L-methionine and the L-type Ca2+ channel agonist BAY K 8644 collaboratively contribute to the reduction of depressive-like behavior in mice.

The L-type Ca2+ channel (LTCC, also known as Cav1,2) is involved in the regulation of key neuronal functions, such as dendritic information integration, cell survival, and neuronal gene expression. Clinical studies have shown an association between L-type calcium channels and the onset of depression, although the precise mechanisms remain unclear. The development of depression results from a combination of environmental and genetic factors. DNA methylation, a significant epigenetic modification, plays a regulatory role in the pathogenesis of psychiatric disorders such as posttraumatic stress disorder (PTSD), depression, and autism. In our study, we observed reduced Dnmt3a expression levels in the hippocampal DG region of mice with LPS-induced depression compared to control mice. The antidepressant Venlafaxine was able to increase Dnmt3a expression levels. Conversely, Bay K 8644, an agonist of the L-type Ca2+ channel, partially ameliorated depression-like behaviors but did not elevate Dnmt3a expression levels. Furthermore, when we manipulated DNA methylation levels during Bay K 8644 intervention in depression-like models, we found that enhancing the expression of Dnmt3a could improve LPS-induced depression/anxiety-like behaviors, while inhibiting DNA methylation exacerbated anxiety-like behaviors, the combined use of BAY K 8644 and L-methionine can better improve depressive-like behavior. These findings indicate that DNA methylation plays a role in the regulation of depression-like behaviors by the L-type Ca2+ channel, and further research is needed to elucidate the interactions between DNA methylation and L-type Ca2+ channels.

Directly Characterizing the Capture Radius of Tethered Double-Stranded DNA by Single-Molecule Nanopipette Manipulation.

The tethered molecule exhibits characteristics of both free and fixed states, with the electrodynamics involved in its diffusion, electrophoresis, and stretching processes still not fully understood. We developed a Single-Molecule Manipulation, Identification, and Length Examination (SMILE) system by integrating piezoelectric devices with nanopipettes. This system enabled successful capture and stretching of tethered double-stranded DNA within the nanopore. Our research unveiled distinct capture (rcapture) and stretch radii (rstretch) surrounding the DNA's anchor point. Notably, consistent ratios of capture radius for DNA of varying lengths (2k, 4k, and 6k base pairs) were observed across different capturing voltages, approximately 1:1.4:1.83, showing a resemblance to their gyration radius ratios. However, the ratios of stretch radius are consistent to their contour length (L0), with the stretching ratio (rstretch/L0) increasing from 70 to 90% as the voltage rose from 100 to 1000 mV. Additionally, through numerical simulations, we identified the origin of capture and stretch radii, determined by the entropic elasticity-induced capture barrier and the electric field-dominant escape barrier. This research introduces an innovative methodology and outlines research perspectives for a comprehensive exploration of the conformational, electrical, and diffusion characteristics of tethered molecules.

starTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis.

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It's worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.

Bright light therapy-induced improvements of mood, cognitive functions and cerebellar functional connectivity in subthreshold depression: A randomized controlled trial.

Background: The efficacy of bright light therapy (BLT) in ameliorating depression has been validated. The present study is to investigate the changes of depressive symptoms, cognitive function and cerebellar functional connectivity (FC) following BLT in individuals with subthreshold depression (StD). Method: Participants were randomly assigned to BLT group (N = 47) or placebo (N = 41) in this randomized controlled trial between March 2020 and June 2022. Depression severity and cognitive function were assessed, as well as resting-state functional MRI scan was conducted before and after 8-weeks treatment. Seed-based whole-brain static FC (sFC) and dynamic FC (dFC) analyses of the bilateral cerebellar subfields were conducted. Besides, a multivariate regression model examined whether baseline brain FC was associated with changes of depression severity and cognitive function during BLT treatment. Results: After 8-week BLT treatment, individuals with StD showed improved depressive symptoms and attention/vigilance cognitive function. BLT also increased sFC between the right cerebellar lobule IX and left temporal pole, and decreased sFC within the cerebellum, and dFC between the right cerebellar lobule IX and left medial prefrontal cortex. Moreover, the fusion of sFC and dFC at baseline could predict the improvement of attention/vigilance in response to BLT. Conclusions: The current study identified that BLT improved depressive symptoms and attention/vigilance, as well as changed cerebellum-DMN connectivity, especially in the cerebellar-frontotemporal and cerebellar internal FC. In addition, the fusion features of sFC and dFC at pre-treatment could serve as an imaging biomarker for the improvement of attention/vigilance cognitive function after BLT in StD.

A Clinically Feasible Diagnostic Typing of Breast Cancer Built on a Homogeneous Electrochemical Biosensor for Simultaneous Multiplex Detection.

Simultaneous detection of multiple tumor markers is of great significance for an accurate diagnosis and early treatment of cancer. Electrochemical homogeneous biosensing strategies have been shown to have advantages, such as high sensitivity and no electrode modification, but they are still a challenge in the field of simultaneous detection of multiple tumor markers. The ER, PR, HER2, and Ki67 proteins are the standard biomarkers for the clinical molecular typing of breast cancer. Precise, sensitive, and simultaneous detection of these four biomarkers is of great importance in the molecular typing of breast cancer, which helps in the creation of personalized treatment plans. In the present study, we developed an electrochemical homogeneous electrochemical bioplatform based on metal ions/SiO2NPs/magnetic beads for detection of the four biomarkers and simultaneous diagnosis of the 10 types of breast cancer directly in human serum at one system by a single electrode. The electrochemical bioplatform has a short detection time of 140 min; however, the current clinical tissue testing time takes about 1 week. Also, the electrochemical bioplatform selectively detects HER2, ER, Ki67, and PR in a range of 0-1000 pg/mL with detection limits of 2, 1.8, 10.36, and 1.33 pg/mL, respectively.

Research Progress on Saccharide Molecule Detection Based on Nanopores.

Saccharides, being one of the fundamental molecules of life, play essential roles in the physiological and pathological functions of cells. However, their intricate structures pose challenges for detection. Nanopore technology, with its high sensitivity and capability for single-molecule-level analysis, has revolutionized the identification and structural analysis of saccharide molecules. This review focuses on recent advancements in nanopore technology for carbohydrate detection, presenting an array of methods that leverage the molecular complexity of saccharides. Biological nanopore techniques utilize specific protein binding or pore modifications to trigger typical resistive pulses, enabling the high-sensitivity detection of monosaccharides and oligosaccharides. In solid-state nanopore sensing, boronic acid modification and pH gating mechanisms are employed for the specific recognition and quantitative analysis of polysaccharides. The integration of artificial intelligence algorithms can further enhance the accuracy and reliability of analyses. Serving as a crucial tool in carbohydrate detection, we foresee significant potential in the application of nanopore technology for the detection of carbohydrate molecules in disease diagnosis, drug screening, and biosensing, fostering innovative progress in related research domains.

Protocol for identifying OXCT1-mediated LACTB succinylation sites in vitro.

OXCT1 acts as a succinyltransferase to promote serine beta-lactamase-like protein (LACTB) K284 succinylation. Here, we present a protocol for detecting OXCT1-mediated LACTB succinylation levels and sites. We describe steps for using western blotting (WB) and mass spectrometry to determine OXCT1-mediated LACTB succinylation levels and sites in vitro. This protocol can be applied to detect and identify succinylation levels and sites on other proteins. For complete details on the use and execution of this protocol, please refer to Ma et al.1.

Investigating the impact of ultrasound-assisted cellulase pretreatment on the nutrients, phytic acid, and phenolics bioaccessibility in sprouted brown rice.

This study aimed to elucidate the impact of ultrasound-assisted cellulase (UC) pretreatment on nutrients, phytic acid, and the bioavailability of phenolics during brown rice sprouting. It sought to unveil the underlying mechanisms by quantifying the activity of key enzymes implicated in these processes. The sprouted brown rice (SBR) surface structure was harmed by the UC pretreatment, which also increased the amount of γ-oryzanol and antioxidant activity in the SBR. Concurrently, the UC pretreatment boosted the activity of phytase, glutamate decarboxylase, succinate semialdehyde dehydrogenase, Gamma-aminobutyric acid (GABA) transaminase, chalcone isomerase, and phenylalanine ammonia lyase, thereby decreasing the phytic acid content and increasing the GABA, flavonoid, and phenolic content in SBR. In addition, UC-pretreated SBR showed increased phenolic release and bioaccessibility during in vitro digestion when compared to the treated group. These findings might offer theoretical direction for using SBR to maximize value.

Obesity and septic patient outcomes: Shaping the puzzle through age and sex perspectives.

BACKGROUND & AIMS: While obesity has been reported as a protective factor in septic patients, little is known about the potential modifying effects of age and sex. The objective of this study is to investigate age and sex-specific associations between obesity and the prognosis of septic patients. METHODS: A retrospective analysis was conducted on a cohort of 15,464 septic patients, categorized by body mass index (BMI) into four groups: underweight (<18.5 kg/m2, n = 483), normal (18.5-24.9 kg/m2, n = 4344), overweight (25-29.9 kg/m2, n = 4949) and obese (≥30 kg/m2, n = 5688). Multivariable logistic regression and inverse probability weighting were employed to robustly confirm the protective effect of a higher BMI on 28-day mortality, with normal weight serving as the reference category. Subgroup analyses based on age (young: 18-39, middle-aged: 40-64 and elderly: ≥65) and sex were performed. RESULTS: The findings demonstrate that high BMI independently confers a protective effect against 28-day mortality in septic patients. However, the relationship between BMI and 28-day mortality exhibits a non-linear trend, with a BMI of 34.5 kg/m2 displaying the lowest odds ratio. Notably, the survival benefits associated with a high BMI were not observed in the young group. Moreover, being underweight emerges as an independent risk factor for middle-aged and elderly female patients, while in males it is only a risk factor in the elderly group. Interestingly, being overweight and obese were identified as independent protective factors in middle-aged and elderly male patients, but not in females. CONCLUSIONS: The effect of BMI on mortality in septic patients varies according to age and sex. Elderly individuals with sepsis may derive more prognostic benefits from obesity.

The combination of DNA nanostructures and materials for highly sensitive electrochemical detection.

Due to the wide range of electrochemical devices available, DNA nanostructures and material-based technologies have been greatly broadened. They have been actively used to create a variety of beautiful nanostructures owing to their unmatched programmability. Currently, a variety of electrochemical devices have been used for rapid sensing of biomolecules and other diagnostic applications. Here, we provide a brief overview of recent advances in DNA-based biomolecular assays. Biosensing platform such as electrochemical biosensor, nanopore biosensor, and field-effect transistor biosensors (FET), which are equipped with aptamer, DNA walker, DNAzyme, DNA origami, and nanomaterials, has been developed for amplification detection. Under the optimal conditions, the proposed biosensor has good amplification detection performance. Further, we discussed the challenges of detection strategies in clinical applications and offered the prospect of this field.

Diagnosis Framework for Probable Alzheimer's Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features.

BACKGROUND: Emotion and cognition are intercorrelated. Impaired emotion is common in populations with Alzheimer's disease (AD) and mild cognitive impairment (MCI), showing promises as an early detection approach. OBJECTIVE: We aim to develop a novel automatic classification tool based on emotion features and machine learning. METHODS: Older adults aged 60 years or over were recruited among residents in the long-term care facilities and the community. Participants included healthy control participants with normal cognition (HC, n = 26), patients with MCI (n = 23), and patients with probable AD (n = 30). Participants watched emotional film clips while multi-dimensional emotion data were collected, including mental features of Self-Assessment Manikin (SAM), physiological features of electrodermal activity (EDA), and facial expressions. Emotional features of EDA and facial expression were abstracted by using continuous decomposition analysis and EomNet, respectively. Bidirectional long short-term memory (Bi-LSTM) was used to train classification model. Hybrid fusion was used, including early feature fusion and late decision fusion. Data from 79 participants were utilized into deep machine learning analysis and hybrid fusion method. RESULTS: By combining multiple emotion features, the model's performance of AUC value was highest in classification between HC and probable AD (AUC = 0.92), intermediate between MCI and probable AD (AUC = 0.88), and lowest between HC and MCI (AUC = 0.82). CONCLUSIONS: Our method demonstrated an excellent predictive power to differentiate HC/MCI/AD by fusion of multiple emotion features. The proposed model provides a cost-effective and automated method that can assist in detecting probable AD and MCI from normal aging.

Carnosine regulation of intracellular pH homeostasis promotes lysosome-dependent tumor immunoevasion.

Tumor cells and surrounding immune cells undergo metabolic reprogramming, leading to an acidic tumor microenvironment. However, it is unclear how tumor cells adapt to this acidic stress during tumor progression. Here we show that carnosine, a mobile buffering metabolite that accumulates under hypoxia in tumor cells, regulates intracellular pH homeostasis and drives lysosome-dependent tumor immune evasion. A previously unrecognized isoform of carnosine synthase, CARNS2, promotes carnosine synthesis under hypoxia. Carnosine maintains intracellular pH (pHi) homeostasis by functioning as a mobile proton carrier to accelerate cytosolic H+ mobility and release, which in turn controls lysosomal subcellular distribution, acidification and activity. Furthermore, by maintaining lysosomal activity, carnosine facilitates nuclear transcription factor X-box binding 1 (NFX1) degradation, triggering galectin-9 and T-cell-mediated immune escape and tumorigenesis. These findings indicate an unconventional mechanism for pHi regulation in cancer cells and demonstrate how lysosome contributes to immune evasion, thus providing a basis for development of combined therapeutic strategies against hepatocellular carcinoma that exploit disrupted pHi homeostasis with immune checkpoint blockade.

OXCT1 functions as a succinyltransferase, contributing to hepatocellular carcinoma via succinylating LACTB.

Metabolic reprogramming is an important feature of cancers that has been closely linked to post-translational protein modification (PTM). Lysine succinylation is a recently identified PTM involved in regulating protein functions, whereas its regulatory mechanism and possible roles in tumor progression remain unclear. Here, we show that OXCT1, an enzyme catalyzing ketone body oxidation, functions as a lysine succinyltransferase to contribute to tumor progression. Mechanistically, we find that OXCT1 functions as a succinyltransferase, with residue G424 essential for this activity. We also identified serine beta-lactamase-like protein (LACTB) as a main target of OXCT1-mediated succinylation. Extensive succinylation of LACTB K284 inhibits its proteolytic activity, resulting in increased mitochondrial membrane potential and respiration, ultimately leading to hepatocellular carcinoma (HCC) progression. In summary, this study establishes lysine succinyltransferase function of OXCT1 and highlights a link between HCC prognosis and LACTB K284 succinylation, suggesting a potentially valuable biomarker and therapeutic target for further development.

Daily diary study of associations between alcohol, cannabis, co-use and sleep quality in individuals with intentions to use cannabis to cope with anxiety.

INTRODUCTION: Sleep problems and anxiety conditions are common comorbidities and may be influenced by cannabis and alcohol use. This study examined daily within-person variation in subjective sleep quality among individuals with anxiety symptoms after cannabis or alcohol were used alone, and after co-use. METHODS: A total of 347 individuals with intentions to use cannabis to cope with anxiety reported their cannabis and alcohol use in the previous 24 h and their previous nights' sleep quality for 30 consecutive days. Mixed-effects models examined whether the within-person daily variation in use of cannabis and alcohol (alone and co-use) was associated with subjective sleep quality. Models also examined whether daily cannabis and alcohol use associations with sleep were moderated by frequency of cannabis, alcohol and co-use during the study period. RESULTS: Compared to non-use, participants reported better sleep after cannabis-use-only and after co-use, but not after alcohol-use-only. People who more frequently use alcohol and cannabis reported sleeping better after cannabis-use-only days compared to those who use cannabis and alcohol less frequently. DISCUSSION AND CONCLUSIONS: The study's utilisation of naturalistic data among individuals with anxiety symptoms replicated previously reported experimental findings among individuals without sleep and anxiety problems that overall, cannabis is associated with higher subjective sleep quality. The results expand upon other research to suggest that more frequent use of alcohol and cannabis may moderate daily associations of cannabis use and sleep, potentially through pharmacokinetics and cross-sensitisation.

Increased functional connectivity between the midbrain and frontal cortex following bright light therapy in subthreshold depression: A randomized clinical trial.

The underlying mechanisms of bright light therapy (BLT) in the prevention of individuals with subthreshold depression symptoms are yet to be elucidated. The goal of the study was to assess the correlation between midbrain monoamine-producing nuclei treatment-related functional connectivity (FC) changes and depressive symptom improvements in subthreshold depression. This double-blind, randomized, placebo-controlled clinical trial was conducted between March 2020 and June 2022. A total of 74 young adults with subthreshold depression were randomly assigned to receive 8-week BLT (N = 38) or placebo (N = 36). Depression severity was measured using the Hamilton Depression Rating Scale (HDRS). The participants underwent resting-state functional magnetic resonance imaging at baseline and after treatment. The dorsal raphe nucleus (DRN), ventral tegmental area (VTA), and habenula seed-based whole-brain FC were analyzed. A multivariate regression model examined whether baseline brain FC was associated with changes in scores on HDRS during BLT treatment. BLT group displayed significantly decreased HDRS scores from pre- to posttreatment compared to the placebo group. BLT increased the FC between the DRN and medial prefrontal cortex (mPFC) and between the left VTA and right superior frontal gyrus (SFG). Altered VTA-SFG connectivity was associated with HDRS changes in the BLT group. Moreover, the baseline FC between DRN and mPFC could predict HDRS changes in BLT. These results suggested that BLT improves depressive symptoms and increases midbrain monoamine-producing nuclei and frontal cortex connectivity in subthreshold depression, which raises the possibility that pretreatment FC of DRN-mPFC could be used as a biomarker for improved BLT treatment in depression. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining.

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .

The effects of esketamine on the intestinal microenvironment and intestinal microbiota in mice.

OBJECTIVE: This study aimed to investigate the impact of esketamine on the intestinal flora and microenvironment in mice using mRNA transcriptome sequencing and 16S rRNA sequencing. METHODS: Ten female mice were randomly assigned to two groups. One group received daily intramuscular injections of sterile water, while the other group received esketamine. After 24 days, the mice were sacrificed, and their intestinal tissues and contents were collected for 16S rRNA sequencing and mRNA transcriptome sequencing. The intergroup differences in the mouse intestinal flora were analyzed. Differentially expressed genes were utilized to construct ceRNA networks and transcription factor regulatory networks to assess the effects of esketamine on the intestinal flora and intestinal tissue genes. RESULTS: Esketamine significantly altered the abundance of intestinal microbiota, including Adlercreutzia equolifaciens and Akkermansia muciniphila. Differential expression analysis revealed 301 significantly upregulated genes and 106 significantly downregulated genes. The ceRNA regulatory network consisted of 6 lncRNAs, 44 miRNAs, and 113 mRNAs, while the regulatory factor network included 13 transcription factors and 53 target genes. Gene Ontology enrichment analysis indicated that the differentially expressed genes were primarily associated with immunity, including B-cell activation and humoral immune response mediation. The biological processes in the ceRNA regulatory network primarily involved transport, such as organic anion transport and monocarboxylic acid transport. The functional annotation of target genes in the TF network was mainly related to epithelial cells, including epithelial cell proliferation and regulation. CONCLUSION: Esketamine induces changes in gut microbiota and the intestinal microenvironment, impacting the immune environment and transport modes.

How do carbon emissions trading impact the financialization of non-financial companies? Evidence from a quasi-natural experiment in China.

This study examines whether and how carbon trading policy impacts the financialization of non-financial firms, using China emission trading scheme as a quasi-natural experiment. We find that the carbon trading policy exerts a substantial and enduring inhibitory effect on corporate financialization. Our findings are robust to possible result bias and more precise control group. Additionally, we explore potential channels through which carbon trading policy can affect financialization, and find that it curbs financialization by reducing financing constraints. Finally, we demonstrate that the relationship between carbon trading policy and financialization of non-financial companies is moderated by company's ownership, region, and industry competition.

The allometric relationship between carbon emission and economic development in Yangtze River Delta: fusion of multi-source remote sensing nighttime light data.

Exploring the allometric relationship between carbon emission and economic development can provide guidance for policy-makers who hope to accelerate carbon emission reduction and achieve high-quality development. First, based on the established DMSP/OLS and NPP/VIIRS nighttime light datasets, this study simulated the carbon emissions of the Yangtze River Delta from 2000 to 2020. Second, our research analyzed the spatiotemporal evolution characteristics of carbon emissions. Third, adopting allometric growth model, we explored the allometric relationship between economic development and carbon emissions in Yangtze River Delta. The main conclusions are as follows. First, four prediction models, namely, linear fitting, support vector machine, random forest, and CNN-BiLSTM deep learning, were compared to simulate the accuracy of carbon emissions. Consequently, the CNN-BiLSTM deep learning estimation model presented the best accuracy. Second, both the carbon emissions in YRD as a whole showed an increasing trend, with the largest growth rate appearing in Shanghai and the smallest growth rate occurring in Lishui. Moreover, the high-carbon emission areas were mainly distributed in the core city cluster, which are enclosed by Shanghai, Nanjing, and Hangzhou. Finally, the allometric relationship between economic development and carbon emissions was dominated by one-level negative during the sample period, and the relative growth rate of carbon emissions is lower than that of the economic development, which made the YRD at a basic coordinate stage of weak expansion of economy.