Oxidative stress is a pivotal factor in the onset and progression of diabetic kidney disease （DKD）， and it plays an essential role in the prevention and treatment of DKD. The "Gaozhuo" pathogenesis posits that DKD is characterized by the invasion of Gaozhuo and damage to the kidney collaterals， with the underlying cause being the insufficiency of spleen Qi and the internal formation of Gaozhuo， which provides valuable guidance on oxidative stress. The insufficiency of spleen Qi and the internal formation of Gaozhuo represent a dynamic， evolving process. Gaozhuo invades the kidney collaterals， impairs kidney Qi， and progressively leads to the congealing and stagnation of Gaozhuo and blood， ultimately resulting in the failure of both the spleen and kidneys. The damage caused by Gaozhuo to the kidney collaterals and kidney Qi is analogous to the organ and functional damage of the kidneys induced by excessive reactive oxygen species and oxidative stress. Damage to the kidney collaterals means organic injuries to the glomeruli， renal tubules， and renal interstitium， and the depletion of kidney Qi refers to damage to glomerular filtration and renal tubular reabsorption. The congealing and stagnation of Gaozhuo and blood in the kidney collaterals is similar to oxidative stress-induced thickening of the glomerular basement membrane and fibrosis. The interaction between spleen and kidney Qi deficiency and the congealing and stagnation of Gaozhuo and blood creates a vicious cycle that exacerbates the condition， ultimately evolving into the failure of both the spleen and kidneys. The failure of the spleen and kidneys is analogous to renal failure， and its extreme manifestation is end-stage renal disease and uremia. The treatment of oxidative stress in DKD with traditional Chinese medicine （TCM） is based on the principles of strengthening the spleen and tonifying the kidneys， and dispelling turbidity and removing blood stasis. According to the syndrome type， it is recommended to use methods such as strengthening the spleen and tonifying Qi while dispelling dampness and removing turbidity， strengthening the spleen and tonifying the kidneys while dispelling dampness and removing turbidity， strengthening the spleen and tonifying the kidneys while dispelling turbidity and removing blood stasis， or consolidating the spleen and kidneys while clearing away turbidity and blood stasis.

ObjectiveHistorically documented Zhejiang Atractylodis Macrocephalae Rhizoma（Baizhu） possesses premium characteristics such as phoenix-like head and crane-like neck， pronounced sweetness， and fragrant aroma. However， its current market circulation is low， and the processed products with Zhejiang-style characteristics are at the risk of being lost. This study aims to preserve the ancient Zhejiang-style processing techniques and evaluate them using modern scientific methods. MethodsMultidimensional intelligent sensory evaluation was used to digitally characterize the "quality-structure" of the external appearance of nine-steamed and nine-processed Baizhu medicinal materials（intermediate processed products） and the "odor-taste" of the internal quality of its decoction pieces（slices）， and the appearance parameters were digitally characterized by colorimeter， texture analyzer， electronic nose and electronic tongue， the chemical composition was analyzed via ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）. Then， cluster analysis on the differences in odor between the medicinal materials（intermediate processed products） and decoction pieces（slices） of nine-steamed and nine-processed Baizhu was conducted， as well as the differences in taste between water-soluble and alcohol-soluble extracts of the decoction pieces（slices）， and the correlation analysis of chroma value-alcohol-soluble extract content-component response value. ResultsThe nine-steamed and nine-processed Baizhu had a dark brown to black epidermis， a brownish-yellow to brownish-gray cross-section， a slightly tough texture， a faint odor， and a slightly sweet， bitter and pungent taste. Texture analyzer measurements revealed minimal adhesion and maximum recovery in the middle section of the characteristic processed Baizhu， consistent with the processing endpoint of thorough steaming and cooking. The head section showed the highest internal hardness， elasticity and chewiness， indicating a denser texture in this area. The electronic nose sensor could clearly distinguish the difference between the medicinal materials and its decoction pieces， with a more significant clustering effect at 60 ℃ for 30 minutes compared to ambient temperature headspace for 2 hours， highlighting the significant impact of the baking degree before slicing on the quality. The electronic tongue taste signal map clearly distinguished the differences between water-soluble and alcohol-soluble extracts of nine-steamed and nine-processed Baizhu decoction pieces， and the addition of auxiliary materials during processing could enhance its alcohol-soluble extract content. A total of 82 chemical components were identified in the characteristic processed Baizhu. After processing， the contents of 58 components increased， while 24 components decreased. Correlation analysis revealed significant negative correlations（P<0.01） between ethanol-soluble extract content and colorimetric values of brightness（L^*）， yellow-bule value（b^*）， and total color difference（E^*_ab）. E^*_ab showed marked negative correlations（P<0.05） with the response values of isochlorogenic acid A and C. ConclusionThis study establishes a modern intelligent sensory evaluation model for multidimensional holographic characterization of nine-steamed and nine-processed Baizhu， clarifying the correlation between increased isochlorogenic acid content and the visual color appearance after different steaming cycles， as well as its intrinsic alcohol-soluble extracts. This provides a reference for quality evaluation and processing standards of the Zhejiang-style characteristic processed products.

With the development of brain-computer interface technology （BCI）， its potential to enhance human cognitive and physical abilities continues to emerge， while also stimulating the“superhuman”enhancement impulse to try to push the boundaries of treatment. Overmodification of the body governed by the pursuit of BCI“superhuman”augmentation may lead to a series of ethical body dilemmas， such as issues of bodily autonomy， the risk and benefit of the body medical dimension， and how to define the idealized body at the social level when the natural body and the BCI technology-enhanced body compete in the future. Therefore， it is necessary to take positive measures to promote the development of brain-computer interface technology in the process of continuous innovation and ethical responsibility.

ObjectiveTo explore the possible mechanisms of Shujin Jiannao Formula （舒筋健脑方） for cerebral palsy. MethodsThirty 7-day-old SD rats were randomly divided into normal group， model group， and Shujin Jiannao Formula group， with 10 rats in each group. The model group and Shujin Jiannao Formula group established a cerebral palsy model by the classic Rice-Vannucci method. After successful modeling， rats in Shujin Jiannao Formula group were given Shujin Jiannao Formula 16 g/（kg·d） by gavage， while the normal group and model group were given normal saline 10 ml/（kg·d） by gavage once a day. After one week of intervention， the rats' body weight was measured， and Zea-Longa scores， the righting reflex test， and the hindlimb suspension test were conducted for assessment； hematoxylin-eosin （HE） staining and Nissl staining were used to observe pathological changes in brain tissue， and the number of Nissl-positive neurons was counted； enzyme-linked immunosorbent assay （ELISA） was employed to measure levels of inflammatory cytokines in the brain tissue， specifically interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）； immunofluorescence was used to detect the expression levels of neurofilament protein 200 （NF200） and myelin basic protein （MBP） in brain tissue； Western Blot analysis was conducted to determine the protein levels of phosphoinositide 3-kinase （PI3K）， protein kinase B （Akt/PKB/Rac）， and mammalian target of rapamycin （mTOR） in brain tissue. ResultsCompared with the normal group， rats in the model group showed significantly higher Zea-Longa scores and lower scores in the hindlimb suspension test （P＜0.01）； pathological findings revealed loose structure in the cerebral cortex， hippocampal atrophy， and neuronal damage in brain tissue. Levels of IL-1β and TNF-α elevated， and the number of Nissl-stained positive neurons in the cortex and hippocampal CA1 region reduced， and immunofluorescence intensity of NF200 and MBP， as well as protein expression levels of PI3K and mTOR， significantly decreased （P＜0.05 or P＜0.01）. Compared with the model group， rats in Shujin Jiannao Formula group showed decreased Zea-Longa scores and increased hindlimb suspension test scores （P＜0.05）； pathological damage in brain tissue alleviated， levels of IL-1β and TNF-α reduced， the number of Nissl-stained positive neurons in the cortex and hippocampal CA1 region increased， and the immunofluorescence intensity of NF200 and MBP， as well as the protein levels of PI3K and mTOR， significantly elevated （P＜0.05 or P＜0.01）. There were no statistically significant differences among the groups in body weight， body-turning time， or AKT protein levels in brain tissue （P＞0.05）. ConclusionShujin Jiannao Formula can improve the neurological function of rats with cerebral palsy， exert neurorestorative effects， and its mechanism of action may be related to the reduction of inflammatory response in brain tissue and the activation of the PI3K/AKT/mTOR signaling pathway.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

BACKGROUND: Generalized pustular psoriasis (GPP), a rare and recurrent autoinflammatory disease, imposes a substantial burden on patients and society. Awareness of GPP in China remains limited. METHODS: This cross-sectional survey, conducted between September 2021 and May 2023 across 14 hospitals in China, included GPP patients of all ages and disease phases. Data collected encompassed demographics, clinical characteristics, economic impact, disease severity, quality of life, and treatment-related complications. Risk factors for GPP recurrence were analyzed. RESULTS: Among 127 patients (female/male ratio = 1.35:1), the mean age of disease onset was 25 years (1st quartile [Q1]-3rd quartile [Q3]: 11-44 years); 29.2% had experienced GPP for more than 10 years. Recurrence occurred in 75.6% of patients, and nearly half reported no identifiable triggers. Younger age at disease onset ( P  = 0.021) and transitioning to plaque psoriasis ( P  = 0.022) were associated with higher recurrence rates. The median diagnostic delay was 8 months (Q1-Q3: 2-41 months), and 32.3% of patients reported misdiagnoses. Comorbidities were present in 53.5% of patients, whereas 51.1% experienced systemic complications during treatment. Depression and anxiety affected 84.5% and 95.6% of patients, respectively. During GPP flares, the median Dermatology Life Quality Index score was 19.0 (Q1-Q3: 13.0-23.5). This score showed significant differences between patients with and without systemic symptoms; it demonstrated correlations with both depression and anxiety scores. Treatment costs caused financial hardship in 55.9% of patients, underscoring the burden associated with GPP. CONCLUSIONS The substantial disease and economic burdens among Chinese GPP patients warrant increased attention. Patients with early onset disease and those transitioning to plaque psoriasis require targeted interventions to mitigate the high recurrence risk.
Humans ; Male ; Female ; Psoriasis/pathology* ; Adult ; Cross-Sectional Studies ; Adolescent ; Child ; Young Adult ; Quality of Life ; Middle Aged ; China/epidemiology* ; Recurrence ; Risk Factors ; Surveys and Questionnaires ; East Asian People

Rubi Fructus has a long history of medicinal and edible use in China. It contains chemical components such as terpenes, flavonoids, phenolic acids, fatty acids, and alkaloids, and possesses various pharmacological activities, including antioxidant, anti-inflammatory, hypoglycemic, anti-tumor, anti-osteoporosis, and liver-protective effects. Rubi Fructus is widely applied in medical, health, and food fields. The quality of Rubi Fructus can directly affect the safety and effectiveness of clinical medication. Therefore, this article reviews the research progress on the chemical constituents and pharmacological effects of Rubi Fructus. Based on the concept of traditional Chinese medicine(TCM) quality markers(Q-markers), the article explores the screening and determination of Q-markers for Rubi Fructus from various aspects, including plant kinship, traditional efficacy, medicinal properties, measurability of chemical composition, different processing methods, producing areas, harvesting periods, and planting conditions. The components ellagic acid, kaempferol, quercetin, kaempferol-3-O-rutinoside, rutin, astragalin, tiliroside, and hyperoside are preliminarily proposed as Q-markers for Rubi Fructus, providing a reference for the quality control of Rubi Fructus.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Rubus/chemistry* ; Fruit/chemistry* ; Quality Control ; Animals