OBJECTIVE Identify the incentive and constraint factors of artificial intelligence （AI） application in the pharmaceutical field， and promote the application of AI in the field of pharmacy. METHODS Based on the technology acceptance model （TAM）， technology-organization-environment （TOE） framework， and diffusion of innovation theory （DOI）， a TAM-TOE-DOI integrated framework was constructed through a four-stage research process of “theoretical review → dimension mapping → mechanism integration → proposition development”. Combining the analytical pathways of the above three theories in AI application in pharmacy with the integration mechanisms and core propositions of the TAM-TOE-DOI， literature review and deductive reasoning were employed to systematically identify the incentive and constraint factors of AI application in pharmacy from three levels：micro （TAM）， meso （TOE）， and macro （DOI）， and to propose optimization strategies. RESULTS & CONCLUSIONS At the micro level， the efficiency transformation and quality improvement brought by AI technology were the main incentive factors for perceived usefulness， while technological complexity and algorithmic opacity were the main constraint factors for perceived ease of use. At the meso level， the completeness of technological infrastructure， the strength of top management support and innovation climate， as well as external institutional pressure and competitive driving forces were the core incentive factors， whereas scarcity of organizational resources and talent shortage were the main constraint factors. At the macro level， relative advantage and observability were typical incentive factors， while technological complexity was a typical constraint factor. China’s health administration， medical insurance authorities， and other relevant departments should coordinate efforts at the macro， meso， and micro levels to advance AI application in pharmacy： optimizing human-computer interaction and implementing tiered training programs at the micro level； reinforcing organizational support systems and capacity building at the meso level； dismantling data barriers and building social trust at the macro level. Differentiated implementation pathways should be developed for medical institutions at different tiers.

This article systematically reviews and examines the historical evolution of Bambusae Succus as a medicinal material， covering aspects such as nomenclature， origin， geographical distribution， harvesting and processing methods， quality assessment， therapeutic effects and indications， by consulting ancient herbal texts， medical compendia， and modern literature. The aim is to provide a reference for the development and utilization of famous classical formulas containing this herb. Research indicated that Bambusae Succus was first documented in the Shennong Bencaojing during the Han dynasty， with Zhuli being the standard name used throughout history， alongside aliases like Zhuzhi， Zhuyou and Huoquan. Historically， the primary source of Bambusae Succus has been Phyllostachys nigra var. henonis（Danzhu）， although other species such as Pleioblastus amarus and Bambusa emeiensis have also been used medicinally. Ancient records predominantly noted its origin in Yizhou（present-day Chengdu and surrounding areas in Sichuan） and the Wuling region（between present-day Hunan， Guangdong， Guangxi and Jiangxi provinces）， while contemporary sources are mainly from regions south of the Yangtze River and southwestern China. Traditionally， Bambusae Succus was harvested from bamboo that had grown for exactly one year， today， it can be collected year-round without strict age requirements. Ancient preparation methods included direct fire roasting or dry distillation， whereas modern industrial production employs dry distillation， reflux extraction， and percolation. In terms of quality evaluation， ancient texts considered a sweet taste to be superior， while today， clarity and transparency are prioritized. Historically， Bambusae Succus was characterized as sweet and cold nature， targeting the lung and stomach meridians， with uses evolving from clearing heat and resolving phlegm to nourishing Yin， moistening dryness， and relaxing tendons and unblocking meridians. Modern descriptions classify it as sweet， bitter， and cold in nature， affecting the heart， liver， and lung meridians， with functions including clearing heat， resolving phlegm， and facilitating orifices. It is indicated for conditions such as stroke with phlegm confusion， lung heat with phlegm congestion， convulsions， epilepsy， excessive phlegm in febrile diseases， high fever with thirst， irritability during pregnancy， and tetanus， with more clearly defined applications. Based on the results of the research， it is recommended that when developing and utilizing famous classical formulas containing Bambusae Succus， the one-year-old Phyllostachys nigra var. Henonis， which has been highly praised throughout history， should be selected as the source material. Industrial production should adopt the dry distillation method. Furthermore， in-depth research should be conducted on the modern technological characterization of the traditional quality control indicator of sweet taste， and reasonable modern quality control standards should be established.

Depression is a common mental disorder that falls under the category of "stagnation syndrome" in traditional Chinese medicine （TCM）. Its complex pathogenesis poses challenges for the development of novel therapeutic agents. Currently， clinically used antidepressants are often accompanied by significant side effects， and statistics show that about one-third of patients do not respond to these medications. TCM demonstrates advantages in the treatment of depression through multi-target， multi-pathway and multi-mechanistic approaches. Pinelliae Rhizoma， a phlegm-resolving herb， exhibits effects such as drying dampness and resolving phlegm， as well as eliminating stuffiness and reducing masses. The characteristics of harmonizing Yin and Yang and resolving stagnation in the middle energizer align precisely with the pathogenesis of depression syndrome， demonstrating therapeutic efficacy in affected patients. Literature studies have found that the active ingredients of Pinelliae Rhizoma， such as cavidine， baicalein， β-sitosterol， as well as Pinelliae Rhizoma herb pairs， such as Pinelliae Rhizoma-Magnoliae Officinalis Cortex， Pinelliae Rhizoma-husked sorghum， Pinelliae Rhizoma-Prunellae Spica， exhibit significant antidepressant effects. Furthermore， TCM formulas containing Pinelliae Rhizoma as the principal therapeutic agent， such as Banxia Xiexin Tang， Banxia Houpo Tang， and Wendan Tang， as well as formulas incorporating Pinelliae Rhizoma like compound Xiaochaihu Tang， Chaihu Jia Longgu Muli Tang， and Erchen Tang， have also demonstrated favorable antidepressant efficacy. The antidepressant mechanism of these agents may involve modulation of 5-hydroxytryptamine （5-HT） and dopamine （DA） levels， up-regulation of brain-derived neurotrophic factor （BDNF） expression， regulation of the hypothalamus-pituitary-adrenal （HPA） axis， reduction of oxidative stress， modulation of nuclear transcription factor-κB （NF-κB） signaling pathway， and inhibition of microglia-mediated inflammatory responses. This review summarized the antidepressant mechanisms and clinical applications of the active components， herb pairs， and TCM formulas containing Pinelliae Rhizoma， aiming to provide a reference for modern research on the use of Pinelliae Rhizoma in antidepressant therapy.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

OBJECTIVE To systematically analyze the trends in the volume and value of out-licensing deals by Chinese biopharmaceutical companies over the past two decades， and to quantitatively identify factors associated with deal value， thereby providing an evidence base for policymakers and corporate managers. METHODS Based on the PharnexCloud database， information on out-licensing deals disclosed by Chinese biopharmaceutical companies between January 1， 2005 and December 31， 2025 was collected， and executed deals with disclosed specific financial terms were included for analysis. Descriptive statistical analyses of deal value， product characteristics and deal characteristics were performed using Stata SE 18.0； with the natural logarithm of deal value as the dependent variable， stepwise multiple regression models were constructed to examine the associations of product and deal characteristics with deal value. RESULTS A total of 210 out-licensing deals were included， with a cumulative deal value of approximately 214.151 billion dollars and a mean deal value of 1.020 billion dollars . The trading market expanded rapidly from 2020 onward， with the annual deal value reaching a record 95.687 billion dollars in 2025. Multiple regression analysis showed that the deal value of products at the drug discovery stage was significantly lower than that at the preclinical stage [regression coefficient （ β ） ＝－1.995， 95% confidence interval （CI）： －3.256 to －0.734， P ＜0.05 ] ； the deal value of oncology products was significantly higher than that of other therapeutic areas （ β ＝0.828， 95%CI： 0.224 to 1.431， P ＜0.05）； global licensing （ β ＝1.690， 95%CI： 1.107 to 2.273， P ＜0.05） and co-development arrangements （ β ＝0.849， 95%CI： 0.084 to 1.615， P ＜0.05） were significantly associated with higher deal value. CONCLUSIONS Out-licensing deals by Chinese biopharmaceutical companies have achieved significant growth in both volume and value over the past two decades. Oncology products， global licensing， and co-development arrangements are significantly and positively associated with higher deal value.

Sanhuang Xiexintang （SHXXT）， originating from the Synopsis of the Golden Chamber （Jin Gui Yao Lue）， comprises three medicinal herbs： Rhei Radix et Rhizoma， Coptidis Rhizoma， and Scutellariae Radix. Renowned for its effects of purging fire， detoxifying， drying dampness， and draining heat， SHXXT primarily treats syndromes characterized by internal excess heat， dampness-heat stagnation， and reckless blood movement due to heat. Because of the concise formulation and remarkable efficacy of this formula， modern research has extensively explored the chemical composition， clinical applications， and pharmacological mechanisms of SHXXT， yielding significant advancements. As a modern derivative of SHXXT， Yiqing granules exemplify the transformation of classical formulas into convenient and precise applications， embodying the paradigm of modern development of classical formulas. This systematic review synthesizes recent research progress in SHXXT. Chemical analyses reveal that the active components—anthraquinones， alkaloids， and flavonoids—of SHXXT exert holistic therapeutic effects through multi-component synergy. Clinical studies demonstrate broad utility of this formula in managing digestive disorders， endocrine diseases， urinary tract infections， dermatological conditions， and systemic inflammatory diseases， highlighting its multi-system regulatory potential. Mechanism investigations elucidate its multi-target mechanism of action， including antimicrobial， anti-inflammatory， metabolic-regulatory， hepatoprotective， and gastrointestinal-modulating effects， reflecting an integrated multi-component， multi-target， multi-pathway mode of action. This review systematically summarizes the modern research progress in SHXXT， providing a theoretical foundation for deciphering the scientific essence of classical formulas and advancing their precision application in clinical practice.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

AIM To investigate the effects of Yunpi Tongchang Formula on intestinal mucosal barrier damage in rats with opioid-induced constipation(OIC)of Spleen-Kidney Yang Deficiency Syndrome.METHODS In contrast to the 10 rats of the blank group,the 50 rats of the modeling group were induced into models of OIC of Spleen-Kidney Yang Deficiency Pattern by 7 days consecutive administration of both subcutaneous loperamide injection and alternating gavage of activated carbon ice water and vinegar.Following successful modeling,rats were randomly allocated into the model group,the mosapride citrate tablet group(1.35 mg/kg),and the high-dose,medium-dose,and low-dose Yunpi Tongchang Formula groups(15.12,7.56,3.78 g/kg),with 8 mice in each group.Upon the completion of the 14 days treatment,the rats had their TCM Syndrome scores assessed;their fecal water content,initial black stool excretion time,and small intestine propulsion rate measured;their colon tissue morphology observed by HE staining;their serum levels of IL-6,TNF-α,and IL-1β detected by ELISA;their expressions of occludin and zonula occludens-1(ZO-1)in colon tissues detected by immunohistochemistry;their mRNA expressions of MyD88,TLR4 and NF-κB p65 in the colon tissues detected by RT-qPCR;and their protein expressions of MyD88,TLR4 and NF-κB p65 in the colon tissues detected by Western blot.RESULTS Compared to the blank group,the model group had higher TCM Syndrome scores(P＜0.01);lower fecal water content and small intestine propulsion rate(P＜0.05,P＜0.01);longer initial black stool excretion time(P＜0.01);more mucosal edema in colon tissue,obvious inflammatory infiltration,and glandular disorder;increased serum levels of IL-6,TNF-α and IL-1 β(P＜0.05);decreased colon expressions of ZO-1 and occludin(P＜0.01);and increased mRNA and protein expressions of TLR4,MyD88 and NF-κB p65(P＜0.01).Compared to the model group,both the medium-dose Yunpi Tongchang Formula group and the mosapride citrate tablet group demonstrated effectively reduced TCM syndrome scores(P＜0.01);increased fecal water content and small intestine propulsion rate(P＜0.05,P＜0.01);and shorter initial black stool excretion time(P＜0.01);improved colon mucosal edema and inflammatory infiltration;decreased serum levels of IL-6,TNF-α and IL-1β(P＜0.01);upregulated protein expressions of ZO-1 and occludin(P＜0.01);and downregulated mRNA and protein expressions of TLR4,MyD88 and NF-κB p65(P＜0.05,P＜0.01).CONCLUSION Yunpi Tongchang Formula significantly ameliorates constipation symptoms in OIC rat models of Spleen-Kidney Yang Deficiency Syndrome because of its efficacy in attenuating intestinal inflammation and preserving the integrity of intestinal epithelial barrier structure,with its mechanistic action in downregulating TLR4/MyD88/NF-κB signaling pathway activation.

By summarizing the mass spectrometric fragmentation patterns of oxyphenisatin substances,an analytical method was established for screening of illegally added oxyphenisatin compounds in weight-loss health foods using high performance liquid chromatography-in-source-fragmentation-quadrupole time-of-flight mass spectrometry(HPLC-ISF-QTOF-MS),along with a quantitative method for 11 kinds of oxyphenisatin compounds.Based on the developed screening method,an oxyphenisatin derivative was discovered in the reference standards,which was tentatively identified as 4-(3-(4-hydroxyphenyl)-2-oxoindolin-3-yl)phenyl acetate and confirmed by MS/MS analysis.The results showed that all 11 kinds of oxyphenisatin compounds had correlation coefficients greater than 0.9971,with limits of detection(LODs)ranging from 0.12 to 0.68 μg/L and limits of quantification(LOQ)from 0.21 to 2.29 μg/L.The LODs for 11 kinds of characteristic ions ranged from 0.45 to 9.11 μg/kg.At spiking levels of 25,50 and 100 μg/kg,the recoveries ranged from 78.9%to 117.3%.The instrumental precision,intra-day method precision and inter-day method precision were 0.23%?1.70%,0.7%?2.4%,and 1.1%?3.3%,respectively.The developed targeted and non-targeted detection method demonstrated high sensitivity,strong stability,rapid analysis,and an expanded screening range for oxyphenisatin substances,and provided robust technical support for regulatory authorities in combating illegal adulteration.