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Reactive oxygen species exacerbate nonalcoholic steatohepatitis (NASH) by oxidizing macromolecules; yet how they promote NASH remains poorly understood. Here, we show that peroxidase activity of global hepatic peroxiredoxin (PRDX) is significantly decreased in NASH, and palmitic acid (PA) binds to PRDX1 and inhibits its peroxidase activity. Using three genetic models, we demonstrate that hepatic PRDX1 protects against NASH in male mice. Mechanistically, PRDX1 suppresses STAT signaling and protects mitochondrial function by scavenging hydrogen peroxide, and mitigating the oxidation of protein tyrosine phosphatases and lipid peroxidation. We further identify rosmarinic acid (RA) as a potent agonist of PRDX1. As revealed by the complex crystal structure, RA binds to PRDX1 and stabilizes its peroxidatic cysteine. RA alleviates NASH through specifically activating PRDX1’s peroxidase activity. Thus, beyond revealing the molecular mechanism underlying PA promoting oxidative stress and NASH, our study suggests that boosting PRDX1’s peroxidase activity is a promising intervention for treating NASH. Oxidative stress is closely linked with nonalcoholic steatohepatitis (NASH). Here, the authors show that palmitic acid stimulates NASH by inhibiting PRDX1 to increase oxidative stress, while rosmarinic acid improves NASH by activating PRDX1 to reduce oxidative stress.

Under the backdrop of global climate warming, both forest vegetation greening and resilience decline coexist, and the consistency of these trends at the regional scale remains controversial. This study uses the kNDVI (Kernel Normalized Difference Vegetation Index) and TAC (Temporal Autocorrelation) index framework, combined with BEAST and Random Forest methods, to quantify and analyze the spatiotemporal evolution of forest resilience and its driving factors in Southwest China from 2000 to 2022. The results show the following: (1) Forest resilience exhibits a “high in the northwest and low in the southeast” spatial distribution, with a temporal pattern of “increase-decrease-increase.” The years 2010 and 2015 are key turning points. Trend shift analysis divides resilience into six types. (2) Although forest vegetation shows a clear greening trend, resilience does not necessarily increase with greening, and in some areas, an “increase in greening—decline in resilience” asynchronous pattern appears. (3) The annual average temperature, precipitation, and solar radiation are the main climate factors and their influence on resilience follows a nonlinear relationship. Higher temperatures and increased radiation may suppress resilience, while increased precipitation can enhance it. This study suggests incorporating the TAC indicator into ecological monitoring and early warning systems, along with applying trend classification results for region-specific management to improve the scientific basis and adaptability of forest governance under climate change.

Background The aim of this study is to evaluate the implant survival/success rate, gain in alveolar bone height, crestal bone loss, and complications associated with implants placed in the posterior maxilla after osteotome sinus floor elevation without bone substitutes. Methods The electronic databases, such as MEDLINE, EMBASE, CENTRAL, and SCOPUS were systematically and manually searched for publications in peer-reviewed journals. The included articles were subjected to qualitative and quantitative analyses, and the meta-analysis was carried out for single-arm studies. Methodological quality assessment was made for all the included studies. Results The included studies were of moderate quality, with the overall implant success and survival rates of 98.3% and 97.9% respectively. The most frequent intra-surgical complication was sinus membrane perforation, accounting for 3.08% of the total implants with reported perforations. The overall crestal bone loss in patients with immediate implants placed with OSFE after a 5-year follow-up was 0.957 mm 95%CI (0.538, 1.377). Conclusion Within the limitations of this review, it can be concluded that the survival and success rates of implants placed immediately along with OSFE without any bone substitutes are acceptable and show adequate implant stability with less crestal bone loss over 5 years.

Cancer nanomedicine has emerged as a transformative approach in oncology, providing targeted therapeutic strategies with enhanced efficacy and reduced systemic toxicity. The integration of multi-omics technologies has further revolutionized this field.This review was conducted based on a systematic literature search using PubMed, Web of Science, and Scopus databases. Keywords included “cancer nanomedicine,” “multi-omics,” “precision oncology,” “nanoparticle,” and related terms. The search was limited to articles published between 2000 and 2024. Only English-language full-text articles reporting original research or clinical studies relevant to omics-enhanced nanomedicine were included. This review uniquely synthesizes recent advances in omics-enhanced nanomedicine, highlighting the integration of multi-omics data for precision cancer therapy. Key innovations include omics-driven nanoparticle design, patient stratification via biomarker panels, and synergy with immunotherapy. Future directions focus on AI-aided data integration and clinical translation.

Background AI learning anxiety and job substitution anxiety hinder the use of AI in healthcare. Primary nurses lack training resources, a strong sense of substitution risk, and weak organizational support. Trust is the key to solving AI anxiety and using AI among primary nurses. Therefore, this study aimed to validate the mediating role of AI trust between AI anxiety and the continuous adoption of artificial intelligence technology. Methods This study was a cross-sectional survey, using purposive sampling to select 550 nurses. Measurement tools included a demographic information questionnaire, an AI trust scale, an organizational trust scale, an artificial intelligence anxiety scale, and continuous adoption of an artificial intelligence technology scale. Results Organizational trust and AI trust mediate AI anxiety and continuous adoption of artificial intelligence technology. Conclusion Primary institutions should enhance AI education and training to alleviate learning anxiety, optimize the human-computer collaboration process to diminish nurses’ feelings of substitution, and bolster nurses’ AI and organizational trust by fostering organizational support and technological transparency, increasing their willingness to maintain adoption.

Aim To explore the attitudes of healthcare professionals and non-healthcare professionals on anorexic behavior on social media. Background The significant function of attitude in alleviating anorexic behavior has been widely recognized. However, traditional methods often fail to capture patients’ hidden emotions due to stigma and fear of judgment. Social media provides a novel platform for anonymously examining these behaviors and emotions, offering insights into anorexic behaviors that can enhance intervention strategies. Design This study has a qualitative design based on machine learning. Methods Data was collected from Zhihu, Weibo, and Xiaohongshu social media platforms up to 1 September 2024. This study method consisted of five steps: data collection, data cleaning, validation of relevance, sentiment analysis, and content analysis using the K-means algorithm. Results This study comprised 1099 comments, comprising 277,793 words. Non-healthcare professionals had seven emotions (good, happy, surprise, anger, disgust, fear, and sad) for anorexic behavior, and negative emotions were predominant. Healthcare professionals had three emotions (happy, good, and sad), and negative emotions were predominant. Healthcare professionals have a role deficit in recognizing negative emotions. Conclusion The study emphasizes the need for healthcare professionals to improve the recognition of negative emotions expressed by non-healthcare professionals/patients and develop data-driven interventions that address psychological barriers, fostering holistic patient care and improving outcomes.

Cancer immunotherapy has transformed oncology, with CAR-T cell therapy emerging as a cornerstone of personalized treatment for hematologic malignancies. In China, rapid advancements in domestically developed CAR-T therapies have achieved clinical outcomes comparable to global benchmarks, with overall response rates (ORR) of 79–89% in B-cell malignancies and 64% 12-month progression-free survival in multiple myeloma. Despite these successes, CAR-T application in solid tumors remains hindered by antigen heterogeneity, immunosuppressive microenvironments, and on-target/off-tumor toxicity. To address these barriers, China has pioneered innovative strategies, including dual-target CAR-T constructs, armored CAR-Ts secreting immunomodulatory cytokines, and synergistic integration with traditional Chinese medicine, which enhances CAR-T efficacy by inhibiting myeloid-derived suppressor cells and reducing cytokine release syndrome. Notably, preclinical studies demonstrate that Huangqin increases tumor regression rates from 40 to 65% in lung cancer models when combined with CAR-T therapy. Concurrently, China is reshaping accessibility through policy innovations such as the “1 + 3 + N” multi-tiered payment system and regional insurance pilots, which reduce patient costs by 50%. Strategic investments in automated manufacturing and global regulatory harmonization further position China as a leader in cost-effective CAR-T development. However, challenges persist in solid tumor targeting, international market integration, and long-term safety monitoring. Future directions emphasize precision engineering, AI-driven treatment optimization, and cross-border collaborations to advance next-generation therapies. By balancing innovation, affordability, and policy agility, China is poised to drive the global evolution of cancer immunotherapy while addressing unmet needs in both hematologic and solid malignancies.

This research examines the distribution features of 4960 caves across Guizhou Province, while probing the relationship between the caves＇ spatial patterns and geographic elements. This study is based on hydrogeological and topographic maps of Guizhou. ArcGIS software was used to process the adjacent index, spatial analysis, and coupling analysis of the caves altitude and longitude, as well as the rock properties, lithology, drainage and tec- tonic division of almost 5000 caves. Based on a point pattern analysis of Guizhou caves, the adjacent index is 0.53, and the coefficient of variation verified by Tyson polygon reached 72.469%. This figure reflects the clustered distribution pattern of the caves. Across the entire province, caves are divided into four concentrated areas and one weakly affected area. The four concentrated areas are Zunyi-Tongren, Bijie, Qianxinan-Liupanshui, and Gui- yang-Anshun-Qinan. The one weakly affected zone is Qiandongnan. The most concentrated among them is the Guiyang-Anshun-Qiannan area, which covers 24.67% of the total province area, and accounts for 36.63% of the total province＇s caves. Cave distribution in Guizhou is characterized as dense in the western part and sparse in the eastern part. Under this study background, the natural elements of formation, including lithology, structure, climate, hydrol- ogy, and altitude, and their effects on the distribution, number, and spatial pattern of cave development is analyzed.

The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting management, precise fertilization, and yield prediction. In the karst mountainous of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats, and uneven plant growth. This study took a tobacco planting area in Guizhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds, and shadow masking, this area was classified into eight habitats. The U-Net model was trained using different habitat datasets. The results show that (1) the overall precision, recall, F1-score, and Intersection over Union (IOU) of tobacco plant information extraction were 0.68, 0.85, 0.75, and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can blur images, reducing the plant-identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.