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Abstract The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude of low frequency fluctuation (ALFF), and regional homogeneity (ReHo) were calculated and extracted from 116 regions of interest in the anatomical automatic labeling atlas. Subsequently, classifiers were developed using different combinations of these selected features to distinguish between MS and NMOSD. Compared to models based on individual MRI features, support vector machine (SVM) and logistic regression (LR) models that integrated multilevel functional features such as RSFC, ALFF, and ReHo demonstrated the highest levels of performance on the testing cohorts (SVM, AUC = 0.857; LR, AUC = 0.929). Adding structural features of gray matter volume (GMV) data did not notably improve the classification performance of the machine learning models using multilevel rs-fMRI features. Notably, similar trends were observed across different brain templates, with models based on RSFC, ALFF, and ReHo yielding optimal performance. These findings suggest that utilizing multilevel fMRI features can effectively differentiate between MS and NMOSD patients.

Abstract The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models’ performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.

Although oxidative stress is closely associated with tumor invasion and metastasis, its’ exact role and mechanism in the initial stage of oral cancer remain ambiguous. Glutamine uptake mediated by alanine-serine-cysteine transporter 2 (ASCT2) participates in glutathione synthesis to resolve oxidative stress. Currently, we firstly found that ASCT2 deletion caused oxidative stress in oral mucosa and promoted oral carcinogenesis induced by 4-Nitroquinoline-1-oxide (4-NQO) using transgenic mice of ASCT2 knockout in oral epithelium. Subsequently, we identified an upregulated gene Thbs1 linked to macrophage infiltration by mRNA sequencing and immunohistochemistry. Importantly, multiplex immunohistochemistry showed M1-like tumor-associated macrophages (TAMs) were enriched in cancerous area. Mechanically, targeted ASCT2 effectively curbed glutamine uptake and caused intracellular reactive oxygen species (ROS) accumulation, which upregulated Thbs1 in oral keratinocytes and then activated p38, Akt and SAPK/JNK signaling to polarize M1-like TAMs via exosome-transferred pathway. Moreover, we demonstrated M1-like TAMs promoted malignant progression of oral squamous cell carcinoma (OSCC) both in vitro and in vivo by a DOK transformed cell line induced by 4-NQO. All these results establish that oxidative stress triggered by ASCT2 deletion promotes oral carcinogenesis through Thbs1-mediated M1 polarization, and indicate that restore redox homeostasis is a new approach to prevent malignant progression of oral potentially malignant disorders.

Abstract Background Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. Methods Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. Results We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. Conclusions The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

Abstract Background Hosts, parasites, and microbiota interact with each other, forming a complex ecosystem. Alterations to the microbial structure have been observed in various enteric parasitic infections (e.g. parasitic protists and helminths). Interestingly, some parasites are associated with healthy gut microbiota linked to the intestinal eubiosis state. So the changes in bacteria and metabolites induced by parasite infection may offer benefits to the host, including protection from other parasitesand promotion of intestinal health. The only ciliate known to inhabit the hindgut of grass carp, Balantidium ctenopharyngodoni, does not cause obvious damage to the intestinal mucosa. To date, its impact on intestinal microbiota composition remains unknown. In this study, we investigated the microbial composition in the hindgut of grass carp infected with B. ctenopharyngodoni, as well as the changes of metabolites in intestinal contents resulting from infection. Results Colonization by B. ctenopharyngodoni was associated with an increase in bacterial diversity, a higher relative abundance of Clostridium, and a lower abundance of Enterobacteriaceae. The family Aeromonadaceae and the genus Citrobacter had significantly lower relative abundance in infected fish. Additionally, grass carp infected with B. ctenopharyngodoni exhibited a significant increase in creatine content in the hindgut. This suggested that the presence of B. ctenopharyngodoni may improve intestinal health through changes in microbiota and metabolites. Conclusions We found that grass carp infected with B. ctenopharyngodoni exhibit a healthy microbiota with an increased bacterial diversity. The results suggested that B. ctenopharyngodoni reshaped the composition of hindgut microbiota similarly to other protists with low pathogenicity. The shifts in the microbiota and metabolites during the colonization and proliferation of B. ctenopharyngodoni indicated that it may provide positive effects in the hindgut of grass carp.

Abstract “”We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798–0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710–0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730–0.879), had the better predictive ability. The Hosmer–Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726–0.945) and 0.779 (95% CI 0.634–0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.

IntroductionParasitic ciliates are protozoans with a global distribution. Along with the gut microbiota, they have formed a micro-ecosystem that affects the host’s nutrition, metabolism, and immunity. The interactions and relationships among the three components of this microecosystem (protozoa, gut microbiota, and host) remain only partially understood. Xenocypris fish and the unique ciliate Balantidium polyvacuolum in its hindgut are good materials to study the interplay.MethodsIn this study, 16S rRNA gene amplicon sequencing and short-chain fatty acids (SCFAs) identification were used. Network was also constructed to understand their relationships.ResultsWe found that the gut microbiota of B. polyvacuolum-infected X. davidi and X. argentea had higher diversity, richness, and evenness than uninfected ones. B. polyvacuolum could lead to an increase of Fusobacterium and Chloroflexi in both X. davidi and X. argentea, while significantly increase the abundance of genera Romboutsia and Clostridium in X. argentea. Besides, B. polyvacuolum could significantly increase the content of total SCFAs and acetic acid in X. davidi and increase the concentrations of propionic, isobutyric and butanoic acids in X. argentea. Furthermore, correlation analyses showed that B. polyvacuolum may alter SCFAs by affecting key SCFAs-producing bacteria such as Clostridium and Cetobacterium.DiscussionThis study greatly expands our understanding of relationships among B. polyvacuolum, gut microbiota and host Xenocypris fish, which sheds new insights into the mechanism of interaction among protozoa, gut microbiota and host.

Accurate prediction of thermoacoustic instability is a prerequisite for thermoacoustic control to avoid the damage of combustion chamber, however, this problem has not been completely solved yet. This paper proposes a data-driven method based on the Elman neural network (ENN) to predict the value of acoustic pressure of combustion instability. As a comparison, a model based on support vector machine (SVM) was built. It is proved that ENN has better prediction performance with a certain predicted time horizon compared to the SVM method. What is more, the prediction model based on ENN can adapt to time-varying characteristics of the transition scenario which is characterized by amplitude modulation, multiple frequencies, and irregular bursts. ENN model still maintains enough prediction accuracy for various input training sets, indicating that ENN can fully mine the features of data and has a strong feature extraction ability in combustion oscillation prediction. Hence, it is demonstrated that ENN is a promising prediction tool for thermoacoustic instability under various combustion conditions. These findings are of great significance for the accurate prediction and control of thermoacoustic instability.

BackgroundEarly gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images.MethodsWe retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance.ResultsThe deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC.ConclusionsThese results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

BackgroundThis study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).Materials and methodsThis study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared.ResultsWe constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847–0.956) and 0.915 (95% CI: 0.850–0.981) in the internal validation and external validation cohorts, respectively.ConclusionWe first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values

Abstract Hierarchical magnetic-dielectric composites are promising functional materials with prospective applications in microwave absorption (MA) field. Herein, a three-dimension hierarchical “nanotubes on microrods,” core–shell magnetic metal–carbon composite is rationally constructed for the first time via a fast metal–organic frameworks-based ligand exchange strategy followed by a carbonization treatment with melamine. Abundant magnetic CoFe nanoparticles are embedded within one-dimensional graphitized carbon/carbon nanotubes supported on micro-scale Mo2N rod (Mo2N@CoFe@C/CNT), constructing a special multi-dimension hierarchical MA material. Ligand exchange reaction is found to determine the formation of hierarchical magnetic-dielectric composite, which is assembled by dielectric Mo2N as core and spatially dispersed CoFe nanoparticles within C/CNTs as shell. Mo2N@CoFe@C/CNT composites exhibit superior MA performance with maximum reflection loss of − 53.5 dB at 2 mm thickness and show a broad effective absorption bandwidth of 5.0 GHz. The Mo2N@CoFe@C/CNT composites hold the following advantages: (1) hierarchical core–shell structure offers plentiful of heterojunction interfaces and triggers interfacial polarization, (2) unique electronic migration/hop paths in the graphitized C/CNTs and Mo2N rod facilitate conductive loss, (3) highly dispersed magnetic CoFe nanoparticles within “tubes on rods” matrix build multi-scale magnetic coupling network and reinforce magnetic response capability, confirmed by the off-axis electron holography.

Background: As a caspase-independent type of cell death, necroptosis plays a significant role in the initiation, and progression of gastric cancer (GC). Numerous studies have confirmed that long non-coding RNAs (lncRNAs) are closely related to the prognosis of patients with GC. However, the relationship between necroptosis and lncRNAs in GC remains unclear.Methods: The molecular profiling data (RNA-sequencing and somatic mutation data) and clinical information of patients with stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted to identify the necroptosis-related lncRNAs (NRLs). Subsequently, univariate Cox regression and LASSO-Cox regression were conducted to establish a 12-NRLs signature in the training set and validate it in the testing set. Finally, the prognostic power of the 12-NRLs signature was appraised via survival analysis, nomogram, Cox regression, clinicopathological characteristics correlation analysis, and the receiver operating characteristic (ROC) curve. Furthermore, correlations between the signature risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed.Results: In the present study, a 12-NRLs signature comprising REPIN1-AS1, UBL7-AS1, LINC00460, LINC02773, CHROMR, LINC01094, FLNB-AS1, ITFG1-AS1, LASTR, PINK1-AS, LINC01638, and PVT1 was developed to improve the prognosis prediction of STAD patients. Unsupervised methods, including principal component analysis and t-distributed stochastic neighbor embedding, confirmed the capability of the present signature to separate samples with RS. Kaplan-Meier and ROC curves revealed that the signature had an acceptable predictive potency in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 12-NRLs signature were risk factors independent of various clinical parameters. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and half-inhibitory concentration differed significantly among different risk subtypes, which implied that the signature could assess the clinical efficacy of chemotherapy and immunotherapy.Conclusion: This 12-NRLs risk signature may help assess the prognosis and molecular features of patients with STAD and improve treatment modalities, thus can be further applied clinically.

BackgroundCircular RNAs (circRNAs) have been recently proposed as hub molecules in various diseases, especially in tumours. We found that circRNAs derived from ribonuclease P RNA component H1 (RPPH1) were highly expressed in colorectal cancer (CRC) samples from Gene Expression Omnibus (GEO) datasets.ObjectiveWe sought to identify new circRNAs derived from RPPH1 and investigate their regulation of the competing endogenous RNA (ceRNA) and RNA binding protein (RBP) networks of CRC immune infiltration.MethodsThe circRNA expression profiles miRNA and mRNA data were extracted from the GEO and The Cancer Genome Atlas (TCGA) datasets, respectively. The differentially expressed (DE) RNAs were identified using R software and online server tools, and the circRNA–miRNA–mRNA and circRNA–protein networks were constructed using Cytoscape. The relationship between targeted genes and immune infiltration was identified using the GEPIA2 and TIMER2 online server tools.ResultsA ceRNA network, including eight circRNAs, five miRNAs, and six mRNAs, was revealed. Moreover, a circRNA–protein network, including eight circRNAs and 49 proteins, was established. The targeted genes, ENOX1, NCAM1, SAMD4A, and ZC3H10, are closely related to CRC tumour-infiltrating macrophages.ConclusionsWe analysed the characteristics of circRNA from RPPH1 as competing for endogenous RNA binding miRNA or protein in CRC macrophage infiltration. The results point towards the development of a new diagnostic and therapeutic paradigm for CRC.

Ichthyophthirius multifiliis is a pathogenic ciliate parasite, which infects almost all freshwater teleost fish and leads to significant economic losses. The present study aimed to evaluate the acute toxicity of berberine to the free-living stages of I. multifiliis, that is, theronts and tomonts. Our results indicated that 99.30% of I. multifiliis theronts were killed by a concentration of 15 mg/L berberine during the 4 h exposure time, while berberine had no effect on protomonts. Nevertheless, berberine at a concentration of 5 mg/L could effectively reduce the release of theronts from tomonts treated for 4 h. Additionally, according to the transmission electron microscopy results, berberine at 15 mg/L could strongly change the shape of protomonts, destroy their organelles, and significantly decrease the number of ribosomes. The median lethal concentration (LC50) of berberine for goldfish at 96 h was 528.44 mg/L, which was almost 67 times the median effective concentration (EC50) of berberine for killing theronts (7.86 mg/L). The results demonstrated that berberine could be an effective and safe potential parasiticide for killing I. multifiliis.

Nd2Fe14B-type permanent magnets have been widely applied in various fields such as wind power, voice coil motors, and medical instruments. The large temperature dependence of coercivity, however, limits their further applications. We have systematically investigated the magnetic properties, thermal stabilities and coercivity mechanisms of the (Pr0.2Nd0.8)13Fe81-xB6Hfx (x=0, 0.5) nanocrystalline magnets fabricated by a spark plasma sintering (SPS) technique. The results indicate that the influence of Hf addition is significant on magnetic properties and thermal stabilities of the (PrNd)2Fe14B-type sintered magnets. It is shown that the sample with x = 0.5 at 300 K has much higher coercivity and remanent magnetization than those counterparts without Hf. The temperature coefficients of remanence (α) and coercivity (β) of the (Pr0.2Nd0.8)13Fe81-xB6Hfx magnets are improved significantly from -0.23 %/K, -0.57 %/K for the sample at x = 0 to -0.17 %/K, -0.49 %/K for the sample at x = 0.5 in the temperature range of 300-400 K. Furthermore, it is found out that the domain wall pinning mechanism is more likely responsible for enhancing the coercivity of the (Pr0.2Nd0.8)13Fe81-xB6Hfx magnets.

The Ce17Fe78-xB6Gax (x=0-1.0) ribbons were fabricated by a melt-spinning technique in order to study the mechanism of the valence variation of Ce and their magnetic properties as well as improve the thermal stability of Ce-based rare earth permanent magnets. The systematic investigations of the Ce17Fe78-xB6Gax (x=0-1.0) alloys show that the room-temperature coercivity increases significantly from 352 kA/m at x = 0 to 492 kA/m at x = 1.0. The Curie temperature (Tc) increases monotonically from 424.5 K to 433.6 K, and the temperature coefficients of remanence (α) and coercivity (β) of the ribbons are better off from -0.56 %/K, -0.75 %/K for x = 0 to -0.45 %/K, -0.65 %/K for x = 0.75 in the temperature range of 300–400 K, respectively. The Ce L3-edge X-ray absorption near edge structure (XANES) spectrums reveal that there is more Ce4+ in ribbons under total electron yield than fluorescence yield as Ce has a high affinity with oxygen. The weight of Ce3+ increases while the weight of Ce4+ decreases in Ga-added alloys. The refined grain size and a more uniform microstructure are mainly attributed to the improved magnetic properties and thermal stability with Ga doping. This paper may serve as a reference for further developing the so-called gap magnets and the effective utilization of the rare earth resources.

BACKGROUND: Curcumin has been demonstrated to have many neuroprotective properties, including improvement of cognition in humans and neurogenesis in animals, yet the mechanism of such effects remains unclear. METHODOLOGY: We assessed behavioural performance and hippocampal cell proliferation in aged rats after 6- and 12-week curcumin-fortified diets. Curcumin enhanced non-spatial and spatial memory, as well as dentate gyrate cell proliferation as compared to control diet rats. We also investigated underlying mechanistic pathways that might link curcumin treatment to increased cognition and neurogenesis via exon array analysis of cortical and hippocampal mRNA transcription. The results revealed a transcriptional network interaction of genes involved in neurotransmission, neuronal development, signal transduction, and metabolism in response to the curcumin treatment. CONCLUSIONS: The results suggest a neurogenesis- and cognition-enhancing potential of prolonged curcumin treatment in aged rats, which may be due to its diverse effects on genes related to growth and plasticity.

Prefrontal cortex plays an important role in working memory, attention regulation and behavioral inhibition. Its functions are associated with NMDA receptors. However, there is little information regarding the roles of NMDA receptor NR2B subunit in prefrontal cortical synaptic plasticity and prefrontal cortex-related working memory. Whether the up-regulation of NR2B subunit influences prefrontal cortical synaptic plasticity and working memory is not yet clear. In the present study, we measured prefrontal cortical synaptic plasticity and working memory function in NR2B overexpressing transgenic mice. In vitro electrophysiological data showed that overexpression of NR2B specifically in the forebrain region resulted in enhancement of prefrontal cortical long-term potentiation (LTP) but did not alter long-term depression (LTD). The enhanced LTP was completely abolished by a NR2B subunit selective antagonist, Ro25-6981, indicating that overexpression of NR2B subunit is responsible for enhanced LTP. In addition, NR2B transgenic mice exhibited better performance in a set of working memory paradigms including delay no-match-to-place T-maze, working memory version of water maze and odor span task. Our study provides evidence that NR2B subunit of NMDA receptor in prefrontal cortex is critical for prefrontal cortex LTP and prefrontal cortex-related working memory.

One major theory in learning and memory posits that the NR2B gene is a universal genetic factor that acts as rate-limiting molecule in controlling the optimal NMDA receptor's coincidence-detection property and subsequent learning and memory function across multiple animal species. If so, can memory function be enhanced via transgenic overexpression of NR2B in another species other than the previously reported mouse species? To examine these crucial issues, we generated transgenic rats in which NR2B is overexpressed in the cortex and hippocampus and investigated the role of NR2B gene in NMDA receptor-mediated synaptic plasticity and memory functions by combining electrophysiological technique with behavioral measurements. We found that overexpression of the NR2B subunit had no effect on CA1-LTD, but rather resulted in enhanced CA1-LTP and improved memory performances in novel object recognition test, spatial water maze, and delayed-to-nonmatch working memory test. Our slices recordings using NR2A- and NR2B-selective antagonists further demonstrate that the larger LTP in transgenic hippocampal slices was due to contribution from the increased NR2B-containing NMDARs. Therefore, our genetic experiments suggest that NR2B at CA1 synapses is not designated as a rate-limiting factor for the induction of long-term synaptic depression, but rather plays a crucial role in initiating the synaptic potentiation. Moreover, our studies provide strong evidence that the NR2B subunit represents a universal rate-limiting molecule for gating NMDA receptor's optimal coincidence-detection property and for enhancing memory function in adulthood across multiple mammalian species.

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy. [ABSTRACT FROM AUTHOR]

The type specimen of Magnolia odoratissima Y.W. Law & R.Z. Zhou (Magnoliaceae) conflicts with the protologue and belongs to the genus Parakmeria Hu & W.C. Cheng. A new name, Magnolia kwangnanensis S.G. Chen & Q.W. Zeng sp. nov., and a new type are designated to accommodate the original description. Here we provide new data and a detailed morphological description for this species. Notes are also presented on its habitat, pollen morphology, and conservation status. [ABSTRACT FROM AUTHOR]

Cerebellar dysfunction causes ataxia characterized by loss of balance and coordination. Until now, the molecular and neuronal mechanisms of several types of inherited cerebellar ataxia have not been completely clarified. Here, we report that leucine-rich G protein-coupled receptor 4 (Lgr4/Gpr48) is highly expressed in Purkinje cells (PCs) in the cerebellum. Deficiency of Lgr4 leads to an ataxia-like phenotype in mice. Histologically, no obvious morphological changes were observed in the cerebellum of Lgr4 mutant mice. However, the number of PCs was slightly but significantly reduced in Lgr4-/- mice. In addition, in vitro electrophysiological analysis showed an impaired long term depression (LTD) at parallel fiber-PC (PF-PC) synapses in Lgr4-/- mice. Consistently, immunostaining experiments showed that the level of phosphorylated cAMP-responsive element-binding protein (Creb) was significantly decreased in Lgr4-/- PCs. Furthermore, treatment with forskolin, an adenylyl cyclase agonist, rescued phospho-Creb in PCs and reversed the impairment in PF-PC LTD in Lgr4-/- cerebellar slices, indicating that Lgr4 is an upstream regulator of Creb signaling, which is underlying PF-PC LTD. Together, our findings demonstrate for first time an important role for Lgr4 in motor coordination and cerebellar synaptic plasticity and provide a potential therapeutic target for certain types of inherited cerebellar ataxia. [ABSTRACT FROM AUTHOR]