Causal association between tea consumption and head and neck cancer: a Mendelian randomization study.

Although evidence supports an observational association between tea consumption and susceptibility to head and neck cancer, the causal nature of this association remains unclear. We performed a two-sample Mendelian randomization (MR) analysis to determine the causal effects of tea consumption on head and neck cancer. We employed a fixed-effects inverse variance-weighted model for the MR analysis. Genome-wide association study (GWAS) summary data for tea consumption were obtained from the UK Biobank Consortium, and GWAS data for head and neck cancer were derived from two data sources and were used as the outcomes. Our MR analysis revealed limited evidence for a causal relationship between various types of tea intake and head and neck cancer. After adjustment for smoking and alcohol consumption, there was no causal relationship between tea consumption and head and neck cancer. Further experimental studies are required to confirm its potential role in these malignancies.

Prevalence and risk factors for subjective cognitive decline and the correlation with objective cognition among community-dwelling older adults in China: Results from the Hubei memory and aging cohort study.

INTRODUCTION: The prevalence and risk factors for subjective cognitive decline (SCD) and its correlation with objective cognition decline (OCD) among community-dwelling older adults is inconsistent. METHODS: Older adults underwent neuropsychological and clinical evaluations to reach a consensus on diagnoses. RESULTS: This study included 7486 adults without mild cognitive impairment and dementia (mean age: 71.35 years [standard deviation = 5.40]). The sex-, age-, and residence-adjusted SCD prevalence was 58.33% overall (95% confidence interval: 58.29% to 58.37%), with higher rates of 61.25% and 59.87% in rural and female subgroups, respectively. SCD global and OCD language, SCD memory and OCD global, SCD and OCD memory, and SCD and OCD language were negatively correlated in fully adjusted models. Seven health and lifestyle factors were associated with an increased risk for SCD. DISCUSSION: SCD affected 58.33% of older adults and may indicate concurrent OCD, which should prompt the initiation of preventative intervention for dementia. HIGHLIGHTS: SCD affects 58.33% of older adults in China. SCD may indicate concurrent objective cognitive decline. Difficulty finding words and memory impairments may indicate a risk for AD. The presence of SCD may prompt preventative treatment initiation of MCI or dementia. Social network factors may be initial targets for the early prevention of SCD.

Electroacupuncture Attenuates Fibromyalgia Pain via Toll-like Receptor 4 in the Mouse Brain.

BACKGROUND: Fibromyalgia (FM) is characterized by complex pain symptoms lacking impersonal considerations in diagnosis and treatment evaluation, which often happens in women. Chronic and persistent widespread pain is the key symptom disturbing patients with FM, leading to depression, obesity, and sleep disturbances. Toll-like receptor 4 (TLR4) activation produces a harmful sensory input involved in central pain; this is the focus of this study. Electroacupuncture (EA) has beneficial effects in reducing FM pain, but its connection with TLR4 signaling is still unknown. METHODS: Intermittent cold stress significantly induced mechanical and thermal pain. EA, but not sham EA, reliably attenuated mechanical and thermal hyperalgesia. The increased inflammatory mediators in FM mice were reduced in the EA group, but not in the sham group. RESULTS: All TLR4 and related molecule levels increased in the FM mice's hypothalamus, periaqueductal gray (PAG), and cerebellum. These increases could be attenuated by EA but not sham stimulation. Activation of TLR4 by lipopolysaccharide (LPS) significantly induced FM and can be further reversed by a TLR4 antagonist. CONCLUSIONS: These mechanisms provide evidence that the analgesic effect of EA is related to the TLR4 pathway. In addition, we showed that inflammation can activate the TLR4 pathway and provided new possible therapeutic targets for FM pain.

Clinical prognosis of surgical resection versus transarterial chemoembolization for single large hepatocellular carcinoma (≥5 cm): A propensity score matching analysis.

Favorable prognostic factors and therapeutic strategies are important for patients with single large hepatocellular carcinoma (HCC). This retrospective study aimed to investigate the prognostic factors in patients with single large (≥5 cm) HCC with Child-Pugh (CP) class A patients and to recommend therapeutic strategies. Overall, 298 HCC patients with single and large (≥5 cm) tumors with CP class A, but without distant metastasis and macrovascular invasion were included, and their clinicopathological data, overall survival (OS), and progression-free survival (PFS) were recorded. OS and PFS was analyzed by the Kaplan-Meier method and Cox regression analysis. Propensity score matching (PSM) analysis was performed. The 298 HCC patients were 79.2% male and median age of 64 years. For the initial treatment, surgical resection (SR) and transarterial chemoembolization (TACE) was 50.8% and 49.2%, respectively. The OS and PFS were significantly higher in patients receiving SR than those receiving TACE before and after PSM. Furthermore, in multivariate analysis, cirrhosis (Hazard ratio [HR]: 2.04; 95% confidence interval [CI]: 1.35-3.03, p < 0.001, CP class A5/6 [HR: 4.01; 95% CI: 2.43-6.66, p < 0.001], and initial treatment [SR vs. TACE HR = 3.23; 95% CI: 2.13-5.01, p < 0.001]) remained significantly associated with mortality. Moreover, in multivariate analysis, CP class A5/6 (HR: 3.23; 95% CI: 1.89-5.88, p < 0.001), and initial treatment (Resection vs. TACE; HR = 4.17; 95% CI: 1.64-8.33, p = 0.039) remained significantly associated with recurrence. In conclusion, SR was associated with significantly higher OS and PFS rates than TACE before and after PSM for single large HCC patients.

SeqCP: A sequence-based algorithm for searching circularly permuted proteins.

Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://pcnas.life.nthu.edu.tw/SeqCP/.

Renal replacement therapy and concurrent fluconazole therapy increase linezolid-related thrombocytopenia among adult patients.

Linezolid has been reported to be associated with thrombocytopenia. However, limited information is available on susceptibility to thrombocytopenia after linezolid usage. We aimed to investigate the risk factors for linezolid-associated thrombocytopenia (LAT). We conducted a retrospective cohort study of patients aged ≥ 18 years who received linezolid for ≥ 5 d during hospitalization in 2019. Information was extracted from electronic medical records. Thrombocytopenia was defined as a platelet count of < 100 × 109/L or a reduction from baseline ≥ 25%. Binary logistic regression and survival analyses were used to evaluate the risk factors for LAT. A total of 98 patients were enrolled. Thrombocytopenia occurred in 53.1% patients, with a median of 9 d after initiation of linezolid. There was no significant difference in the mortality or proportion of platelet transfusions between patients with and without thrombocytopenia. A higher risk of LAT was found in patients who received renal replacement therapy (RRT) (OR 4.8 [1.4-16.4]), or concurrent fluconazole (OR 3.5 [1.2-9.8]). Patients who received RRT (8 vs. 15 d) or concurrent fluconazole (11 vs. 15 d) had a shorter median time to develop thrombocytopenia. Those who simultaneously received RRT and fluconazole had the shortest median of time (6.5 d) and the highest risk of developing thrombocytopenia (87.5%).

Identification of pan-kinase-family inhibitors using graph convolutional networks to reveal family-sensitive pre-moieties.

BACKGROUND: Human protein kinases, the key players in phosphoryl signal transduction, have been actively investigated as drug targets for complex diseases such as cancer, immune disorders, and Alzheimer's disease, with more than 60 successful drugs developed in the past 30 years. However, many of these single-kinase inhibitors show low efficacy and drug resistance has become an issue. Owing to the occurrence of highly conserved catalytic sites and shared signaling pathways within a kinase family, multi-target kinase inhibitors have attracted attention. RESULTS: To design and identify such pan-kinase family inhibitors (PKFIs), we proposed PKFI sets for eight families using 200,000 experimental bioactivity data points and applied a graph convolutional network (GCN) to build classification models. Furthermore, we identified and extracted family-sensitive (only present in a family) pre-moieties (parts of complete moieties) by utilizing a visualized explanation (i.e., where the model focuses on each input) method for deep learning, gradient-weighted class activation mapping (Grad-CAM). CONCLUSIONS: This study is the first to propose the PKFI sets, and our results point out and validate the power of GCN models in understanding the pre-moieties of PKFIs within and across different kinase families. Moreover, we highlight the discoverability of family-sensitive pre-moieties in PKFI identification and drug design.

Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations.

BACKGROUND: While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. RESULTS: In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. CONCLUSION: With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.

Discovery of moiety preference by Shapley value in protein kinase family using random forest models.

BACKGROUND: Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). RESULTS: In this study, we used "merged moiety-based interpretable features (MMIFs)," which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. CONCLUSIONS: In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.

Efficacy of acupuncture at ghost points combined with fluoxetine in treating depression: A randomized study.

BACKGROUND: Depression affects more than 350 million people worldwide. In China, 4.2% (54 million people) of the total population suffers from depression. Psychotherapy has been shown to change cognition, improve personality, and enhance the ability to cope with difficulties and setbacks. While pharmacotherapy can reduce symptoms, it is also associated with adverse reactions and relapse after drug withdrawal. Therefore, there has been an increasing emphasis placed on the use of non-pharmacological therapies for depression. The hypothesis of this study was that acupuncture at ghost points combined with fluoxetine would be more effective than fluoxetine alone for the treatment of depression. AIM: To investigate the efficacy of acupuncture at ghost points combined with fluoxetine for the treatment of patients with depression. METHODS: This randomized controlled trial included patients with mild to moderate depression (n = 160). Patients received either acupuncture at ghost points combined with fluoxetine (n = 80) or fluoxetine alone (control group, n = 80). Needles were retained in place for 30 min, 5 times a week; three treatment cycles were administered. The Mann-Whitney U test was used to compare functional magnet resonance imaging parameters, Hamilton depression rating scale (HAMD) scores, and self-rating depression scale (SDS) scores between the acupuncture group and control group. RESULTS: There were no significant differences in HAMD or SDS scores between the acupuncture group and control group, before or after 4 wk of treatment. The acupuncture group exhibited significantly lower HAMD and SDS scores than the control group after 8 wk of treatment (P < 0.05). The acupuncture group had significantly lower fractional Amplitude of Low Frequency Fluctuations values for the left anterior wedge leaf, left posterior cingulate gyrus, left middle occipital gyrus, and left inferior occipital gyrus after 8 wk. The acupuncture group also had significantly higher values for the right inferior frontal gyrus, right insula, and right hippocampus (P < 0.05). After 8 wk of treatment, the effective rates of the acupuncture and control groups were 51.25% and 36.25%, respectively (P < 0.05). CONCLUSION: The study results suggest that acupuncture at ghost points combined with fluoxetine is more effective than fluoxetine alone for the treatment of patients with mild to moderate depression.

Whole-Blood 3-Gene Signature as a Decision Aid for Rifapentine-based Tuberculosis Preventive Therapy.

BACKGROUND: Systemic drug reaction (SDR) is a major safety concern with weekly rifapentine plus isoniazid for 12 doses (3HP) for latent tuberculosis infection (LTBI). Identifying SDR predictors and at-risk participants before treatment can improve cost-effectiveness of the LTBI program. METHODS: We prospectively recruited 187 cases receiving 3HP (44 SDRs and 143 non-SDRs). A pilot cohort (8 SDRs and 12 non-SDRs) was selected for generating whole-blood transcriptomic data. By incorporating the hierarchical system biology model and therapy-biomarker pathway approach, candidate genes were selected and evaluated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, interpretable machine learning models presenting as SHapley Additive exPlanations (SHAP) values were applied for SDR risk prediction. Finally, an independent cohort was used to evaluate the performance of these predictive models. RESULTS: Based on the whole-blood transcriptomic profile of the pilot cohort and the RT-qPCR results of 2 SDR and 3 non-SDR samples in the training cohort, 6 genes were selected. According to SHAP values for model construction and validation, a 3-gene model for SDR risk prediction achieved a sensitivity and specificity of 0.972 and 0.947, respectively, under a universal cutoff value for the joint of the training (28 SDRs and 104 non-SDRs) and testing (8 SDRs and 27 non-SDRs) cohorts. It also worked well across different subgroups. CONCLUSIONS: The prediction model for 3HP-related SDRs serves as a guide for establishing a safe and personalized regimen to foster the implementation of an LTBI program. Additionally, it provides a potential translational value for future studies on drug-related hypersensitivity.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 µM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Discovering the Ultimate Limits of Protein Secondary Structure Prediction.

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81-86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4-5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84-87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

CirPred, the first structure modeling and linker design system for circularly permuted proteins.

BACKGROUND: This work aims to help develop new protein engineering techniques based on a structural rearrangement phenomenon called circular permutation (CP), equivalent to connecting the native termini of a protein followed by creating new termini at another site. Although CP has been applied in many fields, its implementation is still costly because of inevitable trials and errors. RESULTS: Here we present CirPred, a structure modeling and termini linker design method for circularly permuted proteins. Compared with state-of-the-art protein structure modeling methods, CirPred is the only one fully capable of both circularly-permuted modeling and traditional co-linear modeling. CirPred performs well when the permutant shares low sequence identity with the native protein and even when the permutant adopts a different conformation from the native protein because of three-dimensional (3D) domain swapping. Linker redesign experiments demonstrated that the linker design algorithm of CirPred achieved subangstrom accuracy. CONCLUSIONS: The CirPred system is capable of (1) predicting the structure of circular permutants, (2) designing termini linkers, (3) performing traditional co-linear protein structure modeling, and (4) identifying the CP-induced occurrence of 3D domain swapping. This method is supposed helpful for broadening the application of CP, and its web server is available at http://10.life.nctu.edu.tw/CirPred/ and http://lo.life.nctu.edu.tw/CirPred/ .

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

Reliability Evaluation of Fan-Out Type 3D Packaging-On-Packaging.

The development of fan-out packaging technology for fine-pitch and high-pin-count applications is a hot topic in semiconductor research. To reduce the package footprint and improve system performance, many applications have adopted packaging-on-packaging (PoP) architecture. Given its inherent characteristics, glass is a good material for high-speed transmission applications. Therefore, this study proposes a fan-out wafer-level packaging (FO-WLP) with glass substrate-type PoP. The reliability life of the proposed FO-WLP was evaluated under thermal cycling conditions through finite element simulations and empirical calculations. Considering the simulation processing time and consistency with the experimentally obtained mean time to failure (MTTF) of the packaging, both two- and three-dimensional finite element models were developed with appropriate mechanical theories, and were verified to have similar MTTFs. Next, the FO-WLP structure was optimized by simulating various design parameters. The coefficient of thermal expansion of the glass substrate exerted the strongest effect on the reliability life under thermal cycling loading. In addition, the upper and lower pad thicknesses and the buffer layer thickness significantly affected the reliability life of both the FO-WLP and the FO-WLP-type PoP.

Identifying Patients With Cirrhosis Who Might Avoid Screening Endoscopy Based on Serum Albumin and Bilirubin and Platelet Counts.

The presence of gastroesophageal varices is a major complication of portal hypertension associated with significant morbidity and mortality.1 The Baveno VI criteria state that patients with liver stiffness measurement (LSM) <20 kPa by transient elastography (TE) and platelet count >150,000/µL can avoid screening endoscopy for high-risk varix (HRV).2 However, because TE is not widely available, the Baveno VI criteria could not be applied in many clinical settings. As such, we aim to determine a concise clinical criterion as an alternative noninvasive tool to predict absence of HRV among patients with compensated cirrhosis to avoid screening esophagogastroduodenoscopy (EGD).

Brain surgery in combination with tyrosine kinase inhibitor and whole brain radiotherapy for epidermal growth factor receptor-mutant non-small-cell lung cancer with brain metastases.

The role of brain surgery (BS) on the survival of patients with non-small-cell lung cancer (NSCLC) and brain metastases (BM), particularly those with epidermal growth factor receptor (EGFR) mutations under tyrosine kinase inhibitors (TKIs) is yet to be defined. We aimed to investigate whether BS could improve the survival of patients in addition to the combination of TKIs and whole brain radiotherapy (WBRT). A cohort of 1394 NSCLC patients between 2011 and 2016 was retrospectively studied. One hundred patients with BM receiving TKI + RT were enrolled. Forty patients (40%) received TKI + BS + RT, and 60 patients (60%) received TKI + RT. Survival time was calculated from the date of BM diagnoses to the date of death or last follow-up. With a median follow-up of 25.6 months (95% CI, 18.6-35.7), the median survival after BM was 18.2 months (95% CI, 10.8 to 27.4) in the TKI + BS + RT group and 11.8 months (95% CI, 5.2 to18) in the TKI + RT group. Cox proportional hazards regression model for the patients with the largest BM over 1 cm showed that TKI + BS + RT group was associated with improved survival relative to TKI + RT group (HR, 0.49; 95% CI, 0.29 to 0.83; P = 0.008). BS adds significant survival benefits in addition to TKIs and WBRT, especially for patients with EGFR-mutant NSCLC and the largest BM over 1 cm.

Distal Electroacupuncture at the LI4 Acupoint Reduces CFA-Induced Inflammatory Pain via the Brain TRPV1 Signaling Pathway.

There is accumulating evidence supporting electroacupuncture's (EA) therapeutic effects. In mice, local EA reliably attenuates inflammatory pain and increases the transient receptor potential cation channel, subfamily V, member 1 (TRPV1). However, the effect of distal acupoint EA on pain control has rarely been studied. We used a mouse model to investigate the analgesic effect of distal EA by measuring TRPV1 expression in the brain. Complete Freund's adjuvant (CFA) was injected into mice's hind paws to induce inflammatory pain. The EA-treated group received EA at the LI4 acupoint on the bilateral forefeet on the second and the third days, whereas the control group underwent sham manipulation. Mechanical and thermal pain behavior tests showed that the EA-treated group experienced inflammatory pain alleviation immediately after EA, which did not occur in the sham group. Additionally, following CFA injection, the expression of TRPV1-associated molecules such as phosphorylated protein kinase A (pPKA), extracelluar signal-regulated kinase (pERK), and cAMP-response-element-binding protein (pCREB) increased in the prefrontal cortex (PFC) and the hypothalamus but decreased in the periaqueductal gray (PAG) area. These changes were significantly attenuated by EA but not sham EA. Our results show an analgesic effect of distal EA, which is based on the traditional Chinese medicine theory. The mechanism underlying this analgesic effect involves TRPV1 in the PFC, the hypothalamus, and the PAG. These novel findings are relevant for the evaluation and the treatment of clinical inflammatory pain syndrome.