database from 1991 to 2023. Quantitative analysis of cooperative networks was conducted using bibliometric tools VOSviewer and CiteSpace. The analysis covered 2,649 publications in the field of PD and nursing care, authored by 12,576 researchers from 3,869 institutions across 94 countries. The number of articles has steadily increased over the past 20 years. In this research field, the United States and the United Kingdom emerged as leading countries, and Radboud Universiteit Nijmegen was positioned as an international hub. Movement Disorders was identified as the journal with the highest output and with the most co-citation. Prof. Bastiaan R. Bloem published the most papers in the area, and Prof. Per Odin had the highest average citation. The major fields of these publications are clinical neurology, geriatrics & gerontology, multidisciplinary sciences, and health care sciences & services. Hot topics in the field predominantly revolve around Parkinson's disease, quality of life, and dementia. Research in Parkinson's disease and nursing care is experiencing a period of rapid growth, with continuous expansion in research scope and depth of investigation. One of the trends identified is the increasing focus on quality of life and the management of dementia in PD patients, reflecting the importance of these areas in research. The study further suggests that future advancements in the field May rely significantly on strengthening international collaborations and addressing global disparities in resource distribution, particularly by promoting research inclusivity and cooperation among low-resource countries.

Bibliometric analysis of microRNAs and Parkinson's disease from 2014 to 2023.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the degeneration of dopaminergic neurons. Recent research has emphasized a significant correlation between microRNAs (miRNAs) and PD. To identify key research areas, provide a comprehensive overview of current research in various fields, and propose potential directions for future studies, a bibliometric analysis was conducted on the involvement of miRNAs in Parkinson's disease from 2014 to 2023. Relevant literature records were collected from the Web of Science Core Collection on February 29, 2024. Subsequently, the data underwent analysis using the Bibliometrix R package and VOSviewer (version 1.6.19). The annual scientific publications on miRNAs and Parkinson's disease demonstrated an increasing trend, with an annual growth rate of 12.67%. China, the United States, and India emerged as the top three most productive countries/regions. The University of Barcelona had the highest annual publications, followed by Central South University and the Helmholtz Association. The INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES held the top position in terms of H-index and total citations, reflecting its extensive influence and prolific publication output. Kim, J., Junn, E., Hébert, S.S., and Doxakis, E. were the most frequently co-cited authors in the field. Based on the analysis of keywords, the most frequently occurring terms included "alpha-synuclein," "neurodegenerative disease," "exosome," "neuroinflammation," "oxidative stress," "autophagy," and "amyotrophic lateral sclerosis," which have emerged as prominent research topics. Concurrently, there has been notable interest in topics such as "ceRNA," "lncRNAs," "mitochondrial dysfunction," and "circular RNA." This study focused on identifying emerging trends and critical research topics in the bibliometric analysis of microRNAs related to Parkinson's disease. These findings highlight the diverse research landscape and evolving trend of miRNA-related research in PD. The field of miRNA research in Parkinson's disease is actively exploring the underlying mechanisms of miRNA function, identifying potential diagnostic markers, and developing innovative therapeutic strategies. The results of our study offer significant contributions to researchers' ability to track contemporary developments and guide the trajectory of future research in this domain.

Dual color carbon dots for simultaneous dynamic fluorescence tracking of mitochondria and lysosomes

The development of fluorescent probes to monitor mitochondria and lysosomes will be helpful not only to realize the status monitoring of the organelles, but also to gain insights into their functions in both healthy and diseased states. Carbon Dots (CDs), as a nanomaterial with excellent properties, have provided new tools and methods for research in organelle imaging in recent years. In this study, we synthesized green fluorescence-emitting carbon dots (G-CDs) with mitochondria-targeting ability and red fluorescence-emitting carbon dots (R-CDs) with lysosome-targeting ability by using the same precursors. G-CDs are brightly with a quantum yield of up to 51.8 % and R-CDs have a long-wavelength emission of 611 nm. Based on the unique properties of G-CDs and R-CDs, we observed the changes of mitochondria during apoptosis and mitochondrial autophagy in HeLa cells by using G-CDs. And the dynamics of lysosomes in the inhibition of cellular autophagy and cellular necrosis were observed by utilizing the R-CDs. Furthermore, we observed human intestinal organoids samples stained by G-CDs and R-CDs, extending the relevant applications of carbon dots imaging to the organoids field, which brings forth new possibilities for research and drug development of related diseases.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study uncovers current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.