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Rapid industrialization and urbanization in canal areas have led to significant conversion in production-living-ecological space (PLES), yielding short-term social-economic benefits but causing serious ecological degradation. However, the complex and dynamic interactions between PLES and ecosystem function remain underexplored, despite their importance for guiding sustainable canal development. Therefore, we analyzed the dynamic changes in PLES and ecosystem service value (ESV) in the Jiangsu section of the Beijing-Hangzhou Grand Canal, along with their interrelationships. By optimizing the ESV evaluation model to incorporate natural, social, and economic factors, we examined the spatiotemporal evolution of PLES and ESV from 2000 to 2020. Finally, we quantified the dynamic response of ESV to PLES using contribution rate factors and identified trade-offs and synergies among ESV indicators. Over the past two decades, ecological space (ES) expanded by only 5.31 %, yet contributing 94.36 % of total ESV growth. Despite fluctuations during 2005–2010, synergies among various ESV types remained largely stable. The regulation service shows the most significant response to the conversion of PLES, with an increase in ES’s ESV of CNY 67.87 billion over 20 years, representing 75.34 % of total ESV growth. Notably, the hydrologic regulation contributes substantially, with an increase in ES’s ESV of CNY 60.87 billion, accounting for 67.57 % of total ESV growth. Our findings provide a scientific basis for the sustainable development and resource management in canal areas.

IntroductionUrban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learning and street view images offers a method for large-scale measurement and precise empirical analysis.MethodsThis study focuses on the central area of Shanghai, examining the complex effects of GS exposure on psychological stress perception. By constructing a multidimensional psychological stress perception scale and integrating machine learning algorithms with extensive street view images data, we successfully developed a framework for measuring urban stress perception. Using the scores from the psychological stress perception scale provided by volunteers as labeled data, we predicted the psychological stress perception in Shanghai's central urban area through the Support Vector Machine (SVM) algorithm. Additionally, this study employed the interpretable machine learning model eXtreme Gradient Boosting (XGBoost) algorithm to reveal the nonlinear relationship between GS exposure and residents' psychological stress.ResultsResults indicate that the GS exposure in central Shanghai is generally low, with significant spatial heterogeneity. GS exposure has a positive impact on reducing residents' psychological stress. However, this effect has a threshold; when GS exposure exceeds 0.35, its impact on stress perception gradually diminishes.DiscussionWe recommend combining the threshold of stress perception with GS exposure to identify urban spaces, thereby guiding precise strategies for enhancing GS. This research not only demonstrates the complex mitigating effect of GS exposure on psychological stress perception but also emphasizes the importance of considering the “dose-effect” of it in urban planning and construction. Based on open-source data, the framework and methods developed in this study have the potential to be applied in different urban environments, thus providing more comprehensive support for future urban planning.

The tourism industry is a significant contribution to the economy of many countries, including Pakistan. However, its activities often have a negative impact on the environment, particularly related to climate change. Notably, Pakistan ranks fifth among countries most affected by climate change, which requires a targeted analysis of the tourism sector to determine its potential impacts. Despite the critical nature of this issue, there is currently a lack of research that examines how climate change specifically impacts Pakistan's tourism industry. This study aims to address this gap by using the Tourism Climate Index (TCI) to assess the impact of climate change on the suitability of different tourism locations across the country. Our results show that popular tourist destinations such as Gilgit-Baltistan experience their peak season in the warmer third quarter (summer), which corresponds to the highest observed TCI values. This suggests that warmer temperatures could boost tourism activity and spending in these regions. Furthermore, our analysis shows that air temperature plays a crucial role in determining tourist comfort levels and significantly influences tourists' TCI values and sensory experiences. The results of this study show that the TCI methodology can be an effective tool for further research into the geographical impacts of climate change on tourism. By identifying necessary changes due to climatic changes, future studies could provide valuable insights into how the tourism industry can adapt to and reduce its environmental footprint.

Abstract The “Eight Scenic Views Paintings” represent crucial visual materials for investigating the history of cultural landscapes. However, traditional methods of interpreting materials struggle to discern the inherent connections between different landscape elements. This study proposes an approach for deconstructing historical images, taking the example of the Forty Scenic Views in the Late Ming Dynasty in Nanjing, China. To explore the co-occurrence structure, hierarchical clustering, and correlation features among various elements, various digital humanities quantification methods were applied, including spatial analysis of ArcGIS, co-occurrence and clustering of KH Coder, and correlation analysis of SPSS. This study reveals the characteristics of the landscape construction of Nanjing in the Late Ming: natural landscape as the foundation, artificial landscape as the core, and advocating tradition as the fashion. It also uncovers the landscape order: mountains, waters, and scenic views interweaved and coexisted, as well as nature and culture intertwined and clustered. In addition, multiple information graphs of the subordinate and co-occurrence relationships of 20 landscape elements were constructed, 5 landscape paradigms were extracted, and 36 pairs of related relationships were discovered, deepening the historical understanding of the urban landscape construction of Nanjing in the Late Ming. This paper puts forward the idea of analyzing historical images by digital method, which provides some essential and detailed historical basis for explaining the value of cultural landscape heritage and shaping contemporary urban landscape.

With the development and widespread adoption of smart machines, researchers across various disciplines and fields are exploring the contributions of computers and intelligent machines to human science and society through interdisciplinary collaboration. In this study, we investigated the potential applications of artificial intelligence and multi-source big data in the selection and design of urban greenways, using the city of Nanjing as a case study. Utilizing computer vision technology and the DeepLabV3+ neural network model, we analyzed over 320,000 street view images and 530,000 fine-grained urban data points from Nanjing. We also trained the place space material quantification model using the Street Space Greening Structure (S.S.G.S) dataset. This dataset not only achieved high-precision semantic segmentation but also surpassed previous datasets in predicting greenery at the street level. The performance metrics for this model are as follows: MIoU is 0.6344, Recall is 0.7287, and Precision is 0.8074. Through Robust regression, we identified several micro and macro-level factors influencing the Panoramic View Green View Index (PVGVI). The results indicate that multiple factors have significant positive or negative effects on PVGVI. This research not only provides new decision-making tools for landscape architecture and urban planning but also opens new avenues for applying artificial intelligence in urban environmental studies.

Liriodendron chinense (Hemsl.) Sarg. (Magnoliales: Magnoliaceae), valued for its medicinal properties and timber and as an ornamental plant, is now classified as an endangered species. Investigating how future climate-change scenarios might affect the potential geographic distribution of L. chinense will provide a crucial scientific basis for its protection and management strategies. The MaxEnt model was calibrated using the ENMeval optimization package, and then it was coupled with ArcGIS 10.8 to forecast the possible distribution areas of L. chinense in China, utilizing elevation data, bioclimatic factors, and human footprint as environmental variables. The results indicate: (1) The optimal model parameters were set as follows: FC = LQ, RM = 0.5, the MaxEnt model demonstrated high predictive accuracy and minimal overfitting; (2) The total suitable habitat area for the potential geographical distribution of L. chinense during the current period is estimated at 151.55 × 104 km2, predominantly located in central, eastern, and southwestern regions of China; (3) The minimum temperature of the coldest month (bio6), precipitation of the driest month (bio14), precipitation of the driest quarter (bio17), precipitation of the warmest quarter (bio18), elevation (alt), and human footprint (hf) are the main environmental variables determining the suitable habitat distribution of L. chinense; (4) During the period from 2041 to 2060, under the carbon emission scenarios of SSP126, SSP245, and SSP370, the suitable habitat for L. chinense shows varying degrees of increase compared to the current period. However, under the highest concentration scenario of SSP585, the suitable habitat area decreases to some extent; (5) The distribution of L. chinense is likely to move towards higher latitudes and elevations in the future due to changes in the climate. This research provides a comprehensive analysis of the potential impacts of climate change on L. chinense, offering valuable information for its protection and management under future climatic conditions.

Abstract Background Neuroanatomical alterations have been associated with cognitive deficits in mild traumatic brain injury (MTBI). However, most studies have focused on the abnormal gray matter volume in widespread brain regions using a cross-sectional design in MTBI. This study investigated the neuroanatomical restoration of key regions in salience network and the outcomes in MTBI. Methods Thirty-six MTBI patients with posttraumatic headache (PTH) and 34 matched healthy controls were enrolled in this study. All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Surface-based morphometry was conducted to get cortical thickness (CT) and cortical surface area (CSA) of neuroanatomical regions which were defined by the Desikan atlas. Then mixed analysis of variance models were performed to examine CT and CSA restoration in patients from acute to subacute phase related to controls. Finally, mediation effects models were built to explore the relationships between neuroanatomical restoration and symptomatic improvement in patients. Results MTBI patients with PTH showed reduced headache impact and improved cognitive function from the acute to subacute phase. Moreover, patients experienced restoration of CT of the left caudal anterior cingulate cortex (ACC) and left insula and cortical surface area of the right superior frontal gyrus from acute to subacute phase. Further mediation analysis found that CT restoration of the ACC and insula mediated the relationship between reduced headache impact and improved cognitive function in patients. Conclusions These results showed that neuroanatomical restoration of key regions in salience network correlated reduced headache impact with cognitive function improvement in MTBI with PTH, which further substantiated the vital role of salience network and provided an alternative clinical target for cognitive improvement in MTBI patients with PTH. Graphical Abstract

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

Exposure to green spaces (GSs) has been perceived as a natural and sustainable solution to urban challenges, playing a vital role in rapid urbanization. Previous studies, due to their lack of direct spatial alignment and attention to a human-scale perspective, struggled to comprehensively measure urban GS exposure. To address this gap, our study introduces a novel GS exposure assessment framework, employing machine learning and street view images. We conducted a large-scale, fine-grained empirical study focused on downtown Shanghai. Our findings indicate a pronounced hierarchical structure in the distribution of GS exposure, which initially increases and subsequently decreases as one moves outward from the city center. Further, from both the micro and macro perspectives, we employed structural equation modeling and Geodetector to investigate the impact of the urban built environment on GS exposure. Our results highlight that maintaining an appropriate level of architectural density, enhancing the combination of sidewalks with GSs, emphasizing the diversity of regional characteristics, and avoiding excessive concentration of functions are effective approaches for increasing urban GS exposure and promoting human wellbeing. Our study offers scientific insights for urban planners and administrators, holding significant implications for achieving sustainable urban development.

Territorial spatial planning requires thoughtful consideration of the scientific layout and synergistic control of production, living, and ecological spaces (PLESs). However, research in this field often neglects the human perspective and fails to account for people’s demands and behavioral characteristics. This study evaluates the level and spatial characteristics of residents’ production, living, and ecological behavioral (PLEB) flow, as well as the spatial pattern of the PLES functions, within the framework of the human–land coupling system. Therefore, to analyze the behavior–space coupling coordination relationship, the coupling coordination model is applied. The results indicate that the overall level of residents’ PLEB flow in rural–urban fringe areas is at a lower middle level and the functionality of the PLES is at a medium level, with a spatial distribution pattern of high in the northern and low in the southern areas. Most of the behavior–space matching types are in a state of mismatch between supply and demand. Meanwhile, the PLEB–PLES coupling coordination relationship is generally unbalanced, which is particularly noticeable in the production space. Regardless of whether the behavior–space matching type is a supply deficit or a supply surplus, the mismatch between supply and demand leads to uncoordinated and unreasonable spatial utilization. Overall, the findings of the study provide guidance for future research endeavors about PLESs and suggest embracing a human-centered scientific paradigm. Such a paradigm can promote high-quality, sustainable development of territorial spatial planning while strengthening the capacity and effectiveness of spatial governance and control.

The COVID-19 pandemic has led to a surge in the use of self-service facilities (SSFs) in residential areas worldwide. Previous studies on SSFs mainly focused on their application in commercial or other scenarios. However, SSFs in residential areas have not been thoroughly studied. This study develops an analytical framework for assessing both the supply and demand for SSFs in residential areas. The study evaluates 2693 residential communities and 479 smart parcel lockers (SPLs) in Tianjin, China. The results show that the high-demand area for SPLs is within 300 m of home, while the high-supply area is 300–600 m from home. Further analysis using the Gini coefficient and location quotient shows that the top 20% of the population have access to 80% of SPLs, and most residential communities experience an oversupply. Our study suggests that a mismatch between the supply and demand of SPLs may result in massive public space waste, resource waste, and inequity. Given the many uncertainties of the future, this study highlights the need to consider the dynamic supply–demand relationship of SSFs. This may encourage urban planners, policymakers, and experts in other related disciplines to work towards a more service-efficient and equitable utilization of SSFs in residential areas.

Earth-rock dams are among the most important and expensive infrastructure projects. A key safety issue is dam zone boundary detection to prevent the intrusion of materials from different zones. However, existing detection methods strongly highly depend on human judgement, which is time consuming and labor intensive. To solve this problem, this work proposes a fast boundary detection method based on the Otsu algorithm optimized by enhanced Harris hawks optimization (HHO). Compared with the original Otsu algorithm, the proposed method has a higher computation speed to meet the time requirements of engineering projects. Particle swarm optimization is adopted to enhance the exploration stage of HHO. In addition, a tangent function and chaotic sine map are used to improve the convergence speed and robustness. The application of the proposed method to a real-life project shows that the calculation time can be reduced to 20 s, which is approximately 18.8% of the original calculation time.

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin’s central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin’s development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics.

Multidrug resistance (MDR) and lack of targeting specificity are the main reasons why traditional drug therapies fail and produce toxic side effects in cancer chemotherapy. In order to increase targeting specificity and maximize therapeutic efficacy, new intelligent drug delivery systems are needed. In this study, we prepared the hyaluronic acid (HA) conjugated dasatinib (DAS) and D-α-tocopherol acid polyethylene glycolsuccinate (TPGS) copolymer nanoparticles (THD-NPs). The water solubility of the hydrophobic drug DAS was improved by chemically linking with HA. HA can bind to the over-expressed CD44 protein of tumor cells to increase targeting specificity, TPGS can inhibit the activity of P-glycoprotein (P-gp), and increase the intracellular accumulation of drugs. The prepared drug-loaded nanoparticle has a particle size of 82.23 ± 1.07 nm with good in vitro stability. Our in vitro studies showed that THD-NPs can be released more rapidly in a weakly acidic environment (pH = 5.5) than in a normal physiological environment (pH = 7.4), which can realize the selective release of nanoparticles in tumor cells. Compared to free drugs, THD-NPs showed more efficient cellular uptake, effectively increased the cytotoxic effect of DAS on nasopharyngeal carcinoma HNE1 cells drug resistance HNE1/DDP cells and increased the accumulation of drugs in HNE1/DDP cells, which may be due to the inhibitory effect of TPGS on the efflux function of P-gp. In vivo experiments showed that THD-NPs can effectively inhibit tumor growth without obvious side effects. In conclusion, the targeted and pH-sensitive nanosystem, we designed has great potential to overcome drug resistance and increase therapeutic effects in cancer treatment.

The rapid infrastructure development in densely populated areas has had several negative impacts. Increases in urbanization have led to increased LST, and urban ecological systems have been negatively affected. Urban heat islands (UHIs) can be mitigated by understanding how current and future LST phenomena are linked to changes in landscape composition and land use cover (LUC). This study investigated the multi-scale spatial analysis of LUC and LST in Tianjin using remote sensing and GIS data. We used Landsat data from 2005 to 2020 to examine the effects of LUC on LST in urban agglomeration. According to the Urban Thermal Field Variance Index (UTFVI), the city’s ecological evaluation was carried out. Results show that changes in LUC and other anthropogenic activities affect the spatial distribution of LST. For the study years (2004–2009), the estimated mean LST in Tianjin was 25.32 °C, 26.73 °C, 27.62 °C, and 27.93 °C. Between LST and urban areas with other infrastructures, and NDBI, significant positive correlation values were found about 0.53, 0.48, and 0.76 (p < 0.05), respectively. Temperatures would almost certainly increase by 3.87 °C to 7.26 °C as a result of decreased plant cover and increased settlements. These findings strongly imply a correlation between LST and the vegetation index. Between 2005 and 2020, the anticipated increase in LST of 3.39 °C is expected to harm urban environmental health. This study demonstrates how Tianjin and other cities can achieve ecological sustainability.

Since limited energy density and intrinsic safety issues of commercial lithium-ion batteries (LIBs), solid-state batteries (SSBs) are promising candidates for next-generation energy storage systems. However, their practical applications are restricted by interfacial issues and kinetic problems, which result in energy density decay and safety failure. This review discusses the formation mechanisms of these issues from the perspective of typical solid-state electrolytes (SSEs) and provides an overview of recent advanced anode engineering for SSBs based on representative anodes including Li metal, graphite-based, and Si-based anodes, summarizing the advantages and problems of each strategy. The development of the anode-free batteries concept is demonstrated as well. Finally, recommendations are proposed for the potential directions in future research in anode engineering for SSBs.

IntroductionWhile new targeted therapies have advanced psoriasis treatment, real-world data on comparative effectiveness is lacking. This study analyzed treatment regimens and response in an observational cohort, examining potential disparities between clinical trials and routine practice.MethodsData from the Psoriasis Standardized Diagnosis and Treatment Center registry were analyzed. Patients with ≥1 follow-up were included. Treatment response was assessed using PASI 50/90 criteria. Factors associated with response were analyzed.Results407 patients were included (46 first-time diagnosed, 361 previously diagnosed). A higher proportion of first-time diagnosed patients achieved treatment response than previously diagnosed (76.1% vs. 62.6%). Multivariable analysis identified factors associated with reduced response in previously treated patients.ConclusionThis real-world study found lower treatment response rates compared to clinical trials, especially in previously treated patients. Disparities highlight remaining unmet needs for psoriasis management. Combination and rotational strategies may improve outcomes in patients unresponsive to available therapies. Ongoing research on novel targets and pathways is warranted to address treatment gaps.

Objective To investigate the effect of transfusion-transmitted Zika virus (ZIKV) on the expression of circular RNA (hsa_circ_0000290) and the role of hsa_circ_0000290 in ZIKV replication. Methods A549 or U251 cells were infected with ZIKV, and the total RNA were extracted continuously for 1-4 days. The relative expression level of hsa_circ_0000290 was detected by RT-qPCR; hsa_circ_0000290 was produced by transcription of PDHX (pyruvate dehydrogenase complex component X). After knocking down the expression of hsa_circ_0000290 by transfection with siRNA, the effect on ZIKV and PDHX expression was detected by RT-qPCR and Western blot. The localization of hsa_circ_0000290 in cells was verified by cytoplasmic localization experiment. Results Compared with the control group, hsa_circ_0000290 was up-regulated in A549 and U251 cells after 1-4 days of ZIKV infection; hsa_circ_0000290 was mainly localized in the nucleus. Knockdown the expression level of hsa_circ_0000290 in cells can inhibit the replication of ZIKV RNA and the expression of ZIKV protein, and promote the expression of PDHX. Conclusion The expression level of hsa_circ_0000290 is significantly increased after ZIKV infection. Hsa_circ_0000290 is localized in the nucleus, and the specific knockdown of hsa_circ_0000290 expression level significantly inhibits ZIKV replication.

Molecular imprinting technology is widely used for the specific identification of compounds, but the selective recognition mechanisms of the same compounds still need to be further studied. Based on differences in hydrogen bond size and orientation, molecularly imprinted polymers (MIPs) were designed to adsorb flavonols with the same parent core and different hydroxyl groups. A surface-imprinted material was designed with silicon dioxide as the carrier, myricetin as the template molecule, and methacrylic acid (MAA) as the functional monomer. Scanning electron microscopy (SEM), Brunauer–Emmett–Teller surface area (BET) analyses, Fourier-transform infrared spectroscopy (FT-IR), and other characterization experiments were carried out. The intrinsic mechanism of the MIPs was also explored. The MIPs showed good adsorption of myricetin and other flavonoids through hydrogen bonding and steric hindrance. The adsorption capacity was 3.12–9.04 mg/g, and the imprinting factor was 1.78–3.37. Flavonoids with different hydroxyl groups in different numbers and directions had different hydrogen bond strengths with functional monomers. R2, R4, and R1 on 2-phenylchromogenone had stronger electronegativity, and the hydroxyl group was also more likely to form and have stronger hydrogen bonds. The hydroxyl negativity and the degree of steric hindrance of flavonoids played a major role in the recognition of molecularly imprinted materials. This study is of great significance for the synthesis of and selection of templates for analogous molecular imprinting materials.

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A simple water soaking treatment significantly weakened the strong covalent metal-support interaction between the atomically dispersed Pt and CoFe2O4, which leads to an enhanced activity towards methane combustions by 55 times. This work highlights the critical role of altering the coordination structure of single-atom active sites and provides a new strategy to modulate metal-support interaction regulation.

Semi-hydrogenation of acetylene in excess ethylene is a key industrial process for ethylene purification. Here the authors develop highly stable Pd1/TiO2 single-atom catalyst for photo-thermo semi-hydrogenation of acetylene.

Abstract Pilomatricoma (PM; calcifying epithelioma of Malherbe) is an uncommon adnexal tumour originating from the matrix of the hair follicles. Bullous appearance is a rare variant of PM, and its pathogenesis remains unclear. Here, we present a case of a 17‐year‐old girl with a pseudobullous PM on the right shoulder. Lymphatic dilatation and collagen disorder were histopathologically observed in this case, which may provide clues to elucidate the pathogenesis of pseudobullous PM.

Abstract Background The MVD gene mutations are identified in porokeratosis, which is considered a skin-specific autoinflammatory keratinization disease. However, the biological function of MVD gene remains largely unknown. Therefore, we analyzed the function of mvda gene, orthologous to the human MVD gene, in developing zebrafish. Methods Morpholino antisense oligonucleotide technique was used to generate mvda loss-of-function phenotypes. Knockdown of mvda was confirmed by RT-PCR and Sanger sequencing. Scanning and transmission electron microscopy were performed to analyze the morphology of the epidermis. Angiogenesis study was presented using the Tg(fli1a:EGFP) y1 transgenic strain. In addition, acridine orange staining was used to examine the apoptotic cells in vivo. Results As expected, the mvda morphants showed abnormal morphology of the epidermis. Moreover, we observed ectopic sprouts in trunk angiogenesis and impaired formation of the caudal vein plexus in the mvda-deficient zebrafish. Besides, increased apoptosis was found throughout the tail, heart, and eyes in mvda zebrafish morphants. Conclusions These findings indicated the essential role of mvda in the early development of zebrafish. This was the first in vivo knockdown study of the zebrafish mvda gene, which might offer insight into the biological function of the human MVD gene.