Sign of Neck Mass as the Chief Complaint: A Case Report and Literature Review About Thyroid Metastasis of Colorectal Cancer.

Background: Commonly, the thyroid gland is regarded as an organ with fewer metastatic diseases, and colorectal metastasis to the thyroid (CMT) is rarely reported, especially, with that the clinical sign of thyroid metastasis nidus is the chief complaint. The CMT occurs in advanced colorectal cancer and is associated with poor prognosis and short survival. Case Report: In this case, we reported a patient with the sign of neck mass as the first manifestation of CMT. The patient underwent a partial thyroidectomy in June 2019, immunohistochemical findings of thyroid carcinoma suggested the possibility of adenocarcinoma of gastrointestinal tract. The patient underwent a colonoscopy in July 2019 and a colonic mass was found. Pathological examination diagnosed rectal adenocarcinoma. The patient underwent neoadjuvant chemotherapy, surgical treatment, postoperative adjuvant chemotherapy and targeted therapy. The patient died in June 2022. Conclusion: The metastasis disease would not be ignored at all, when a patient complains at signs of neck mass. Further, the possibility of metastasis cancer should be considered once thyroid nodules occur in patients with colorectal cancer. Even though the biological characteristics and stage of the primary tumor have an important impact on the prognosis, positive standardized treatments can also be helpful.

A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis.

BACKGROUND: Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment. METHODS: We proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date. RESULTS: Here, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 - 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance. CONCLUSIONS: Such a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.

Breast cancer is one of the most common cancers, and finding it early can greatly improve patients' chances of survival and recovery. We create a tool based on artificial intelligence (AI)­whereby computer software learns to perform tasks that normally require human thinking­called MUP-Net. MUP-Net can analyze medical images to predict a patient's risk of having breast cancer. To make this AI tool usable in clinical practice, we enabled doctors to see the reasoning behind the AI's predictions by visualizing the key image features it analyzed. We showed that our AI tool not only makes doctors more confident in their diagnosis but also helps them make better decisions, especially for less experienced doctors. With further testing, our AI tool may help clinicians to diagnose breast cancer more accurately and quickly, potentially improving patient outcomes.

A vision chip with complementary pathways for open-world sensing.

Image sensors face substantial challenges when dealing with dynamic, diverse and unpredictable scenes in open-world applications. However, the development of image sensors towards high speed, high resolution, large dynamic range and high precision is limited by power and bandwidth. Here we present a complementary sensing paradigm inspired by the human visual system that involves parsing visual information into primitive-based representations and assembling these primitives to form two complementary vision pathways: a cognition-oriented pathway for accurate cognition and an action-oriented pathway for rapid response. To realize this paradigm, a vision chip called Tianmouc is developed, incorporating a hybrid pixel array and a parallel-and-heterogeneous readout architecture. Leveraging the characteristics of the complementary vision pathway, Tianmouc achieves high-speed sensing of up to 10,000 fps, a dynamic range of 130 dB and an advanced figure of merit in terms of spatial resolution, speed and dynamic range. Furthermore, it adaptively reduces bandwidth by 90%. We demonstrate the integration of a Tianmouc chip into an autonomous driving system, showcasing its abilities to enable accurate, fast and robust perception, even in challenging corner cases on open roads. The primitive-based complementary sensing paradigm helps in overcoming fundamental limitations in developing vision systems for diverse open-world applications.

Trilaciclib dosage in Chinese patients with extensive-stage small cell lung cancer: a pooled pharmacometrics analysis.

This study aimed to analyze potential ethnic disparities in the dose-exposure-response relationships of trilaciclib, a first-in-class intravenous cyclin-dependent kinase 4/6 inhibitor for treating chemotherapy-induced myelosuppression in patients with extensive-stage small cell lung cancer (ES-SCLC). This investigation focused on characterizing these relationships in both Chinese and non-Chinese patients to further refine the dosing regimen for trilaciclib in Chinese patients with ES-SCLC. Population pharmacokinetic (PopPK) and exposure-response (E-R) analyses were conducted using pooled data from four randomized phase 2/3 trials involving Chinese and non-Chinese patients with ES-SCLC. PopPK analysis revealed that trilaciclib clearance in Chinese patients was approximately 17% higher than that in non-Chinese patients with ES-SCLC. Sex and body surface area influenced trilaciclib pharmacokinetics in both populations but did not exert a significant clinical impact. E-R analysis demonstrated that trilaciclib exposure increased with a dosage escalation from 200 to 280 mg/m2, without notable changes in myeloprotective or antitumor efficacy. However, the incidence of infusion site reactions, headaches, and phlebitis/thrombophlebitis rose with increasing trilaciclib exposure in both Chinese and non-Chinese patients with ES-SCLC. These findings suggest no substantial ethnic disparities in the dose-exposure-response relationship between Chinese and non-Chinese patients. They support the adoption of a 240-mg/m2 intravenous 3-day or 5-day dosing regimen for trilaciclib in Chinese patients with ES-SCLC.

The individual contributions of blaB, blaGOB and blaCME on MICs of ß-lactams in Elizabethkingia anophelis.

BACKGROUND: The blaB, blaGOB and blaCME genes are thought to confer ß-lactam resistance to Elizabethkingia anophelis, based on experiments conducted primarily on Escherichia coli. OBJECTIVES: To determine the individual contributions of ß-lactamase genes to increased MICs in E. anophelis and to assess their impact on the in vivo efficacy of carbapenem therapy. METHODS: Scarless gene deletion of one or more ß-lactamase gene(s) was performed in three clinical E. anophelis isolates. MICs were determined by broth microdilution. Hydrolytic activity and expressions of ß-lactamase genes were measured by an enzymatic assay and quantitative RT-PCR, respectively. In vivo efficacy was determined using Galleria mellonella and murine thigh infection models. RESULTS: The presence of blaB resulted in >16-fold increases, while blaGOB caused 4-16-fold increases of carbapenem MICs. Hydrolysis of carbapenems was highest in lysates of blaB-positive strains, possibly due to the constitutionally higher expression of blaB. Imipenem was ineffective against blaB-positive isolates in vivo in terms of improvement of the survival of wax moth larvae and reduction of murine bacterial load. The deletion of blaB restored the efficacy of imipenem. The blaB gene was also responsible for a >4-fold increase of ampicillin/sulbactam and piperacillin/tazobactam MICs. The presence of blaCME, but not blaB or blaGOB, increased the MICs of ceftazidime and cefepime by 8-16- and 4-8-fold, respectively. CONCLUSIONS: The constitutionally and highly expressed blaB gene in E. anophelis was responsible for increased MICs of carbapenems and led to their poor in vivo efficacy. blaCME increased the MICs of ceftazidime and cefepime.

Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors.

BACKGROUND: Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis. METHODS: We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model. RESULTS: In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant. CONCLUSION: Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.

Possible mechanisms of auricular acupoint stimulation in the treatment of migraine by activating auricular vagus nerve. / 从耳迷走神经探讨耳穴治疗偏头痛的可能机制.

Under the guidance of traditional Chinese medicine theory, the clinical research of auricular acupoint stimulation in the treatment of migraine has gained a lot, and the curative efficacy is definite, but its mechanism remains unclear. In the present paper, we discussed the efficacy of auricular acupoint stimulation including "transcutaneous auricular vagus nerve stimulation" (taVNS) in the treatment of migraine in recent years. Through bibliometric analysis, we screened out top 10 auricular acupoints (Shenmenï¼»TF4ï¼½, Pizhixiaï¼»AT4ï¼½, Jiaoganï¼»AH6aï¼½, Ganï¼»CO12ï¼½, Yidanï¼»CO11ï¼½, Neifenmiï¼»CO18ï¼½, Shenï¼»CO10ï¼½, Nieï¼»AT2ï¼½, Zhenï¼»AT3ï¼½ and Eï¼»AT1ï¼½) which were the most frequently used for migraine. Majority of these auricular acupoints just distributed in the region innervated by auricular vagus nerve. Thus, we thought that the analgesic effect of needling these auricular acupoints for migraine was produced by triggering the auricular vagus nerve, and concluded that the central mechanism underlying induction of analgesic effect by activating auricular vagus nerve may be achieved by activating the descending pain regulation pathway of the locus coeruleus nucleus and dorsal raphe nucleus. In addition, taVNS-induced 1) regulation of the activities of brain's default network and pain matrix, 2) activation of the cortical descending pain regulation pathway, and 3) inhibition of the neuroinflammatory response may also contribute to its ameliorating effect of migraine. This paper may provide ideas for the future research on the mechanism of auricular acupoint treatment of migraine.

Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness.

Synaptic plasticity plays a critical role in the expression power of brain neural networks. Among diverse plasticity rules, synaptic scaling presents indispensable effects on homeostasis maintenance and synaptic strength regulation. In the current modeling of brain-inspired spiking neural networks (SNN), backpropagation through time is widely adopted because it can achieve high performance using a small number of time steps. Nevertheless, the synaptic scaling mechanism has not yet been well touched. In this work, we propose an experience-dependent adaptive synaptic scaling mechanism (AS-SNN) for spiking neural networks. The learning process has two stages: First, in the forward path, adaptive short-term potentiation or depression is triggered for each synapse according to afferent stimuli intensity accumulated by presynaptic historical neural activities. Second, in the backward path, long-term consolidation is executed through gradient signals regulated by the corresponding scaling factor. This mechanism shapes the pattern selectivity of synapses and the information transfer they mediate. We theoretically prove that the proposed adaptive synaptic scaling function follows a contraction map and finally converges to an expected fixed point, in accordance with state-of-the-art results in three tasks on perturbation resistance, continual learning, and graph learning. Specifically, for the perturbation resistance and continual learning tasks, our approach improves the accuracy on the N-MNIST benchmark over the baseline by 44% and 25%, respectively. An expected firing rate callback and sparse coding can be observed in graph learning. Extensive experiments on ablation study and cost evaluation evidence the effectiveness and efficiency of our nonparametric adaptive scaling method, which demonstrates the great potential of SNN in continual learning and robust learning.

Potential Value of HSP90α in Prognosis of Triple-Negative Breast Cancer.

BACKGROUND Triple-negative breast cancer (TNBC) is a distinct subtype of breast cancer, accounting for 12-18% of all breast cancer cases. It exhibits high heterogeneity and aggressiveness, resulting in a poorer prognosis with a high risk of early recurrence and metastasis. Due to the lack of expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2), as well as insensitivity to endocrine therapy, determining a standard treatment for TNBC is challenging. The identification of potential prognostic biomarkers is crucial for developing personalized treatment strategies for patients. MATERIAL AND METHODS Our study investigated the potential value of HSP90a in TNBC prognosis. A retrospective analysis was conducted on 127 TNBC patients and 127 Healthy controls from March 1, 2019 to July 31, 2022. Venous blood was collected and tested for HSP90alpha, CEA, CA199, and CA125, and we recorded the clinical characteristics of the patients, including age, BMI, alcohol consumption status, surgical history, CEA level, CA199 level, CA125 level, HSP90alpha level, tumor size, distant metastases, lymph node metastasis, and TNM stage. Univariate and multivariate methods were used to screen independent risk factors for progression-free survival (PFS) and overall survival (OS). RESULTS HSP90alpha is not only upregulated in TNBC but is also highly correlated with lymph node metastasis and TNM stage. The results of multivariate analysis showed that distant metastasis, TNM stage and HSP90a level were independent factors associated with PFS. BMI, tumor size, TNM stage, surgical history, and HSP90a level were independent factors influencing OS. CONCLUSIONS Our research findings demonstrate a significant association between high HSP90alpha expression and adverse clinical features, suggesting a poorer prognosis for TNBC patients.

Isolation and characterization of polyester polyurethane-degrading bacterium Bacillus sp. YXP1.

Microorganisms have the potential to be applied for the degradation or depolymerization of polyurethane (PU) and other plastic waste, which have attracted global attention. The appropriate strain or enzyme that can effectively degrade PU is the key to treat PU plastic wastes by biological methods. Here, a polyester PU-degrading bacterium Bacillus sp. YXP1 was isolated and identified from a plastic landfill. Three PU substrates with increasing structure complexities, including Impranil DLN, poly (1,4-butylene adipate)-based PU (PBA-PU), and polyester PU foam, were used to evaluate the degradation capacity of Bacillus sp. YXP1. Under optimal conditions, strain YXP1 could completely degrade 0.5% Impranil DLN within 7 days. After 30 days, the weight loss of polyester PU foam by strain YXP1 was as high as 42.1%. In addition, PBA-PU was applied for degradation pathway analysis due to its clear composition and chemical structure. Five degradation intermediates of PBA-PU were identified, including 4,4'-methylenedianiline (MDA), 1,4-butanediol, adipic acid, and two MDA derivates, indicating that strain YXP1 could depolymerize PBA-PU by the hydrolysis of ester and urethane bonds. Furthermore, the extracellular enzymes produced by strain YXP1 could hydrolyze PBA-PU to generate MDA. Together, this study provides a potential bacterium for the biological treatment of PU plastic wastes and for the mining of functional enzymes.

The Role of Acetylation and Methylation of Rat Hippocampal Histone H3 in the Mechanism of Aluminum-Induced Neurotoxicity.

Aluminum is a known neurotoxin and a major environmental contributor to neurodegenerative diseases such as Alzheimer's disease (AD). We uesd a subchronic aluminum chloride exposure model in offspring rats by continuously treating them with AlCl3 solution from the date of birth until day 90 in this research. Then evaluated the neurobehavioral changes in rats, observed the ultrastructural changes of hippocampal synapses and neurons, and examined the level of hippocampal acetylated histone H3 (H3ac), the activity and protein expression of hippocampal HAT1 and G9a, and the protein expression level of H3K9 dimethylation (H3K9me2). The findings demonstrated that aluminum-treated offspring rats had impaired learning and memory abilities as well as ultrastructural alterations in hippocampal synapses and neurons. The level of histone H3ac was decreased along with decreased protein expression and activity of HAT1, while level of H3K9me2 was increased along with increased protein expression and activity of G9a.

Effects of miR-204-5p and Target Gene EphB2 on Cognitive Impairment Induced by Aluminum Exposure in Rats.

Aluminum is a common environmental neurotoxin. Aluminum ions can cross the blood-brain barrier and accumulate in different brain regions, damage brain tissue, and cause cognitive impairment, but the molecular mechanism of aluminum neurotoxicity is not precise. This study investigated the effects of miR-204-5p, target gene EphB2, and downstream signaling pathway NMDAR-ERK-CREB-Arc on cognitive dysfunction induced by aluminum exposure. The results showed that the learning and memory of the rats were impaired in behavior. The accumulation of aluminum in the hippocampus resulted in the damage of nerve cell morphology in the CA1 region of the hippocampus. The expression level of miR-204-5p was increased, and the mRNA and protein expressions of EphB2, NMDAR2B, ERK1/2, CREB, and Arc were decreased. The results indicated that the mechanism of impaired learning and memory induced by aluminum exposure might promote the expression of miR-204-5P and further inhibit the expression of the target gene EphB2 and its downstream signaling pathway NMDAR-ERK-CREB-Arc.

The Role of Histone H2B Acetylation Modification in Aluminum-Induced Cognitive Dysfunction.

Aluminum (Al) is a low toxic trace element that can accumulate in the nervous system and induce cognitive disorders characterized by reduced learning and memory ability. Neuroepigenetic effects are structural changes in cellular function by the brain in response to environmental stimuli by altering the expression of specific genes and repressing normal cellular transcription, leading to abnormalities in a variety of biological processes within the nervous system and affecting neurobehavioral responses. One of the most important mechanisms of epigenetic control on chromatin shape is histone modification. In the present study, we established an offspring rat model of Al intoxication to investigate the changes in spatial learning and memory retention abilities and the relationship with histone H2B acetylation modification in rats exposed to different doses of Al over a long period of time. The results demonstrated that long-term AlCl3 staining resulted in decreased CBP gene and protein expression, increased HDAC3 gene and protein levels, as well as decreased histone H2B and acH2BK20 protein expression levels in the hippocampus of rats. In conclusion, long-term exposure to Al may vary the expression of histone H2B and acH2BK20 through the regulation of enzymes that specifically regulate histone acetylation, hence hastening the deterioration of the nervous system that impairs cognitive function.

Complex resistivity spectrum of pollutant soils with low-concentration heavy metals.

Soil contamination by heavy metals occurs globally, with varying degrees of severity, especially in agricultural fields. Investigating the frequency response characteristics of different types of heavy metal pollutants through induced polarization can provide valuable evidence for surveys based on this method. Soil specimens with varying low concentrations of copper (Cu), chromium (Cr), cadmium (Cd), and lead (Pb) heavy metals were prepared for this study, and parameters including complex resistivity, amplitude-frequency, and resistivity phase were measured. Our findings reveal the following trends: Complex resistivity decreases as heavy metal concentrations increase, demonstrating significant shifts within lower concentration ranges but presenting limitations for assessing pollution in high-concentration areas. Conversely, amplitude-frequency increases with higher heavy metal concentrations, displaying excellent performance in high-concentration scenarios. The differences in complex resistivity and amplitude-frequency among different types of heavy metal pollutants are distinct. In contrast, the absolute phase decreases with rising heavy metal concentrations. The resistivity phase spectra for various heavy metal pollutants exhibit unique patterns. For example, copper-contaminated soil exhibits phase peaks in the frequency range of 8-32 Hz, whereas chromium-contaminated soil shows phase peaks at 16-64 Hz. Cadmium-contaminated soil displays phase peaks ranging from 0.25 Hz to 2 Hz, while lead-contaminated soil exhibits phase peaks within the 0.5 Hz-4 Hz range. Leveraging the frequency range corresponding to phase peaks as an identification method for heavy metal pollution types proves effective. The frequency response characteristics of induced polarization vary significantly among different types and concentrations of heavy metal pollutants, providing important foundations for the application of induced polarization method in the field of heavy metal pollution detection.

CDKN2A was a cuproptosis-related gene in regulating chemotherapy resistance by the MAGE-A family in breast cancer: based on artificial intelligence (AI)-constructed pan-cancer risk model.

BACKGROUND: Before the discovery of cuproptosis, copper-loaded nanoparticle is a wildly applied strategy for enhancing the tumor-cell-killing effect of chemotherapy. Although copper(ii)-related researches are wide, details of cuproptosis-related bioprocess in pan-cancer are not clear yet now, especially for prognosis and drug sensitivity prediction yet now. METHODS: In this study, VOSviewer is used for the literature review, and R4.2.0 is used for data analysis. Public data are collected from TCGA and GEO, local breast cancer cohort is collected to verify the expression level of CDKN2A. RESULTS: 7036 published articles exhibited a time-dependent linear relationship (R=0.9781, p<0.0001), and breast cancer (33.4%) is the most researched topic. Cuproptosis-related-genes (CRGs)-based unsupervised clustering divides pan-cancer subgroups into four groups (CRG subgroup) with differences in prognosis and tumor immunity. 44 tumor-driver-genes (TDGs)-based prediction model of drug sensitivity and prognosis is constructed by artificial intelligence (AI). Based on TDGs and clinical features, a nomogram is (C- index: 0.7, p= 6.958e- 12) constructed to predict the prognosis of breast cancer. Importance analysis identifies CDKN2A has a pivotal role in AI modeling, whose higher expression indicates worse prognosis in breast cancer. Furthermore, inhibition of CDKN2A down-regulates decreases Snail1, Twist1, Zeb1, vimentin and MMP9, while E-cadherin is increased. Besides, inhibition of CDKN2A also decreases the expression of MEGEA4, phosphorylated STAT3, PD-L1, and caspase3, while cleaved-caspase3 is increased. Finally, we find down-regulation of CDKN2A or MAGEA inhibits cell migration and wound healing, respectively. CONCLUSIONS: AI identified CRG subgroups in pan-cancer based on CRGs-related TDGs, and 44-gene-based AI modeling is a novel tool to identify chemotherapy sensitivity in breast cancer, in which CDKN2A/MAGEA4 pathway played the most important role.

Spike Attention Coding for Spiking Neural Networks.

Spiking neural networks (SNNs), an important family of neuroscience-oriented intelligent models, play an essential role in the neuromorphic computing community. Spike rate coding and temporal coding are the mainstream coding schemes in the current modeling of SNNs. However, rate coding usually suffers from limited representation resolution and long latency, while temporal coding usually suffers from under-utilization of spike activities. To this end, we propose spike attention coding (SAC) for SNNs. By introducing learnable attention coefficients for each time step, our coding scheme can naturally unify rate coding and temporal coding, and then flexibly learn optimal coefficients for better performance. Several normalization and regularization techniques are further incorporated to control the range and distribution of the learned attention coefficients. Extensive experiments on classification, generation, and regression tasks are conducted and demonstrate the superiority of the proposed coding scheme. This work provides a flexible coding scheme to enhance the representation power of SNNs and extends their application scope beyond the mainstream classification scenario.

[Effect of transcutaneous auricular vagus nerve stimulation on the splenic α7nAchR/JAK2/STAT3 signaling pathway in LPS-induced depressive-like behavior rats].

OBJECTIVE: To observe the effect of transcutaneous auricular vagus nerve stimulation (taVNS) on the improvement of depressive-like behavior and the splenic α7 nicotinic acetylcholine receptor (α7nAchR) / Janus kinase 2 (JAK2 / signal transducer and activator of transcription 3 (STAT3) signaling pathway in lipopolysaccharide (LPS)-induced depressive-like behavior rats, so as to investigate the antidepressant mechanism of taVNS. METHODS: SD rats were randomly divided into SD control group, SD model group and SD taVNS group, and α7nAchR knockout rats were also randomly divided into α7 control group, α7 model group and α7 taVNS group, with 6 rats in each group. Rat model of depressive-like behavior was established by intraperitoneal injection of LPS (1 mg/kg). Rats in both SD taVNS and α7 taVNS groups received taVNS intervention once a day (2 Hz/15 Hz, 2 mA, 30 min) from 7 days before LPS injection to 2 days after LPS injection, respectively. The mean speed, activity time and side immobility time in the open field test were recorded after taVNS. The contents of interleukin 10 (IL-10) and chemokine (C-X-C motif) ligand 1 (CXCL1) in serum were detected by electrochemiluminescence multifactorial method. The splenic phosphorylated (p)-JAK2 and p-STAT3 protein expressions were detected by Western blot. RESULTS: Compared with their respective control groups, the mean speed and active time were reduced (P<0.01, P<0.05, P<0.001) and the side immobility time was increased (P<0.001) in the open field test, serum IL-10 and CXCL1 levels were up-regulated (P<0.01, P<0.05, P<0.001), and splenic p-JAK2 protein expressions were down-regulated (P<0.05, P<0.01) in SD and α7nAchR knockout rats, and splenic p-STAT3 protein expression were down-regulated (P<0.05) in SD rats after LPS injection. Following taVNS intervention and in comparison with the model group , the mean speed and active time were increased (P<0.01) and the side immobility time was decreased (P<0.001) in the open field test, serum IL-10 and CXCL1 levels down-regulated (P<0.05), while splenic p-JAK2 and p-STAT3 protein expressions were up-regulated (P<0.01, P<0.001) in the SD taVNS group rather than in the α7 taVNS group. Compared with SD taVNS group, the α7 taVNS group showed increased (P<0.001, P<0.05) side immobility time in the open field test and serum IL-10, decreased splenic p-JAK2 and p-STAT3 protein expressions (P<0.01, P<0.05). CONCLUSION: taVNS may exert anti-inflammatory effects through modulating the splenic α7nAchR/JAK2/STAT3 signaling pathway, thereby ameliorating LPS-induced depressive-like behavior in rats.