Author Correction: The Apostasia genome and the evolution of orchids.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Comprehensive analysis of the global impact and distribution of tick paralysis, a deadly neurological yet fully reversible condition.

SUMMARYTick paralysis is a potentially fatal condition caused by neurotoxins secreted by the salivary glands of certain ticks. Documented cases have been reported worldwide, predominantly in the United States, Canada, and Australia, with additional reports from Europe and Africa. This condition also affects animals, leading to significant economic losses and adverse impacts on animal health and welfare. To date, 75 tick species, mostly hard ticks, have been identified as capable of causing this life-threatening condition. Due to symptom overlap with other conditions, accurate diagnosis of tick paralysis is crucial to avoid misdiagnosis, which could result in adverse patient outcomes. This review provides a comprehensive analysis of the current literature on tick paralysis, including the implicated tick species, global distribution, tick toxins, molecular pathogenesis, clinical manifestations, diagnosis, treatment, control, and prevention. Enhancing awareness among medical and veterinary professionals is critical for improving the management of tick paralysis and its health impacts on both humans and animals.

Polytypic metal chalcogenide nanocrystals.

By engineering chemically identical but structurally distinct materials into intricate and sophisticated polytypic nanostructures, which often surpass their pure phase objects and even produce novel physical and chemical properties, exciting applications in the fields of photovoltaics, electronics and photocatalysis can be achieved. In recent decades, various methods have been developed for synthesizing a library of polytypic nanocrystals encompassing IV, III-V and II-VI polytypic semiconductors. The exceptional performances of polytypic metal chalcogenide nanocrystals have been observed, making them highly promising candidates for applications in photonics and electronics. However, achieving high-precision control over the morphology, composition, crystal structure, size, homojunctions, and periodicity of polytypic metal chalcogenide nanostructures remains a significant synthetic challenge. This review article offers a comprehensive overview of recent progress in the synthesis and control of polytypic metal chalcogenide nanocrystals using colloidal synthetic strategies. Starting from a concise introduction on the crystal structures of metal chalcogenides, the subsequent discussion delves into the colloidal synthesis of polytypic metal chalcogenide nanocrystals, followed by an in-depth exploration of the key factors governing polytypic structure construction. Subsequently, we provide comprehensive insights into the physical properties of polytypic metal chalcogenide nanocrystals, which exhibit strong correlations with their applications. Thereafter, we emphasize the significance of polytypic nanostructures in various applications, such as photovoltaics, photocatalysis, transistors, thermoelectrics, stress sensors, and the electrocatalytic hydrogen evolution. Finally, we present a summary of the recent advancements in this research field and provide insightful perspectives on the forthcoming challenges, opportunities, and future research directions.

Exploring the Genetic Basis of Calonectria spp. Resistance in Eucalypts.

Selecting high-quality varieties with disease resistance by artificial crossbreeding is the most fundamental way to address the damage caused by Calonectria spp. in eucalypt plantations. However, understanding the mechanism of disease-resistant heterosis occurrence in eucalypts is crucial for successful crossbreeding. Two eucalypt hybrids, the susceptible EC333 (H1522 × unknown) and the resistant EC338 (W1767 × P9060), were screened through infection with Calonectria isolates, a pathogen that causes eucalypt leaf blight. RNA-Seq was performed on the susceptible hybrid, the disease-resistant hybrid, and their parents. The gene differential expression analysis showed that there were 3912 differentially expressed genes between EC333 and EC338, with 1631 up-regulated and 2281 down-regulated genes. The expression trends of the differential gene sets in P9060 and EC338 were similar. However, the expression trend of W1767 was opposite that of EC338. The similarity of the expression and the advantage of stress resistance in E. pellita suggested that genes with significant differences in expression likely relate to disease resistance. A GSEA based on GO annotations revealed that the carbohydrate binding pathway genes were differentially expressed between EC338 and EC333. The gene pathways that were differentially expressed between EC338 and EC333 revealed by the GSEA based on KEGG annotations were the sesquiterpenoid and triterpenoid biosynthesis pathways. The alternative splicing analysis demonstrated that an AS event between EC338 and EC333 occurred in LOC104426602. According to our SNP analysis, EC338 had 626 more high-impact mutation loci than the male parent P9060 and 396 more than the female parent W1767; W1767 had 259 more mutation loci in the downstream region than EC338, while P9060 had 3107 fewer mutation loci in the downstream region than EC338. Additionally, EC338 had 9631 more mutation loci in the exon region than EC333. Modules were found via WGCNA that were strongly and oppositely correlated with EC338 and EC333, such as module MEsaddlebrown, likely associated with leaf blight resistance. The present study provides a detailed explanation of the genetic basis of eucalypt leaf blight resistance, providing the foundation for exploring genes related to this phenomenon.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (µg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Overexpression of NtEXPA7 promotes seedling growth and resistance to root-knot nematode in tobacco.

Root-knot nematode (RKN) is one of the most damaging plant pathogen in the world. They exhibit a wide host range and cause serious crop losses. The cell wall, encasing every plant cell, plays a crucial role in defending of RKN invasion. Expansins are a group of cell wall proteins inducing cell wall loosening and extensibility. They are widely involved in the regulation of plant growth and the response to biotic and abiotic stresses. In this study, we have characterized the biological function of tobacco (Nicotiana tabacum) NtEXPA7, the homologue of Solyc08g080060.2 (SlEXPA18), of which the transcription level was significantly reduced in susceptible tomato upon RKN infection. The expression of NtEXPA7 was up-regulated after inoculation of RKNs. The NtEXPA7 protein resided in the cell wall. Overexpression of NtEXPA7 promoted the seedling growth of transgenic tobacco. Meanwhile the increased expression of NtEXPA7 was beneficial to enhance the resistance against RKNs. This study expands the understanding of biological role of expansin in coordinate plant growth and disease resistance.

Porous Tellurium-Doped Ruthenium Dioxide Nanotubes for Enhanced Acidic Water Oxidation.

Electrocatalysts with high activity and durability for acidic oxygen evolution reaction (OER) play a crucial role in achieving cost-effective hydrogen production via proton exchange membrane water electrolysis. A novel electrocatalyst, Te-doped RuO2 (Te-RuO2) nanotubes, synthesized using a template-directed process, which significantly enhances the OER performance in acidic media is reported. The Te-RuO2 nanotubes exhibit remarkable OER activity in acidic media, requiring an overpotential of only 171 mV to achieve an anodic current density of 10 mA cm-2. Furthermore, they maintain stable chronopotentiometric performance under 10 mA cm-2 in acidic media for up to 50 h. Based on the experimental results and density functional calculations, this significant improvement in OER performance to the synergistic effect of large specific surface area and modulated electronic structure resulting from the doping of Te cations is attributed.

Multifunctional Heterostructured Fe3O4-FeTe@MCM Electrocatalyst Enabling High-Performance Practical Lithium-Sulfur Batteries Via Built-in Electric Field.

The development of capable of simultaneously modulating the sluggish electrochemical kinetics, shuttle effect, and lithium dendrite growth is a promising strategy for the commercialization of lithium-sulfur batteries. Consequently, an elaborate preparation method is employed to create a host material consisting of multi-channel carbon microspheres (MCM) containing highly dispersed heterostructure Fe3O4-FeTe nanoparticles. The Fe3O4-FeTe@MCM exhibits a spontaneous built-in electric field (BIEF) and possesses both lithophilic and sulfophilic sites, rendering it an appropriate host material for both positive and negative electrodes. Experimental and theoretical results reveal that the existence of spontaneous BIEF leads to interfacial charge redistribution, resulting in moderate polysulfide adsorption which facilitates the transfer of polysulfides and diffusion of electrons at heterogeneous interfaces. Furthermore, the reduced conversion energy barriers enhanced the catalytic activity of Fe3O4-FeTe@MCM for expediting the bidirectional sulfur conversion. Moreover, regulated Li deposition behavior is realized because of its high conductivity and remarkable lithiophilicity. Consequently, the battery exhibited long-term stability for 500 cycles with 0.06% capacity decay per cycle at 5 C, and a large areal capacity of 7.3 mAh cm-2 (sulfur loading: 9.73 mg cm-2) at 0.1 C. This study provides a novel strategy for the rational fabrication of heterostructure hosts for practical Li-S batteries.

Boosting Polysulfide Redox Kinetics by Temperature-Induced Metal-Insulator Transition Effect of Tungsten-Doped Vanadium Dioxide for High-Temperature Lithium-Sulfur Batteries.

The practical application of Li-S batteries is still severely restricted by poor cyclic performance caused by the intrinsic polysulfides shuttle effect, which is even more severe under the high-temperature condition owing to the inevitable increase of polysulfides' solubility and diffusion rate. Herein, tungsten-doped vanadium dioxide (W-VO2) micro-flowers are employed with first-order metal-insulator phase transition (MIT) property as a robust and multifunctional modification layer to hamper the shuttle effect and simultaneously improve the thermotolerance of the common separator. Tungsten doping significantly reduces the transition temperature from 68 to 35 °C of vanadium dioxide, which renders the W-VO2 easier to turn from the insulating monoclinic phase into the metallic rutile phase. The systematic experiments and theoretical analysis demonstrate that the temperature-induced in-suit MIT property endows the W-VO2 catalyst with strong chemisorption against polysulfides, low energy barrier for liquid-to-solid conversion, and outstanding diffusion kinetics of Li-ion under high temperatures. Benefiting from these advantages, the Li-S batteries with W-VO2 modified separator exhibit significantly improved rate and long-term cyclic performance under 50 °C. Remarkably, even at an elevated temperature (80 °C), they still exhibit superior electrochemical performance. This work opens a rewarding avenue to use phase-changing materials for high-temperature Li-S batteries.

High Performance Thermoelectric Power of Bi0.5Sb1.5Te3 Through Synergistic Cu2GeSe3 and Se Incorporations.

Bi2Te3-based alloys are the benchmark for commercial thermoelectric (TE) materials, the widespread demand for low-grade waste heat recovery and solid-state refrigeration makes it imperative to enhance the figure-of-merits. In this study, high-performance Bi0.5Sb1.5Te3 (BST) is realized by incorporating Cu2GeSe3 and Se. Concretely, the diffusion of Cu and Ge atoms optimizes the hole concentration and raises the density-of-states effective mass (md *), compensating for the loss of "donor-like effect" exacerbated by ball milling. The subsequent Se addition further increases md *, enabling a total 28% improvement of room-temperature power factor (S2σ), reaching 43.6 µW cm-1 K-2 compared to the matrix. Simultaneously, the lattice thermal conductivity is also significantly suppressed by multiscale scattering sources represented by Cu-rich nanoparticles and dislocation arrays. The synergistic effects yield a peak ZT of 1.41 at 350 K and an average ZT of 1.23 (300-500 K) in the Bi0.5Sb1.5Te2.94Se0.06 + 0.11 wt.% Cu2GeSe3 sample. More importantly, the integrated 17-pair TE module achieves a conversion efficiency of 6.4%, 80% higher than the commercial one at ΔT = 200 K. These results validate that the facile composition optimization of the BST/Cu2GeSe3/Se is a promising strategy to improve the application of BST-based TE modules.

Optimizing Surface State Electrons of Topological Semi-Metal by Atomic Doping for Enhanced Hydrogen Evolution Reaction.

Topological materials carrying topological surface states (TSSs) have extraordinary carrier mobility and robustness, which provide a new platform for searching for efficient hydrogen evolution reaction (HER) electrocatalysts. However, the majority of these TSSs originate from the sp band of topological quantum catalysts rather than the d band. Here, based on the density functional theory calculation, it is reported a topological semimetal Pd3Sn carrying TSSs mainly derived from d orbital and proposed that optimizing surface state electrons of Pd3Sn by introduction heteroatoms (Ni) can promote hybridization between hydrogen atoms and electrons, thereby reducing the Gibbs free energy (ΔGH) of adsorbed hydrogen and improving its HER performance. Moreover, this is well verified by electrocatalytic experiment results, the Ni-doped Pd3Sn (Ni0.1Pd2.9Sn) show much lower overpotential (-29 mV vs RHE) and Tafel slope (17 mV dec-1) than Pd3Sn (-39 mV vs RHE, 25 mV dec-1) at a current density of 10 mA cm-2. Significantly, the Ni0.1Pd2.9Sn nanoparticles exhibit excellent stability for HER. The electrocatalytic activity of Ni0.1Pd2.9Sn nanoparticles is superior to that of commercial Pt. This work provides an accurate guide for manipulating surface state electrons to improve the HER performance of catalysts.

Effect of high-fat diet on the fatty acid profiles of brain in offspring mice exposed to maternal gestational diabetes mellitus.

OBJECTIVE: Fatty acids play a critical role in the proper functioning of the brain. This study investigated the effects of a high-fat (HF) diet on brain fatty acid profiles of offspring exposed to maternal gestational diabetes mellitus (GDM). METHODS: Insulin receptor antagonist (S961) and HF diet were used to establish the GDM animal model. Brain fatty acid profiles of the offspring mice were measured by gas chromatography at weaning and adulthood. Protein expressions of the fatty acid transport pathway Wnt3/ß-catenin and the target protein major facilitator superfamily domain-containing 2a (MFSD2a) were measured in the offspring brain by Western blot. RESULTS: Maternal GDM increased the body weight of male offspring (P < 0.05). In weaning offspring, factorial analysis showed that maternal GDM increased the monounsaturated fatty acid (MUFA) percentage of the weaning offspring's brain (P < 0.05). Maternal GDM decreased offspring brain arachidonic acid (AA), but HF diet increased brain linoleic acid (LA) (P < 0.05). Maternal GDM and HF diet reduced offspring brain docosahexaenoic acid (DHA), and the male offspring had higher DHA than the female offspring (P < 0.05). In adult offspring, factorial analysis showed that HF diet increased brain MUFA in offspring, and male offspring had higher brain MUFA than female offspring (P < 0.05). The HF diet increased brain LA in the offspring. Male offspring had higher level of AA than female offspring (P < 0.05). HF diet reduced DHA in the brains of female offspring. The brain protein expression of ß-catenin and MFSD2a in both weaning and adult female offspring was lower in the HF + GDM group than in the CON group (P < 0.05). CONCLUSIONS: Maternal GDM increased the susceptibility of male offspring to HF diet-induced obesity. HF diet-induced adverse brain fatty acid profiles in both male and female offspring exposed to GDM.

Water addition but not reduction alters plant biomass-diversity relationship.

The relationship between plant aboveground biomass and diversity typically follows a unimodal pattern, showing a positive correlation in resource-poor habitats and a negative correlation in resource-rich environments. Precipitation is a crucial resource for both plant biomass and diversity in terrestrial ecosystems. However, the impact of precipitation changes on the relationship between plant biomass and diversity remains unclear. We conduct a water addition field experiment in a semiarid grassland and identify a unimodal relationship between plant biomass and species richness under ambient conditions. Water addition delays the declining phase of this unimodal curve and shift it upward compared to ambient conditions. Our meta-analysis of water addition experiments conducted across major biomes worldwide (grassland, shrubland, desert, and forest) supports this finding, while water reduction does not alter the biomass-diversity relationship. Water addition increases biomass in all climate but only increases species richness in arid and semiarid climate. Similarly, water reduction decreases biomass in all climate but only reduces species richness in arid and semiarid climate. Species richness in dry subhumid and humid climate does not change significantly. Furthermore, our field experiment shows that water addition increases plant diversity while decreasing soil inorganic nitrogen levels. The increase in one resource, such as water, leads to the scarcity of another, such as nutrient, thus postponing the declining phase of the plant biomass-diversity relationship typically observed in resource-rich habitats. Our research contributes to predicting the plant biomass-diversity relationship under changing precipitation conditions and highlights the complex interplay between water availability, nutrient level, and plant diversity.

Transition-Metal-Free Radical Relay Cascade Annulation of Amides: Access to Antitumor Active Benzo[b]azepine and Oxindole Derivatives.

Efficient transition-metal-free synthesis of benzo[b]azepines and oxindoles is achieved via a radical relay cascade strategy employing halogen atom transfer (XAT) for aryl radical generation followed by intramolecular hydrogen atom transfer (HAT). Optimization yielded moderate to substantial yields under visible light irradiation. Preliminary biological assessments revealed promising anti-tumor activity for select compounds. This study underscores the potential of XAT-mediated radical relay cascades in medicinal chemistry and anticancer drug discovery.

Gestational Diabetes Mellitus Remodels the Fetal Brain Fatty Acid Profile Through Placenta-Brain Lipid Axis in C57BL/6J Mice.

BACKGROUND: Polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), are critical for proper fetal brain growth and development. Gestational diabetes mellitus (GDM) could affect maternal-fetal fatty acid metabolism. OBJECTIVE: This study aimed to explore the effect of GDM and high-fat (HF) diet on the DHA transport signaling pathway in the placenta-brain axis and fatty acid concentrations in the fetal brain. METHODS: Insulin receptor antagonist (S961) and HF diet were used to establish an animal model of GDM. Eighty female C57BL/6J mice were randomly divided into control (CON), GDM, HF, and HF+GDM groups. The fatty acid profiles of the maternal liver and fetal brain were analyzed by gas chromatography. In addition, we analyzed the protein amounts of maternal liver fatty acid desaturase (FADS1/3), elongase (ELOVL2/5) and the regulatory factor sterol-regulatory element-binding protein (SREBP)-1c, and the DHA transport signaling pathway (Wnt3/ß-catenin/MFSD2a) of the placenta and fetal brain using western blotting. RESULTS: GDM promoted the decrease of maternal liver ELOVL2, ELOVL5, and SREBP-1c. Accordingly, we observed a significant decrease in the amount of maternal liver arachidonic acid (AA), DHA, and total n-3 PUFA and n-6 PUFA induced by GDM. GDM also significantly decreased the amount of DHA and n-3 PUFA in the fetal brain. GDM downregulated the Wnt3/ß-catenin/MFSD2a signaling pathway, which transfers n-3 PUFA in the placenta and fetal brain. The HF diet increased n-6 PUFA amounts in the maternal liver, correspondingly increasing linoleic acid, gamma-linolenic acid, AA, and total n-6 PUFA in the fetal brain, but decreased DHA amount in the fetal brain. However, HF diet only tended to decrease placental ß-catenin and MFSD2a amounts (P = 0.074 and P = 0.098, respectively). CONCLUSIONS: Maternal GDM could affect the fatty acid profile of the fetal brain both by downregulating the Wnt3/ß-catenin/MFSD2a pathway of the placental-fetal barrier and by affecting maternal fatty acid metabolism.

Stop codon variant in EFEMP1 is associated with primary open-angle glaucoma due to impaired regulation of aqueous humor outflow.

It is known that the actin cytoskeleton and its associated cellular interactions in the trabecular meshwork (TM) and juxtacanalicular tissues mainly contribute to the formation of resistance to aqueous outflow of the eye. Fibulin-3, encoded by EFEMP1 gene, has a role in extracellular matrix (ECM) modulation, and interacts with enzymatic ECM regulators, but the effects of fibulin-3 on TM cells has not been explored. Here, we report a stop codon variant (c.T1480C, p.X494Q) of EFEMP1 that co-segregates with primary open angle glaucoma (POAG) in a Chinese pedigree. In the human TM cells, overexpression of wild-type fibulin-3 reduced intracellular actin stress fibers formation and the extracellular fibronectin levels by inhibiting Rho/ROCK signaling. TGFß1 up-regulated fibulin-3 protein levels in human TM cells by activating Rho/ROCK signaling. In rat eyes, overexpression of wild-type fibulin-3 decreased the intraocular pressure and the fibronectin expression of TM, however, overexpression of mutant fibulin-3 (c.T1480C, p.X494Q) showed opposite effects in cells and rat eyes. Taken together, the EFEMP1 variant may impair the regulatory capacity of fibulin-3 which has a role for modulating the cell contractile activity and ECM synthesis in TM cells, and in turn may maintain normal resistance of aqueous humor outflow. This study contributes to the understanding of the important role of fibulin-3 in TM pathophysiology and provides a new possible POAG therapeutic approach.