Nickel-Catalyzed Atroposelective C-H Alkylation Enabled by Bimetallic Catalysis with Air-Stable Heteroatom-Substituted Secondary Phosphine Oxide Preligands.

The catalytic asymmetric construction of axially chiral C-N atropisomers remains a formidable challenge due to their low rotational barriers and is largely reliant on toxic, cost-intensive, and precious metal catalysts. In sharp contrast, we herein describe the first nickel-catalyzed atroposelective C-H alkylation for the construction of C-N axially chiral compounds with the aid of a chiral heteroatom-substituted secondary phosphine oxide (HASPO)-ligated Ni-Al bimetallic catalyst. A wide range of alkenes, including terminal and internal alkenes, were well compatible with the reaction, providing a variety of benzimidazole derivatives in high yields and enantioselectivities (up to 97:3 e.r.). The key to success was the identification of novel HASPOs as highly effective chiral preligands. Mechanistic studies revealed the catalyst mode of action, and in-depth data science analysis elucidated the key features of the responsible chiral preligands in controlling the enantioselectivity.

Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial-spectral redundancy-based denoising method.

Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal-to-noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial-spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw-rank constraint and Spectral local-smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low-dimensional subspace by leveraging the global spectral low-rank prior. Subsequently, a spatial nonlocal self-similarity prior is applied to the subspace-based images. Simultaneously, the spectral local-smoothness property of Z-spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state-of-the-art algorithms in terms of noise elimination. As a cost-effective and widely available post-processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.

Correlation Between N-Demethyl Imatinib Trough Concentration and Serious Adverse Reactions in Patients with Gastrointestinal Stromal Tumors: A Retrospective Cohort Study.

BACKGROUND: Imatinib is the first-line treatment for gastrointestinal stromal tumors; however, the clinical prognosis and adverse reactions of patients vary owing to individualized discrepancies in plasma exposure. METHODS: To determine the safe interval for steady-state plasma trough concentrations (C min ) of imatinib and its active metabolite, N-demethyl imatinib (NDI), 328 plasma samples from 273 patients treated with imatinib were retrospectively analyzed. Imatinib C min and NDI C min were tested, and adverse reactions were recorded. The association between imatinib C min , NDI C min , and serious adverse reactions was evaluated. RESULTS: The C min range of imatinib was 209.5-4950.0 ng/mL, with the mean value and SD of 1491.8 ± 731.4 ng/mL. The C min range of NDI was 80.0-2390.0 ng/mL with the mean value and SD of 610.8 ± 281.5 ng/mL. NDI C min was positively correlated with imatinib C min , whereas the ratio of NDI C min to imatinib C min (NDI C min /imatinib C min ) was negatively correlated with imatinib C min . Univariate logistic regression analysis demonstrated that the treatment objective, daily dose, imatinib C min , NDI C min , and imatinib C min + NDI C min were significantly associated with serious adverse reactions. Multivariate logistic regression analysis showed that NDI C min was an independent risk factor for serious adverse reactions, with a threshold of 665 ng/mL. CONCLUSIONS: NDI C min was an independent risk factor for serious adverse reactions, with a threshold of 665 ng/mL. Monitoring NDI C min was beneficial for the rational application of imatinib and individualized treatment of patients with gastrointestinal stromal tumors.

Application of deep learning in isolated tooth identification.

BACKGROUND: Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be challenging due to the anatomical similarities between categories. In this study, we aim to explore the possibility of using a deep learning model to classify isolated tooth by a set of photographs. METHODS: A collection of 5,100 photographs from 850 isolated human tooth specimens were assembled to serve as the dataset for this study. Each tooth was carefully labeled during the data collection phase through direct observation. We developed a deep learning model that incorporates the state-of-the-art feature extractor and attention mechanism to classify each tooth based on a set of 6 photographs captured from multiple angles. To increase the validity of model evaluation, a voting-based strategy was applied to refine the test set to generate a more reliable label, and the model was evaluated under different types of classification granularities. RESULTS: This deep learning model achieved top-3 accuracies of over 90% in all classification types, with an average AUC of 0.95. The Cohen's Kappa demonstrated good agreement between model prediction and the test set. CONCLUSIONS: This deep learning model can achieve performance comparable to that of human experts and has the potential to become a valuable tool for dental education and various applications in accurately identifying isolated tooth.

Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

PURPOSE: To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising. METHODS: DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods. RESULTS: Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes. CONCLUSIONS: DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.

IMPULSED model based cytological feature estimation with U-Net: Application to human brain tumor at 3T.

PURPOSE: This work introduces and validates a deep-learning-based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion-weighted MRI data. METHODS: The U-Net was applied to rapidly quantify extracellular diffusion coefficient (Dex ), cell size (d), and intracellular volume fraction (vin ) of brain tumor. At the training stage, the image-based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U-Net. At the test stage, the pre-trained U-Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U-Net was compared with conventional non-linear least-squares (NLLS) fitting in simulations in terms of estimation accuracy and precision. RESULTS: Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U-Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). CONCLUSION: The image-based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep-learning-based fitting method can estimate the cell microstructural parameters fast and accurately.

Rational Design of a Triple Tumor Microenvironment-Responsive Nanoplatform for Enhanced Tumor Theranostics.

The development of responsive nanoplatforms based on the tumor microenvironment (TME) is critical for tumor diagnosis and treatment. Concentrating on a single TME-responsive nanoplatform, however, may result in insufficient diagnostic accuracy and treatment efficacy. Herein, layered double-hydroxides (LDHs) and rare earth nanomaterials (Er@Lu) were combined to create a triple TME-responsive nanoplatform that was then modified with cypate (a fluorescent dye with strong absorbance) by a peptide chain and loaded with epigallocatechin gallate (EGCG), a chemotherapeutic drug. Multiple responses to TME occurred when Er@Lu/LDH-EGCG reached the colorectal tumor region. Based on an acidic TME, the nanoplatform cracked and released Ni2+ and EGCG. NiS, which was produced by the reaction of Ni2+ with abundant H2 S in tumor cells, was used for photothermal therapy and the released EGCG was used for chemotherapy. The MMP-7 enzyme specifically expressed in tumor cells recognized and cut the peptide chain, resulting in cypate release. The fluorescence of the Er@Lu was then restored along with the release of cypate because of the absorption competition disappearance. Compared to a single TME response, Er@Lu/LDH-EGCG with a triple TME response led to a better synergistic therapeutic effect in vitro and in vivo. This work has provided new approaches for developing multiple TME-responsive therapeutic nanoplatforms for synergistic therapy with improved diagnosis and therapeutic efficiency.

Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI.

BACKGROUND Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study aimed to examine how visceral fat area (VFA) and body mass index (BMI) correlate with intraoperative complexities, thereby guiding the selection of surgical techniques for RAPN. MATERIAL AND METHODS The study analyzed the medical records of 213 Chinese patients diagnosed with a range of benign and malignant renal neoplasms and treated with RAPN in 2020. Visceral fat area was quantified using computed tomography (CT) scans taken at the umbilical level. Various perioperative indicators, such as demographic details, clinicopathological parameters, operation time, estimated blood loss (EBL), warm ischemic time (WIT), and intraoperative complications, were assessed. RESULTS For the retroperitoneal approach, patients with either visceral obesity or general obesity had longer operation times (P<0.001 and P=0.004) and had a tendency for higher EBL (P=0.003 and P=0.001) compared to non-obese patients. In the transperitoneal approach, those with visceral obesity had significantly longer operation times (P=0.008) than their non-viscerally obese counterparts; however, general obesity showed no impact on operation time (P=0.251). Estimated blood loss was higher for patients with visceral obesity (P=0.004), but no significant difference was noted among those with general obesity (P=0.980). CONCLUSIONS VFA appears to offer predictive advantages over BMI in assessing intraoperative complexities for transperitoneal RAPN. When used in conjunction with BMI, it could serve as a valuable tool in selecting the most appropriate surgical approach for RAPN.

Norvancomycin plasma concentration monitoring in hemodialysis patients with end stage kidney disease: A retrospective cohort study.

Blood concentration monitoring plays an important role in the rational use of norvancomycin. However, the reference interval for the norvancomycin plasma concentration in the treatment of infections in hemodialysis patients with end stage kidney disease is undefined. To determine the safe and effective interval for the norvancomycin plasma trough concentration, 39 patients treated with hemodialysis and norvancomycin were analyzed retrospectively. The norvancomycin plasma concentration before hemodialysis was tested as the trough concentration. The associations of the norvancomycin trough concentration with efficacy and adverse reactions were evaluated. No norvancomycin concentration above 20 µg/mL was detected. The trough concentration, but not the dose, had a significant effect on the anti-infectious efficacy. Compared with the low norvancomycin trough concentration group (<9.30 µg/mL), the high concentration group (9.30-20.0 µg/mL) had improved efficacy (OR = 15.45, p < 0.01) with similar side effects (OR = 0.5417, p = 0.4069). It is beneficial to maintain the norvancomycin trough concentration at 9.30-20.0 µg/mL to achieve a good anti-infectious effect in hemodialysis patients with end stage kidney disease. Plasma concentration monitoring provides a data basis for the individual treatment of infections with norvancomycin in hemodialysis patients.

Close-Shell Reductive Elimination versus Open-Shell Radical Coupling for Site-Selective Ruthenium-Catalyzed C-H Activations by Computation and Experiments.

Ruthenium-catalyzed σ-bond activation-assisted meta-C-H functionalization has emerged as a useful tool to forge distal C-C bonds. However, given the limited number of mechanistic studies, a clear understanding of the origin of the site-selectivity and the complete reaction pattern is not available. Here, we present systematic computational studies on ruthenium-catalyzed C-H functionalization with primary, secondary, tertiary alkyl bromides and aryl bromides. The C-H scission and the C-C formation were carefully examined. Monocyclometalated ruthenium(II) complexes were identified as the active species, which then underwent inner-sphere single electron transfer (ISET) to activate the organic bromides. The site-selectivity results from the competition between the close-shell reductive elimination and the open-shell radical coupling. Based on this mechanistic understanding, a multilinear regression model was built to predict the site-selectivity, which was further validated by experiments.

Enantioconvergent Palladium-Catalyzed Alkylation of Tertiary Allylic C-H Bonds.

Enantioconvergent catalysis enables the conversion of racemic molecules into a single enantiomer in perfect yield and is considered an ideal approach for asymmetric synthesis. Despite remarkable advances in this field, enantioconvergent transformations of inert tertiary C-H bonds remain largely unexplored due to the high bond dissociation energy and the surrounding steric repulsion that pose unparalleled constraints on bond cleavage and formation. Here, we report an enantioconvergent Pd-catalyzed alkylation of racemic tertiary allylic C-H bonds of α-alkenes, providing a unique approach to access a broad range of enantioenriched γ,δ-unsaturated carbonyl compounds featuring quaternary carbon stereocenters. Mechanistic studies reveal that a stereoablative event occurs through the rate-limiting cleavage of tertiary allylic C-H bonds to generate σ-allyl-Pd species, and the achieved E/Z-selectivity of σ-allyl-Pd species effectively regulates the diastereoselectivity via a nucleophile coordination-enabled SN 2'-allylation pathway.

Ultrafast water-fat separation using deep learning-based single-shot MRI.

PURPOSE: To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images. METHODS: Spatiotemporally encoded MRI is an ultrafast branch that can encode chemical shift information due to its special quadratic phase modulation. A deep learning approach using a 2D U-Net was proposed to reconstruct spatiotemporally encoded signal and obtain water and fat images simultaneously. The training data for U-Net were generated by MRiLab software (version 1.3) with various synthetic models. Numerical simulations and experiments on ex vivo pork and in vivo rats at a 7.0 T Varian MRI system (Agilent Technologies, Santa Clara, CA) were performed, and the deep learning results were compared with those obtained by state-of-the-art algorithms. The structural similarity index and signal-to-ghost ratio were used to evaluate the residual artifact of different reconstruction methods. RESULTS: With a well-trained neural network, the proposed deep learning approach can accomplish signal reconstruction within 0.46 s on a personal computer, which is comparable with the conjugate gradient method (0.41 s) and much faster than the state-of-the-art super-resolved water-fat image reconstruction method (30.31 s). The results of numerical simulations, ex vivo pork experiments, and in vivo rat experiments demonstrate that the deep learning approach can achieve better fidelity and higher spatial resolution compared to the other 2 methods. The deep learning approach also has a great advantage in artifact suppression, as indicated by the signal-to-ghost ratio results. CONCLUSION: Spatiotemporally encoded MRI with deep learning can provide ultrafast water-fat separation with better performance compared to the state-of-the-art methods.

Regioselective C3-Fluoroalcoholation of Indoles with Heptafluoroisopropyl Iodide via Palladium-Catalyzed C(sp2)-C(sp3) Cross-Coupling in the Presence of O2.

An efficient method for C3-fluoroalcoholation of indole derivatives was developed by merging C-F cleavage and C-C bond coupling, using free (NH)-indoles and heptafluoroisopropyl iodides as precursors. Preliminary mechanistic studies indicate that the bimetallic co-mediated C-F bond cleavage and the trifluoroacetate moiety play an essential role. Notably, this strategy constructs derivatizations through the modifiable carbon-oxygen bond. A broad range of structurally valuable organofluorine products was obtained, which shows excellent functional group tolerance. Furthermore, easily accessible materials were utilized and circumvented two troublesome steps of installing and removing an external auxiliary. This is the first report to introduce 3-fluoroalcoholated indoles via fluorohalides. This reaction offers a straightforward and efficient platform to access worthwhile fluorinated free (NH)-heteroarenes derivatives.

Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

Photo-Induced Ruthenium-Catalyzed Double Remote C(sp2 )-H / C(sp3 )-H Functionalizations by Radical Relay.

Distal C(sp2 )-H and C(sp3 )-H functionalizations have recently emerged as step-economical tools for molecular synthesis. However, while the C(sp2 )-C(sp3 ) construction is of fundamental importance, its formation through double remote C(sp2 )-H/C(sp3 )-H activation has proven elusive. By merging the ruthenium-catalyzed meta-C(sp2 )-H functionalization with an aliphatic hydrogen atom transfer (HAT) process, we, herein, describe the catalyzed twofold remote C(sp2 )-H/C(sp3 )-H functionalizations via photo-induced ruthenium-mediated radical relay. Thus, meta-C(sp2 )-H arene bonds and remote C(sp3 )-H alkane bonds were activated by a single catalyst in a single operation. This process was accomplished at room temperature by visible light-notably without exogenous photocatalysts. Experimental and computational theory studies uncovered a manifold comprising ortho-C-H activation, single-electron-transfer (SET), 1,n-HAT (n=5-7) and σ-activation by means of a single ruthenium(II) catalyst.

Distal Ruthenaelectro-Catalyzed meta-C-H Bromination with Aqueous HBr.

While electrochemical ortho-selective C-H activations are well established, distal C-H activations continue to be underdeveloped. In contrast, we herein describe the electrochemical meta-C-H functionalization. The remote C-H bromination was accomplished in an undivided cell by RuCl3 ⋅3 H2 O with aqueous HBr. The electrohalogenation proceeded under exogenous ligand- and electrolyte-free conditions. Notably, pyrazolylarenes were meta-selectively brominated at the benzenoid moiety, rather than on the electron-rich pyrazole ring for the first time. Mechanistic studies were suggestive of an initial ruthenacycle formation, and a subsequent ligand-to-ligand hydrogen transfer (LLHT) process to liberate the brominated product.

Exergaming as a Community Program for Older Adults: The Effects of Social Interaction and Competitive Information.

This study examined how different types of social interaction and competitive information in exergaming affect older adults' motivation and attitude toward playing exergames. A 2 (time: pre- vs. posttest) × 3 (social interaction: play alone vs. play with peer vs. play with youth) × 2 (competition: competition informed vs. noncompetition informed) mixed experiment was conducted with 319 Singaporean older adults over 6 weeks through a three-way analysis of variance. Social interaction was found to significantly affect the changes of extrinsic motivation over time, while competitive information affected intrinsic motivation significantly. The results showed significant three-way interaction effects between time, social interaction, and competitive information on older adults' extrinsic and intrinsic motivations. The changes of attitude over time were not affected by either social interaction or competitive information. The findings contribute to aging research and advance the knowledge of potential factors that promote the effective implementation of exergames for community older adults.

Predisposing factors and radiological features in patients with internal carotid artery dissection or vertebral artery dissection.

BACKGROUND: Cervicocerebral artery dissection is an important cause of ischemic stroke in young and middle-aged individuals. However, very few studies have compared the differential features between internal carotid artery dissection (ICAD) and vertebral artery dissection (VAD), including both cervical and intracranial artery dissections. We conducted a study to investigate the predisposing factors and radiological features in patients with ICAD or VAD. METHODS: All cases diagnosed with cervicocerebral artery dissection, ICAD, or VAD were identified through a medical records database, between January 2010 and January 2020. Baseline characteristics, predisposing factors, and radiological features of ICAD versus VAD were compared. RESULTS: A total of 140 patients with cervicocerebral artery dissection were included in the study, including 84 patients in the ICAD group and 56 in the VAD group. The mean age of patients in the ICAD and VAD groups was 43.37 ± 14.01 and 41.00 ± 12.98 years old, respectively. Patients with ICAD were more likely to be men compared with VAD (85.71% vs. 67.86%, p = 0.012). The frequency of hypertension, diabetes, smoking, drinking, and cervical trauma did not differ between ICAD and VAD. Dissections of ICAD were more frequently at the extracranial portions of the artery compared with those of VAD (70.24% vs. 44.64%, p = 0.003). In contrast, dissections of VAD were more common in the intracranial artery (55.36% vs. 29.76%, p = 0.003). Radiologically, double lumen (36.90% vs. 19.64%, p = 0.029) and intimal flap (11.90% vs. 1.79%, p = 0.029) were more frequently observed in ICAD than in VAD, and dissecting aneurysms were less frequent (13.10% vs. 26.79%, p = 0.041). CONCLUSIONS: The distributions of cervical and intracranial artery dissections were different between ICAD and VAD. The frequencies of radiological features detected in patients with ICAD and VAD also differed.

Complete genome sequence of PFP1, a novel T7-like Pseudomonas fluorescens bacteriophage.

The complete genomic sequence of Pseudomonas fluorescens bacteriophage PFP1, isolated from sewage samples collected in Liaoning Province, China, were sequenced in this study and found to be 40,914 bp long. The PFP1 genome is composed of linear double-stranded DNA with 55.81% G+C content and 45 putative protein-coding genes, and no rRNA and tRNA genes. Comparative genomics and phylogenetic analysis revealed that the Pseudomonas fluorescens phage PFP1 is a new member of the genus T7virus. This information can be used to develop novel phage-based control strategies against Pseudomonas fluorescens.

B7-H4 facilitates proliferation of esophageal squamous cell carcinoma cells through promoting interleukin-6/signal transducer and activator of transcription 3 pathway activation.

B7-H4, one of the costimulatory molecules of the B7 family, has been found to be widely expressed in many kinds of tumor tissues and to play an important part in tumor progression and poor prognosis. However, the role of B7-H4 in esophageal squamous cell carcinoma (ESCC) cells has not been elucidated. In this study, we found that, compared with normal esophageal tissue, B7-H4 was highly expressed in three ESCC cell lines, Eca109, TE1, and TE13. B7-H4 silenced cells suppressed cellular proliferation and colony formation. Additionally, compared with control cells, B7-H4 silenced cells showed higher apoptosis rates, Bcl-2 and Survivin upregulation, and BAX downregulation. Further study revealed that B7-H4 silenced cells also showed reduction in interleukin-6 (IL-6) secretion, signal transducer and activator of transcription 3 (STAT3) activation, and p-STAT3 translocation from cytoplasm to nucleus. Moreover, B7-H4 depletion inhibited the IL-6 secretion of control cells but not JAK2/STAT3 inhibitor FLLL32-treated cells. Interleukin-6 receptor antagonist tocilizumab did not block the p-JAK2 or p-STAT3 downregulation induced by B7-H4 silence. It was suggested that B7-H4 silence suppressed IL-6 secretion through JAK2/STAT3 inactivation. Furthermore, cell proliferation and colony formation were downregulated by tocilizumab in control cells but not in B7-H4 silenced cells, indicating that IL-6 upregulation induced by B7-H4 was necessary for cell growth. On the other hand, B7-H4 expression was downregulated by tocilizumab. In all, our study provided the first evidence that B7-H4 facilitated ESCC cell proliferation through promoting IL-6/STAT3 positive loopback pathway activation.