Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Three SARS-CoV-2 spike protein variants delivered intranasally by measles and mumps vaccines are broadly protective.

As the new SARS-CoV-2 Omicron variants and subvariants emerge, there is an urgency to develop intranasal, broadly protective vaccines. Here, we developed highly efficacious, intranasal trivalent SARS-CoV-2 vaccine candidates (TVC) based on three components of the MMR vaccine: measles virus (MeV), mumps virus (MuV) Jeryl Lynn (JL1) strain, and MuV JL2 strain. Specifically, MeV, MuV-JL1, and MuV-JL2 vaccine strains, each expressing prefusion spike (preS-6P) from a different variant of concern (VoC), were combined to generate TVCs. Intranasal immunization of IFNAR1-/- mice and female hamsters with TVCs generated high levels of S-specific serum IgG antibodies, broad neutralizing antibodies, and mucosal IgA antibodies as well as tissue-resident memory T cells in the lungs. The immunized female hamsters were protected from challenge with SARS-CoV-2 original WA1, B.1.617.2, and B.1.1.529 strains. The preexisting MeV and MuV immunity does not significantly interfere with the efficacy of TVC. Thus, the trivalent platform is a promising next-generation SARS-CoV-2 vaccine candidate.

Role of AMPK-regulated autophagy in retinal pigment epithelial cell homeostasis: A review.

The retinal pigment epithelium (RPE) is a regularly arranged monolayer of cells in the outermost layer of the retina. It is crucial for transporting nutrients and metabolic substances in the retina and maintaining the retinal barrier. RPE dysfunction causes diseases related to vision loss. Thus, understanding the mechanisms involved in normal RPE function is vital. Adenosine monophosphate-activated protein kinase (AMPK) is an RPE energy sensor regulating various signaling and metabolic pathways to maintain cellular energetic homeostasis. AMPK activation is involved in multiple signaling pathways regulated by autophagy in the RPE, thereby protecting the cells from oxidative stress and slowing RPE degeneration. In this review, we attempt to broaden the understanding of the pathogenesis of RPE dysfunction by focusing on the role and mechanism of AMPK regulation of autophagy in the RPE. The correlation between RPE cellular homeostasis and role of AMPK was determined by analyzing the structure and mechanism of AMPK and its signaling pathway in autophagy. The protective effect of AMPK-regulated autophagy on the RPE for gaining insights into the regulatory pathways of RPE dysfunction has been discussed.

Diagnostic Value of Superb Microvascular Imaging of the Rotator Cuff Interval for the Early Diagnosis of Frozen Shoulder.

Purpose: To explore the early diagnostic value of superb microvascular imaging (SMI) features within the rotator cuff gap for frozen shoulder. Patients and Methods: This prospective study enrolled patients with acute early-stage frozen shoulder seeking treatment at Zhabei Central Hospital in Jing'an District, Shanghai, between July 2021 and December 2022 were enrolled in this study. Healthy controls were collected in a 1:1 ratio from the same hospital's physical examination center. All participants underwent SMI and power Doppler ultrasound (PDUS) of the rotator cuff gap. Results: The study included 79 patients with frozen shoulder and 77 healthy controls. Compared with the healthy control group, the patient group had a higher proportion of hypoechoic rotator cuff gap (81.0% vs 48.1%, P<0.001), a thicker coracohumeral ligament (2.60±1.01 vs 2.03±0.97, P<0.001), a thicker glenohumeral joint capsule (3.10±0.99 vs 2.46±1.17, P<0.001), and elevated blood grading using SMI (P<0.001) and PDUS (P=0.014). The highest area under the curve (AUC) was observed for SMI blood flow grading (AUC=0.824, 95% CI: 0.755-0.880, P<0.001), resulting in 82% sensitivity and 77% specificity when using a cutoff of 1. SMI blood flow grading was associated with external rotation <30° (P=0.007) and abduction <30° (P=0.013) but not with internal rotation <30° (P=0.630) or flexion <30° (P=0.562). Conclusion: The grading of SMI blood flow may emerge as a valuable predictive indicator for the early stages of frozen shoulder. This simple ultrasound technique holds the potential to enhance the diagnostic process, enabling early initiation of treatment and potentially improving patient outcomes.

Isolation and Characterization of Contemporary Bovine Coronavirus Strains.

Bovine coronavirus (BCoV) poses a threat to cattle health worldwide, contributing to both respiratory and enteric diseases. However, few contemporary strains have been isolated. In this study, 71 samples (10 nasal and 61 fecal) were collected from one farm in Ohio in 2021 and three farms in Georgia in 2023. They were screened by BCoV-specific real-time reverse transcription-PCR, and 15 BCoV-positive samples were identified. Among them, five BCoV strains from fecal samples were isolated using human rectal tumor-18 (HRT-18) cells. The genomic sequences of five strains were obtained. The phylogenetic analysis illustrated that these new strains clustered with US BCoVs that have been detected since the 1990s. Sequence analyses of the spike proteins of four pairs of BCoVs, with each pair originally collected from the respiratory and enteric sites of one animal, revealed the potential amino acid residue patterns, such as D1180 for all four enteric BCoVs and G1180 for three of four respiratory BCoVs. This project provides new BCoV isolates and sequences and underscores the genetic diversity of BcoVs, the unknown mechanisms of disease types, and the necessity of sustained surveillance and research for BCoVs.

Functional near-infrared spectroscopy-based neurofeedback training regulates time-on-task effects and enhances sustained cognitive performance.

Time-on-task effect is a common consequence of long-term cognitive demand work, which reflects reduced behavioral performance and increases the risk of accidents. Neurofeedback is a neuromodulation method that can guide individuals to regulate their brain activity and manifest as changes in related symptoms and cognitive behaviors. This study aimed to examine the effects of functional near-infrared spectroscopy-based neurofeedback training on time-on-task effects and sustained cognitive performance. A randomized, single-blind, sham-controlled study was performed: 17 participants received feedback signals of their own dorsolateral prefrontal cortex activity (neurofeedback group), and 16 participants received feedback signals of dorsolateral prefrontal cortex activity from the neurofeedback group (sham-neurofeedback group). All participants received 5 neurofeedback training sessions and completed 2 sustained cognitive tasks, including a 2-back task and a psychomotor vigilance task, to evaluate behavioral performance changes following neurofeedback training. Results showed that neurofeedback relative to the sham-neurofeedback group exhibited increased dorsolateral prefrontal cortex activation, increased accuracy in the 2-back task, and decreased mean response time in the psychomotor vigilance task after neurofeedback training. In addition, the neurofeedback group showed slower decline performance during the sustained 2-back task after neurofeedback training compared with sham-neurofeedback group. These findings demonstrate that neurofeedback training could regulate time-on-task effects on difficult task and enhance performance on sustained cognitive tasks by increasing dorsolateral prefrontal cortex activity.

Measuring Human Auditory Evoked Fields with a Flexible Multi-Channel OPM-Based MEG System.

BACKGROUND: Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. METHODS: An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. RESULTS: High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. CONCLUSIONS: The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems.

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

C6orf15 promotes liver metastasis via WNT/ß-catenin signalling in colorectal cancer.

BACKGROUND: Colon cancer ranks third among global tumours and second in cancer-related mortality, prompting an urgent need to explore new therapeutic targets. C6orf15 is a novel gene that has been reported only in Sjogren's syndrome and systemic lupus erythematosus patients. We found a close correlation between increased C6orf15 expression and the occurrence of colon cancer. The aim of this study was to explore the potential of C6orf15 as a therapeutic target for colorectal cancer. METHOD: RNA-seq differential expression analysis of the TCGA database was performed using the R package 'limma.' The correlation between target genes and survival as well as tumour analysis was analysed using GEPIA. Western blot and PCR were used to assess C6orf15 expression in colorectal cancer tissue samples. Immunofluorescence and immunohistochemistry were used to assess C6orf15 subcellular localization and tissue expression. The role of C6orf15 in liver metastasis progression was investigated via a mouse spleen infection liver metastasis model. The association of C6orf15 with signalling pathways was assessed using the GSEA-Hallmark database. Immunohistochemistry (IHC), qPCR and western blotting were performed to assess the expression of related mRNAs or proteins. Biological characteristics were evaluated through cell migration assays, MTT assays, and Seahorse XF96 analysis to monitor fatty acid metabolism. RESULTS: C6orf15 was significantly associated with liver metastasis and survival in CRC patients as determined by the bioinformatic analysis and further verified by immunohistochemistry (IHC), qPCR and western blot results. The upregulation of C6orf15 expression in CRC cells can promote the nuclear translocation of ß-catenin and cause an increase in downstream transcription. This leads to changes in the epithelial-mesenchymal transition (EMT) and alterations in fatty acid metabolism, which together promote liver metastasis of CRC. CONCLUSION: Our study identified C6orf15 as a marker of liver metastasis in CRC. C6orf15 can activate the WNT/ß-catenin signalling pathway to promote EMT and fatty acid metabolism in CRC.

Relationships between blood chromium exposure and liver injury: Exploring the mediating role of systemic inflammation in a chromate-exposed population.

Hexavalent chromium and its compounds are prevalent pollutants, especially in the work environment, pose a significant risk for multisystem toxicity and cancers. While it is known that chromium accumulation in the liver can cause damage, the dose-response relationship between blood chromium (Cr) and liver injury, as well as the possible potential toxic mechanisms involved, remains poorly understood. To address this, we conducted a follow-up study of 590 visits from 305 participants to investigate the associations of blood Cr with biomarkers for liver injury, including serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and direct bilirubin (DBIL), and to evaluate the mediating effects of systemic inflammation. Platelet (PLT) and the platelet-to-lymphocyte ratio (PLR) were utilized as biomarkers of systemic inflammation. In the linear mixed-effects analyses, each 1-unit increase in blood Cr level was associated with estimated effect percentage increases of 0.82% (0.11%, 1.53%) in TBIL, 1.67% (0.06%, 3.28%) in DBIL, 0.73% (0.04%, 1.43%) in ALT and 2.08% (0.29%, 3.87%) in AST, respectively. Furthermore, PLT mediated 10.04%, 11.35%, and 10.77% increases in TBIL, DBIL, and ALT levels induced by chromate, respectively. In addition, PLR mediated 8.26% and 15.58% of the association between blood Cr and TBIL or ALT. These findings shed light on the mechanisms underlying blood Cr-induced liver injury, which is partly due to worsening systemic inflammation.

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) account for a significant proportion of gynecological malignancies and represent a major global health concern. Globally, CESC is ranked as the fourth most common cancer among women. Conventional treatment of this disease has a less favorable prognosis for most patients. However, the discovery of early molecular biomarkers is therefore important for the diagnosis of CESC, as well as for slowing down their progression process. Methods: To identify differentially expressed genes strongly associated with prognosis, univariate Cox proportional hazard analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used. Using multiple Cox proportional hazard regression, a multifactorial model for prognostic risk assessment was then created. Results: The expression of biological clock-related genes, which varied considerably among distinct subtypes and were associated with significantly diverse prognoses, was used to categorize CESC patients. These findings demonstrate how the nomogram developed based on the 7-CRGs signature may assist physicians in creating more precise, accurate, and successful treatment plans that can aid CESC patients at 1, 3, and 5 years. Conclusions: By using machine learning techniques, we thoroughly investigated the impact of CRGs on the prognosis of CESC patients in this study. By creating a unique nomogram, we were able to accurately predict patient prognosis. At the same time, we showed new perspectives on the development of CESC and its treatment by analyzing the associations of the prognostic model with immunity, enrichment pathways, chemotherapy sensitivity, and so on. This research provides a new direction for clinical treatment.

Longitudinal multi-omics analysis uncovers the altered landscape of gut microbiota and plasma metabolome in response to high altitude.

BACKGROUND: Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed. METHODS: In this study, we investigated the microbiome profile of 610 healthy young men from three different places in China, grouped by altitude, duration of residence, and ethnicity. We conducted widely targeted metabolomic profiling and clinical testing to explore metabolic characteristics. RESULTS: Our findings revealed that as the Han individuals migrated from low altitude to high latitude, the gut microbiota gradually converged towards that of the Tibetan populations but reversed upon returning to lower altitude. Across different cohorts, we identified 51 species specifically enriched during acclimatization and 57 species enriched during deacclimatization to high altitude. Notably, Prevotella copri was found to be the most enriched taxon in both Tibetan and Han populations after ascending to high altitude. Furthermore, significant variations in host plasma metabolome and clinical indices at high altitude could be largely explained by changes in gut microbiota composition. Similar to Tibetans, 41 plasma metabolites, such as lactic acid, sphingosine-1-phosphate, taurine, and inositol, were significantly elevated in Han populations after ascending to high altitude. Germ-free animal experiments demonstrated that certain species, such as Escherichia coli and Klebsiella pneumoniae, which exhibited altitude-dependent variations in human populations, might play crucial roles in host purine metabolism. CONCLUSIONS: This study provides insights into the dynamics of gut microbiota and host plasma metabolome with respect to altitude changes, indicating that their dynamics may have implications for host health at high altitude and contribute to host adaptation. Video Abstract.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

BERT-siRNA: siRNA target prediction based on BERT pre-trained interpretable model.

Silencing mRNA through siRNA is vital for RNA interference (RNAi), necessitating accurate computational methods for siRNA selection. Current approaches, relying on machine learning, often face challenges with large data requirements and intricate data preprocessing, leading to reduced accuracy. To address this challenge, we propose a BERT model-based siRNA target gene knockdown efficiency prediction method called BERT-siRNA, which consists of a pre-trained DNA-BERT module and Multilayer Perceptron module. It applies the concept of transfer learning to avoid the limitation of a small sample size and the need for extensive preprocessing processes. By fine-tuning on various siRNA datasets after pretraining on extensive genomic data using DNA-BERT to enhance predictive capabilities. Our model clearly outperforms all existing siRNA prediction models through testing on the independent public siRNA dataset. Furthermore, the model's consistent predictions of high-efficiency siRNA knockdown for SARS-CoV-2, as well as its alignment with experimental results for PDCD1, CD38, and IL6, demonstrate the reliability and stability of the model. In addition, the attention scores for all 19-nt positions in the dataset indicate that the model's attention is predominantly focused on the 5' end of the siRNA. The step-by-step visualization of the hidden layer's classification progressively clarified and explained the effective feature extraction of the MLP layer. The explainability of model by analysis the attention scores and hidden layers is also our main purpose in this work, making it more explainable and reliable for biological researchers.

Deciphering a Prognostic Signature Based on Soluble Mediators Defines the Immune Landscape and Predicts Prognosis in HNSCC.

BACKGROUND: The study on Head and Neck Squamous Cell Carcinoma (HNSCC), a prevalent and aggressive form of head and neck cancer, focuses on the often-overlooked role of soluble mediators. The objective is to leverage a transcriptome-based risk analysis utilizing soluble mediator-related genes (SMRGs) to provide novel insights into prognosis and immunotherapy efficacy in HNSCC patients. METHODS: We analyzed the expression and prognostic significance of 10,859 SMRGs using 502 HNSCC and 44 normal samples from the TCGA-HNSC cohort in The Cancer Genome Atlas (TCGA). The samples were divided into training and test sets in a 7:3 ratio, with an additional external validation using 40 tumor samples from the International Cancer Genome Consortium (ICGC). Key differentially expressed genes (DEGs) with prognostic significance were identified through univariate and Lasso-Cox regression analyses. A prognostic model based on 20 SMRGs was developed using Lasso and multivariate Cox regression. We assessed the clinical outcomes and immune status in high-risk (HR) and low-risk (LR) HNSCC patients utilizing the BEST databases and single-sample Gene Set Enrichment Analysis (ssGSEA). RESULTS: The 20 SMRGs were crucial in predicting the prognosis of HNSCC, with the SMRG signature emerging as an independent prognostic indicator. Patients classified in the HR group exhibited poorer outcomes compared to those in the LR group. A nomogram, integrating clinical characteristics and risk scores, demonstrated substantial prognostic value. Immunotherapy appeared to be more effective in the LR group, possibly attributed to enhanced immune infiltration and expression of immune checkpoints. CONCLUSIONS: The model based on soluble mediator-associated genes offers a fresh perspective for assessing the pre-immune efficacy and showcases robust predictive capabilities. This innovative approach holds significant promise in advancing the field of precision immuno-oncology research, providing valuable insights for personalized treatment strategies in HNSCC.

Corrigendum: Prediction of severe CRS and determination of biomarkers in B cell-acute lymphoblastic leukemia treated with CAR-T cells.

[This corrects the article DOI: 10.3389/fimmu.2023.1273507.].

GPR39 regulated spinal glycinergic inhibition and mechanical inflammatory pain.

G protein-coupled receptor 39 (GPR39) senses the change of extracellular divalent zinc ion and signals through multiple G proteins to a broad spectrum of downstream effectors. Here, we found that GPR39 was prevalent at inhibitory synapses of spinal cord somatostatin-positive (SOM+) interneurons, a mechanosensitive subpopulation that is critical for the conveyance of mechanical pain. GPR39 complexed specifically with inhibitory glycine receptors (GlyRs) and helped maintain glycinergic transmission in a manner independent of G protein signalings. Targeted knockdown of GPR39 in SOM+ interneurons reduced the glycinergic inhibition and facilitated the excitatory output from SOM+ interneurons to spinoparabrachial neurons that engaged superspinal neural circuits encoding both the sensory discriminative and affective motivational domains of pain experience. Our data showed that pharmacological activation of GPR39 or augmenting GPR39 interaction with GlyRs at the spinal level effectively alleviated the sensory and affective pain induced by complete Freund's adjuvant and implicated GPR39 as a promising therapeutic target for the treatment of inflammatory mechanical pain.

[Tacrolimus Induces Pain by Upregulating the Synaptic Expression of AMPA Receptors in the Spinal Cord Dorsal Horn]. / ä»–å ‹èŽ«å¸é€šè¿‡ä¸Šè°ƒè„Šé«“èƒŒè§’AMPA受体的突触表达诱发疼痛.

Objective: To investigate the effects of long-term administration of tacrolimus (also known as FK506) on the pain-related behaviors in mice and to study the underlying mechanism of pain induced by FK506 via measuring the effect of FK506 on the synaptic expression and phosphorylation of alpha-amino-3-hyroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor in the spinal cord dorsal horn of mice. Methods: 1) A total of 24 mice were evenly and randomly assigned to two groups, a FK506 group and a Saline group. The FK506 group was given daily intraperitoneal injection of FK506 and the Saline group received normal saline. Both groups received injection once a day for 7 days in a row. Some of the mice ( n=6 in each group) were monitored for the changes in the paw withdrawal threshold (PWT), the paw withdrawal latency (PWL), and the spontaneous pain behaviors to establish the pain model. The other mice ( n=6 in each group) of each group underwent isolation of the dorsal horn when obvious pain symptoms were induced on day 7 of injection. Then, immunoblotting was performed to determine the synaptic expression and phosphorylation levels of GluA1 and GluA2 subunits of AMPA receptors. 2) The mice were randomly divided into two groups, FK506+calcineurin (CaN) group and FK506+Saline group ( n=6 in each group). After the pain model was constructed, the mice were given intrathecal injection of recombinant CaN (also know as 33 U) or normal saline. Then, 60 minutes later, the PWT and the PWL of the mice were measured to investigate the role of CaN in FK506-induced pain. 3) Another18 mice were selected. The mice were randomly and evenly assigned to three groups, a control group (receiving intraperitoneal injection of normal saline followed by intrathecal injection of normal saline), FK506+Saline group (receiving intraperitoneal injection of FK506 followed by intrathecal injection of normal saline) and FK506+CaN group (receiving intraperitoneal injection of FK506 followed by intrathecal injection of CaN). Then, 60 minutes later, the spinal cords were isolated and subjected to immunoblotting assay to determine the role of CaN in FK506-induced AMPA receptor modification. Results: 1) After 7 consecutive days of intraperitoneal injection of FK506, the PWT and PWL of mice dropped significantly, reaching on day 7 as low as 22.3%±0.05% and 66.6%±0.05% of the control group, respectively ( P<0.01). The FK506-treated mice displayed evident spontaneous pain behavior, presenting significantly increased licking activities ( P<0.01). These results indicated that FK506-induced pain model was successfully established. Immunoblotting assay showed that the total expressions of GluA1 and GluA2 subunits in the spinal dorsal horn of the FK506 group remained unchanged in comparison with those of the Saline group. However, FK506 specifically induced an increase in the synaptic expression of GluA1. In addition, the phosphorylation levels of GluA1 at Ser845 and Ser831 in FK506-treated mice were significantly increased in comparison with those of the control group ( P<0.05). 2) Compared with those of the mice in the FK506+Saline group, the PWT and the PWL of mice in the FK506+CaN group were significantly increased ( P<0.05). 3) Compared with those of the FK506+Saline group, the synaptic expression of GluA1 were decreased in FK506+CaN group ( P<0.01) and the phosphorylation levels of GluA1 at Ser845 and Ser831 were significantly downregulated ( P<0.001). Conclusion: The hyper-expression and hyperphosphorylation of GluA1 subunit in the spinal cord dorsal horn resulting from CaN inhibition contributes to the FK506-induced pain syndrome. FK506 induces the synaptic hyper-expression and hyperphosphorylation of GluA1 in the dorsal horn of the spinal cord through CaN inhibition, thereby inducing pain.

Green manufacturing for achieving carbon neutrality goal requires innovative technologies: A bibliometric analysis from 1991 to 2022.

Recent years have seen a significant increase in interest in green manufacturing as a key driver of global carbon-neutral efforts and sustainable development. To find the research hotspots of green manufacturing and reveal future research trends, this study reviewed and analyzed research articles from the Web of Science database on green manufacturing from 1991 to 2022 using a bibliometric method. The findings indicate a significant rise in the number of articles related to green manufacturing since the 2010s. Moreover, there has been an increase in the involvement of scholars from developing countries such as China and India in this field. Based on the literature review and bibliometric cluster analysis on green manufacturing, we believed that future research may continue following the lines of intelligent technology integration, adoption of frontier engineering techniques, and industry development in line with carbon reduction targets. A framework for future green manufacturing development is proposed, with a focus on Chinese policies. The framework could provide policy implications for developing countries looking to pursue opportunities for development in green manufacturing.