Effects of non-pharmacological interventions for adults with subjective cognitive decline: a network meta-analysis and component network meta-analysis.

BACKGROUND: Non-pharmacological interventions have a myriad of available intervention options and contain multiple components. Whether specific components of non-pharmacological interventions or combinations are superior to others remains unclear. The main aim of this study is to compare the effects of different combinations of non-pharmacological interventions and their specific components on health-related outcomes in adults with subjective cognitive decline. METHODS: PubMed, Embase, Cochrane, CINAHL, PsycINFO, CENTRAL, Web of Science, and China's two largest databases, CNKI and Wanfang, were searched from inception to 22nd, January 2023. Randomized controlled trials using non-pharmacological interventions and reporting health outcomes in adults with subjective cognitive decline were included. Two independent reviewers screened studies, extracted data, and assessed risk of bias. Component network meta-analysis was conducted employing an additive component model for network meta-analysis. This study followed the PRISMA reporting guideline and the PRISMA checklist is presented in Additional file 2. RESULTS: A total of 39 trials with 2959 patients were included (range of mean ages, 58.79-77.41 years). Resistance exercise might be the optimal intervention for reducing memory complaints in adults with subjective cognitive decline; the surface under the cumulative ranking p score was 0.888, followed by balance exercise (p = 0.859), aerobic exercise (p = 0.832), and cognitive interventions (p = 0.618). Music therapy, cognitive training, transcranial direct current stimulation, mindfulness therapy, and balance exercises might be the most effective intervention components for improving global cognitive function (iSMD, 0.83; 95% CI, 0.36 to 1.29), language (iSMD, 0.31; 95% CI, 0.24 to 0.38), ability to perform activities of daily living (iSMD, 0.55; 95% CI, 0.21 to 0.89), physical health (iSMD, 3.29; 95% CI, 2.57 to 4.00), and anxiety relief (iSMD, 0.71; 95% CI, 0.26 to 1.16), respectively. CONCLUSIONS: The form of physical activity performed appears to be more beneficial than cognitive interventions in reducing subjective memory complaints for adults with subjective cognitive decline, and this difference was reflected in resistance, aerobic, and balance exercises. Randomized clinical trials with high-quality and large-scale are warranted to validate the findings. TRIAL REGISTRATION: PROSPERO registry number. CRD42022355363.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

Elucidation of triacylglycerol catabolism in Yarrowia lipolytica: how cells balance acetyl-CoA and excess reducing equivalents.

Yarrowia lipolytica is an industrial yeast that can convert waste oil to value-added products. However, it is unclear how this yeast metabolizes lipid feedstocks, specifically triacylglycerol (TAG) substrates. This study used 13C-metabolic flux analysis (13C-MFA), genome-scale modeling, and transcriptomics analyses to investigate Y. lipolytica W29 growth with oleic acid, glycerol, and glucose. Transcriptomics data was used to guide 13C-MFA model construction and to validate the 13C-MFA results. The 13C-MFA data was then used to constrain a genome-scale model (GSM), which predicted Y. lipolytica fluxes, cofactor balance, and theoretical yields of terpene products. The three data sources provided new insights into cellular regulation during catabolism of glycerol and fatty acid components of TAG substrates, and how their consumption routes differ from glucose catabolism. We found that (1) over 80% of acetyl-CoA from oleic acid is processed through the glyoxylate shunt, a pathway that generates less CO2 compared to the TCA cycle, (2) the carnitine shuttle is a key regulator of the cytosolic acetyl-CoA pool in oleic acid and glycerol cultures, (3) the oxidative pentose phosphate pathway and mannitol cycle are key routes for NADPH generation, (4) the mannitol cycle and alternative oxidase activity help balance excess NADH generated from ß-oxidation of oleic acid, and (5) asymmetrical gene expressions and GSM simulations of enzyme usage suggest an increased metabolic burden for oleic acid catabolism.

IAIH-PG consensus for histological criteria of AIH: Multicentre validation with focus on chronic liver diseases in China.

BACKGROUND AND AIMS: The International AIH Pathology Group (IAIH-PG) put forward the new histological criteria of autoimmune hepatitis (AIH) in 2022, which have not undergone adequate verification. In this study, we verified the applicability of the new histological criteria in the population of Chinese patients with chronic liver disease, comparing it with the simplified criteria. METHODS: The gold standard for diagnosis in all patients was based on histological findings, combined with clinical manifestations and laboratory tests and determined after a follow-up period of at least 3 years. A total of 640 patients with various chronic liver diseases from multiple centres underwent scoring using the new histological criteria and the simplified criteria, comparing their diagnostic performance. RESULTS: In this study, the new histological criteria showed a sensitivity of 73.6% and 100% for likely and possible AIH, with specificities of 100% and 69.0% respectively. The coincidence rates of possible AIH for the new histological criteria, simplified histological criteria and simplified score were 81.7%, 72.8% and 69.7% respectively. For likely AIH, the rates were 89.2%, 75.9% and 65.6% respectively. Based on the new histological criteria, all patients with AIH were correctly diagnosed. Specifically, 73.6% were diagnosed with likely AIH and 26.4% were possible AIH. Additionally, the simplified histological criteria achieved a diagnosis rate of 98.6% for AIH, while the simplified score could only diagnose 53.8% of AIH. CONCLUSIONS: Compared with the simplified score and simplified histological criteria, the sensitivity and specificity of the new histological criteria for AIH were significantly improved. The results indicate that the new histological criteria exhibit high sensitivity and specificity for diagnosing AIH in China.

Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Development and validation of a noninvasive prediction model of autoimmune hepatitis in patients with liver diseases.

BACKGROUND AND AIMS: There is no golden standard for the diagnosis of autoimmune hepatitis which still dependent on liver biopsy currently. So, we developed a noninvasive prediction model to help optimize the diagnosis of autoimmune hepatitis. METHODS: From January 2017 to December 2019, 1739 patients who had undergone liver biopsy were seen in the second hospital of Nanjing, of which 128 were here for consultation. Clinical, laboratory, and histologic data were obtained retrospectively. Multivariable logistic regression analysis was employed to create a nomogram model that predicting the risk of autoimmune hepatitis. Internal and external validation was both performed to evaluate the model. RESULTS: A total of 1288 patients with liver biopsy were enrolled (1184 from the second hospital of Nanjing, the remaining 104 from other centers). After the univariate and multivariate logistic regression analysis, nine variables including ALT, IgG, ALP/AST, ALB, ANA, AMA, HBsAg, age, and gender were selected to establish the noninvasive prediction model. The nomogram model exhibits good prediction in diagnosing autoimmune hepatitis with AUROC of 0.967 (95% CI: 0.776-0.891) in internal validation and 0.835 (95% CI: 0.752-0.919) in external validation. CONCLUSIONS: ALT, IgG, ALP/AST, ALB, ANA, AMA, HBsAg, age, and gender are predictive factors for the diagnosis of autoimmune hepatitis in patients with unexplained liver diseases. The predictive nomogram model built by the nine predictors achieved good prediction for diagnosing autoimmune hepatitis.

Breaking the Hoff/Le Bel rule by an electron-compensation strategy: the global energy minimum of NGa4S4.

In tetracoordinate chemistry, there is an attractive scientific problem of how to make the planar configuration more stable than the tetrahedral configuration. For tetracoordinate nitrogen, the abundant studies indicate that the planar tetracoordinate nitrogen (ptN) is far less stable than the tetrahedral tetracoordinate nitrogen (ttN). Herein, we introduced four S atoms to the unstable ptN-NGa4+ and stable ttN-NGa4+ by following an electron-compensation strategy. Surprisingly, ptN-NGa4S4+ is more stable than ttN-NGa4S4+. Thermodynamically, ptN-NGa4S4+ is the global energy minimum, which is 46.7 kcal mol-1 lower in energy than ttN-NGa4S4+. Dynamically, the BOMD simulations indicated that ptN-NGa4S4+ has excellent dynamic stability at 4, 298, 500 and 1000 K, but the ttN-NGa4S4+ is isomerized at 1000 K. Electronically, the HOMO-LUMO gap of ptN-NGa4S4+ (6.91 eV) is much wider than that of ttN-NGa4S4+ (5.25 eV). Moreover, AdNDP analyses showed that the eight 2c-2e Ga-S σ-bonds eliminated the 4s2 lone pair/4s2 lone pair repulsion between the four Ga atoms and provided a strong spatial protection for ptN-NGa4S4+; and that the four 3c-2e Ga-S-Ga π back-bonds could compensate electrons for Ga, weakening the electron-deficiency of Ga. Simultaneously, the double 6σ/2π aromaticity further enhanced the stability of ptN-NGa4S4+. Thus, as the dynamically stable global energy minimum displaying double aromaticity, ptN-NGa4S4+ will be more promising than ttN-NGa4S4+ in gas phase generation.

CircNSD1 promotes cardiac fibrosis through targeting the miR-429-3p/SULF1/Wnt/ß-catenin signaling pathway.

Cardiac fibrosis is a detrimental pathological process, which constitutes the key factor for adverse cardiac structural remodeling leading to heart failure and other critical conditions. Circular RNAs (circRNAs) have emerged as important regulators of various cardiovascular diseases. It is known that several circRNAs regulate gene expression and pathological processes by binding miRNAs. In this study we investigated whether a novel circRNA, named circNSD1, and miR-429-3p formed an axis that controls cardiac fibrosis. We established a mouse model of myocardial infarction (MI) for in vivo studies and a cellular model of cardiac fibrogenesis in primary cultured mouse cardiac fibroblasts treated with TGF-ß1. We showed that miR-429-3p was markedly downregulated in the cardiac fibrosis models. Through gain- and loss-of-function studies we confirmed miR-429-3p as a negative regulator of cardiac fibrosis. In searching for the upstream regulator of miR-429-3p, we identified circNSD1 that we subsequently demonstrated as an endogenous sponge of miR-429-3p. In MI mice, knockdown of circNSD1 alleviated cardiac fibrosis. Moreover, silence of human circNSD1 suppressed the proliferation and collagen production in human cardiac fibroblasts in vitro. We revealed that circNSD1 directly bound miR-429-3p, thereby upregulating SULF1 expression and activating the Wnt/ß-catenin pathway. Collectively, circNSD1 may be a novel target for the treatment of cardiac fibrosis and associated cardiac disease.

The impact of visit-to-visit heart rate variability on all-cause mortality in atrial fibrillation.

OBJECTIVE: We aimed to investigate the association between visit-to-visit heart rate variability (VVHRV) and all-cause mortality in patients diagnosed with atrial fibrillation (AF). Previous studies have shown a positive correlation between VVHRV and several adverse outcomes. However, the relationship between VVHRV and the prognosis of AF remains uncertain. METHODS: In our study, we aimed to examine the relationship between VVHRV and mortality rates among 3983 participants with AF, who were part of the AFFIRM study (Atrial Fibrillation Follow-Up Investigation of Rhythm Management). We used the standard deviation of heart rate (HRSD) to measure VVHRV and divided the patients into four groups based on quartiles of HRSD (1st, <5.69; 2nd, 5.69-8.00; 3rd, 8.01-11.01; and 4th, ≥11.02). Our primary endpoint was all-cause death, and we estimated the hazard ratios for mortality using the Cox proportional hazard regressions. RESULTS: Our analysis included 3983 participants from the AFFIRM study and followed for an average of 3.5 years. During this period, 621 participants died from all causes. In multiple-adjustment models, we found that the lowest and highest quartiles of HRSD independently predicted an increased risk of all-cause mortality compared to the other two quartiles, presenting a U-shaped relationship (1st vs 2nd, hazard ratio = 2.28, 95% CI = 1.63-3.20, p < .01; 1st vs. 3rd, hazard ratio = 2.23, 95% CI = 1.60-3.11, p < .01; 4th vs. 2nd, hazard ratio = 1.82, 95% CI = 1.26-2.61, p < .01; and 4th vs. 3rd, hazard ratio = 1.78, 95% CI = 1.25-2.52, p < .01). CONCLUSION: In patients with AF, we found that both lower VVHRV and higher VVHRV increased the risk of all-cause mortality, indicating a U-shaped curve relationship.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Triangular pulley traction facilitates endoscopic full-thickness resection of exogenous gastric tumor in the fornix of the stomach.

A 65-year-old woman was diagnosed with an exogenous submucosal tumor located in the fornix of the stomach, on the basis of the endoscopic ultrasound and enhanced CT findings. She refused surgery and referred for EFTR. It is difficult to perform EFTR at the gastric fornix and suture the large surgical defect. Therefore, we created technique of triangular pulley traction combined with pre-closure.

Bioinformatic and functional analysis of OsDHN2 under cadmium stress.

As a toxic heavy metal, cadmium (Cd) is one of the principal pollutants influencing rice productivity and food security. Despite several studies, the underlying mechanism of Cd response in plants remains largely unclear. Dehydrins are part of the late embryogenesis abundant (LEA) family which protect plants against abiotic stresses. In this study, a Cd-responsive LEA gene, OsDHN2, was functionally characterized. The chromosome localization results indicated that OsDHN2 was located on chromosome 2 of rice. Meanwhile, cis-acting elements, such as MBS (MYB binding site involved in drought-inducibility), ARE (anaerobic induction), and ABRE (abscisic acid), were present in the OsDHN2 promoter region. Expression pattern analysis also showed that OsDHN2 expression was induced in both roots and shoots under Cd stress. Overexpression of OsDHN2 improved Cd tolerance and reduced Cd concentration in yeast. Moreover, increased expression levels of SOD1, CTA1, GSH1, or CTT1 were found in transgenic yeast under Cd stress, suggesting the increased antioxidant enzymatic activities. These results suggested that OsDHN2 is a Cd-responsive gene that has the potential to improve resistance to Cd in rice.

Long intergenic non-coding RNA DIO3OS promotes osteosarcoma metastasis via activation of the TGF-ß signaling pathway: a potential diagnostic and immunotherapeutic target for osteosarcoma.

BACKGROUND: The aim of this study was to determine the underlying potential mechanisms and function of DIO3OS, a lincRNA in osteosarcoma and clarify that DIO3OS can be used as a potential diagnostic biomarker and immunotherapeutic target. METHODS: The expression matrix data and clinical information were obtained from XENA platform of UCSC and GEO database as the test cohorts. The external validation cohort was collected from our hospital. Bioinformatics analysis was used to annotate the biological function of DIO3OS. Immune infiltration and immune checkpoint analysis were applied to evaluate whether DIO3OS can be used as an immunotherapeutic target. ROC curves and AUC were established to assess the diagnostic value of DIO3OS for differentiating patients from other subtypes sarcoma. The expression analysis was detected by qRT-PCR, western blot, and immunohistochemical. Wound healing assay and Transwell assay were applied to determine the migration and invasion function of DIO3OS in osteosarcoma cell lines. The tail vein injection osteosarcoma cells metastases model was used in this research. RESULTS: High expression of DIO3OS was identified as a risk lincRNA for predicting overall survival of osteosarcoma in test cohort. The outcomes of experiments in vitro and in vivo showed that low expression of DIO3OS limited osteosarcoma tumor metastasis with inhibiting TGF-ß signaling pathway. Immune checkpoint genes (CD200 and TNFRSF25) expressions were inhibited in the low DIO3OS expression group. The DIO3OS expression can be applied to reliably distinguish osteosarcoma from lipomatous neoplasms, myomatous neoplasms, nerve sheath tumors, and synovial-like neoplasms. This result was further validated in the validation cohort. CONCLUSIONS: In conclusion, our outcomes indicated that DIO3OS is a potential diagnostic and prognostic biomarker of osteosarcoma, emphasizing its potential as a target of immunotherapy to improve the treatment of osteosarcoma through TGF-ß signaling pathway. TRIAL REGISTRATION NUMBER: The present retrospectively study was approved by the Ethics Committee of The Second Affiliated Hospital of Nanchang University [Review (2020) No. (115)].

Visible-Light-Driven Organocatalytic Alkoxylation of Benzylic C-H Bonds.

A variety of strategies for direct alkoxylation of the benzyl C-H bond have been developed toward the construction of benzyl ethers. The light-induced benzyl C-H bond alkoxylation provides an alternative strategy for the synthesis of these important intermediates. The photocatalyzed alkoxylation of the benzyl C-H bond has dominated by metal-catalyzed methods. Herein, we reported a light-driven organocatalytic approach for alkoxylation of the benzyl C-H bond by the use of 9,10-dibromoanthracene as a photocatalyst and employing N-fluorobenzenesulfonimide as an oxidant. This reaction proceeds at room temperature and is capable of converting a variety of alkyl biphenyl and coupling partners, including a variety of alcohol and carboxylic acid, as well as peroxide, to the desired products under 400 nm light irradiation.

An updated phylogeny, biogeography, and PhyloCode-based classification of Cornaceae based on three sets of genomic data.

PREMISE: A major goal of systematic biology is to uncover the evolutionary history of organisms and translate that knowledge into stable classification systems. Here, we integrate three sets of genome-wide data to resolve phylogenetic relationships in Cornaceae (containing only Cornus s.l.), reconstruct the biogeographic history of the clade, and provide a revised classification using the PhyloCode to stabilize names for this taxonomically controversial group. METHODS: We conducted phylogenetic analyses using 312 single-copy nuclear genes and 70 plastid genes from Angiosperms353 Hyb-Seq, plus numerous loci from RAD-Seq. We integrated fossils using morphological data and produced a dated phylogeny for biogeographical analysis. RESULTS: A well-resolved, strongly supported, comprehensive phylogeny was obtained. Biogeographic analyses support an origin and rapid diversification of Cornus into four morphologically distinct major clades in the Northern Hemisphere (with an eastern Asian ancestor) during the late Cretaceous. Dispersal into Africa from eastern Asia likely occurred along the Tethys Seaway during the Paleogene, whereas dispersal into South America likely occurred during the Neogene. Diversification within the northern hemisphere likely involved repeated independent colonization of new areas during the Paleogene and Neogene along the Bering Land Bridge, the North Atlantic Land Bridge, and the Tethys Seaway. Thirteen strongly supported clades were named following rules of the PhyloCode. CONCLUSIONS: Our study provides an example of integrating genomic and morphological data to produce a robust, explicit species phylogeny that includes fossil taxa, which we translate into an updated classification scheme using the PhyloCode to stabilize names.

Highly asymmetric aldol reaction of isatins and ketones catalyzed by chiral bifunctional primary-amine organocatalyst on water.

Herein, we have reported an environmentally friendly asymmetric aldol reaction between isatins and ketones catalyzed by double-hydrogen-bonded primary amine organocatalysts on water under mild conditions. Enantioenriched 3-hydroxy-2-oxindoles were obtained in high yields (up to 99%) and excellent stereoselectivities (up to 99 : 1 dr and 99% ee) under optimal conditions. Furthermore, the model reaction involving isatin and cyclohexanone was successfully scaled to 10 mmol with no reduction in yield or stereoselectivity. In addition, the catalyst was recovered via simple filtration and was subsequently reused on water, which highlights its good application potential.

Selection of reference genes for RT-qPCR analysis in developing chicken embryonic ovary.

BACKGROUND: Normalization of the expression profiling of target genes, in a tissue-specific manner and under different experimental conditions, requires stably expressed gene(s) to be used as internal reference(s). However, to study the molecular regulation of oocyte meiosis initiation during ovary development in chicken embryos, stable reference gene(s) still need to be compared and confirmed. METHODS AND RESULTS: Six candidate genes previously used as internal references for the chicken embryo (Actb, Cvh, Dazl, Eef1a, Gapdh and Rpl15) were chosen, and their expression profiles in left ovaries dissected at five chicken embryonic days (E12.5, E15.5, E17.5, E18.5 and E20.5) were evaluated, respectively. Separately, GeNorm, NormFinder, BestKeeper and Comparative ΔCt methods were used to assess the stability of candidate reference genes, and all results were combined to give the final rank by RefFinder. All methods identified that Eef1a and Rpl15 were the two most stable internal reference genes, whereas Cvh is the most unstable one. Moreover, expression levels of three marker genes for chicken oocyte meiosis entry (Stra8, Scp3 and Dmc1) were normalized, based on Eef1a, Rpl15, or their combinations, respectively. CONCLUSION: Our findings provide the most suitable internal reference genes (Eef1a and Rpl15), to investigate further molecular regulation of ovary development and oocyte meiosis initiation in chicken embryos.

18-valence-electron rule lighted planar tetracoordinate carbon and nitrogen: the global energy minima of CAl4Zn and NAl4Zn.

The exploration of planar hypercoordinate carbon (phC) is challenging and significant. It is often puzzling to chemists whether the designed phC species should satisfy the 18-valence-electron rule, an authoritative rule in the phC field. In this study, we introduced a zinc atom into the extremely unstable 16-valence-electron planar tetracoordinate carbon (ptC) species CAl4 and its isoelectronic structure NAl4+ with a planar tetracoordinate nitrogen (ptN), and designed the 18-valence-electron CAl4Zn and NAl4Zn+ possessing a ptC and ptN, respectively. The thermodynamic results indicate that the ptC/N species CAl4Zn and NAl4Zn+ are the global energy minima, and also showed that the 18-valence-electron rule is more appropriate in designing ptC/N species having the CAl4 and NAl4+ skeletons, compared with the 16-valence-electron rule. Simultaneously, the BOMD simulations found that CAl4Zn is dynamically stable. Although NAl4Zn+ was isomerized at 298 and 500 K, it is dynamically viable. The excellent stability may be explained by the perfect electronic structure. First, the HOMO-LUMO gaps became much wider after the introduction of the Zn atom. Second, AdNDP analysis indicated that the introduction of the Zn atom promoted the formation of peripheral Al-Al and Al-Zn covalent bonds, providing a stable and comfortable bonding environment for ptC/N. In addition, the σ and π double aromaticity further stabilized the ptC/N species. Hence, as dynamic global energy minima display σ and π double aromaticity, the ptC/N species CAl4Zn and NAl4Zn+ are promising in gas phase generation.

SHED-exos promote saliva secretion by suppressing p-ERK1/2-mediated apoptosis in glandular cells.

OBJECTIVES: Confirm that stem cells from human exfoliated deciduous teeth-derived exosomes (SHED-exos) can limit inflammation-triggered epithelial cell apoptosis and explore the molecular mechanism. METHODS: SHED-exos were injected into the submandibular glands (SMGs) of non-obese diabetic (NOD) mice, an animal model of Sjögren's syndrome (SS). Cell death was evaluated by western blotting and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labelling staining. RESULTS: SHED-exos treatment promoted the saliva flow rates of NOD mice, accompanied by decreased cleaved caspase-3 levels and apoptotic cell numbers in SMGs. SHED-exos inhibited autophagy, pyroptosis, NETosis, ferroptosis, necroptosis and oxeiptosis marker expression in SS-damaged glands. Mechanistically, Kyoto Encyclopedia of Genes and Genomes analysis of exosomal miRNAs suggested that the rat sarcoma virus (RAS)/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) pathway might play an important role. In vivo, the expression of Kirsten RAS, Harvey RAS, MEK1/2 and p-ERK1/2 was upregulated in SMGs, and this change was blocked by SHED-exos treatment. In vitro, SHED-exos suppressed p-ERK1/2 activation and increased cleaved caspase-3 and apoptotic cell numbers, which were induced by IFN-γ. CONCLUSION: SHED-exos suppress epithelial cell death, which is responsible for promoting salivary secretion. SHED-exos inhibited inflammation-triggered epithelial cell apoptosis by suppressing p-ERK1/2 activation, which is involved in these effects.