Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins.

Fungal pathogens pose significant threats to plant health by secreting effectors that manipulate plant-host defences. However, identifying effector proteins remains challenging, in part because they lack common sequence motifs. Here, we introduce Fungtion (Fungal effector prediction), a toolkit leveraging a hybrid framework to accurately predict and visualize fungal effectors. By combining global patterns learned from pretrained protein language models with refined information from known effectors, Fungtion achieves state-of-the-art prediction performance. Additionally, the interactive visualizations we have developed enable researchers to explore both sequence- and high-level relationships between the predicted and known effectors, facilitating effector function discovery, annotation, and hypothesis formulation regarding plant-pathogen interactions. We anticipate Fungtion to be a valuable resource for biologists seeking deeper insights into fungal effector functions and for computational biologists aiming to develop future methodologies for fungal effector prediction: https://step3.erc.monash.edu/Fungtion/.

Cu0-based nanoparticles boost anti-tumor efficacy via synergy of cuproptosis and ferroptosis enhanced by cuproptosis-induced glutathione synthesis disorder.

Apoptotic resistance of tumor often leads to poor efficacy from mono-therapy based on apoptosis. Cuproptosis, a new type of non-apoptotic cell death related to mitochondrial dysfunction, can alter metabolism and enhance ferroptosis, providing a promising strategy for effective synergistic cancer treatment. In this work, Cu0-based nanoparticles (denoted as HA-ZCu) were successfully developed to improve anti-tumor efficacy by combining cuproptosis with enhanced ferroptosis, which was achieved by cuproptosis-induced glutathione synthesis disorder. In vitro studies revealed that HA-ZCu effectively induced cuproptosis and ferroptosis in HepG2 cells. Moreover, HA-ZCu induced mitochondrial dysfunction and decreased intracellular adenosine triphosphate (ATP), glutamate, and glutathione, demonstrating the effective synergy. In vivo studies further approved the synergistic therapeutic efficacy of HA-ZCu, where the inhibition rate of tumor growth reached 83.2 %. This work represents the first example of enhanced anti-tumor efficacy via cuproptosis and ferroptosis synergy through cuproptosis-induced glutathione synthesis disorder.

Exploring charge transfer and schottky barrier modulation at monolayer Ge2Sb2Te5-metal interfaces.

Monolayer Ge2Sb2Te5exhibits great potential in non-volatile memory technology due to its excellent electronic properties and phase-change characteristics, while the fundamental nature of Ge2Sb2Te5-metal contacts has not been well understood yet. Here, we provide a comprehensiveab initiostudy of the electronic properties between monolayer Ge2Sb2Te5and Pt, Pd, Au, Cu, Cr, Ag, and W contacts based on first-principles calculations. We find that the strong interaction interfaces formed between monolayer Ge2Sb2Te5and Pt, Pd, Cr, and W contacts show chemical bonding and strong charge transfer. In contrast, no apparent chemical bonding and weak charge transfer are observed in the weak interaction interfaces formed with Au, Cu, and Ag. Additionally, our study reveals the presence of a pronounced Fermi level pinning effect between monolayer Ge2Sb2Te5and metals, with pinning factors ofSn=0.325andSp=0.350. By increasing the interlayer distance, an effective transition fromn-type Ohmic contact ton-type Schottky contact is facilitated because the band edge of Ge2Sb2Te5is shifted upwards. Our study not only provides a theoretical basis for selecting suitable metal electrodes in Ge2Sb2Te5-based devices but also holds significant implications for understanding Schottky barrier height modulation between semiconductors and metals.

Optimizing the hemodynamics of hand injections in neuroangiography.

PURPOSE: Optimizing the preparation of a 10 ml syringe for manual injection of contrast media can help operators obtain easier and faster injections. This study aims to compare the flow rates of different contrast media injection methods. METHODS: Different contrast media solutions were compared: 100% contrast (10 ml contrast), mixed contrast solution (8:2 contrast/saline), and layered contrast below saline ("Parfait") in different volumes. Contrast media were injected at room temperature (20°C) and after heating (37°C). Four operators injected 10 ml syringes filled with different mediums through 5-French angiographic catheters. The average flow rate was used to compare different contrast injection mediums. The Kruskal-Wallis test with post-hoc pairwise comparisons using Bonferroni correction or Mann-Whitney U-tests were employed depending on the type of comparison. RESULTS: Compared to the 100% contrast solution, every Parfait media and mixed contrast solution demonstrated significantly higher flow rates (p < 0.001). The 5 ml saline Parfait had the highest flow rate among the Parfait solutions. The 5 ml saline Parfait and the mixed solution had comparable flow rates (p = 0.237). Higher flow rates were observed upon heating both 100% contrast (p < 0.001) and mixed contrast solutions (p < 0.001) in comparison to their flow rates at room temperature. CONCLUSION: This study demonstrates the capability of the Parfait and mixed contrast injections to achieve higher flow rates than the 100% contrast solutions. Heating the contrast media to 37°C also proves to be a viable strategy for further enhancing the flow rates for 100% and mixed contrast solutions.

Emerging trends and hotspots in peptic ulcer from 2008 to 2023: A bibliometric analysis.

Peptic ulcer (PU) is a common digestive disorder in the gastroduodenal. Although bibliometrics has become very popular in the medical field, a bibliometric analysis of research related to PU has yet to be reported. Therefore, this research aims to analyze the trends and hotspots of PU in the last 15 years. Literature data related to PU retrieved from the Web of Science Core Collection database from 2008 to 2023 were visualized and analyzed using CiteSpace 6.1.6.msi, VOSviewer 1.6.19, and SCImago Graphica Beta 1.0.35. Six thousand four hundred ninety-one papers were collected based on inclusion and exclusion criteria. The country with the highest number of publications was China. The institution with the highest number of publications was Baylor College of Medicine. The most prolific author was Yamaoka Yoshio. Malfertheiner Peter had the highest number of citations. The journal with the most publications is World Journal of Gastroenterology. The most cited Journal is Gastroenterology. The most cited reference was published by Marshall B. J. et al in 1984. The article with the highest burst strength was published in 2012 by Malfertheiner Peter. The keyword with the highest burst strength was "oxidative stress." Our research provides a bibliometric analysis of PU research to reveal the trends and hotspots of PU for 2008 to 2023. Our findings will help researchers to quickly understand the current state of research and provide a reference for in-depth studies in this area to foster the development of PU research.

Dental panoramic X-ray image segmentation for multi-feature coordinate position learning.

Objective: To achieve an accurate assessment of orthodontic and restorative treatments, tooth segmentation of dental panoramic X-ray images is a critical preliminary step, however, dental panoramic X-ray images suffer from poorly defined interdental boundaries and low root-to-alveolar bone contrast, which pose significant challenges to tooth segmentation. In this article, we propose a multi-feature coordinate position learning-based tooth image segmentation method for tooth segmentation. Methods: For better analysis, the input image is randomly flipped horizontally and vertically to enhance the data. Our method extracts multi-scale tooth features from the designed residual omni-dimensional dynamic convolution and the designed two-stream coordinate attention module can further complement the tooth boundary features, and finally the two features are fused to enhance the local details of the features and global contextual information, which achieves the enrichment and optimization of the feature information. Results: The publicly available adult dental datasets Archive and Dataset and Code were used in the study. The experimental results were 87.96% and 92.04% for IoU, 97.79% and 97.32% for ACC, and 86.42% and 95.64% for Dice. Conclusion: The experimental results show that the proposed network can be used to assist doctors in quickly viewing tooth positions, and we also validate the effectiveness of the proposed two modules in fusing features.

Predicting all-cause mortality and premature death using interpretable machine learning among a middle-aged and elderly Chinese population.

Objective: To develop machine learning-based prediction models for all-cause and premature mortality among the middle-aged and elderly population in China. Method: Adults aged 45 years or older at baseline of 2011 from the China Health and Retirement Longitudinal Study (CHARLS) were included. The stacked ensemble model was built utilizing five selected machine learning algorithms. These models underwent training and testing using the CHARLS 2011-2015 cohort (derivation cohort) and subsequently underwent external validation using the CHARLS 2015-2018 cohort (validation cohort). SHapley Additive exPlanations (SHAP) was introduced to quantify the importance of risk factors and explain machine learning algorithms. Result: In derivation cohort, a total of 10,677 subjects were included, 478 died during the follow-up. The stacked ensemble model demonstrated the highest efficacy in terms of its discrimination capability for predicting all-cause mortality and premature death, with an AUC[95 % CI] of 0.826[0.792-0.859] and 0.773[0.725-0.821], respectively. In validation cohort, the corresponding AUC[95 % CI] were 0.803[0.743-0.864] and 0.791[0.719-0.863], respectively. Risk factors including age, sex, self-reported health, activities of daily living, cognitive function, ever smoker, levels of systolic blood pressure, Cystatin C and low density lipoprotein were strong predictors for both all-cause mortality and premature death. Conclusion: Stacked ensemble models performed well in predicting all-cause and premature death in this Chinese cohort. Interpretable techniques can aid in identifying significant risk factors and non-linear relationships between predictors and mortality.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner.

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Multiantigen T-Cell Hybridizers: A Two-Component T-Cell-Activating Therapy.

Multispecific T-cell-engaging scaffolds have emerged as effective anticancer therapies for the treatment of hematological malignancies. Approaches that modulate cancer cell targeting and provide personalized, multispecific immunotherapeutics are needed. Here, we report on a modular, split antibody-like approach consisting of Fab' fragments modified with complementary morpholino oligonucleotides (MORFs). We synthesized a library of B-cell-targeting Fab'-MORF1 conjugates that self-assemble, via a Watson-Crick base pairing hybridization, with a complementary T-cell-engaging Fab'-MORF2 conjugate. We aptly titled our technology multiantigen T-cell hybridizers (MATCH). Using MATCH, cancer-specific T-cell recruitment was achieved utilizing four B-cell antigen targets: CD20, CD38, BCMA, and SLAMF7. The antigen expression profiles of various malignant B-cell lines were produced, and using these distinct profiles, cell-specific T-cell activation was attained on lymphoma, leukemia, and multiple myeloma cell lines in vitro. T-cell rechallenge experiments demonstrated the modular approach of MATCH by sequentially activating the same T-cell cohort against three different cancers using cancer antigen-specific Fab'-MORF1 conjugates. Furthermore, MATCH's efficacy was demonstrated in vivo by treating xenograft mouse models of human non-Hodgkin's lymphoma with CD20-directed MATCH therapy. In the pilot study, a single dose of MATCH allowed for long-term survival of all treated mice compared to saline control. In a second in vivo model, insights regarding optimal T-cell-to-target cell ratio were gleaned when a ratio of 5:1 T-cell-to-target cell MATCH-treated mice significantly delayed the onset of disease compared to higher and lower ratios.

A transformer-based unified multimodal framework for Alzheimer's disease assessment.

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.

Ultrahigh Single Au Atoms Loaded Porous Aromatic Frameworks for Enhanced Photocatalytic Hydrogen Evolution.

Supported single-atom catalysts (SACs) are promising in heterogeneous catalysis because of their atom economy, unusual transformations, and mechanistic clarity. The metal SAs loading, however, limits the catalytic efficiency. Herein, an in situ pre-metallated monomer-based preparation strategy is shown to achieve ultrahigh Au SAs loading in catalyst formations. The polymerization of single-atom loaded monomers yield a new porous aromatic framework (PAF-164) with Au SAs loading up to a record high 45.3 wt.%. SACs of Au-PAFs exhibit excellent photocatalytic activity in hydrogen (H2) evolution, and the H2 evolution rate of Au100%-SAs-PAF-164 can reach 4.82 mmol g-1 h-1 with great recyclability.

Development and evaluation of vaccination strategies for addressing the continuous evolution SARS-CoV-2 based on recombinant trimeric protein technology: Potential for cross-neutralizing activity and broad coronavirus response.

Given the significant decline in vaccine efficacy against Omicron, the development of novel vaccines with specific or broad-spectrum effectiveness is paramount. In this study, we formulated four monovalent vaccines based on recombinant spike trimer proteins, along with three bivalent vaccines, and five monovalent vaccines based on recombinant spike proteins. We evaluated the efficacy of different vaccination regimens in eliciting neutralizing antibodies in mice through pseudovirus neutralization assays. Following two doses of primary immunization with D614G, mice received subsequent immunizations with Omicron (BA.1, BA.2, BA.4/5) boosters individually, which led to the generation of broader and more potent cross-neutralizing activity compared to D614G boosters. Notably, the BA.4/5 booster exhibited superior efficacy. Following two doses of primary immunization with Omicron (BA.1, BA.2, BA.4/5), mice were subsequently immunized with one dose of D614G booster which resulted in broader neutralizing activity compared to one dose of Omicron (BA.1, BA.2, or BA.4/5). In unvaccinated mice, full-course immunization with different bivalent vaccines induced broad neutralizing activity against Omicron and pre-Omicron variants, with D614G&BA.4/5 demonstrating superior efficacy. However, compared to other variants, the neutralizing activity against XBB.1.5/1.9.1 is notably reduced. This observation emphasizes the necessity of timely updates to the vaccine antigen composition. Based on these findings and existing studies, we propose a vaccination strategy aimed at preserving the epitope repertoire to its maximum potential: (1) Individuals previously vaccinated or infected with pre-Omicron variants should inoculate a monovalent vaccine containing Omicron components; (2) Individuals who have only been vaccinated or infected with Omicron should be inoculated a monovalent vaccine containing pre-Omicron variants components; (3) Individuals without SARS-CoV-2 infection and vaccination should inoculate a bivalent vaccine comprising both pre-Omicron and Omicron components for primary immunization. Additionally, through cross-inoculation of SARS-CoV-2 D614G spike trimer protein and SARS-CoV-1 spike protein in mice, we preliminarily demonstrated the possibility of cross-reaction between different coronavirus vaccines to produce resistance to the pan-coronavirus.

Pharmacovigilance analysis of orlistat adverse events based on the FDA adverse event reporting system (FAERS) database.

Objective: Based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database, we analyzed the signals of potential adverse events (AEs) of orlistat in the real world to provide a reference for its safe clinical use. Methods: The FAERS database and OpenVigil 2.1 were used to obtain data on adverse events of orlistat from the first quarter of 2004 to the first quarter of 2023, and to analyze the population in which the adverse events occurred. And the signals of their potential adverse events were mined using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and empirical Bayesian geometric mean (EBGM). Result: A total of 21,079 reports of adverse events with orlistat as the primary suspected drug were collected in this study. Using four disproportionate analyses, we screened 117 preferred terms (PTs) involving 18 system organ classes (SOCs). We found that the most common adverse events at SOC level for orlistat remained "gastrointestinal disorders", while "metabolism and nutrition disorders", "renal and urinary disorders", "musculoskeletal and connective tissue disorders" and "hepatobiliary disorders" also ranked high in the number of case reports. In addition, at the PT level, we identified several new signals of adverse events not mentioned in the specification, including "lipiduria", "anal haemorrhage", "rectal haemorrhage", "haematochezia", "sigmoiditis", "diverticulitis" and "muscle spasms". Conclusion: Most of the adverse events found in this study are consistent with the results described in the drug label. At the same time, we also found some new adverse events, which require more prospective studies to verify and elucidate their relationship with orlistat.

Generation of mouse testicular organoids with highly compartmentalized tubular lumen structure and their cryopreservation.

Testicular organoids have great potential for maintaining male fertility and even restoring male infertility. However, existing studies on generating organoids with testis-specific structure and function are scarce and come with many limitations. Research on cryopreservation of testicular organoids is even more limited, and inappropriate cryopreservation methods may result in the loss of properties in resuscitated or regenerated organoids, rendering them unsuitable for clinical or research needs. In this paper, we investigated the effects of mouse age and cell number on the self-aggregation of testicular cells into spheres in low-adsorption plates. Various media compositions, culture systems, and cell numbers were used to culture cell spheres for 14 days to form testicular organoids, and the self-organization of the organoids was assessed by histological and immunofluorescence staining. We determined the appropriate cryopreservation conditions for testicular cells, cell spheres, and tissues. Subsequently, organoids derived from cryopreserved testicular tissues, testicular cells, and testicular cell spheres were compared and evaluated by histological and immunofluorescence staining. The results indicate that testicular cell spheres consisting of 30 × 104 testicular cells from 2-week-old mice were able to form organoids highly similar to the luminal structure and cell distribution of natural mouse testicular tissues. This transformation occurred over 14 days of incubation in α-MEM medium containing 10 % knockout serum replacer (KSR) using an agarose hydrogel culture system. Additionally, the Sertoli cells were tightly connected to form a blood-testis barrier. The relative rates of tubular area, germ cells, Sertoli cells, and peritubular myoid cells were 36.985 % ± 0.695, 13.347 % ± 3.102, 47.570 % ± 0.379, and 27.406 % ± 1.832, respectively. The optimal cryopreservation protocol for primary testicular cells involved slow freezing with a cryoprotectant consisting of α-MEM with 10 % dimethyl sulfoxide (DMSO). Slow freezing with cryoprotectants containing 5 % DMSO and 5 % ethylene glycol (EG) was optimal for all different volumes of testicular cell spheres. Compared to testicular organoids generated from frozen testicular tissue and cell spheres, freezing testicular cells proved most effective in maintaining organoid differentiation characteristics and cell-cell interactions. The findings of this study contribute to a "universal" testicular organoid in vitro culture protocol with promising applications for fertility preservation and restoration in prepubertal cancer patients and adult infertile patients.

The combined application of pulsed electromagnetic fields and platelet-rich plasma in the treatment of early-stage knee osteoarthritis: A randomized clinical trial.

BACKGROUND: This study aims to evaluate the therapeutic efficacy of combined treatment with pulsed electromagnetic fields (PEMFs) and platelet-rich plasma (PRP) injection in improving pain and functional mobility among patients with early-stage knee osteoarthritis (KOA). We hypothesize that this combined therapy can yield superior treatment outcomes. METHODS: Based on the different treatment regimens, we divided 48 patients diagnosed with Kellgren-Lawrence grades I-III KOA into 3 groups: the PRP group, the PEMFs group, and the PRP + PEMFs group. Each subtype of KOA patients was randomly assigned to different treatment groups. In the PRP group, patients received intra-articular injections of leukocyte-rich platelet-rich plasma once a month for 3 consecutive months. In the PEMFs group, patients receive low-frequency PEMFs irradiation therapy with a frequency of 30 Hz and intensity of 1.5 mT, once daily, 5 times a week, for a consecutive treatment period of 12 weeks. In the PRP + PEMFs group, patients receive both of the aforementioned treatment protocol. The treatment effects on patients are evaluated at baseline and at weeks 4, 8, and 12 post-treatment. Assessment parameters include visual analog scale for pain, Western Ontario and McMaster Universities Osteoarthritis Index, Lequesne Index score, and knee joint range of motion. RESULTS: From the 4th to the 12th week of treatment, the visual analog scale scores, Western Ontario and McMaster Universities Osteoarthritis Index scores, and Lequesne index scores of patients in all 3 groups gradually decreased, while knee joint mobility gradually increased (P < .05). At weeks 4, 8, and 12 after treatment, the PRP combined with PEMFs group showed significantly better scores compared to the PRP group and the PEMFs group, with statistically significant differences (P < .05). A total of 7 patients experienced adverse reactions such as knee joint swelling, low-grade fever, and worsening knee joint pain after treatment, all of which disappeared within 1 week after treatment. The incidence of complications did not differ significantly among the 3 groups (P = .67). CONCLUSION: PRP, PEMFs, and the combination of PRP and PEMFs therapy all effectively alleviate knee joint pain and improve joint function. However, compared to single treatment modalities, the combined therapy of PRP and PEMFs demonstrates more pronounced efficacy.

An EEG-based marker of functional connectivity: detection of major depressive disorder.

Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains a pressing challenge. In this study, we present a fusion feature called P-MSWC, as a novel marker to construct brain functional connectivity matrices and utilize the convolutional neural network (CNN) to identify MDD based on electroencephalogram (EEG) signal. Firstly, we combine synchrosqueezed wavelet transform and coherence theory to get synchrosqueezed wavelet coherence. Then, we obtain the fusion feature by incorporating synchrosqueezed wavelet coherence value and phase-locking value, which outperforms conventional functional connectivity markers by comprehensively capturing the original EEG signal's information and demonstrating notable noise-resistance capabilities. Finally, we propose a lightweight CNN model that effectively utilizes the high-dimensional connectivity matrix of the brain, constructed using our novel marker, to enable more accurate and efficient detection of MDD. The proposed method achieves 99.92% accuracy on a single dataset and 97.86% accuracy on a combined dataset. Moreover, comparison experiments have shown that the performance of the proposed method is superior to traditional machine learning methods. Furthermore, visualization experiments reveal differences in the distribution of brain connectivity between MDD patients and healthy subjects, including decreased connectivity in the T7, O1, F8, and C3 channels of the gamma band. The results of the experiments indicate that the fusion feature can be utilized as a new marker for constructing functional brain connectivity, and the combination of deep learning and functional connectivity matrices can provide more help for the detection of MDD.

CD38 deficiency prevents diabetic nephropathy by inhibiting lipid accumulation and oxidative stress through activation of the SIRT3 pathway.

Diabetic nephropathy (DN) is one of the most common complications of diabetes. Our previous study showed that CD38 knockout (CD38KO) mice had protective effects on many diseases. However, the roles and mechanisms of CD38 in DN remain unknown. Here, DN mice were generated by HFD feeding plus streptozotocin (STZ) injection in male CD38KO and CD38flox mice. Mesangial cells (SV40 MES 13 cells) were used to mimic the injury of DN with palmitic acid (PA) treatment in vitro. Our results showed that CD38 expression was significantly increased in kidney of diabetic CD38flox mice and SV40 MES 13 cells treated with PA. CD38KO mice were significantly resistant to diabetes-induced renal injury. Moreover, CD38 deficiency markedly decreased HFD/STZ-induced lipid accumulation, fibrosis and oxidative stress in kidney tissue. In contrast, overexpression of CD38 aggravated PA-induced lipid accumulation and oxidative stress. CD38 deficiency increased expression of SIRT3, while overexpression of CD38 decreased its expression. More importantly, 3-TYP, an inhibitor of SIRT3, significantly enhanced PA-induced lipid accumulation and oxidative stress in CD38 overexpressing cell lines. In conclusion, our results demonstrated that CD38 deficiency prevented DN by inhibiting lipid accumulation and oxidative stress through activation of the SIRT3 pathway.

Gastrodin Alleviates DSS-Induced Colitis in Mice through Strengthening Intestinal Barrier and Modulating Gut Microbiota.

Inflammatory bowel diseases (IBDs) are commonly associated with dysfunctional intestinal barriers and disturbed gut microbiota. Gastrodin, a major bioactive ingredient of Gastrodia elata Blume, has been shown to exhibit anti-oxidation and anti-inflammation properties and could mitigate non-alcoholic fatty liver disease, but its role in modulating IBD remains elusive. The aim of this study was to investigate the impact of gastrodin on DSS-induced colitis in mice and explore its potential mechanisms. Gastrodin supplementation alleviated clinical symptoms such as weight loss, a shortened colon, and a high disease activity index. Meanwhile, gastrodin strengthened the intestinal barrier by increasing the 0expression of tight junction proteins and mucin. Furthermore, Gastrodin significantly reduced pro-inflammatory cytokine secretion in mice by downregulating the NF-κB and MAPK pathways. Gut microbiota analysis showed that gastrodin improved the DSS-disrupted microbiota of mice. These findings demonstrate that gastrodin could attenuate DSS-induced colitis by enhancing the intestinal barrier and modulating the gut microbiota, providing support for the development of a gastrodin-based strategy to prevent or combat IBD.

Development and application of a serious adverse events risk model for concurrent chemoradiotherapy in patients with nasopharyngeal carcinoma.

The objective of this study was to construct a concise prediction model for serious adverse events (SAEs) in order to assess the likelihood of SAE occurrence among hospitalized patients undergoing concurrent chemoradiotherapy. An electronic database of a Cancer Centre was utilized to conduct a cross-sectional review survey. Our research involved the recruitment of 239 patients who were undergoing concurrent chemoradiotherapy in the Department of Nasopharynx and Radiotherapy. The clinical prediction rule was derived using logistic regression analysis, with SAE serving as the primary outcome. Internal verification was conducted. The occurrence rate of SAE in the derivation cohort was 59.4%. The ultimate model used had 3 variables, namely cystatin C, C-reactive protein, and serum amyloid A. The model exhibited an area under the curve of 0.626 (95% CI: 0.555-0.696; P < .001). The model accurately predicts the occurrence of SAE, and the variable data can be easily obtained, and the assessment technique is straightforward.

Whole genome resequencing reveals the adaptability of native chickens to drought, tropical and frigid environments in Xinjiang.

Chickens exhibit extensive genetic diversity and are distributed worldwide. Different chicken breeds have evolved to thrive in diverse environmental conditions. However, research on the genetic mechanisms underlying chicken adaptation to extreme environments, such as tropical, frigid and drought-prone regions, remains limited. In this study, we conducted whole-genome sequencing of 240 individuals from six native chicken breeds in Xinjiang, China, as well as 4 publicly available chicken breeds inhabiting regions with varying annual precipitations, temperatures, and altitudes. Our analysis revealed several genetic variants among the examined breeds. Furthermore, we investigated the genetic diversity and population structure of breeds residing in extreme drought and temperature environments by comparing them. Notably, native chicken breeds exhibited different genetic diversity and population structures. Moreover, we identified candidate genes associated with chicken adaptability to the environment, such as CORO2A, CTNNA3, AGMO, GRID2, BBOX1, COL3A1, INSR, SOX5, MAP2 and PLPPR1. Additionally, pathways such as lysosome, cysteine and methionine metabolism, glycosaminoglycan degradation, and Wnt signaling may be play crucial roles in regulating chicken adaptation to drought environments. Overall, these findings contribute to our understanding of the genetic mechanisms governing chicken adaptation to extreme environments, and also offer insights for enhancing the resilience of chicken breeds to different climatic conditions.