Serum exosomal small nucleolar RNA (snoRNA) signatures as a predictive biomarker for benign and malignant pulmonary nodules.

Low-dose CT (LDCT) is increasingly recognized as the preferred method for detecting pulmonary nodules. However, distinguishing whether a nodule is benign or malignant often necessitates repeated scans or invasive tissue sampling procedures. Therefore, there is a pressing need for non-invasive techniques to minimize unnecessary interventions. This study aim to investigate the expression profile of exosomal snoRNA in the serum of patients with benign and malignant pulmonary nodules. We identified a total of 278 snoRNAs in serum exosomes, revealing significant differences in snoRNA levels between patients with malignant and benign nodules. Specifically, the upregulated snoRNAs U78 and U37 were validated through qRT-PCR and were found significantly elevated in the serum of patients with malignant pulmonary nodules, positioning them as promising biomarkers for the early detection of lung cancer. This study underscores the potential of serum exosomal U78 and U37 as critical tools for assessing the risk of pulmonary nodules identified through CT screening.

Identification of candidate single-nucleotide polymorphisms (SNPs) and genes associated with sugarcane leaf scald disease.

Leaf scald, caused by Xanthomonas albilineans, is a severe disease affecting sugarcane worldwide. One of the most practical ways to control it is by developing resistant sugarcane cultivars. It is essential to identify genes associated with the response to leaf scald. A panel of 170 sugarcane genotypes was evaluated for resistance to leaf scald in field conditions for 2 years, followed by a 1-year greenhouse experiment. The phenotypic evaluation data showed a wide continuous distribution, with heritability values ranging from 0.58 to 0.84. Thirteen single nucleotide polymorphisms (SNPs) were identified, significantly associated with leaf scald resistance. Among these, eight were stable across multiple environments and association models. The candidate genes identified and validated based on RNA-seq and qRT-PCR included two genes that encode NB-ARC leucine-rich repeat (LRR)-containing domain disease-resistance protein. These findings provide a basis for developing marker-assisted selection strategies in sugarcane breeding programs.

Integrated analysis of transcriptome and miRNAome reveals the heat stress response of Pinellia ternata seedlings.

Pinellia ternata (Thunb.) Briet., a valuable herb native to China, is susceptible to the "sprout tumble" phenomenon because of high temperatures, resulting in a significant yield reduction. However, the molecular regulatory mechanisms underlying the response of P. ternata to heat stress are not well understood. In this study, we integrated transcriptome and miRNAome sequencing to identify heat-response genes, microRNAs (miRNAs), and key miRNA-target pairs in P. ternata that differed between heat-stress and room-temperature conditions. Transcriptome analysis revealed extensive reprogramming of 4,960 genes across various categories, predominantly associated with cellular and metabolic processes, responses to stimuli, biological regulation, cell parts, organelles, membranes, and catalytic and binding activities. miRNAome sequencing identified 1,597 known/conserved miRNAs that were differentially expressed between the two test conditions. According to the analysis, genes and miRNAs associated with the regulation of transcription, DNA template, transcription factor activity, and sequence-specific DNA binding pathways may play a major role in the resistance to heat stress in P. ternata. Integrated analysis of the transcriptome and miRNAome expression data revealed 41 high-confidence miRNA-mRNA pairs, forming 25 modules. MYB-like proteins and calcium-responsive transcription coactivators may play an integral role in heat-stress resistance in P. ternata. Additionally, the candidate genes and miRNAs were subjected to quantitative real-time polymerase chain reaction to validate their expression patterns. These results offer a foundation for future studies exploring the mechanisms and critical genes involved in heat-stress resistance in P. ternata.

Muscle density, but not size, is independently associated with cognitive health in older adults with hip fractures.

Emerging evidence indicates a complex interplay between skeletal muscle and cognitive function. Despite the known differences between muscle quantity and quality, which can be measured via computed tomography (CT), the precise nature of their associations with cognitive performance remain underexplored. To investigate the links between muscle size and density and cognitive impairment (CI) in the older adults with hip fractures, we conducted a post hoc, cross-sectional analysis within a prospective cohort study on 679 patients with hip fractures over 65. Mini-Mental State Examination (MMSE) and routine hip CT imaging were utilized to assess cognition function and muscle characteristics in older adults with hip fractures. The CT scans provided data on cross-sectional area and attenuation for the gluteus maximus (G.MaxM) and the combined gluteus medius and minimus (G.Med/MinM). Participants were categorized into CI and non-CI groups based on education levels and MMSE scores. Multivariate logistic regressions, propensity score (PS) methods, and subgroup analysis were employed to analyze associations and validate findings. This study included 123 participants (81.6 ± 6.8 years, 74% female) with CI and 556 participants (78.5 ± 7.7 years, 72% female) without. Compared to the non-CI group, muscle parameters, especially density, were significantly lower in the CI group. Specifically, G.Med/Min muscle density, but not size was robustly associated with CI (odds ratio (OR) = 0.77, 95% confidence interval = 0.62-0.96, P = 0.02), independent of other medical situations. Sensitivity analysis corroborated that G.Med/Min muscle density was consistently lower in the CI group than the non-CI group, as evidenced in the PS matched (P = 0.024) and weighted cohort (P = 0.033). Enhanced muscle parameters, particularly muscle density in the G.Med/MinM muscle, correlate with a lower risk of CI. Muscle density demonstrates a stronger association with cognitive performance than muscle size, highlighting its potential as a key focus in future cognitive health research.

Assembly and comparative analysis of the complete mitochondrial genome of Pinellia ternata.

Pinellia ternata is an important natural medicinal herb in China. However, it is susceptible to withering when exposed to high temperatures during growth, which limits its tuber production. Mitochondria usually function in stress response. The P . ternata mitochondrial (mt) genome has yet to be explored. Therefore, we integrated PacBio and Illumina sequencing reads to assemble and annotate the mt genome of P . ternata . The circular mt genome of P . ternata is 876 608bp in length and contains 38 protein-coding genes (PCGs), 20 tRNA genes and three rRNA genes. Codon usage, sequence repeats, RNA editing and gene migration from chloroplast (cp) to mt were also examined. Phylogenetic analysis based on the mt genomes of P . ternata and 36 other taxa revealed the taxonomic and evolutionary status of P . ternata . Furthermore, we investigated the mt genome size and GC content by comparing P . ternata with the other 35 species. An evaluation of non-synonymous substitutions and synonymous substitutions indicated that most PCGs in the mt genome underwent negative selection. Our results provide comprehensive information on the P . ternata mt genome, which may facilitate future research on the high-temperature response of P . ternata and provide new molecular insights on the Araceae family.

Thyrotropin induces atherosclerosis by upregulating large conductance Ca2+-activated K+ channel subunits.

Hypothyroidism is associated with elevated levels of serum thyrotropin (TSH), which have been shown to promote abnormal proliferation of vascular smooth muscle cells and contribute to the development of atherosclerosis. However, the specific mechanisms underlying the TSH-induced abnormal proliferation of vascular smooth muscle cells remain unclear. The objective of this study was to investigate the role of TSH in the progression of atherosclerosis. Our research findings revealed that hypothyroidism can trigger early atherosclerotic changes in the aorta of Wistar rats. In alignment with our in vitro experiments, we observed that TSH induces abnormal proliferation of aortic smooth muscle cells by modulating the expression of α and ß1 subunits of large conductance Ca2+-activated K+ (BKCa) channels within these cells via the cAMP/PKA signaling pathway. These results collectively indicate that TSH acts through the cAMP/PKA signaling pathway to upregulate the expression of α and ß1 subunits of BKCa channels, thereby promoting abnormal proliferation of arterial smooth muscle cells. These findings may provide a basis for the clinical prevention and treatment of atherosclerosis caused by elevated TSH levels.

Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.

BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. OBJECTIVE: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. METHODS: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models' outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. RESULTS: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of code-mixed training instances is essential for the model's performance. Furthermore, the LLM-based deidentification method was a feasible and appealing approach that can be controlled and enhanced through natural language prompts. CONCLUSIONS: The study contributes to understanding the underlying mechanism of PLMs in addressing the deidentification process in the code-mixed context and highlights the significance of incorporating code-mixed training instances into the model training phase. To support the advancement of research, we created a manipulated subset of the resynthesized data set available for research purposes. Based on the compiled data set, we found that the LLM-based deidentification method is a feasible approach, but carefully crafted prompts are essential to avoid unwanted output. However, the use of such methods in the hospital setting requires careful consideration of data security and privacy concerns. Further research could explore the augmentation of PLMs and LLMs with external knowledge to improve their strength in recognizing rare PHI.

Effectiveness of nonpharmacological multi-component intervention on depressive symptoms in patients with mild cognitive impairment and dementia: A systematic review and meta-analysis.

Patients with mild cognitive impairment (MCI) and dementia are more prone to depression than people without MCI or dementia. Some studies have found nonpharmacological multi-component intervention to be more effective than single-component intervention in improving the condition of patients with MCI and dementia; however, their effect on depressive symptoms is still inconsistent. Therefore, it is necessary to explore the effectiveness of nonpharmacological multi-component intervention in improving depressive symptoms in patients with MCI and dementia. This review retrieved papers from PubMed, Embase, Cochrane Library, CINAHL, PsycINFO and CNKI. The retrieval time limit was set from 1 January 1990 to 25 November 2022. The PRISMA 2020 guideline was used to report the included studies. The result showed that nonpharmacological multi-component intervention could improve depressive symptoms in patients with MCI and dementia. Among them, nonpharmacological multi-component intervention with a duration of <6 months, physical and cognitive activities, or other activities had significant effects. However, each study differed in terms of specific measures, duration and frequency of intervention methods. Accordingly, more randomized controlled trials with larger samples are required to discover the best scheme for nonpharmacological multi-component intervention.

Risk prediction models for deep venous thrombosis in patients with acute stroke: A systematic review and meta-analysis.

BACKGROUND: The number of risk prediction models for deep venous thrombosis (DVT) in patients with acute stroke is increasing, while the quality and applicability of these models in clinical practice and future research remain unknown. OBJECTIVE: To systematically review published studies on risk prediction models for DVT in patients with acute stroke. DESIGN: Systematic review and meta-analysis of observational studies. METHODS: China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase were searched from inception to November 7, 2022. Data from selected studies were extracted, including study design, data source, outcome definition, sample size, predictors, model development and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability. RESULTS: A total of 940 studies were retrieved, and after the selection process, nine prediction models from nine studies were included in this review. All studies utilized logistic regression to establish DVT risk prediction models. The incidence of DVT in patients with acute stroke ranged from 0.4 % to 28 %. The most frequently used predictors were D-dimer and age. The reported area under the curve (AUC) ranged from 0.70 to 0.912. All studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of the five validated models was 0.76 (95 % confidence interval: 0.70-0.81), indicating a fair level of discrimination. CONCLUSION: Although the included studies reported a certain level of discrimination in the prediction models of DVT in patients with acute stroke, all of them were found to have a high risk of bias according to the PROBAST checklist. Future studies should focus on developing new models with larger samples, rigorous study designs, and multicenter external validation. REGISTRATION: The protocol for this study is registered with PROSPERO (registration number: CRD42022370287).

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China.

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

A method of gas-related pollution source layout based on multi-source data: A case study of shaanxi province, China.

The location and layout of enterprises have an important impact on local air quality. However, a few studies on exploring of the optimal layout of gas-related enterprises from the perspective of optimizing the layout of air pollution sources. This study developed a method for the evaluation of air pollution source layout based on air pollutant emission inventory data, atmospheric self-purification capacity data, and satellite remote sensing air quality data. Taking Shaanxi Province as an example, the Moran's I index and GIS spatial analysis techniques were used to evaluate the layout of air pollution sources, analyze the spatial variation characteristics of air pollution sources, and propose specific countermeasures to optimize the layout of air pollution sources. Results showed that northern Shaanxi and Guanzhong Plain are the most unsuitable for the distribution of NOx and CO sources, accounting for 13.78% and 21.77% of the total area, respectively. The most suitable area for the distribution of NOx is southern Shaanxi, accounting for 65.77% of the total area, mainly concentrated in Hanzhong and Ankang regions. The most suitable area for the distribution of CO is southern Shaanxi, accounting for 40.97% of the total area, mainly concentrated in Hanzhong and Shangluo regions. The findings of this study could supplement and improve the evaluation of the layout of industrial enterprises in China from technical and methodological aspects, and provide new insight for local governments to adjust and optimize the layout of air pollution sources.

Transcriptome Analysis Reveals Long Non-Coding RNAs Involved in Shade-Induced Growth Promotion in Pinellia ternata.

BACKGROUND: High temperature and drought environments are important limiting factors for Pinellia ternata growth, whereas shading can promote growth by relieving these stresses. However, the mechanism of growth promotion by shading in P. ternata is unknown. Long non-coding RNAs (lncRNAs) play important roles in the plant's growth and environmental response, but few analyses of lncRNAs in P. ternata have been reported. METHODS: We performed lncRNAs analysis of P. ternata in response to shading using RNA-seq data from our previous studies. A total of 13,927 lncRNAs were identified, and 145 differentially expressed lncRNAs (DELs) were obtained from the comparisons of 5 days shade (D5S) vs. 5 days of natural light (D5CK), 20 days of shade (D20S) vs. 20 days of natural light (D20CK), D20S vs. D5S, and D20CK vs. D5CK. Of these, 119 DELs (82.07%) were generated from the D20S vs. D20CK comparison. RESULTS: Gene ontology (GO) analysis indicated that the reactive oxygen (ROS) metabolism and programmed cell death (PCD) processes might regulate shade-induced growth promotion. The "signal transduction" and "environmental adaptation" in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for lncRNA-mRNA regulatory network construction and showed that the lncRNAs might mediate P. ternata growth by regulating ROS accumulation and light signals. CONCLUSIONS: This study explores lncRNAs' functions and regulatory mechanisms related to P. ternata growth and lays a foundation for further research on P. ternata.

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China.

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.

A multi-block data approach to assessing beef quality: ComDim analysis of hyperspectral imaging, 1H NMR, electronic nose and quality parameters data.

Several factors affect the quality of beef. In the field of chemometrics, multi-block data analysis methods are useful for examining multiple sources of information from a sample. This study focuses on the application of ComDim, a multi-block data analysis method, to evaluate beef from different parts of hyperspectral spectrum and image texture information, 1H NMR fingerprints, quality parameters and electronic nose. Compared to principal component analysis (PCA) methods based on low-level data fusion, ComDim is more efficient and powerful, because it reveals the relationships between the methods and techniques studied, as well as the variability of beef quality across multiple metrics. The quality and metabolite composition of beef tenderloin and hindquarters were differentiated, with low L* value and high shear tenderloin distinguished from hindquarters with opposite characteristics. The proposed strategy demonstrates that ComDim approach can be used to characterize samples when different techniques describe the same set of samples.

Qualitative and quantitative analysis of four benzimidazole residues in food by surface-enhanced Raman spectroscopy combined with chemometrics.

In this study, surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods were developed for qualitative and quantitative analysis of four benzimidazole (BMZs) residues in corn. Sulfhydryl functionalized Fe3O4@SiO2@Ag-SH magnetic SERS substrates were prepared to obtain the SERS spectra of four BMZs for chemometric analysis. The partial least squares regression discrimination analysis (PLS-DA) model performed best, with a recall rate upwards 99.17%, and could successfully distinguish four BMZs. Under the support vector machine regression (SVR) model, the detection limits of carbendazim, benomyl, thiophanate-methyl and thiabendazole were 0.055 mg/L, 0.056 mg/L, 0.067 mg/L and 0.093 mg/L, respectively; the average recovery was in the range of 85.6%-107.5%. Furthermore, the method verified by HPLC, and the results showed that there was no significant difference between two methods (p > 0.05). Therefore, the strategy based on SERS coupling chemometrics can be served as a promising tool for rapid determination of BMZs residues in food.

Estimating Changes in Contact Patterns in China Over the First Year of the COVID-19 Pandemic: Implications for SARS-CoV-2 Spread - Four Cities, China, 2020.

Introduction: Previous studies have demonstrated significant changes in social contacts during the first-wave coronavirus disease 2019 (COVID-19) in Chinese mainland. The purpose of this study was to quantify the time-varying contact patterns by age in Chinese mainland in 2020 and evaluate their impact on the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: Diary-based contact surveys were performed for four periods: baseline (prior to 2020), outbreak (February 2020), post-lockdown (March-May 2020), and post-epidemic (September-November 2020). We built a Susceptible-Infected-Recovered (SIR) model to evaluate the effect of reducing contacts on transmission. Results: During the post-epidemic period, daily contacts resumed to 26.7%, 14.8%, 46.8%, and 44.2% of the pre-COVID levels in Wuhan, Shanghai, Shenzhen, and Changsha, respectively. This suggests a moderate risk of resurgence in Changsha, Shenzhen, and Wuhan, and a low risk in Shanghai. School closure alone was not enough to interrupt transmission of SARS-CoV-2 Omicron BA.5, but with the addition of a 75% reduction of contacts at the workplace, it could lead to a 16.8% reduction of the attack rate. To control an outbreak, concerted strategies that target schools, workplaces, and community contacts are needed. Discussion: Monitoring contact patterns by age is key to quantifying the risk of COVID-19 outbreaks and evaluating the impact of intervention strategies.

Relationship between social support and fear of cancer recurrence among Chinese cancer patients: A systematic review and meta-analysis.

Background: To quantitatively analyze the association between social support (SS) and fear of cancer recurrence (FCR) by reviewing current evidence from observational studies. Methods: A comprehensive literature search was performed in nine databases from inception to May 2022. Observational studies that used both SS and FCR as study variables were included. Regression coefficient (ß') and correlation coefficient (r) were calculated with R software. Subgroup analysis was utilized to investigate the degree of the relationship between SS and FCR as well as the impact of various forms of SS on FCR in cancer patients. Results: Thirty-seven studies involving 8,190 participants were identified. SS significantly reduced FCR risk [pooled ß' = -0.27, 95% confidence interval (CI) = -0.364 to -0.172], with moderate negative correlations (summary r = -0.52, 95% CI = -0.592 to -0.438). Meta-regression and subgroup analysis showed that types of cancer and study type were the source of heterogeneity. However, types of SS [actual SS, perceived social support (PSS), and others], source of actual SS, and source of PSS were not significant moderators. Conclusion: To the best of our knowledge, this is the first systematic review and meta-analysis to quantitatively investigate the association between SS and FCR in Chinese cancer patients using ß' and r coefficients. The results re-emphasized that social workers should enhance the use of SS by cancer patients and establish a sound SS system by either implementing more relevant research or developing targeted policies. Based on meta-regression and subgroup analyses, moderators of the association between SS and FCR should also be studied closely as they may help identify patients in need. In addition, longitudinal research, as well as mixed research, should be conducted to more comprehensively explore the relationship between SS and FCR. Systematic review registration: https://www.crd.york.ac.uk/prospero, identifier CRD42022332718.

Mapping and validation of the epistatic D and P genes controlling anthocyanin biosynthesis in the peel of eggplant (Solanum melongena L.) fruit.

Fruit color is an important trait influencing the commercial value of eggplant fruits. Three dominant genes (D, P and Y) cooperatively control the anthocyanin coloration in eggplant fruits, but none has been mapped. In this study, two white-fruit accessions (19 141 and 19 147) and their F2 progeny, with 9:7 segregation ratio of anthocyanin pigmented versus non-pigmented fruits, were used for mapping the D and P genes. A high-density genetic map was constructed with 5270 SNPs spanning 1997.98 cM. Three QTLs were identified, including two genes on chromosome 8 and one on chromosome 10. Gene expression analyses suggested that the SmANS on chromosome 8 and SmMYB1 on chromosome 10 were the putative candidate genes for P and D, respectively. We further identified (1) a SNP leading to a premature stop codon within the conserved PLN03176 domain of SmANS in 19 141, (2) a G base InDel in the promoter region leading to an additional cis-regulatory element and (3) a 6-bp InDel within the R2-MYB DNA binding domain of SmMYB1, in 19 147. Subsequently, these three variations were validated by PARMS technology as related to phenotypes in the F2 population. Moreover, silencing of SmANS or SmMYB1 in the purple red fruits of F1 (E3316) led to inhibition of anthocyanin biosynthesis in the peels. Conversely, overexpression of SmANS or SmMYB1 restored anthocyanin biosynthesis in the calli of 19 141 and 19 147 respectively. Our findings demonstrated the epistatic interactions underlying the white color of eggplant fruits, which can be potentially applied to breeding of eggplant fruit peel color.