Effects of Patient-Controlled Transcutaneous Electrical Acupoint Stimulation on Cancer Induced Bone Pain Relief in Patients with Non-Small Cell Lung Cancer: Study Protocol for a Randomized Controlled Trial.

Background: Transcutaneous Electrical Acupoint Stimulation (TEAS) therapy opens up the possibility for individuals with Cancer-induced bone pain (CIBP) to receive a home-based, patient-controlled approach to pain management. The aim of this study is designed to evaluate the efficacy of patient-controlled TEAS (PC-TEAS) for relieving CIBP in patients with non-small cell lung cancer (NSCLC). Methods/Design: This is a study protocol for a prospective, triple-blind, randomized controlled trial. We anticipate enrolling 188 participants with NSCLC bone metastases who are also using potent opioid analgesics from 4 Chinese medical centers. These participants will be randomly assigned in a 1:1 ratio to either the true PC-TEAS or the sham PC-TEAS group. All participants will receive standard adjuvant oncology therapy. The true group will undergo patient-controlled TEAS intervention as needed, while the sham group will follow the same treatment schedule but with non-conductive gel patches. Each treatment course will span 7 days, with a total of 4 courses administered. There will be 4 assessment time points: baseline, the conclusion of weeks 4, 8, and 12. The primary outcome of this investigation is the response rate of the average pain on the Brief Pain Inventory (BPI) scale at week 4 after treatment. Secondary outcomes include pain related indicators, quality of life scale, mood scales, and routine blood counts on the assessment days. Any adverse events will be promptly addressed and reported if they occur. We will manage trial data using the EDC platform, with a data monitoring committee providing regular quality oversight. Discussion: PC-TEAS interventions offer an attempt to achieve home-based acupuncture treatment and the feasibility of achieving triple blinding in acupuncture research. This study is designed to provide more rigorous trial evidence for the adjuvant treatment of cancer-related pain by acupuncture and to explore a safe and effective integrative medicine scheme for CIBP. Trial Registration: ClinicalTrials.gov NCT05730972, registered February 16, 2023.

Unveiling Low Temperature Assembly of Dense Fe-N4 Active Sites via Hydrogenation in Advanced Oxygen Reduction Catalysts.

The single-atom Fe-N-C is a prominent material with exceptional reactivity in areas of sustainable energy and catalysis research. It is challenging to obtain the dense Fe-N4 site without the Fe nanoparticle (NPs) sintering during the Fe-N-C synthesis via high-temperature pyrolysis. Thus, a novel approach is devised for the Fe-N-C synthesis at low temperatures. Taking FeCl2 as Fe source, a hydrogen environment can facilitate oxygen removal and dechlorination processes in the synthesis, efficiently favouring Fe-N4 site formation without Fe nanoparticle clustering at as low as 360 °C. We shed light on the reaction mechanism about hydrogen promoting Fe-N4 formation in the synthesis. By adjusting the temperature and duration, the Fe-N4 structural evolution and site density can be precisely tuned to directly influence the catalytic behavior of the Fe-N-C material. The FeNC-H2-360 catalyst demonstrates a remarkable Fe dispersion (8.3 wt%) and superior acid ORR activity with a half-wave potential of 0.85 V and a peak power density of 1.21 W cm-2 in fuel cell. This method also generally facilitates the synthesis of various high-performance M-N-C materials (M = Fe, Co, Mn, Ni, Zn, Ru) with elevated single-atom loadings.

Regulation Strategies on Fe-N-C and Co-N-C Catalysts for Oxygen Reduction Reaction: A Review.

Proton exchange membrane fuel cells (PEMFCs) and alkaline membrane fuel cells (AEMFCs) have received great attention as the energy device of next generation. Accelerating oxygen reduction reaction (ORR) kinetics is the key to improve PEMFCs and AEMFCs performance. Platinum-based catalysts are the most widely used catalysts for ORR, but their high price and low abundance limit the commercialization of fuel cell. Non-noble metal-nitrogen-carbon (M-N-C) is considered to be the most likely material to replace Pt-based catalysts, among which Fe-N-C and Co-N-C have been widely studied due to their excellent intrinsic ORR performance and have made great progress in the past decades. With the improvement of synthesis technology and deepening understanding of the ORR mechanism, some reported Fe-N-C and Co-N-C catalysts have shown excellent ORR activity close to that of commercial Pt/C catalysts. Inspired by the progress, regulation strategies on Fe-N-C or Co-N-C catalysts are summarized in this review from 5 aspects: (1) coordinated atoms, (2) environmental heteroatoms and defects, (3) dual-metal active sites, (4) metal-based particle promoter and (5) curved carbon layer. Next, we make our own suggestions on some challenges facing Fe-N-C and Co-N-C research.

Unlocking single molecule magnetism: a supramolecular strategy for isolating neutral MnIII salen-type dimer in crystalline environments.

In [Mn(5-MeOsalen)(Cl)]2(dibenzo[24]crown-8), dibenzo[24]crown-8 formed a supramolecule via multi-point interactions with the [Mn(5-MeOsalen)(Cl)] dimer. The dimer was magnetically isolated with ST = 4 and weak interdimer magnetic interactions. The crystal exhibited single-molecule magnet behaviour with an anisotropic barrier of 26(1) K, which is the highest among the Mn-salen series reported to date.

Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.

BACKGROUND: Although papillary thyroid cancer (PTC) patients are known to have an excellent prognosis, up to 30% of patients experience disease recurrence after initial treatment. Accurately predicting disease prognosis remains a challenge given that the predictive value of several predictors remains controversial. Thus, we investigated whether machine learning (ML) approaches based on comprehensive predictors can predict the risk of structural recurrence for PTC patients. METHODS: A total of 2244 patients treated with thyroid surgery and radioiodine were included. Twenty-nine perioperative variables consisting of four dimensions (demographic characteristics and comorbidities, tumor-related variables, lymph node (LN)-related variables, and metabolic and inflammatory markers) were analyzed. We applied five ML algorithms-logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and neural network (NN)-to develop the models. The area under the receiver operating characteristic (AUC-ROC) curve, calibration curve, and variable importance were used to evaluate the models' performance. RESULTS: During a median follow-up of 45.5 months, 179 patients (8.0%) experienced structural recurrence. The non-stimulated thyroglobulin, LN dissection, number of LNs dissected, lymph node metastasis ratio, N stage, comorbidity of hypertension, comorbidity of diabetes, body mass index, and low-density lipoprotein were used to develop the models. All models showed a greater AUC (AUC = 0.738 to 0.767) than did the ATA risk stratification (AUC = 0.620, DeLong test: P < 0.01). The SVM, XGBoost, and RF model showed greater sensitivity (0.568, 0.595, 0.676), specificity (0.903, 0.857, 0.784), accuracy (0.875, 0.835, 0.775), positive predictive value (PPV) (0.344, 0.272, 0.219), negative predictive value (NPV) (0.959, 0.959, 0.964), and F1 score (0.429, 0.373, 0.331) than did the ATA risk stratification (sensitivity = 0.432, specificity = 0.770, accuracy = 0.742, PPV = 0.144, NPV = 0.938, F1 score = 0.216). The RF model had generally consistent calibration compared with the other models. The Tg and the LNR were the top 2 important variables in all the models, the N stage was the top 5 important variables in all the models. CONCLUSIONS: The RF model achieved the expected prediction performance with generally good discrimination, calibration and interpretability in this study. This study sheds light on the potential of ML approaches for improving the accuracy of risk stratification for PTC patients. TRIAL REGISTRATION: Retrospectively registered at www.chictr.org.cn (trial registration number: ChiCTR2300075574, date of registration: 2023-09-08).

A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity.

Introduction: Mummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult. Methods: Using real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate. Results: Experimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16. Discussion: These features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification.

REV-ERBα negatively regulates NLRP6 transcription and reduces the severity of Salmonella infection in mice.

Non-typhoidal Salmonella infection is among the most frequent foodborne diseases threatening human health worldwide. The host circadian clock orchestrates daily rhythms to adapt to environmental changes, including coordinating immune function in response to potential infections. However, the molecular mechanisms underlying the interplay between the circadian clock and the immune system in modulating infection processes are incompletely understood. Here, we demonstrate that NLRP6, a novel nucleotide-oligomerization domain (NOD)-like receptor (NLR) family member highly expressed in the intestine, is closely associated with the differential day-night response to Salmonella infection. The core clock component REV-ERBα negatively regulates NLRP6 transcription, leading to the rhythmic expression of NLRP6 and the secretion of IL-18 in intestinal epithelial cells, playing a crucial role in mediating the differential day-night response to Salmonella infection. Activating REV-ERBα with agonist SR9009 in wild-type mice attenuated the severity of infection by decreasing the NLRP6 level in intestinal epithelial cells. Our findings provide new insights into the association between the host circadian clock and the immune response to enteric infections by revealing the regulation of Salmonella infection via the inhibitory effect of REV-ERBα on NLRP6 transcription. Targeting REV-ERBα to modulate NLRP6 activation may be a potential therapeutic strategy for bacterial infections.

Clinical outcomes of treatment-naïve HBeAg-negative patients with chronic hepatitis B virus infection with low serum HBsAg and undetectable HBV DNA.

Serum hepatitis B surface antigen (HBsAg) level < 100 IU/ml and undetectable hepatitis B virus (HBV) DNA have been recently proposed as an alternate endpoint of "partial cure" in chronic hepatitis B (CHB). We investigated clinical outcomes of hepatitis B e antigen (HBeAg)-negative CHB patients with HBsAg <100 IU/ml and undetectable HBV DNA. Treatment-naïve HBeAg-negative CHB patients with undetectable HBV DNA and normal alanine aminotransferase were retrospectively included from three institutions. Patients were classified into the low HBsAg group (<100 IU/ml) and the high HBsAg group (≥100 IU/ml). Liver fibrosis was evaluated by noninvasive tests (NITs). A total of 1218 patients were included and the median age was 41.5 years. Patients with low HBsAg were older (45.0 vs. 40.0 years, P < 0.001) than those in the high HBsAg group, while the NIT parameters were comparable between groups. During a median follow-up of 25.7 months, patients with low HBsAg achieved a higher HBsAg clearance rate (13.0% vs. 0%, P < 0.001) and a lower rate of significant fibrosis development (2.2% vs. 7.0%, P = 0.049) compared to patients with high HBsAg. No patient developed HCC in either group. HBsAg level was negatively associated with HBsAg clearance (HR 0.213, P < 0.001) and patients with HBsAg < 100 IU/ml had a low risk of significant fibrosis development (HR 0.010, P = 0.002). The optimal cutoff value of HBsAg for predicting HBsAg clearance was 1.1 Log10 IU/ml. Treatment-naïve HBeAg-negative CHB patients with HBsAg <100 IU/ml and undetectable HBV DNA had favourable outcomes with a high rate of HBsAg clearance and a low risk of fibrosis progression.

Targeting PARG induces tumor cell growth inhibition and antitumor immune response by reducing phosphorylated STAT3 in ovarian cancer.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy, with limited treatment options after failure of standard therapies. Despite the potential of poly(ADP-ribose) polymerase inhibitors in treating DNA damage response (DDR)-deficient ovarian cancer, the development of resistance and immunosuppression limit their efficacy, necessitating alternative therapeutic strategies. Inhibitors of poly(ADP-ribose) glycohydrolase (PARG) represent a novel class of inhibitors that are currently being assessed in preclinical and clinical studies for cancer treatment. METHODS: By using a PARG small-molecule inhibitor, COH34, and a cell-penetrating antibody targeting the PARG's catalytic domain, we investigated the effects of PARG inhibition on signal transducer and activator of transcription 3 (STAT3) in OVCAR8, PEO1, and Brca1-null ID8 ovarian cancer cell lines, as well as in immune cells. We examined PARG inhibition-induced effects on STAT3 phosphorylation, nuclear localization, target gene expression, and antitumor immune responses in vitro, in patient-derived tumor organoids, and in an immunocompetent Brca1-null ID8 ovarian mouse tumor model that mirrors DDR-deficient human high-grade serous ovarian cancer. We also tested the effects of overexpressing a constitutively activated STAT3 mutant on COH34-induced tumor cell growth inhibition. RESULTS: Our findings show that PARG inhibition downregulates STAT3 activity through dephosphorylation in ovarian cancer cells. Importantly, overexpression of a constitutively activated STAT3 mutant in tumor cells attenuates PARG inhibitor-induced growth inhibition. Additionally, PARG inhibition reduces STAT3 phosphorylation in immune cells, leading to the activation of antitumor immune responses, shown in immune cells cocultured with ovarian cancer patient tumor-derived organoids and in immune-competent mice-bearing mouse ovarian tumors. CONCLUSIONS: We have identified a novel antitumor mechanism underlying PARG inhibition beyond its primary antitumor effects through blocking DDR in ovarian cancer. Furthermore, targeting PARG activates antitumor immune responses, thereby potentially increasing response rates to immunotherapy in patients with ovarian cancer.

Relationship between leaf vein traits of three dominant Quercus species and ecological factors in the Qinling Mountains, China.

We investigated the inter- and intra-species differences of leaf vein traits of three dominant Quercus species, Q. wutaishanica, Q. aliena var. acutiserrata, and Q. variabilis of Niubeiling (subtropical humid climate) and Taohuagou (warm temperate semi-humid climate), located in the eastern and western Qinling Mountains. The nine examined leaf vein traits included primary leaf vein width, secondary leaf vein width, mean fine vein width, primary vein density, fine vein density, vein areole diameter, areole density, 3D fine vein surface area, and fine vein volume. We further elucidated the influencing mechanisms and regulatory pathways of biotic and abiotic factors on leaf vein traits. The results showed that species identity had significant effects on eight out of nine leaf vein traits except 3D fine vein surface area, while habitat had significant effects on primary leaf vein width, secondary leaf vein width, vein areole diameter, fine vein density, and areole density. Altitude had significant effects on primary vein density, mean fine vein width, vein areole diameter, fine vein density and areole density. Habitat, tree species identity, and altitude had significantly interactive effects on primary leaf vein density, 3D fine vein surface area, and fine vein volume. There were significant differences in primary leaf vein width, mean fine vein width, areole density, 3D fine vein surface area, fine vein volume, primary vein density of Q. wutaishanica between the two studied habitats, but the differences were only found in secondary leaf vein width and areole density of Q. aliena var. acutiserrata and Q. variabilis. The examined leaf vein traits were influenced both by biotic and abiotic factors, with varying effect sizes. Among the biotic factors, petiole length, leaf length and width ratio had strong effect on leaf vein traits. Among the abiotic factors, climatic and soil factors had high effect size on vein traits, with the former being higher than the latter. Leaf vein traits were affected directly by biotic factors, but indirectly by abiotic factors (soil and climatic factors) via regulating biotic factors (leaf stoichiometry and leaf phenotypic traits).

Symmetry Evolution Induced Two-Dimensional Pt Single Atom Catalyst with High Density for Alkaline Hydrogen Oxidation.

The performance of single-atom catalysts is greatly influenced by the chemical environment surrounding the central atom. In this study, a salt-assisted method was employed to transform the tetrahedral coordination structure of ZIF-8 into a planar square coordination structure without altering the ligands. During the subsequent carbonization process, concurrent with the evaporation of zinc atoms, the structure of the nitrogen and carbon carriers (NC carriers) undergo a transition from five-membered rings to six-membered rings to preserve the 2D structure. Following carbonization, the 2d-NC carrier predominantly comprises pyridine N within six-membered rings, whereas the 3d-NC carrier contains a higher proportion of pyrrole N within five-membered rings, along with graphite N that unavailable for coordination sites. This transition result in the generation of additional defect sites on the 2d-NC substrates. Hence, the Pt single-atom catalysts with planar square coordination symmetries can be precisely prepared via electrodeposition (denoted as 2d-Pt SAC). By utilizing the structural characteristics of the 2d-NC carrier, it is beneficial to construct Pt SAs with higher density than that on 3d-NC. The Pt loading of 2d-Pt SAC is 0.49 ± 0.03 µg cm-2, higher than that of 3d-Pt SAC (0.37 ± 0.04 µg cm-2). In the context of the hydrogen oxidation reaction (HOR) electrocatalysis, these single atom catalysts with 2d coordination exhibited exchange current densities of 1.47 mA cm-2. Moreover, under an overpotential of 50 mV, it achieved mass activities of 2396 A gPt -1 (32 times higher than commercial Pt/C catalyst, 2 times higher than 3d-PtNC). Our findings elucidate the influence of coordination symmetry on the performance of single-atom catalysts, offering a novel synthetic approach that may have implications for future industrial synthesis endeavors. This article is protected by copyright. All rights reserved.

Photoinduced Decarboxylative Difluoroalkylation and Perfluoroalkylation of α-Fluoroacrylic Acids.

Herein, a novel and practical methodology for the photoinduced decarboxylative difluoroalkylation and perfluoroalkylation of α-fluoroacrylic acids is reported. A wide range of α-fluoroacrylic acids can be used as applicable feedstocks, allowing for rapid access to structurally important difluoroalkylated and polyfluoroalkylated monofluoroalkenes with high Z-stereoselectivity under mild conditions. The protocol demonstrates excellent functional group compatibility and provides a platform for modifying complex biologically active molecules.

Soluble Programmed Cell Death 1 Protein Is a Promising Biomarker to Predict Severe Liver Inflammation in Chronic Hepatitis B Patients.

Background and Aims: Liver inflammation is important in guiding the initiation of antiviral treatment and affects the progression of chronic hepatitis B(CHB). The soluble programmed cell death 1 protein (sPD-1) was upregulated in inflammatory and infectious diseases and correlated with disease severity. We aimed to investigate the correlation between serum sPD-1 levels and liver inflammation in CHB patients and their role in indicating liver inflammation. Methods: 241 CHB patients who underwent liver biopsy were enrolled. The correlation between sPD-1 levels and the degree of liver inflammation was analyzed. Univariate and multivariate logistic regression analyses were performed to analyze independent variables of severe liver inflammation. Binary logistic regression analysis was conducted to construct a predictive model for severe liver inflammation, and the receiver operating characteristic curve (ROC) was used to evaluate the diagnostic accuracy of the predictive model. Results: sPD-1 was highest in CHB patients with severe liver inflammation, which was higher than that in CHB patients with mild or moderate liver inflammation (P < 0.001). Besides, sPD-1 was weakly correlated with AST (r = 0.278, P < 0.001). Multivariable analysis showed that sPD-1 was an independent predictor of severe liver inflammation. The predictive model containing sPD-1 had areas under the ROC (AUROCs) of 0.917 and 0.921 in predicting severe liver inflammation in CHB patients and CHB patients with ALT ≤ 1× upper limit of normal (ULN), respectively. Conclusions: Serum sPD-1 level is associated with liver inflammation in CHB patients, and high levels of sPD-1 reflect severe liver inflammation. Serum sPD-1 is an independent predictor of severe liver inflammation and shows improved diagnostic accuracy when combined with other clinical indicators.

Association of liver function and prognosis in patients with severe fever with thrombocytopenia syndrome.

OBJECTIVES: Severe fever with thrombocytopenia syndrome (SFTS) is an epidemic emerging infectious disease with high mortality rate. We investigated the association between liver injury and clinical outcomes in patients with SFTS. METHODS: A total of 291 hospitalized SFTS patients were retrospectively included. Cox proportional hazards model was adopted to identify risk factors of fatal outcome and Kaplan-Meier curves were used to estimate cumulative risks. RESULTS: 60.1% of patients had liver injury at admission, and the median alanine transaminase, aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin (TBil) levels were 76.4 U/L, 152.3 U/L, 69.8 U/L and 9.9 µmol/L, respectively. Compared to survivors, non-survivors had higher levels of AST (253.0 U/L vs. 131.1 U/L, P < 0.001) and ALP (86.2 U/L vs. 67.9 U/L, P = 0.006), higher proportion of elevated ALP (20.0% vs. 4.4%, P < 0.001) and liver injury (78.5% vs. 54.9%, P = 0.001) at admission. The presence of liver injury (HR 2.049, P = 0.033) at admission was an independent risk factor of fatal outcome. CONCLUSIONS: Liver injury was a common complication and was strongly associated with poor prognosis in SFTS patients. Liver function indicators should be closely monitored for SFTS patients.

Characterization the microbial diversity and metabolites of four varieties of Dry-Cured ham in western Yunnan of China.

In this study, high-throughput sequencing and metabolomics analysis were conducted to analyze the microbial and metabolites of dry-cured Sanchuan ham, Laowo ham, Nuodeng ham, and Heqing ham that have fermented for two years produced from western Yunnan China. Results showed that at the genus level, the dominant bacteria in the four types of ham were Halomonas and Staphylococcus, while the dominant fungi were Aspergillus and Yamadazyma. A total 422 different metabolites were identified in four types of ham, mainly amino acids, peptides, fatty acids, and their structural analogs, which were involved in pantothenate and coenzyme A biosynthesis, caffeine, and tyrosine metabolism. The dominant microorganisms of the four types of ham were mainly related to the metabolism of fatty acids and amino acids. This research enhances the identification degree of these four types of dry-cured ham and provides a theoretical basis for developing innovative and distinctive ham products.

NLRP6 induces RIP1 kinase-dependent necroptosis via TAK1-mediated p38MAPK/MK2 phosphorylation in S. typhimurium infection.

Programmed cell death (PCD) is tightly orchestrated by molecularly defined executors and signaling pathways. NLRP6, a member of cytoplasmic pattern recognition receptors, has a multifaceted role in host resistance to bacterial infection. However, whether and how NLRP6 may contribute to regulate host PCD during Gram-negative bacterial infection remain to be illuminated. Here, we report that NLRP6 promotes RIP1 kinase-mediated necroptosis, a form of lytic PCD, in both an in vitro and in vivo model of Salmonella typhimurium infection. By downregulating TAK1-mediated p38MAPK/MK2 phosphorylation, NLRP6 decreased RIP1 phosphorylation at residue S321 and subsequently increased RIP1 kinase-dependent MLKL phosphorylation. Suppression of p38MAPK/MK2 cascade not only reduced the number of dead cells caused by NLRP6 but also decreased the systemic dissemination of S. typhimurium resulting from NLRP6. Taken together, our findings provide new insights into the role and regulatory mechanism of NLRP6-associated antimicrobial responses by revealing a function for NLRP6 in regulating necroptosis.

Emergent Conformal Boundaries from Finite-Entanglement Scaling in Matrix Product States.

The use of finite entanglement scaling with matrix product states (MPS) has become a crucial tool for studying one-dimensional critical lattice theories, especially those with emergent conformal symmetry. We argue that finite entanglement introduces a relevant deformation in the critical theory. As a result, the bipartite entanglement Hamiltonian defined from the MPS can be understood as a boundary conformal field theory with a physical and an entanglement boundary. We are able to exploit the symmetry properties of the MPS to engineer the physical conformal boundary condition. The entanglement boundary, on the other hand, is related to the concrete lattice model and remains invariant under this relevant perturbation. Using critical lattice models described by the Ising, Potts, and free compact boson conformal field theories, we illustrate the influence of the symmetry and the relevant deformation on the conformal boundaries in the entanglement spectrum.