Sequential safe static and dynamic screening rule for accelerating support tensor machine.

Support tensor machine (STM), as a higher-order extension of support vector machine, is adept at effectively addressing tensorial data classification problems, which maintains the inherent structure in tensors and mitigates the curse of dimensionality. However, it needs to resort to the alternating projection iterative technique, which is very time-consuming. To overcome this shortcoming, we propose an efficient sequential safe static and dynamic screening rule (SS-SDSR) for accelerating STM in this paper. Its main idea is to reduce every projection iterative sub-model by identifying and deleting the redundant variables before and during the training process without sacrificing accuracy. Its construction mainly consists of two parts: (1) The static screening rule and dynamic screening rule are first built based on the variational inequality and duality gap, respectively. (2) The sequential screening process is achieved by using the static screening rule with the different adjacent parameters and applying the dynamic screening rule under the same parameter. In the experiment, on the one hand, to verify the influence of different parameter intervals, screening frequencies, and forms of data on the effectiveness of our method, three experiments on artificial datasets are conducted, which indicate that our method is effective for any forms of data when the parameter interval is small and the screening frequency is appropriate. On the other hand, to demonstrate the feasibility and validity of our SS-SDSR, numerical experiments on eleven vector-based datasets, and six tensor-based datasets are conducted and compared with the other five algorithms. Experimental results illustrate the effectiveness and safety of our SS-SDSR.

The role of microbiota in gastric cancer: A comprehensive review.

BACKGROUND: Gastric cancer (GC) continues to pose a significant global threat in terms of cancer-related fatalities. Despite notable advancements in medical research and therapies, further investigation is warranted to elucidate its underlying etiology and risk factors. Recent times have witnessed an escalated emphasis on comprehending the role of the microbiota in cancer development. METHODS: This review briefly delves into recent developments in microbiome-related research pertaining to gastric cancer. RESULTS: According to studies, the microbiota can influence GC growth by inciting inflammation, disrupting immunological processes, and generating harmful microbial metabolites. Furthermore, there is ongoing research into how the microbiome can impact a patient's response to chemotherapy and immunotherapy. CONCLUSION: The utilization of the microbiome for detecting, preventing, and managing stomach cancer remains an active area of exploration.

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer.

Immune checkpoint inhibitors (ICIs) drastically improve therapeutic outcomes for lung cancer, but accurately predicting individual patient responses to ICIs remains a challenge. We performed the genome-wide profiling of 5-hydroxymethylcytosine (5hmC) in 85 plasma cell-free DNA (cfDNA) samples from lung cancer patients and developed a 5hmC signature that was significantly associated with progression-free survival (PFS). We built a 5hmC predictive model to quantify the 5hmC level and validated the model in the validation, test, and control sets. Low weighted predictive scores (wp-scores) were significantly associated with a longer PFS compared to high wp-scores in the validation [median 7.6 versus 1.8 months; p = 0.0012; hazard ratio (HR) 0.12; 95% confidence interval (CI), 0.03-0.54] and test (median 14.9 versus 3.3 months; p = 0.00074; HR 0.10; 95% CI, 0.02-0.50) sets. Objective response rates in patients with a low or high wp-score were 75.0% (95% CI, 42.8-94.5%) versus 0.0% (95% CI, 0.0-60.2%) in the validation set (p = 0.019) and 80.0% (95% CI, 44.4-97.5%) versus 0.0% (95% CI, 0.0-36.9%) in the test set (p = 0.0011). The wp-scores were also significantly associated with PFS in patients receiving single-agent ICI treatment (p < 0.05). In addition, the 5hmC predictive signature demonstrated superior predictive capability to tumor programmed death-ligand 1 and specificity to ICI treatment response prediction. Moreover, we identified novel 5hmC-associated genes and signaling pathways integral to ICI treatment response in lung cancer. This study provides proof-of-concept evidence that the cfDNA 5hmC signature is a robust biomarker for predicting ICI treatment response in lung cancer.

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC.

Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be immune cold and hence immunotherapy resistant tumors, and EA TNBC as inflamed tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.

MTKSVCR: A novel multi-task multi-class support vector machine with safe acceleration rule.

Regularized multi-task learning (RMTL) has shown good performance in tackling multi-task binary problems. Although RMTL can be used to handle multi-class problems based on "one-versus-one" and "one-versus-rest" techniques, the information of the samples is not fully utilized and the class imbalance problem occurs. Motivated by the regularization technique in RMTL, we propose an original multi-task multi-class model termed MTKSVCR based on "one-versus-one-versus-rest" strategy to achieve better testing accuracy. Due to the utilization of the idea of RMTL, the related information included in multiple tasks is mined by setting different penalty parameters before task-common and task-specific regularization terms. However, the proposed MTKSVCR is time-consuming since it employs all samples in each optimization problem. Therefore, a multi-parameter safe acceleration rule termed SA is further presented to reduce the time consumption. It identifies and deletes most of the superfluous samples corresponding to 0 elements in the dual optimal solution before solving. Then, only a reduced dual problem is to be solved and the computational efficiency is improved accordingly. The biggest advantage of the proposed SA lies in safety. Namely, it derives an identical optimal solution to the primal problem without SA. In addition, our method remains effective when multiple parameters change simultaneously. Experiments on different artificial datasets and benchmark datasets verify the validity of the proposed methods.

A Phase 2 Study of In Situ Oncolytic Virus Therapy and Stereotactic Body Radiation Therapy Followed by Pembrolizumab in Metastatic Non-Small Cell Lung Cancer.

PURPOSE: A phase 2 study of stereotactic body radiation therapy (SBRT) and in situ oncolytic virus therapy in metastatic non-small cell lung cancer (mNSCLC) followed by pembrolizumab (STOMP) was designed to explore the dual approach in enhancing single pembrolizumab with ADV/HSV-tk plus valacyclovir gene therapy and SBRT in mNSCLC. METHODS AND MATERIALS: STOMP is a single-arm, open-label phase 2 study. Patients with mNSCLC received intratumoral injections of ADV/HSV-tk (5 × 1011 vp) and SBRT (30 Gy in 5 fractions) followed by pembrolizumab 200 mg IV every 3 weeks until disease progression or intolerable toxicity. The primary endpoint was overall response rate (ORR) (complete response [CR] and partial response [PR]). Secondary endpoints included clinical benefit rate (CBR) (CR, PR and stable disease [SD]), progression-free survival (PFS), overall survival (OS), and safety. RESULTS: 28 patients were enrolled, of whom 27 were evaluated for response. The ORR was 33.3%, including 2 CR (7.4%) and 7 PR (25.9%). CBR was 70.4%. Six of eight (75.0%) patients who were immune checkpoint inhibitor (ICI) refractory derived clinical benefits. Responders had durable responses with median PFS, and OS not reached. The entire cohort had a median PFS of 7.4 months (95% CI, 5.1-9.6 months), and median OS of 18.1 months (95% CI, 15.4-20.9 months). The combination was well tolerated, with grade 3 or higher toxicity in 6 (21.4%) patients. CONCLUSIONS: The dual approach of in situ ADV/HSV-tk plus valacyclovir gene therapy and SBRT as a chemotherapy-sparing strategy to enhance the antitumor effect of pembrolizumab is a well-tolerated encouraging treatment in patients with mNSCLC.

A predictive nomogram developed and validated for gastric cancer patients with triple-negative tumor markers.

Aim: To predict the prognosis of gastric cancer patients with triple-negative tumor markers. Materials & methods: Prognostic factors of the nomogram were identified through univariate and multivariate Cox regression analyses. Calibration and receiver operating characteristic curves were used to assess accuracy. Decision curve analysis and concordance indexes were utilized to compare the nomogram with the pathological tumor, node, metastasis stage. Results: A nomogram incorporating log odds of positive lymph nodes, tumor size and lymphocyte-to-monocyte ratio was constructed. The calibration and receiver operating characteristic curves (area under the curve >0.85) showed high accuracy in predicting overall survival. The concordance indexes (0.832 vs 0.760; p < 0.001) and decision curve analysis demonstrated that the nomogram was superior to the pathological tumor, node, metastasis stage. Conclusion: A prediction and risk stratification nomogram has been developed and validated for gastric cancer patients with triple-negative tumor markers.

LILRB3 Modulates Acute Myeloid Leukemia Progression and Acts as an Effective Target for CAR T-cell Therapy.

Identifying novel cell surface receptors that regulate leukemia cell differentiation and can be targeted to inhibit cellular proliferation is crucial to improve current treatment modalities in acute myeloid leukemia (AML), especially for relapsed or chemotherapy-refractory leukemia. Leukocyte immunoglobulin-like receptor type B (LILRB) is an immunomodulatory receptor originally found to be expressed in myeloid cells. In this study, we found that LILRB receptors can be induced under inflammatory stimuli and chemotherapy treatment conditions. Blockade of LILRB3 inhibited leukemia cell proliferation and leukemia progression. In addition, treatment with LILRB3 blocking antibodies upregulated myeloid lineage differentiation transcription factors, including PU.1, C/EBP family, and IRF, whereas phosphorylation of proliferation regulators, for example, AKT, cyclin D1, and retinoblastoma protein, was decreased. Conversely, transcriptomic analysis showed LILRB3 activation by agonist antibodies may enhance leukemia survival through upregulation of cholesterol metabolism, which has been shown to promote leukemia cell survival. Moreover, LILRB3-targeted CAR T cells exhibited potent antitumor effects both in vitro and in vivo. Taken together, our results suggest that LILRB3 is a potentially potent target for multiple treatment modalities in AML. SIGNIFICANCE: LILRB3 regulates differentiation and proliferation in acute myeloid leukemia and can be targeted with monoclonal antibodies and CAR T cells to suppress leukemia growth.

Shade-induced RTFL/DVL peptides negatively regulate the shade response by directly interacting with BSKs in Arabidopsis.

For shade-intolerant species, shade light indicates the close proximity of neighboring plants and triggers the shade avoidance syndrome (SAS), which causes exaggerated growth and reduced crop yield. Here, we report that non-secreted ROT FOUR LIKE (RTFL)/DEVIL (DVL) peptides negatively regulate SAS by interacting with BRASSINOSTEROID SIGNALING KINASEs (BSKs) and reducing the protein level of PHYTOCHROME INTERACTING FACTOR 4 (PIF4) in Arabidopsis. The transcription of at least five RTFLs (RTFL13/16/17/18/21) is induced by low R:FR light. The RTFL18 (DVL1) protein is stabilized under low R:FR conditions and localized to the plasma membrane. A phenotype analysis reveals that RTFL18 negatively regulates low R:FR-promoted petiole elongation. BSK3 and BSK6 are identified as partners of RTFL18 through binding assays and structural modeling. The overexpression of RTFL18 or knockdown of BSK3/6 reduces BRASSINOSTEROID signaling and reduces low R:FR-stabilized PIF4 levels. Genetically, the overexpression of BSK3/6 and PIF4 restores the petiole phenotype acquired by RTFL18-overexpressing lines. Collectively, our work characterizes a signaling cascade (the RTFLs-BSK3/6-PIF4 pathway) that prevents the excessive activation of the shade avoidance response in Arabidopsis.

Chronic unpredictable mild stress promotes atherosclerosis via adipose tissue dysfunction in ApoE-/- mice.

Background: Chronic unpredictable mild stress (CUMS) has been shown to exacerbate atherosclerosis, but the underlying mechanism remains unknown. Adipose tissue is an energy storage organ and the largest endocrine organ in the human body, playing a key role in the development of cardiovascular disease. In this research, it was hypothesized that CUMS may exacerbate the development of atherosclerosis by inducing the hypertrophy and dysfunction of white adipocytes. Methods: The CUMS-induced atherosclerosis model was developed in Western diet-fed apolipoprotein E (ApoE)-/- mice. White adipose tissue (WAT), serum, aortic root, and the brachiocephalic trunk were collected and tested after 12 weeks of CUMS development. The mouse model of CUMS was evaluated for depression-like behavior using the open field test (OFT) and the elevated plus maze (EPM) test. Enzyme-linked immunosorbent assay (ELISA) was conducted to detect serum noradrenaline and urine adrenaline protein levels. Serological assays were used to detect serum low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), and free fatty acid (FFA) concentrations. Hematoxylin and eosin (H&E) staining and oil red O were used to detect atherosclerotic plaque area, lipid deposition, and adipocyte size. The mRNA levels of genes related to aberrant adipose tissue function were determined using real-time PCR. Immunofluorescence assay and western blotting were conducted to examine the expression of proteins in the adipose tissue samples. Results: CUMS aggravated vascular atherosclerotic lesions in ApoE-/- mice. It decreased body weight while increasing the percentage of WAT. The serological results indicated that the concentration of HDL decreased in CUMS mice. Notably, adipocyte hypertrophy increased, whereas the mRNA levels of Pparg and its target genes (Slc2a4 (encodes for GLUT4), Adipoq, and Plin1) decreased. Further investigation revealed that CUMS increased subcutaneous inguinal WAT (iWAT) lipid synthesis and adipocyte inflammation while decreasing lipid hydrolysis and the expression of HDL-associated protein ApoA-I. Moreover, CUMS aggravated insulin resistance in mice and inhibited the insulin pathway in iWAT. Conclusions: These findings indicated that CUMS induces adipose tissue dysfunction via a mechanism that leads to dyslipidemia, increased inflammation, and insulin resistance in the body, thereby exacerbating atherosclerosis. Notably, CUMS that is involved in decreasing the expression of HDL-associated proteins in adipose tissue may be a crucial link between adipose hypertrophy and advanced atherosclerosis. This study reveals a novel mechanism via which CUMS exacerbates atherosclerosis from the novel perspective of abnormal adipose function and identifies a novel potential therapeutic target for this disease.

Multiview Structural Large Margin Classifier and Its Safe Acceleration Strategy.

Multiview learning (MVL) concentrates on the problem where each instance is represented by multiple different feature sets. Efficiently exploring and exploiting the common and complementary information among different views remains challenging in MVL. Nevertheless, many existing algorithms deal with multiview problems via pairwise strategies, which limit the exploration of relationships among different views and dramatically increase the computational cost. In this article, we propose a multiview structural large margin classifier (MvSLMC) that simultaneously satisfies the consensus and complementarity principles in all views. Specifically, on the one hand, MvSLMC employs a structural regularization term to promote cohesion within-class and separability between-class in each view. On the other hand, different views provide extra structural information to each other, which favors the diversity of the classifier. Moreover, the introduction of hinge loss in MvSLMC results in sample sparsity, which we leverage to construct a safe screening rule (SSR) for accelerating MvSLMC. To the best of our knowledge, this is the first attempt at safe screening in MVL. Numerical experimental results demonstrate the effectiveness of MvSLMC and its safe acceleration method.

Pharmacological properties and underlying mechanisms of aurantio­obtusin (Review).

Herbal medicine has been widely applied for a range of diseases in China since antiquity. Cassia obtusifolia L. and Cassia tora L. are plants whose seeds have high reported medicinal values and have been documented to function as a laxative, to lower lipid level and to lower blood pressure. The main active ingredient in Cassia seeds is aurantio-obtusin (AO), which is an anthraquinone monomer compound. Currently, AO is listed in China as a quality control index component of Cassia seeds. In clinical practice in China, AO is typically used to treat obesity, diabetes and its complications, non-alcoholic fatty liver disease and allergic reactions. In addition, AO has been reported to confer insecticidal activities and antimalarial effects. Previous studies have even suggested that AO is a potential therapeutic candidate for a variety of diseases with research value. Therefore, the present review summarizes and discuss the existing literature on AO to provide a review of its pharmacological activity and mechanism of action, with the aim of providing a basis for its development and utilization in a clinical setting.

Molecular architecture of proliferative lupus nephritis as elucidated using 50-plex imaging mass cytometry proteomics.

Due to unique advantages that allow high-dimensional tissue profiling, we postulated imaging mass cytometry (IMC) may shed novel insights on the molecular makeup of proliferative lupus nephritis (LN). This study interrogates the spatial expression profiles of 50 target proteins in LN and control kidneys. Proliferative LN glomeruli are marked by podocyte loss with immune infiltration dominated by CD45RO+, HLA-DR+ memory CD4 and CD8 T-cells, and CD163+ macrophages, with similar changes in tubulointerstitial regions. Macrophages are the predominant HLA-DR expressing antigen presenting cells with little expression elsewhere, while macrophages and T-cells predominate cellular crescents. End-stage sclerotic glomeruli are encircled by an acellular fibro-epithelial Bowman's space surrounded by immune infiltrates, all enmeshed in fibronectin. Proliferative LN also shows signs indicative of epithelial to mesenchymal plasticity of tubular cells and parietal epithelial cells. IMC enabled proteomics is a powerful tool to delineate the spatial architecture of LN at the protein level.

Aerosolized miR-138-5p and miR-200c targets PD-L1 for lung cancer prevention.

The development of chemopreventive strategies with the ability to prevent the progression of lung lesions to malignant cancers would reduce the mortality and morbidity resulting from this deadly disease. Delivery of microRNA (miRNA) by inhalation is a novel method for lung cancer prevention. In this study, we investigated the combined efficacy of aerosolized miR-138-5p and miR-200c miRNA mimics in lung cancer prevention. Combination of the two miRNAs inhibited Benzo(a)pyrene (B((a))P)-induced lung adenomas and N-nitroso-tris-chloroethylurea (NTCU)-induced lung squamous cell carcinomas with no detectable side effects. Using single-cell RNA sequencing (scRNA-seq) and imaging mass cytometry (IMC), we found that both miRNAs inhibited programmed cell death ligand 1 (PD-L1) expression. Our flow cytometry results showed that aerosolized delivery of combined miRNAs increased CD4+ and CD8+ T cells and reduced the expression of programmed cell death protein 1 (PD-1) and T-regulatory cells. Our results demonstrated that the delivery of aerosolized microRNAs targeting PD-L1 can be highly effective in preventing lung cancer development and progression in mice.

Sustained Intratumoral Administration of Agonist CD40 Antibody Overcomes Immunosuppressive Tumor Microenvironment in Pancreatic Cancer.

Agonist CD40 monoclonal antibodies (mAb) is a promising immunotherapeutic agent for cold-to-hot tumor immune microenvironment (TIME) conversion. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer known as an immune desert, and therefore urgently needs more effective treatment. Conventional systemic treatment fails to effectively penetrate the characteristic dense tumor stroma. Here, it is shown that sustained low-dose intratumoral delivery of CD40 mAb via the nanofluidic drug-eluting seed (NDES) can modulate the TIME to reduce tumor burden in murine models. NDES achieves tumor reduction at a fourfold lower dosage than systemic treatment while avoiding treatment-related adverse events. Further, abscopal responses are shown where intratumoral treatment yields growth inhibition in distant untreated tumors. Overall, the NDES is presented as a viable approach to penetrate the PDAC immune barrier in a minimally invasive and effective manner, for the overarching goal of transforming treatment.

Integrated exposure assessment and potential risks of neonicotinoids in vegetables from three different sources in Zhejiang, China (2018-2020).

Dietary exposure is the primary route of human exposure to neonicotinoids (neonics), and vegetables are essential foods in people's daily diet. However, the residues and potential health risks of neonics in vegetables from different sources have not been well examined. In this study, we collected 1588 samples of vegetables from organic vegetable bases, farmer's markets, and supermarkets in Zhejiang, China. Three frequently used neonics, acetamiprid, imidacloprid, and thiamethoxam, were selected and analyzed. We investigated the residue, temporal and spatial distribution, and potential health risks. Cowpea was detected with the highest mean imidacloprid-equivalent total neonics (IMIRPF) by value of 655 µg/kg. Vegetable samples from farmer's markets were detected with the highest mean IMIRPF by value of 168 µg/kg, followed by supermarkets (134 µg/kg) and the lowest in organic vegetable bases (76.9 µg/kg). The outcomes of integrated risk assessment for dietary intake of those three neonics in vegetables were all within the safety.

Machine-learning models utilizing CYP3A4*1G show improved prediction of hypoglycemic medication in Type 2 diabetes.

The effectiveness and side effects of Type 2 diabetes (T2D) medication are related to individual genetic background. SNPs CYP3A4 and CYP2C19 were introduced to machine-learning models to improve the performance of T2D medication prediction. Two multilabel classification models, ML-KNN and WRank-SVM, trained with clinical data and CYP3A4/CYP2C19 SNPs were evaluated. Prediction performance was evaluated with Hamming loss, one-error, coverage, ranking loss and average precision. The average precision of ML-KNN and WRank-SVM using clinical data was 92.74% and 92.9%, respectively. Combined with CYP2C19*2*3, the average precision dropped to 88.84% and 89.93%, respectively. While combined with CYP3A4*1G, the average precision was enhanced to 97.96% and 97.82%, respectively. Results suggest that CYP3A4*1G can improve the performance of ML-KNN and WRank-SVM models in predicting T2D medication performance.

About 10% of adults around the world are living with Type 2 Diabetes (T2D). Due to the huge number of patients and the complexity of individual makeup, it is a challenge for doctors to prescribe appropriate hypoglycemic drugs. To aid prescribing, machine-learning models were developed to predict medication schemes based on patients' demographic information and laboratory test results. These models treat prediction as a multilabel classification problem, with each class of medication as a label. This work was designed to determine whether the introduction of genetic information would improve prediction performance. The machine-learning models were trained using datasets with and without genetic information and their performance was compared. The performance of the machine-learning models was improved by incorporating the SNP CYP3A4*1G into the datasets. Thus, this work demonstrates a novel strategy to improve the prediction of T2D hypoglycemic medication performance and provides new ideas for how to support the T2D health system with machine-learning techniques.

The integrated exposure assessment and potential risks of five organophosphorus pesticides in vegetables in Zhejiang, China (2018-2020).

This research surveyed the concentrations of five organophosphorus pesticides (OPs) in vegetables with the purpose of assessing the potential integrated health risks of residents. From 2018 to 2020, 870 samples of eight kinds of vegetables from Zhejiang Province were collected. Gas chromatography coupled with a flame photometric detector (GC-FPD) analyzed the five OPs. OPs were most frequently detected in celery (18.9% of samples), cowpeas (18.3% of samples), and leeks (16.9% of samples) compared to other vegetables. Among the 11 cities in Zhejiang, the cities with high detection rates of OPs were Ningbo and Hangzhou. The integrated concentrations of OPs in different cities ranged from 71.9 to 376 µg/kg. The cumulative risk assessment revealed that the estimated daily intake (EDI) of leek in Wenzhou was the highest, which was 0.0077 (mg/kg bw) and 0.0059 (mg/kg bw) in adults and children respectively. The health risks of residents who consume these vegetables were within a safe range. The data provided demonstrate the distribution and potential health hazards of OPs in commonly consumed vegetables.

CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images.

Since the emergence of the novel coronavirus in December 2019, it has rapidly swept across the globe, with a huge impact on daily life, public health and the economy around the world. There is an urgent necessary for a rapid and economical detection method for the Covid-19. In this study, we used the transformers-based deep learning method to analyze the chest X-rays of normal, Covid-19 and viral pneumonia patients. Covid-Vision-Transformers (CovidViT) is proposed to detect Covid-19 cases through X-ray images. CovidViT is based on transformers block with the self-attention mechanism. In order to demonstrate its superiority, this research is also compared with other popular deep learning models, and the experimental result shows CovidViT outperforms other deep learning models and achieves 98.0% accuracy on test set, which means that the proposed model is excellent in Covid-19 detection. Besides, an online system for quick Covid-19 diagnosis is built on http://yanghang.site/covid19.

Sequential safe feature elimination rule for L1-regularized regression with Kullback-Leibler divergence.

The L1-regularized regression with Kullback-Leibler divergence (KL-L1R) is a popular regression technique. Although many efforts have been devoted to its efficient implementation, it remains challenging when the number of features is extremely large. In this paper, to accelerate KL-L1R, we introduce a novel and fast sequential safe feature elimination rule (FER) based on its sparsity, local regularity properties, and duality theory. It takes negligible time to select and delete most redundant features before and during the training process. Only one reduced model needs to be solved, which makes the computational time shortened. To further speed up the reduced model, the Newton coordinate descent method (Newton-CDM) is chosen as a solver. The superiority of FER is safety, i.e., its solution is exactly the same as the original KL-L1R. Numerical experiments on three artificial datasets, five real-world datasets, and one handwritten digit dataset demonstrate the feasibility and validity of our FER.