Accelerated discovery and miniaturization of novel single-stranded cytidine deaminases.

Cytidine base editors (CBEs) hold significant potential in genetic disease treatment and in breeding superior traits into animals. However, their large protein sizes limit their delivery by adeno-associated virus (AAV), given its packing capacity of <4.7 kb. To overcome this, we employed a web-based fast generic discovery (WFG) strategy, identifying several small ssDNA deaminases (Sdds) and constructing multiple Sdd-CBE 1.0 versions. SflSdd-CBE 1.0 demonstrated high C-to-T editing efficiency, comparable to AncBE4max, while SviSdd-CBE 1.0 exhibited moderate C-to-T editing efficiency with a narrow editing window (C3 to C5). Utilizing AlphaFold2, we devised a one-step miniaturization strategy, reducing the size of Sdds while preserving their efficiency. Notably, we administered AAV8 expressing PCSK9 targeted sgRNA and SflSdd-CBEs (nSaCas9) 2.0 into mice, leading to gene-editing events (with editing efficiency up to 15%) and reduced serum cholesterol levels, underscoring the potential of Sdds in gene therapy. These findings offer new single-stranded editing tools for the treatment of rare genetic diseases.

Writers, readers, and erasers RNA modifications and drug resistance in cancer.

Drug resistance in cancer cells significantly diminishes treatment efficacy, leading to recurrence and metastasis. A critical factor contributing to this resistance is the epigenetic alteration of gene expression via RNA modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G), pseudouridine (Ψ), and adenosine-to-inosine (A-to-I) editing. These modifications are pivotal in regulating RNA splicing, translation, transport, degradation, and stability. Governed by "writers," "readers," and "erasers," RNA modifications impact numerous biological processes and cancer progression, including cell proliferation, stemness, autophagy, invasion, and apoptosis. Aberrant RNA modifications can lead to drug resistance and adverse outcomes in various cancers. Thus, targeting RNA modification regulators offers a promising strategy for overcoming drug resistance and enhancing treatment efficacy. This review consolidates recent research on the role of prevalent RNA modifications in cancer drug resistance, with a focus on m6A, m1A, m5C, m7G, Ψ, and A-to-I editing. Additionally, it examines the regulatory mechanisms of RNA modifications linked to drug resistance in cancer and underscores the existing limitations in this field.

From Bench to Clinic: A Nitroreductase Rv3368c-Responsive Cyanine-Based Probe for the Specific Detection of Live Mycobacterium tuberculosis.

Tuberculosis (TB), characterized by high mortality and low diagnosis, is caused by a single pathogen, Mycobacterium tuberculosis (Mtb). Imaging tools that can be used to track Mtb without pre-labeling and to diagnose live Mtb in clinical samples can shorten the gap between bench and clinic, fuel the development of novel anti-TB drugs, strengthen TB prevention, and improve patient treatment. In this study, we report an unprecedented novel nitroreductase-responsive cyanine-based fluorescent probe (Cy3-NO2-tre) that rapidly and specifically labels Mtb and detects it in clinical samples. Cy3-NO2-tre generated fluorescence after activation by a specific nitroreductase, Rv3368c, which is conserved in the Mycobacteriaceae. Cy3-NO2-tre effectively imaged mycobacteria within infected host cells, tracked the infection process, and visualized Mycobacterium smegmatis being endocytosed by macrophages. Cy3-NO2-tre also detected Mtb in the sputum of patients with TB and exhibited excellent photostability. Furthermore, the Cy3-NO2-tre/auramine O percentage change within 7 ± 2 days post drug treatment in the sputum of inpatients was closely correlated with the reexamination results of the chest computed tomography, strongly demonstrating the clinical application of Cy3-NO2-tre as a prognostic indicator in monitoring the therapeutic efficacy of anti-TB drugs in the early patient care stage.

Identification and function analysis of GABA branch three gene families in the cotton related to abiotic stresses.

γ -aminobutyric acid (GABA) is closely related to the growth, development and stress resistance of plants. Combined with the previous study of GABA to promote the cotton against abiotic stresses, the characteristics and expression patterns of GABA branch gene family laid the foundation for further explaining its role in cotton stress mechanism. Members of GAD, GAB-T and SSADH (three gene families of GABA branch) were identified from the Gossypium hirsutum, Gossypium barbadense, Gossypium arboreum and Gossypium raimondii genome. The GABA branch genes were 10 GAD genes, 4 GABA-T genes and 2 SSADH genes. The promoter sequences of genes mainly contains response-related elements such as light, hormone and environment.Phylogenetic analysis shows that GAD indicating that even in the same species, the homologous sequences in the family. The GABA-T gene of each cotton genus was in sum the family had gene loss in the process of dicotyledon evolution. SSADH families Gossypium hirsutum, Gossypium barbadense, Gossypium arboreum and Gossypium raimondii were closely related to the dicot plants.GABA gene is involved in the regulation of salt stress and high temperature in Gossypium hirsutum.GABA attenuated part of the abiotic stress damage by increasing leaf protective enzyme activity and reducing reactive oxygen species production.This lays the foundation for a thorough analysis of the mechanism of GABA in cotton stress resistance.

Investigating the Formation of Polymer-Nanoparticle Complex Coacervate Hydrogels Using Polymerization-Induced Self-Assembly-Derived Nanogels with a Succinate-Functional Core.

This paper reports polymer-nanoparticle-based complex coacervate (PNCC) hydrogels prepared by mixing anionic nanogels synthesized by polymerization-induced self-assembly (PISA) and cationic branched poly(ethylenimine) (bPEI). Specifically, poly(3-sulfopropyl methacrylate)58-b-poly(2-(methacryloyloxy)ethyl succinate)500 (PKSPMA58-PMES500) nanogels were prepared by reversible addition-fragmentation chain-transfer (RAFT)-mediated PISA. These nanogels swell on increasing the solution pH and form free-standing hydrogels at 20% w/w and pH ≥ 7.5. However, the addition of bPEI significantly improves the gel properties through the formation of PNCCs. Diluted bPEI/nanoparticle mixtures were analyzed by dynamic light scattering (DLS) and aqueous electrophoresis to examine the mechanism of PNCC formation. The influence of pH and the bPEI-to-nanogel mass ratio (MR) on the formation of these PNCC hydrogels was subsequently investigated. A maximum gel strength of 1300 Pa was obtained for 20% w/w bPEI/PKSPMA58-PMES500 PNCC hydrogels prepared at pH 9 with an MR of 0.1, and shear-thinning behavior was observed in all cases. After the removal of shear, these PNCC gels recovered rapidly, with the recovery efficiency being pH-dependent.

Ultrasound-Based Multi-Planar Bilateral Comparisons as a Diagnostic and Treatment-Definition Method for Unilateral Peripheral Nerve Entrapment.

OBJECTIVE: The primary goal was to determine the performance of the cross-section area swelling rate (CSASR) for diagnostic and therapeutic purposes based on the reference standard of electrodiagnosis examination (EDX) in this diagnostic test study. METHODS: First, patients with symptoms like unilateral carpal tunnel syndrome (CTS), cubital tunnel syndrome (CuTS), and radial nerve compression (RNC) underwent EDX and ultrasound examination. Second, patients with positive ultrasound were calculated for the CSASR of diseased nerve. Based on previously established CSASR criteria, each patient was categorized as having or not having peripheral nerve entrapment, and for those meeting diagnostic criteria, non-surgical or surgical treatment was recommended. Then, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy rate (ACC) of ultrasound diagnosis and therapeutic decision-making were calculated based on the reference standard of EDX that had been historically used in the practice. RESULTS: The total sensitivity, specificity, PPV, NPV, and ACC of ultrasound diagnosis are respectively 93.4, 85.2, 94.7, 82.1, and 91.3%. Which of therapeutic decision-making by ultrasound are, respectively, 83.3, 52.2, 78.4, 60.0, and 73.2%. CONCLUSION: The sensitivity and Youden's index of CSASR diagnostic threshold for CuTS is higher than other ultrasound methods. The CSASR diagnostic threshold for CuTS has a potential diagnostic role, but the current date is still not enough to support the potential diagnostic role for CTS or RNS. There is insufficient evidence to suggest that CSASR for CuTS can be used in isolation for diagnosis. Additional research is needed to confirm the diagnostic role of CSASR. The current results suggest that this ultrasound examination method is not suitable for therapeutic decision-making.

Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature.

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUCODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects.

Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic Scenarios.

The decision-making algorithm serves as a fundamental component for advancing the level of autonomous driving. The end-to-end decision-making algorithm has a strong ability to process the original data, but it has grave uncertainty. However, other learning-based decision-making algorithms rely heavily on ideal state information and are entirely unsuitable for autonomous driving tasks in real-world scenarios with incomplete global information. Addressing this research gap, this paper proposes a stable hierarchical decision-making framework with images as the input. The first step of the framework is a model-based data encoder that converts the input image data into a fixed universal data format. Next is a state machine based on a time series Graph Convolutional Network (GCN), which is used to classify the current driving state. Finally, according to the state's classification, the corresponding rule-based algorithm is selected for action generation. Through verification, the algorithm demonstrates the ability to perform autonomous driving tasks in different traffic scenarios without relying on global network information. Comparative experiments further confirm the effectiveness of the hierarchical framework, model-based image data encoder, and time series GCN.

LncRNA WEE2-AS1 is a diagnostic biomarker that predicts poor prognoses in patients with glioma.

BACKGROUND: Glioma is characterized by high morbidity, high mortality, and poor prognosis. Despite tremendous advances in the treatment of glioma, the prognosis of patients with glioma is still unsatisfactory. There is an urgent need to discover novel molecular markers that effectively predict prognosis in patients with glioma. The investigation of the role of WEE2-AS1 in various tumors is an emerging research field, but the biological function and prognostic value of WEE2-AS1 in glioma have rarely been reported. This study aimed to assess the value of WEE2-AS1 as a potential prognostic marker of glioma. METHODS: Gene expression (RNA-Seq) data of patients with glioma were extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. The Wilcoxon rank sum test was used to analyze the expression of WEE2-AS1 in the cells and tissues of glioma. The Kruskal-Wallis rank sum test, Wilcoxon rank sum test, and logistic regression were used to evaluate the relationship between clinical variables and expression of WEE2-AS1. Cox regression analysis and the Kaplan-Meier method were used to evaluate the prognostic factors in glioma. A nomogram based on Cox multivariate analysis was used to predict the impact of WEE2-AS1 on glioma prognosis. Gene Set Enrichment Analysis (GSEA) was used to identify key WEE2-AS1-associated signaling pathways. Spearman's rank correlation was used to elucidate the association between WEE2-AS1 expression and immune cell infiltration levels. RESULTS: We found that WEE2-AS1 was overexpressed in a variety of cancers, including glioma. High expression of WEE2-AS1 was associated with glioma progression. We determined that the expression of WEE2-AS1 might be an independent risk factor for the survival and prognosis of patients with glioma. We further observed that the mechanism of WEE2-AS1-mediated tumorigenesis involved neuroactive ligand-receptor interaction, cell cycle, and the infiltration of immune cells into the glioma microenvironment. CONCLUSION: These findings demonstrate that WEE2-AS1 is a promising biomarker for the diagnosis and prognosis of patients with glioma. An increased understanding of its effects on the regulation of cell growth may lead to the development of clinical applications that improve the prognostic status of patients with glioma.

3D printable, thermo-responsive, self-healing, graphene oxide containing self-assembled hydrogels formed from block copolymer wormlike micelles.

Graphene oxide (GO) containing block copolymer nanocomposite hydrogels formed from poly(glycerol monomethacrylate-block-hydroxypropyl methacrylate) (PGMA-PHPMA) wormlike micelles were prepared by either mixing GO and copolymer at low temperature or via in situ reversible addition-fragmentation chain-transfer (RAFT) polymerisation-induced self-assembly (PISA) of HPMA in the presence of a PGMA macromolecular chain-transfer agent and GO flakes. Hydrogels containing 15-25% w/w copolymer and 0 and 8% w/w GO, based on copolymer, were investigated and the maximum gel strength measured was ∼33 kPa for a 25% w/w copolymer gel prepared by in situ polymerisation and containing 2% w/w GO based on copolymer. This gel strength represents a fifteen-fold increase over the same copolymer gel without the addition of GO. The nanocomposite gels were found to recover efficiently after the application of high shear, with up to 98% healing efficiency within seconds. These gels are also 3D printable, self-healing, adhesive and temperature responsive on cooling and re-heating. The observed properties were both GO and copolymer concentration dependent, and tensile testing demonstrated that the nanocomposite gels had higher moduli, elongation at break and toughness than gels prepared without GO.

Follow-up outcomes of different bypass surgical modalities for adults with ischaemic-type moyamoya disease.

PURPOSE: The preferred surgical method for treating adults with moyamoya disease (MMD) remains controversial. The purpose of this study was to compare the efficacy of different surgical methods in the treatment of adults with ischaemic-type MMD. METHODS: We retrospectively analyzed the data of patients with ischaemic-type MMD who underwent indirect bypass (IB), direct bypass (DB), or combined bypass (CB) at the First Affiliated Hospital of Zhengzhou University from January 2013 to December 2019. Postoperative complications, improvements in neurological function, haemodynamics, recurrent stroke and neovascularization were compared. RESULTS: A total of 310 adults (371 hemispheres) with ischaemic-type MMD were included in our study. Ninety, 127, and 154 hemispheres underwent IB, DB and CB, respectively. A total of 24 (6.5%) ischaemic events and 8 (2.8%) symptomatic hyperperfusion events occurred after the operations. There was no significant difference in postoperative complications among the three types of surgery (p = 0.300). During the follow-up period, there were 21 cases (5.7%) of recurrent ischaemia and 12 cases (3.2%) of recurrent haemorrhage. Kaplan-Meier survival analysis showed that the ischaemia-free survival of the CB group was significantly longer than that of the IB group (p = 0.047), but there was no significant difference in haemorrhage-free survival among the three groups (p = 0.660). Six months after the operation, DB and CB were superior to IB in improving cerebral blood flow and neovascularization (p = 0.002), but there was no significant difference in the improvement of neurological function among the three groups at the last follow-up (p = 0.784). CONCLUSION: The three surgical methods achieved satisfactory results in the treatment of ischaemic-type MMD. DB and CB can significantly improve haemodynamics and reduce recurrent stroke. In terms of improving neurological function, the curative effect of the three surgical methods remains to be further explored.

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.

The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

Research into Autonomous Vehicles Following and Obstacle Avoidance Based on Deep Reinforcement Learning Method under Map Constraints.

Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained by some map elements such as lane boundaries, lane driving rules, and lane center lines. In this paper, a deep reinforcement learning approach seriously considering map elements is proposed to deal with the autonomous driving issues of vehicles following and obstacle avoidance. When the deep reinforcement learning method is modeled, an obstacle representation method is proposed to represent the external obstacle information required by the ego vehicle input, aiming to address the problem that the number and state of external obstacles are not fixed.

Nerve merging repair in the replantation of a severed limb with defects in multiple nerves: five cases and long-term follow-up.

BACKGROUND: Repairing all nerves is challenging in cases of upper arm avulsion combined with defects in multiple nerves because the donor area for autogenous nerve transplantation is limited and the outcomes of long-segment allogeneic nerve transplantation are poor. Based on the principle of magnified nerve regeneration, we present a method called nerve merging repair, the feasibility of which needs to be confirmed in clinical practice. METHODS: The nerve merging repair method relies on the use of fewer proximal nerves to innervate more distal nerves and depends mainly on whether the radial nerve (RN) can repair itself. In the case of defects in multiple nerves precluding RN self-repair, median-(median + radial) (M-(M + R)) repair is performed. If the RN can undergo self-repair, median-(median + ulnar) (M-(M + U)) or ulnar-(ulnar + median) (U-(U + M)) is used to repair the three nerves. Five cases were included in the study and involved the analysis of joint motor function, muscle strength and sensory recovery of the affected limb. RESULTS: The replanted limb survived in all 5 cases. Follow-up visits were conducted with the patients for 51-80 months, during which they experienced satisfactory recovery of skin sensation, elbow flexion and extension and partial recovery of hand muscle strength. CONCLUSIONS: To a certain extent, treatment with the nerve merging repair method improved the sensory and motor function of the affected limb and limited the loss of function of the donor nerve area. This intervention provides a new approach for repairing long-segment defects in multiple nerves caused by avulsion amputation of the upper limb.

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning.

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on decision-making styles and further emphasis on incentives and punishments. Additionally, we model a traffic scene via graph model to better represent the interaction between vehicles, and adopt the graph convolutional network (GCN) to extract the features of the graph structure to help the connected autonomous vehicles perform decision-making directly. Furthermore, we combine GCN with deep Q-learning and multi-step double deep Q-learning to train four decision-making modes, which are named the graph convolutional deep Q-network (GQN) and the multi-step double graph convolutional deep Q-network (MDGQN). In the simulation, the superiority of the reward function matrix is proved by comparing it with the baseline, and evaluation metrics are proposed to verify the performance differences among decision-making modes. Results show that the trained decision-making modes can satisfy various driving requirements, including task completion rate, safety requirements, comfort level, and completion efficiency, by adjusting the weight values in the reward function matrix. Finally, the decision-making modes trained by MDGQN had better performance in an uncertain highway exit scene than those trained by GQN.

Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving.

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.

A combination of genome-wide and transcriptome-wide association studies reveals genetic elements leading to male sterility during high temperature stress in cotton.

Global warming has reduced the productivity of many field-grown crops, as the effects of high temperatures can lead to male sterility in such plants. Genetic regulation of the high temperature (HT) response in the major crop cotton is poorly understood. We determined the functionality and transcriptomes of the anthers of 218 cotton accessions grown under HT stress. By analyzing transcriptome divergence and implementing a genome-wide association study (GWAS), we identified three thermal tolerance associated loci which contained 75 protein coding genes and 27 long noncoding RNAs, and provided expression quantitative trait loci (eQTLs) for 13 132 transcripts. A transcriptome-wide association study (TWAS) confirmed six causal elements for the HT response (three genes overlapped with the GWAS results) which are involved in protein kinase activity. The most susceptible gene, GhHRK1, was confirmed to be a previously uncharacterized negative regulator of the HT response in both cotton and Arabidopsis. These functional variants provide a new understanding of the genetic basis for HT tolerance in male reproductive organs.

Disrupted Genome Methylation in Response to High Temperature Has Distinct Affects on Microspore Abortion and Anther Indehiscence.

High-temperature (HT) stress induces male sterility, leading to yield reductions in crops. DNA methylation regulates a range of processes involved in plant development and stress responses, but its role in male sterility under HT remains unknown. Here, we investigated DNA methylation levels in cotton (Gossypium hirsutum) anthers under HT and normal temperature (NT) conditions by performing whole-genome bisulfite sequencing to investigate the regulatory roles of DNA methylation in male fertility under HT. Global disruption of DNA methylation, especially CHH methylation (where H = A, C, or T), was detected in an HT-sensitive line. Changes in the levels of 24-nucleotide small-interfering RNAs were significantly associated with DNA methylation levels. Experimental suppression of DNA methylation led to pollen sterility in the HT-sensitive line under NT conditions but did not affect the normal dehiscence of anther walls. Further transcriptome analysis showed that the expression of genes in sugar and reactive oxygen species (ROS) metabolic pathways were significantly modulated in anthers under HT, but auxin biosynthesis and signaling pathways were only slightly altered, indicating that HT disturbs sugar and ROS metabolism via disrupting DNA methylation, leading to microspore sterility. This study opens up a pathway for creating HT-tolerant cultivars using epigenetic techniques.

Review on Vehicle Detection Technology for Unmanned Ground Vehicles.

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology-vehicle detection-are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

Ruthenium-Catalyzed Selectively Oxidative C-H Alkenylation of N-Acylated Aryl Sulfonamides by Using Molecular Oxygen as an Oxidant.

A ruthenium-catalyzed sulfonamide-directed ortho aryl C-H alkenylation/annulation to afford five-membered sultam by using of molecular oxygen as an oxidant is reported in this article. Compared to the previous transition-metal-catalyzed C-H alkenylation of aryl sulfonamides, no excess metal salt oxidant was required in this method. A wide sulfonamide substrates scope and good regioselectivity and site-selectivity make this Ru-catalyzed method more attractive. Importantly, this method can not only be used to effectively prepare the biologically active five-membered sultam molecules but also be efficiently applied to the late-stage modification of sulfonamide drugs.