ST3GAL1 Promotes Malignant Phenotypes in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) has a poor prognosis, and elucidation of the molecular mechanisms underlying iCCA malignancy is of great significance. Glycosylation, an important post-translational modification, is closely associated with tumor progression. Altered glycosylation, including aberrant sialylation resulting from abnormal expression of sialyltransferases (STs) and neuraminidases (NEUs), is a significant feature of cancer cells. However, there is limited information on the roles of STs and NEUs in iCCA malignancy. Here, utilizing our proteogenomic resources from a cohort of 262 patients with iCCA, we identified ST3GAL1 as a prognostically relevant molecule in iCCA. Moreover, overexpression of ST3GAL1 promoted proliferation, migration, and invasion and inhibited apoptosis of iCCA cells in vitro. Through proteomic analyses, we identified the downstream pathway potentially regulated by ST3GAL1, which was the NF-κB signaling pathway, and further demonstrated that this pathway was positively correlated with malignancy in iCCA cells. Notably, glycoproteomics showed that O-glycosylation was changed in iCCA cells with high ST3GAL1 expression. Importantly, the altered O-glycopeptides underscored the potential utility of O-glycosylation profiling as a discriminatory marker for iCCA cells with ST3GAL1 overexpression. Additionally, miR-320b was identified as a post-transcriptional regulator of ST3GAL1, capable of suppressing ST3GAL1 expression and then reducing the proliferation, migration, and invasion abilities of iCCA cell lines. Taken together, these results suggest ST3GAL1 could serve as a promising therapeutic target for iCCA.

High exposure of global tree diversity to human pressure.

Safeguarding Earth's tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50.2% of a tree species' range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average. Protecting high-priority areas for the top 17% and 50% priority thresholds would increase the average protected proportion of each tree species' range to 65.5% and 82.6%, respectively, leaving many fewer species (2,151 and 2,010) completely unprotected. The priority areas identified for trees match well to the Global 200 Ecoregions framework, revealing that priority areas for trees would in large part also optimize protection for terrestrial biodiversity overall. Based on range estimates for >46,000 tree species, our findings show that a large proportion of tree species receive limited protection by current PAs and are under substantial human pressure. Improved protection of biodiversity overall would also strongly benefit global tree diversity.

Carbon encapsulated nanoparticles: materials science and energy applications.

The technological implementation of electrochemical energy conversion and storage necessitates the acquisition of high-performance electrocatalysts and electrodes. Carbon encapsulated nanoparticles have emerged as an exciting option owing to their unique advantages that strike a high-level activity-stability balance. Ever-growing attention to this unique type of material is partly attributed to the straightforward rationale of carbonizing ubiquitous organic species under energetic conditions. In addition, on-demand precursors pave the way for not only introducing dopants and surface functional groups into the carbon shell but also generating diverse metal-based nanoparticle cores. By controlling the synthetic parameters, both the carbon shell and the metallic core are facilely engineered in terms of structure, composition, and dimensions. Apart from multiple easy-to-understand superiorities, such as improved agglomeration, corrosion, oxidation, and pulverization resistance and charge conduction, afforded by the carbon encapsulation, potential core-shell synergistic interactions lead to the fine-tuning of the electronic structures of both components. These features collectively contribute to the emerging energy applications of these nanostructures as novel electrocatalysts and electrodes. Thus, a systematic and comprehensive review is urgently needed to summarize recent advancements and stimulate further efforts in this rapidly evolving research field.

Temporal dynamics of Grime's CSR strategies in plant communities during 60 years of succession.

Grime's competitive, stress-tolerant, ruderal (CSR) theory predicts a shift in plant communities from ruderal to stress-tolerant strategies during secondary succession. However, this fundamental tenet lacks empirical validation using long-term continuous successional data. Utilizing a 60-year longitudinal data of old-field succession, we investigated the community-level dynamics of plant strategies over time. Our findings reveal that while plant communities generally transitioned from ruderal to stress-tolerant strategies during succession, initial abandonment conditions crucially shaped early successional strategies, leading to varied strategy trajectories across different fields. Furthermore, we found a notable divergence in the CSR strategies of alien and native species over succession. Initially, alien and native species exhibited similar ruderal strategies, but in later stages, alien species exhibited higher ruderal and lower stress tolerance compared to native species. Overall, our findings underscore the applicability of Grime's predictions regarding temporal shifts in CSR strategies depending on both initial community conditions and species origin.

Histamine H4 receptor and TRPV1 mediate itch induced by cadaverine, a metabolite of the microbiome.

Cadaverine is an endogenous metabolite produced by the gut microbiome with various activity in physiological and pathological conditions. However, whether cadaverine regulates pain or itch remains unclear. In this study, we first found that cadaverine may bind to histamine 4 receptor (H4R) with higher docking energy score using molecular docking simulations, suggesting cadaverine may act as an endogenous ligand for H4R. We subsequently found intradermal injection of cadaverine into the nape or cheek of mice induces a dose-dependent scratching response in mice, which was suppressed by a selective H4R antagonist JNJ-7777120, transient receptor potential vanilloid 1 (TRPV1) antagonist capsazepine and PLC inhibitor U73122, but not H1R antagonist or TRPA1 antagonist or TRPV4 antagonist. Consistently, cadaverine-induced itch was abolished in Trpv1-/- but not Trpa1-/- mice. Pharmacological analysis indicated that mast cells and opioid receptors were also involved in cadaverine-induced itch in mice. scRNA-Seq data analysis showed that H4R and TRPV1 are mainly co-expressed on NP2, NP3 and PEP1 DRG neurons. Calcium imaging analysis showed that cadaverine perfusion enhanced calcium influx in the dissociated dorsal root ganglion (DRG) neurons, which was suppressed by JNJ-7777120 and capsazepine, as well as in the DRG neurons from Trpv1-/- mice. Patch-clamp recordings found that cadaverine perfusion significantly increased the excitability of small diameter DRG neurons, and JNJ-7777120 abolished this effect, indicating involvement of H4R. Together, these results provide evidences that cadaverine is a novel endogenous pruritogens, which activates H4R/TRPV1 signaling pathways in the primary sensory neurons.

Gastrodin regulates the expression of renin-angiotensin system-SIRT3 and proinflammatory mediators in reactive astrocytes via activated microglia.

Gastrodin, an anti-inflammatory herbal agent, is known to suppress microglia activation. Here, we investigated whether it would exert a similar effect in reactive astrocytes and whether it might act through the renin-angiotensin system (RAS) and sirtuin 3 (SIRT3). Angiotensinogen (ATO), angiotensin-converting enzyme (ACE), angiotensin II type 1 (AT1) and type 2 (AT2) receptor and SIRT3 expression was detected in TNC-1 astrocytes treated with BV-2 microglia conditioned medium (CM) with or without gastrodin and lipopolysaccharide (LPS) pre-treatment by RT-PCR, immunofluorescence and western blotting analysis. Expression of C3 (A1 astrocyte marker), S100A10 (A2 astrocyte marker), proinflammatory cytokines and neurotrophic factors was then evaluated. The results showed a significant increase of ATO, ACE, AT1, SIRT3, C3, proinflammatory cytokines and neurotrophic factors expression in TNC-1 astrocytes incubated in CM + LPS when compared with cells incubated in the CM, but AT2 and S100A10 expression was reduced. TNC-1 astrocytes responded vigorously to BV-2 CM treated with gastrodin + LPS as compared with the control. This was evident by the decreased expression of the abovementioned protein markers, except for AT2 and S100A10. Interestingly, SIRT3, IGF-1 and BDNF expression was enhanced, suggesting that gastrodin inhibited the expression of RAS and proinflammatory mediators but promoted the expression of neurotrophic factors. And gastrodin regulated the phenotypic changes of astrocytes through AT1. Additionally, azilsartan (a specific inhibitor of AT1) inhibited the expression of C3 and S100A10, which remained unaffected in gastrodin and azilsartan combination treatment. These findings provide evidence that gastrodin may have a therapeutic effect via regulating RAS-SIRT3.

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.

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). METHODS: Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. RESULTS: The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. CONCLUSION: The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.

Aggregation-Induced Emissive Feringa-Type Motor: Toward the Dual-Functional Motor in a Single Molecular Aggregation System.

Aggregation-induced emission (AIE)allows tunable photoluminescence via the simple regulation of molecular aggregation. The research spurt along this vein has also offered tremendous opportunities for light-responsive artificial molecular machines that are to be fully explored for performing versatile functions. Herein, the study reports a light-driven Feringa-type motor, when in the appropriate aggregation state, not only demonstrates the light-activated rotary motion but emits photons with good quantum yield. A semi-quantitative TD-DFT calculation is also conducted to aid the understanding of the competitive photoluminescence and photoisomerization processes of the motor. Cytotoxicity test shows this motor possesses good biocompatibility, laying a solid foundation for applying it in the bio-environment. The results demonstrated that the engagement of the aggregation-induced emission concept and light-driven Feringa-motor can lead to the discovery of the novel motorized AIEgen, which will further stimulate the rise of more advanced molecular motors capable of executing multi-functionalities.

Breaking Scaling Relations for Highly Efficient Electroreduction of CO2 to CO on Atomically Dispersed Heteronuclear Dual-Atom Catalyst.

Conversion of CO2 into value-added products by electrocatalysis provides a promising way to mitigate energy and environmental problems. However, it is greatly limited by the scaling relationship between the adsorption strength of intermediates. Herein, Mn and Ni single-atom catalysts, homonuclear dual-atom catalysts (DACs), and heteronuclear DACs are synthesized. Aberration-corrected annular dark-field scanning transmission electron microscopy (ADF-STEM) and X-ray absorption spectroscopy characterization uncovered the existence of the Mn─Ni pair in Mn─Ni DAC. X-ray photoelectron spectroscopy and X-ray absorption near-edge spectroscopy reveal that Mn donated electrons to Ni atoms in Mn─Ni DAC. Consequently, Mn─Ni DAC displays the highest CO Faradaic efficiency of 98.7% at -0.7 V versus reversible hydrogen electrode (vs RHE) with CO partial current density of 16.8 mA cm-2. Density functional theory calculations disclose that the scaling relationship between the binding strength of intermediates is broken, resulting in superior performance for ECR to CO over Mn─Ni─NC catalyst.

Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD.

RATIONALE & OBJECTIVE: Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study. STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy. EXPOSURE: Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy. OUTCOME: Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records. ANALYTICAL APPROACH: Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals. RESULTS: We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD. LIMITATIONS: Potential underreporting of CKD cases because of case identification via electronic health records. CONCLUSIONS: Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD. PLAIN-LANGUAGE SUMMARY: The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.

PASTICCINO2 interacting with Golgi Anti-Apoptotic Proteins resists endoplasmic reticulum stress dependent on very long-chain fatty acids.

The endoplasmic reticulum (ER) is crucial for maintaining cell homeostasis because it is the primary site for synthesizing secreted and transmembrane proteins and lipids. The unfolded protein response (UPR) is activated to restore ER homeostasis under ER stress. However, the relationship between lipids and the ER stress response in plants is not well understood. Arabidopsis Golgi anti-apoptotic proteins (GAAPs) are involved in resisting ER stress. To elucidate the function of GAAPs, PASTICCINO2 (PAS2), involved in very long-chain fatty acid (VLCFA) synthesis, was found to interact with GAAPs and IRE1. Single pas2 and gaap1/gaap2pas2 double mutants exhibited increased seedling damage and impaired UPR response under chronic ER stress. Site mutation combined with genetic analysis revealed that the role of PAS2 in resisting ER stress depended on its VLCFA synthesis domain. VLCFA contents were upregulated under ER stress, which required GAAPs. Exogenous VLCFAs partially restored the defect in UPR upregulation caused by PAS2 or GAAP mutations under chronic ER stress. These findings demonstrate that the association of PAS2 with GAAPs confers plant resistance to ER stress by regulating VLCFA synthesis and the UPR. This provides a basis for further studies on the connection between lipids and cell fate decisions under stress.

Compact high-power 320-nm continuous-wave single-frequency laser based on power scaling of a diode-pumped Pr:LiYF4 ring laser at red.

Single-frequency (SF) lasers in the visible spectral region are usually obtained through an indirect method, i.e., frequency doubling of near-infrared SF lasers. In this work, we report on the direct generation of a high-power continuous-wave (CW) SF laser in red based on a diode-pumped Pr:LiYF4 (YLF) ring cavity technology. A maximum output power is scaled to 3.98 W at 640 nm with a linewidth of about 17.2 MHz and a power stability of 0.6%. Moreover, by inserting a LBO crystal into the ring cavity for intracavity frequency doubling of the 640 nm SF laser, we have also successfully demonstrated an ultraviolet (UV) SF laser at 320 nm, for the first time to the best of our knowledge, with a maximum power of 670 mW. This work provides a promising route for the development of simple, compact, and high-power SF lasers operating in visible and UV spectral regions.

The impact of MIF deficiency on alterations of fecal microbiota in C57BL/6 mice induced by Trichinella spiralis infection.

Trichinellosis caused by Trichinella spiralis (T. spiralis) is a major food-borne parasitic zoonosis worldwide. Prevention of trichinellosis is an effective strategy to improve patient quality of life. Macrophage migration inhibitory factor (MIF) is closely related to the occurrence and development of several parasitic diseases. Studying the impact of MIF deficiency (Mif-/- ) on the alterations in host fecal microbiota due to T. spiralis infection may contribute to proposing a novel dual therapeutic approach for trichinellosis. To reveal the diversity and differences in fecal microbial composition, feces were collected from T. spiralis-uninfected and T. spiralis-infected wild-type (WT) and MIF knockout (KO) C57BL/6 mice at 0, 7, 14, and 35 days post-infection (dpi), and the samples were sent for 16S rRNA amplicon sequencing on the Illumina NovaSeq platform. Flow cytometry was used to determine the expression levels of IFN-γ and IL-4 in the CD4+ /CD8+ T-cell sets of mouse spleens. The results showed that operational taxonomic unit (OTU) clustering, relative abundance of microbial composition, alpha diversity, and beta diversity exhibited significant changes among the eight groups. The LEfSe analysis selected several potential biomarkers at the genus or species level, including Akkermansia muciniphila, Lactobacillus murinus, Coprococcus catus, Firmicutes bacterium M10_2, Parabacteroides sp. CT06, and Bacteroides between the KTs and WTs groups. The predicted bacterial functions of the fecal microbiota were mainly involved in metabolism, such as the metabolism of carbohydrates, amino acids, energy, cofactors, vitamins, nucleotides, glycans, and lipids. Flow cytometry revealed an increased CD3+ CD8- /CD3+ CD8+ T-cell ratio and increased IFN-γ and IL-4 levels in CD3+ CD8- T-cell sets from WT and MIF KO mice at 7 dpi. The results indicated that both MIF KO and infection time have a significant influence on the CD3+ CD8- IFN-γ+ and CD3+ CD8- IL-4+ response in mice after T. spiralis. In conclusion, this research showed alterations of the fecal microbiota and immune response in both WT and MIF KO mice before and after T. spiralis infection. These results revealed a potential role of MIF in regulating the pathogenesis of trichinellosis related to the intestinal microbiota. Importantly, the selected potential biomarkers combined with MIF will also offer a novel therapeutic approach to treat trichinellosis in the future.

Peptide RL-QN15 promotes regeneration of epidermal nerve fibers and recovery of sensory function in diabetic skin wounds.

Epidermal nerve fiber regeneration and sensory function are severely impaired in skin wounds of diabetic patients. To date, however, research on post-traumatic nerve regeneration and sensory reconstruction remains scarce, and effective clinical therapeutics are lacking. In the current study, localized treatment with RL-QN15, considered as a drug candidate for intervention in skin wounds in our previous research, accelerated the healing of full-thickness dorsal skin wounds in diabetic mice and footpad skin wounds in diabetic rats. Interestingly, nerve density and axonal plasticity in the skin wounds of diabetic rats and mice, as well as plantar sensitivity in diabetic rats, were markedly enhanced by RL-QN15 treatment. Furthermore, RL-QN15 promoted the proliferation, migration, and axonal length of neuron-like PC12 cells, which was likely associated with activation of the phosphatidylinositol-3 kinase/protein kinase B (PI3K/Akt) signaling pathway. The therapeutic effects of RL-QN15 were partially reduced by blocking the PI3K/Akt signaling pathway with the inhibitor LY294002. Thus, RL-QN15 showed positive therapeutic effects on the distribution of epidermal nerve fibers and stimulated the recovery of sensory function after cutaneous injury. This study lays a solid foundation for the development of RL-QN15 peptide-based therapeutics against diabetic skin wounds.