Awake ripples enhance emotional memory encoding in the human brain.

Enhanced memory for emotional experiences is hypothesized to depend on amygdala-hippocampal interactions during memory consolidation. Here we show using intracranial recordings from the human amygdala and the hippocampus during an emotional memory encoding and discrimination task increased awake ripples after encoding of emotional, compared to neutrally-valenced stimuli. Further, post-encoding ripple-locked stimulus similarity is predictive of later memory discrimination. Ripple-locked stimulus similarity appears earlier in the amygdala than in hippocampus and mutual information analysis confirms amygdala influence on hippocampal activity. Finally, the joint ripple-locked stimulus similarity in the amygdala and hippocampus is predictive of correct memory discrimination. These findings provide electrophysiological evidence that post-encoding ripples enhance memory for emotional events.

Effects of dietary supplement of ε-polylysine hydrochloride on laying performance, egg quality, serum parameters, organ index, intestinal morphology, gut microbiota and volatile fatty acids in laying hens.

BACKGROUND: ε-polylysine hydrochloride (ε-PLH) is a naturally occurring antimicrobial peptide extensively utilized in the food and medical industries. However, its impact on animal husbandry remains to be further explored. Therefore, the present study aimed to determine the effect of ε-PLH on laying hens' health and laying performance. RESULTS: Dietary supplementation with ε-PLH to the diet significantly increased average egg weight during weeks 1-8. Meanwhile, compared with the control group, supplementation with ε-PLH decreased the feed egg ratio during weeks 9-12 and egg breakage rate during weeks 9-16 ，whereas it increased eggshell strength during weeks 1-4 and 13-16 . The ε-PLH 0.05% group increased yolk percentage during weeks 5-8 and yolk color during weeks 1-4 . Furthermore, ε-PLH supplementation significantly increased the concentrations of total protein, albumin, globulin and reproductive hormones estradiol, as well as decreased interleukin-1 beta and malondialdehyde in the serum. Compared with the control group, supplementation with 0.05% ε-PLH significantly increased the relative abundance of Cyanobacteria and Gastranaerophilales and decreased the abundance of Desulfovibrio and Streptococcus in the cecum microbiota. In addition, ε-PLH 0.1% supplementation also increased acetic acid content in the cecum. CONCLUSION: Dietary supplementation with ε-PLH has a positive impact on both productive performance and egg quality in laying hens. Furthermore, ε-PLH can also relieve inflammation by promoting the immunity and reducing oxidative damage during egg production. ε-PLH has been shown to improve intestinal morphology, gut microbial diversity and intestinal health. © 2023 Society of Chemical Industry.

Characteristics of oral microbiota and oral health in the patients treated with clear aligners: a prospective study.

OBJECTIVES: In this prospective clinical study, the effect of clear aligners on periodontal health and oral hygiene was examined. As the same time, microbial changes of the aligner tray and subgingival microbiota community were investigated. METHODS: The study recruited fifteen patients, and clinical parameters were recorded at three different time points: before the initiation of aligner treatment (T0), 1 month after treatment onset (T1), and 3 months after treatment onset (T3). Plaque samples were collected from the inner surface of aligners and subgingival sulcus at each of these time points. The microbial composition of the samples was analyzed using 16S rRNA gene sequencing, and changes were evaluated based on the abundance of amplicon sequence variants (ASVs). RESULTS: Reduction in plaque index and improvement in periodontal health were observed. In aligner tray plaque samples, the relative abundance of Streptococcus increased significantly, as well as the richness and diversity of microbiota decreased substantially as the duration of treatment time. In subgingival plaque samples, alpha and beta diversity of microbiota did not change significantly. CONCLUSIONS: During the clear aligner treatment, the patients' periodontium remained in a healthy condition, and clear aligner treatment had no significant impact on the composition of subgingival microbiota. The structure of the aligner tray microbiota altered significantly at both phylum and genus levels and attracted a unique and less diverse microbiota community. CLABSINABSICAL RELEVANCE: Clear aligner treatment has no significant impact on periodontal health and subgingival microbiota composition of patients.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.

BACKGROUND: Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. METHODS: We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. RESULTS: We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. CONCLUSIONS: By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Comprehensive analysis identifies cuproptosis-related gene DLAT as a potential prognostic and immunological biomarker in pancreatic adenocarcinoma.

BACKGROUND: Cuproptosis is a regulated cell death form associated with tumor progression, clinical outcomes, and immune response. However, the role of cuproptosis in pancreatic adenocarcinoma (PAAD) remains unclear. This study aims to investigate the implications of cuproptosis-related genes (CRGs) in PAAD by integrated bioinformatic methods and clinical validation. METHODS: Gene expression data and clinical information were downloaded from UCSC Xena platform. We analyzed the expression, mutation, methylation, and correlations of CRGs in PAAD. Then, based on the expression profiles of CRGs, patients were divided into 3 groups by consensus clustering algorithm. Dihydrolipoamide acetyltransferase (DLAT) was chosen for further exploration, including prognostic analysis, co-expression analysis, functional enrichment analysis, and immune landscape analysis. The DLAT-based risk model was established by Cox and LASSO regression analysis in the training cohort, and then verified in the validation cohort. Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays were performed to examine the expression levels of DLAT in vitro and in vivo, respectively. RESULTS: Most CRGs were highly expressed in PAAD. Among these genes, increased DLAT could serve as an independent risk factor for survival. Co-expression network and functional enrichment analysis indicated that DLAT was engaged in multiple tumor-related pathways. Moreover, DLAT expression was positively correlated with diverse immunological characteristics, such as immune cell infiltration, cancer-immunity cycle, immunotherapy-predicted pathways, and inhibitory immune checkpoints. Submap analysis demonstrated that DLAT-high patients were more responsive to immunotherapeutic agents. Notably, the DLAT-based risk score model possessed high accuracy in predicting prognosis. Finally, the upregulated expression of DLAT was verified by RT-qPCR and IHC assays. CONCLUSIONS: We developed a DLAT-based model to predict patients' clinical outcomes and demonstrated that DLAT was a promising prognostic and immunological biomarker in PAAD, thereby providing a new possibility for tumor therapy.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance.

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

The effects of aligner anchorage preparation on mandibular first molars during premolar-extraction space closure with clear aligners: A finite element study.

INTRODUCTION: This study aimed to determine the effectiveness of different aligner anchorage preparations on mandibular first molars during premolar-extraction space closure with clear aligners and to assess the effects of different modes of Class II elastics on mandibular first molars. METHODS: Finite element models were constructed on the basis of cone-beam computed tomography data from an orthodontic patient. The models comprised maxilla, mandible, maxillary and mandibular teeth without first premolars, periodontal ligaments, attachments and aligners. Tooth displacement tendencies were calculated using different aligner anchorage preparations and Class II elastics on the models from the same patient. Three group sets were designed on the basis of the positions of aligner cutouts and buttons (mesiobuccal, distobuccal and lingual). Four groups were established in each of the 3 group sets. Four groups were created: (1) no elastic traction + no anchorage preparation, (2) anchorage preparation only, (3) elastic traction only, and (4) elastic traction + anchorage preparation. Different aligner anchorage preparations (0°, 1°, 2°, 3°) were applied on mandibular second premolars and molars. The Class II traction force was set to 100 g. RESULTS: With clear aligners, mandibular first molars were subject to mesial tipping, lingual tipping and intrusion. In the condition of no elastic traction, aligner anchorage preparation resulted in distal tipping, buccal tipping, and extrusion effect on mandibular first molars. Aligner anchorage preparation was more effective in the distal and lingual cutout groups than in the mesial cutout group. In the condition of Class II elastic traction, the bodily movement of mandibular first molars was achieved with a 3° anchorage preparation for the mesial cutout group and a 1.7° anchorage preparation for distal and lingual cutout groups. Absolute maximal anchorage was achieved with a 2° anchorage preparation for distal and lingual cutout groups. CONCLUSIONS: Clear aligner therapy caused mesial tipping, lingual tipping and intrusion of mandibular first molars during premolar-extraction space closure. Aligner anchorage preparation effectively prevented mesial and lingual tipping of mandibular molars. Distal and lingual cutout modes were more effective than mesial cutout modes in aligner anchorage preparation. For each aligner stage (0.25 mm), 1.7° aligner anchorage preparation and Class II elastics with distal or lingual cutouts led to the bodily movement of mandibular first molars, whereas 2° anchorage preparation reached absolute maximal anchorage.

Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses.

The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy. However, most studies on the pathogenic and drug-resistant mechanisms of ovarian cancer have focused on the expression and signaling pathways of individual transcription factors. To improve the prognosis and treatment of patients with ovarian cancer, multiple transcription factors should be evaluated simultaneously to determine the effects of their protein activity on drug therapies. In this study, the transcription factor activity of ovarian cancer samples was inferred from virtual inference of protein activity by enriched regulon algorithm using mRNA expression data. Patients were clustered according to their transcription factor protein activities to investigate the association of transcription factor activities of different subtypes with prognosis and drug sensitivity for filtering subtype-specific drugs. Meanwhile, master regulator analysis was utilized to identify master regulators of differential protein activity between clustering subtypes, thereby identifying transcription factors associated with prognosis and assessing their potential as therapeutic targets. Master regulator risk scores were then constructed for guiding patients' clinical treatment, providing new insights into the treatment of ovarian cancer at the level of transcriptional regulation.

A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer.

As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.

EGCG Alleviates Obesity-Induced Myocardial Fibrosis in Rats by Enhancing Expression of SCN5A.

Object: Obesity is an increase in body weight beyond the limitation of skeletal and physical requirement, as the result of an excessive accumulation of fat in the body. Obesity could increase the risk of myocardial fibrosis. (-)-Epigallocatechin-3-gallate (EGCG) is the most abundant substance in green tea and has been reported to have multiple pharmacological activities. However, there is not enough evidence to show that EGCG has a therapeutic effect on obesity-induced myocardial fibrosis. This study aims to investigate whether EGCG is a potential drug for obesity-induced myocardial fibrosis. Methods: Obesity-induced myocardial fibrosis rat model was established by HFD feeding for 36 weeks. EGCG was intragastrically administered at 160 mg/kg/d for the last 4 weeks. The pathological changes of myocardial fibrosis were evaluated by tissue pathological staining and collagen quantification. Furthermore, total RNA was extracted from the heart for RNA-seq to identify the changes in the transcript profile, and the relevant hub genes were verified by quantitative real-time PCR and western blot. Results: EGCG significantly relieved HFD diet-induced obesity and alleviated the pathology of myocardial fibrosis. Biochemical analysis showed that EGCG could relieve the burden of lipid metabolism and injury to the myocardium and transcript profile analysis showed that EGCG could alleviate obesity-induced myocardial fibrosis by increasing the level of Scn5a in the heart. Furthermore, quantitative real-time PCR and western blot analysis for SCN5A also confirmed this finding. Conclusion: Taken together, these results suggest that EGCG could protect against the obesity-induced myocardial fibrosis. EGCG plays an anti-myocardial fibrosis role by regulating the expression of SCN5A in the heart.

Single-Channel EEG Based Arousal Level Estimation Using Multitaper Spectrum Estimation at Low-Power Wearable Devices.

This paper proposes a novel lightweight method using the multitaper power spectrum to estimate arousal levels at wearable devices. We show that the spectral slope (1/f) of the electrophysiological power spectrum reflects the scale-free neural activity. To evaluate the proposed feature's performance, we used scalp EEG recorded during anesthesia and sleep with technician-scored Hypnogram annotations. It is shown that the proposed methodology discriminates wakefulness from reduced arousal solely based on the neurophysiological brain state with more than 80% accuracy. Therefore, our findings describe a common electrophysiological marker that tracks reduced arousal states, which can be applied to different applications (e.g., emotion detection, driver drowsiness). Evaluation on hardware shows that the proposed methodology can be implemented for devices with a minimum RAM of 512 KB with 55 mJ average energy consumption.

On-line SERS detection of bilirubin based on the optofluidic in-fiber integrated GO/Ag NPs for rapid diagnosis of jaundice.

In this paper, we propose a self-assembled graphene oxide (GO)/Ag NPs SERS Raman sensor based on a novel type of optofluidic MHF as a point-of-care testing (POCT) device. This device is used to diagnose jaundice and its related diseases through on-line detection of free bilirubin content in human serum. This optofluidic Raman sensor is composed of a microstructured hollow fiber (MHF) with a microstructured channel and a suspended core, which allows the sample solution to flow in the channel while interacting with the strong evanescent field on the suspended core. Here, the suspended core was modified by a GO/Ag NPs SERS substrate. When the sample flows through the channel, and interacts with the strong evanescent field generated by the suspended core, the on-line SERS signal is generated and can be coupled back to the suspended core to be detected. In addition, both the electrostatic interaction and interference between GO/Ag NPs with the target enriched bilirubin. The results show that the detection concentration range of bilirubin aqueous, bilirubin in albumin and bilirubin in human blood are all in the range of 2 µM-100 µM, and all have a good linear response. The limit of detection reaches the order of 10-6 M. This rapid, sensitive and label-free SERS Raman sensor of free bilirubin in blood can detect excessive levels of bilirubin in the actual blood environment of the human body, providing a broad prospect for clinically accurate diagnosis of jaundice and related diseases.

Coupling between slow waves and sharp-wave ripples engages distributed neural activity during sleep in humans.

Hippocampal-dependent memory consolidation during sleep is hypothesized to depend on the synchronization of distributed neuronal ensembles, organized by the hippocampal sharp-wave ripples (SWRs, 80 to 150 Hz), subcortical/cortical slow-wave activity (SWA, 0.5 to 4 Hz), and sleep spindles (SP, 7 to 15 Hz). However, the precise role of these interactions in synchronizing subcortical/cortical neuronal activity is unclear. Here, we leverage intracranial electrophysiological recordings from the human hippocampus, amygdala, and temporal and frontal cortices to examine activity modulation and cross-regional coordination during SWRs. Hippocampal SWRs are associated with widespread modulation of high-frequency activity (HFA, 70 to 200 Hz), a measure of local neuronal activation. This peri-SWR HFA modulation is predicted by the coupling between hippocampal SWRs and local subcortical/cortical SWA or SP. Finally, local cortical SWA phase offsets and SWR amplitudes predicted functional connectivity between the frontal and temporal cortex during individual SWRs. These findings suggest a selection mechanism wherein hippocampal SWR and cortical slow-wave synchronization governs the transient engagement of distributed neuronal populations supporting hippocampal-dependent memory consolidation.

In-fiber optofluidic online SERS detection of trace uremia toxin.

In this Letter, we propose a microstructured in-fiber optofluidic surface-enhanced Raman spectroscopy (SERS) sensor for the initial inspection of uremia through the detection of unlabeled urea and creatinine. As a natural microfluidic device, microstructured hollow fiber has a special structure inside. Through chemical bonds, the SERS substrate can be modified and grown on the surface of the suspended core. Here, the silver nanoparticles (Ag NPs) are embedded on the poly diallyl dimethyl ammonium chloride-modified graphene oxide sheet to achieve the self-assembled SERS substrate. The reduced distance between Ag NPs can increase the strong hot spots that generate enhanced Raman signals. Therefore, it can effectively detect the Raman signal of unlabeled trace uremic toxin analytes (urea, creatinine) inside the optical fiber. The results show that under simulated biophysical conditions, the limit detection (LOD) for urea is 10-4M and the linearity is good, especially at the clinical conventional concentration range (2.5-6.5×10-3M). In addition, the online Raman detection of creatinine aqueous solution LOD is 10-6M, which also has good linearity. Significantly, this Letter provides a microstructured optofluidic in-fiber Raman sensor for the preliminary detection of uremia, which will have good development prospects in the field of clinical biomedicine.

Scutellarin Reduce the Homocysteine Level and Alleviate Liver Injury in Type 2 Diabetes Model.

Scutellarin (SCU) is an active ingredient extracted from Erigeron breviscapus (Vaniot) Hand.-Mazz. Its main physiological functions are anti-inflammatory and antioxidant. In this study, we established a STZ-induced model of type 2 diabetes (T2DM) and a homocysteine (Hcy)-induced apoptosis model of LO2 to investigate whether SCU can alleviate liver damage by regulating Hcy in type 2 diabetes. Biochemical analysis indicated that SCU could improve the lipid metabolism disorder and liver function in diabetic rats by downregulating the levels of triglycerides (TG), cholesterol (CHO), low-density lipoprotein (LDL), alanine transaminase (ALT) and aspartate transaminase (AST), and by upregulating the level of high-density lipoprotein (HDL). Interestingly, SCU also could down-regulate the levels of Hcy and insulin and enhance the ability of type 2 diabetic rats to regulate blood glucose. Mechanistically, our results indicated that SCU may control the level of Hcy through regulating the levels of ß-Cystathionase (CBS), γ-Cystathionase (CSE) and 5,10-methylenetetrahydrofolate (MTHFR) in liver tissue, and up-regulate folic acid, VitB6 and VitB12 levels in serum. Furthermore, SCU inhibits apoptosis in the liver of T2DM rats and in cultured LO2 cells treated with Hcy. Together, our findings suggest that SCU may alleviate the liver injury thorough downregulating the level of Hcy in T2DM rats.

A novel optimal sensitivity design scheme for yarn tension sensor using surface acoustic wave device.

In this paper, we propose a novel optimal sensitivity design scheme for the yarn tension sensor using surface acoustic wave (SAW) device. In order to obtain the best sensitivity, the regression model between the size of the SAW yarn tension sensor substrate and the sensitivity of the SAW yarn tension sensor was established using the least square method. The model was validated too. Through analyzing the correspondence between the regression function monotonicity and its partial derivative sign, the effect of the SAW yarn tension sensor substrate size on the sensitivity of the SAW yarn tension sensor was investigated. Based on the regression model, a linear programming model was established to gain the optimal sensitivity of the SAW yarn tension sensor. The linear programming result shows that the maximum sensitivity will be achieved when the SAW yarn tension sensor substrate length is equal to 15 mm and its width is equal to 3mm within a fixed interval of the substrate size. An experiment of SAW yarn tension sensor about 15 mm long and 3mm wide was presented. Experimental results show that the maximum sensitivity 1982.39 Hz/g was accomplished, which confirms that the optimal sensitivity design scheme is useful and effective.