Advances in Yupingfeng San Research: Multi-Target Mechanisms and Clinical Evidence

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Shiyu Gao · Yanfang Wang · Xiaoya Li · Lin Zheng · Zhaoshuang Zhan · Jiafeng Wang

25 May 2026 at 18:00

Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

Advances in Yupingfeng San Research: Multi-Target Mechanisms and Clinical Evidence

Omics In Lung

By: Shiyu Gao · Yanfang Wang · Xiaoya Li · Lin Zheng · Zhaoshuang Zhan · Jiafeng Wang

25 May 2026 at 18:00

Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality

cs.AI, q-bio.NC updates on arXiv.org

By: Xiaoyuan Zhu · Kimberly Le Truong · Riccardo Fogliato · Gokul Swamy · Weijian Zhang · Minglai Yang · Longtian Ye · Bangya Liu · Minghao Liu · Andrew Ilyas · Steven Wu

7 April 2026 at 12:00

arXiv:2604.04418v1 Announce Type: cross Abstract: As LLMs are deployed in high-stakes settings, users must judge the correctness of individual responses, often relying on model-generated justifications such as reasoning chains or explanations. Yet, no standard measure exists for whether these justifications help users distinguish correct answers from incorrect ones. We formalize this idea as error verifiability and propose $v_{\text{bal}}$, a balanced metric that measures whether justifications enable raters to accurately assess answer correctness, validated against human raters who show high agreement. We find that neither common approaches, such as post-training and model scaling, nor more targeted interventions recommended improve verifiability. We introduce two methods that succeed at improving verifiability: reflect-and-rephrase (RR) for mathematical reasoning and oracle-rephrase (OR) for factual QA, both of which improve verifiability by incorporating domain-appropriate external information. Together, our results establish error verifiability as a distinct dimension of response quality that does not emerge from accuracy improvements alone and requires dedicated, domain-aware methods to address.

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

cs.AI, q-bio.NC updates on arXiv.org

By: Xiaojie Xu · Zongyuan Li · Chang Lu · Runnan Qi · Yanan Ni · Lumin Jiang · Xiangbei Liu · Xuebo Zhang · Yongchun Fang · Kuihua Huang · Xian Guo · Zhanghua Wu · Zhenya Li

7 April 2026 at 12:00

arXiv:2502.13388v3 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key information in the game through a keyframe selection method, then makes decisions based on expert experience and self-experience. After a game is completed, it reflects on the previous experience to obtain new self-experience. Finally, in the experiment, our method beat the robot under the Very Hard difficulty in TextStarCraft II. We analyze the data of the LLM in the process of the game in detail, verified its effectiveness.

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

cs.AI, q-bio.NC updates on arXiv.org

By: Xiaojie Xu · Zongyuan Li · Chang Lu · Runnan Qi · Yanan Ni · Lumin Jiang · Xiangbei Liu · Xuebo Zhang · Yongchun Fang · Kuihua Huang · Xian Guo · Zhanghua Wu · Zhenya Li

3 April 2026 at 12:00

arXiv:2502.13388v2 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key information in the game through a keyframe selection method, then makes decisions based on expert experience and self-experience. After a game is completed, it reflects on the previous experience to obtain new self-experience. Finally, in the experiment, our method beat the robot under the Very Hard difficulty in TextStarCraft II. We analyze the data of the LLM in the process of the game in detail, verified its effectiveness.

Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Fold Paralysis

cs.AI, q-bio.NC updates on arXiv.org

By: Yucong Zhang · Xin Zou · Jinshan Yang · Wenjun Chen · Juan Liu · Faya Liang · Ming Li

10 March 2026 at 12:00

arXiv:2409.03597v4 Announce Type: replace-cross Abstract: This paper presents the Multimodal Laryngoscopic Video Analyzing System (MLVAS), a novel system that leverages both audio and video data to automatically extract key video segments and metrics from raw laryngeal videostroboscopic videos for assisted clinical assessment. The system integrates video-based glottis detection with an audio keyword spotting method to analyze both video and audio data, identifying patient vocalizations and refining video highlights to ensure optimal inspection of vocal fold movements. Beyond key video segment extraction from the raw laryngeal videos, MLVAS is able to generate effective audio and visual features for Vocal Fold Paralysis (VFP) detection. Pre-trained audio encoders are utilized to encode the patient voice to get the audio features. Visual features are generated by measuring the angle deviation of both the left and right vocal folds to the estimated glottal midline on the segmented glottis masks. To get better masks, we introduce a diffusion-based refinement that follows traditional U-Net segmentation to reduce false positives. We conducted several ablation studies to demonstrate the effectiveness of each module and modalities in the proposed MLVAS. The experimental results on a public segmentation dataset show the effectiveness of our proposed segmentation module. In addition, unilateral VFP classification results on a real-world clinic dataset demonstrate MLVAS's ability of providing reliable and objective metrics as well as visualization for assisted clinical diagnosis.

Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model

cs.AI, q-bio.NC updates on arXiv.org

By: Yuhang Liu · Yueyang Cang · Wenge Que · Xinru Bai · Xingtong Wang · Kuisheng Chen · Jingya Li · Xiaoteng Zhang · Xinmin Li · Lixia Zhang · Pingge Hu · Qiaoting Xie · Peiyu Xu · Xianxu Zeng · Li Shi

4 March 2026 at 13:00

arXiv:2603.02704v1 Announce Type: cross Abstract: The pathological diagnosis of gestational trophoblastic disease(GTD) takes a long time, relies heavily on the experience of pathologists, and the consistency of initial diagnosis is low, which seriously threatens maternal health and reproductive outcomes. We developed an expert model for GTD pathological diagnosis, named GTDoctor. GTDoctor can perform pixel-based lesion segmentation on pathological slides, and output diagnostic conclusions and personalized pathological analysis results. We developed a software system, GTDiagnosis, based on this technology and conducted clinical trials. The retrospective results demonstrated that GTDiagnosis achieved a mean precision of over 0.91 for lesion detection in pathological slides (n=679 slides). In prospective studies, pathologists using GTDiagnosis attained a Positive Predictive Value of 95.59% (n=68 patients). The tool reduced average diagnostic time from 56 to 16 seconds per case (n=285 patients). GTDoctor and GTDiagnosis offer a novel solution for GTD pathological diagnosis, enhancing diagnostic performance and efficiency while maintaining clinical interpretability.

RRPO: Robust Reward Policy Optimization for LLM-based Emotional TTS

cs.AI, q-bio.NC updates on arXiv.org

By: Cong Wang · Changfeng Gao · Yang Xiang · Zhihao Du · Keyu An · Han Zhao · Qian Chen · Xiangang Li · Yingming Gao · Ya Li

17 February 2026 at 13:00

arXiv:2512.04552v3 Announce Type: replace-cross Abstract: Differentiable reinforcement learning (RL) frameworks like DiffRO offer a powerful approach for controllable text-to-speech (TTS), but are vulnerable to reward hacking, particularly for nuanced tasks like emotion control. The policy model can exploit a vanilla Reward Model (RM) by generating acoustic artifacts to achieve spurious rewards, but at the cost of degrading perceptual quality. To address this, we propose Robust Reward Policy Optimization (RRPO), a novel framework that employs a hybrid regularization scheme. This scheme develops a robust RM whose reward signal is more reliably aligned with human perception, compelling the policy to abandon detrimental shortcuts and instead learn the complex features of genuine emotions. Our ablation study confirms the enhanced robustness of our RM, as evidenced by its strong cross-lingual generalization. The subjective evaluation demonstrates that this robust RM effectively mitigates reward hacking, leading to significant improvements in both emotional expressiveness and naturalness over all baselines. Demo page: https://lrwinr.github.io/RRPO-CosyVoice.

Reading view