ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

OBJECTIVE To construct an intelligent pharmaceutical Q&A platform for precision medication with low “artificial intelligence （AI） hallucination”， aiming to enhance the accuracy， consistency， and traceability of medication consultations. METHODS Medication package inserts were batch-processed and converted into structured data through Python programming to build a local pharmaceutical knowledge base. The retrieval and question-answering processes were designed based on large language models， and system integration and localized deployment were completed on Dify platform. By designing typical clinical medication questions and comparing the output of the intelligent pharmaceutical Q&A platform with the online version of DeepSeek across dimensions such as peak time retrieval， half-life， and dosage adjustment reasoning for patients with renal impairment， the accuracy and reliability of its retrieval and reasoning results were evaluated. RESULTS The intelligent pharmaceutical Q&A platform， constructed based on local drug package inserts， achieved 100% accuracy in retrieval and reasoning for peak time， half-life， and dosage adjustment schemes. In comparison， the online version of DeepSeek demonstrated accuracies of 30%（6/20）， 50%（10/20）， and 38%（23/60） across these three dimensions， respectively. CONCLUSIONS The constructed intelligent pharmaceutical Q&A platform is capable of accurately retrieving and extracting information from the local knowledge base based on clinical inquiries， thereby avoiding the occurrence of AI hallucinations and providing reliable medication decision support for healthcare professionals.

Objective: To assess the effect of 12-year-old children s pit and fissure sealants on the health of first permanent molars, so as to provide evidence for optimizing caries prevention strategies among children. Methods: In March 2025, a cluster random sampling method was used to conduct oral examinations on 965 students aged 12 from Chengdu s 2021 Comprehensive Intervention Program for Pediatric Oral Diseases. Data from the Comprehensive Intervention System for Children s Oral Diseases were referenced. Participants were divided into a sealed group ( n =755) and an unsealed group ( n =210) based on whether they had received sealants on their first permanent molars. Chi square test or analysis of variance were used to compare indicators such as caries incidence, new caries detection rate, and new caries mean (DMFT increment) between the two groups Results: The sealed group showed significantly lower caries incidence, new caries detection rate, and new caries mean (33.38%, 17.65%, 0.59±1.00) compared to the unsealed group (43.81%, 24.70%, 0.87±1.22)( χ 2/F =7.79, 18.26, 9.55, all P <0.05). However, no significant difference was found in the filled teeth ratio between the two groups (20.38% , 20.16%; χ 2=0.01, P =0.94). In girls, the sealed group exhibited significantly lower caries incidence, new caries detection rate, and new caries mean (36.78%, 20.99%, 0.69± 1.10 ) than the unsealed group (57.55%, 33.52%, 1.15±1.29) ( χ 2/F =14.42, 23.76, 10.92, all P <0.05), whereas no significant differences were observed between boys in the sealed (30.47%, 14.85%, 0.50±0.89) and unsealed groups (29.81%, 16.18%, 0.59± 1.08) ( χ 2/F =0.02, 0.41, 0.74, all P >0.05). Boys had significantly lower new caries detection rates and new caries means than girls in both groups ( χ 2/F =16.20, 6.94; 29.93, 11.84, all P <0.05). In urban areas, the sealed group had lower new caries detection rates and new caries means (19.37%, 0.68±1.04) than the unsealed group (24.66%, 0.90±1.20) ( χ 2/F =6.86, 3.94, both P <0.05). In suburban areas, all indicators for the sealed group (24.71%, 13.77%, 0.42±0.87) were significantly lower than those for the unsealed group (38.81%, 24.77%, 0.82±1.28) ( χ 2/F =5.28, 15.36, 6.00, all P <0.05). Indicators from specialized dental institutions (11.25%, 4.81%, 0.16±0.56) were significantly lower than those from county level or above general hospitals (33.33%, 19.11%, 0.38±1.00) and primary healthcare institutions (37.59%, 19.24%, 0.67±1.05) ( χ 2/F =20.99, 34.31, 21.08 , all P <0.01). Conclusions The 12-year-old children s pit and fissure sealants effectively reduce the caries incidence in first permanent molars, particularly showing significant effectiveness in girls and suburban children. Intervention strategies should be optimized according to gender.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

AIM: To analyze the factors affecting non-contact intraocular pressure(IOPNCT)measurements after femtosecond laser-assisted small incision lenticule extraction(SMILE), explore the correlation of IOPNCT with central corneal thickness(CCT)and corneal curvature after SMILE, and construct the corresponding regression model which will provide scientific basis for clinical evaluation of the true IOP of patients after SMILE.METHODS: Data from a retrospective analysis of 107 myopic patients(206 eyes)who underwent SMILE and 107 myopic patients(201 eyes)received femtosecond laser-assisted in situ keratomileusis(FS-LASIK)surgery from June 2023 to May 2024 were examined. IOPNCT, CCT, and corneal curvature before surgery and at 1 and 3 mo were collected. The preoperative and postoperative IOPNCT, CCT and corneal curvature were analyzed by ANOVA and Pearson correlation analysis, and multiple linear regression models were constructed to evaluate the association of postoperative changes of IOPNCT, CCT and corneal curvature.RESULTS: There were significant differences in IOPNCT, CCT, and corneal curvature of both SMILE and FS-LASIK patients(all P<0.001), there was no significant difference between two groups and interaction effects(all P>0.05), and the IOPNCT, CCT and corneal curvature at 1 and 3 mo post-surgery were significantly lower than preoperative(all P<0.05). Pearson correlation analysis showed a positive correlation between IOPNCT and CCT at 1 and 3 mo after SMILE(r=0.261, 0.267, all P<0.001), but no significant correlation with corneal curvature(all P>0.05). Multiple linear regression analysis of IOPNCT with CCT and corneal curvature at 1 mo after SMILE indicated that the regression equation was: Y=3.426+0.019X1-0.058X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistical significant difference in the equation(F=7.654, P=0.001); the regression equation for 3 mo after surgery was: Y=2.056+0.020X1-0.038 X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistically significance in the equation(F=7.903, P<0.001). The regression equation of postoperative IOPNCT change(△IOPNCT)and intraoperative cutting corneal thickness(△CCT)and corneal curvature at 1 mo was Y=-2.252+0.008X1+0.587X2(Y represents △IOPNCT, X1 stands for the △CCT, X2 represents the corneal curvature change value), with statistical significant difference in the equation(F=17.550, P<0.001); the regression equation for 3 mo after surgery was: Y=-2.168+0.024X1+0.281X2(Y represents △IOPNCT, X1 represents △CCT, X2 indicates the corneal curvature change values), with statistical significant difference in the equation(F=16.030, P<0.001).CONCLUSION: After SMILE and FS-LASIK surgery, the IOPNCT value of patients was mainly affected by CCT compared with preoperative surgery, and the short-term use of hormone eye drops, fluorometholone, did not cause a significant increase in IOP; both the IOP correction formula at 1 and 3 mo postoperatively can be used clinically to evaluate and correct actual IOP in patients after SMILE.

AIM: To analyze the factors affecting non-contact intraocular pressure(IOPNCT)measurements after femtosecond laser-assisted small incision lenticule extraction(SMILE), explore the correlation of IOPNCT with central corneal thickness(CCT)and corneal curvature after SMILE, and construct the corresponding regression model which will provide scientific basis for clinical evaluation of the true IOP of patients after SMILE.METHODS: Data from a retrospective analysis of 107 myopic patients(206 eyes)who underwent SMILE and 107 myopic patients(201 eyes)received femtosecond laser-assisted in situ keratomileusis(FS-LASIK)surgery from June 2023 to May 2024 were examined. IOPNCT, CCT, and corneal curvature before surgery and at 1 and 3 mo were collected. The preoperative and postoperative IOPNCT, CCT and corneal curvature were analyzed by ANOVA and Pearson correlation analysis, and multiple linear regression models were constructed to evaluate the association of postoperative changes of IOPNCT, CCT and corneal curvature.RESULTS: There were significant differences in IOPNCT, CCT, and corneal curvature of both SMILE and FS-LASIK patients(all P<0.001), there was no significant difference between two groups and interaction effects(all P>0.05), and the IOPNCT, CCT and corneal curvature at 1 and 3 mo post-surgery were significantly lower than preoperative(all P<0.05). Pearson correlation analysis showed a positive correlation between IOPNCT and CCT at 1 and 3 mo after SMILE(r=0.261, 0.267, all P<0.001), but no significant correlation with corneal curvature(all P>0.05). Multiple linear regression analysis of IOPNCT with CCT and corneal curvature at 1 mo after SMILE indicated that the regression equation was: Y=3.426+0.019X1-0.058X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistical significant difference in the equation(F=7.654, P=0.001); the regression equation for 3 mo after surgery was: Y=2.056+0.020X1-0.038 X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistically significance in the equation(F=7.903, P<0.001). The regression equation of postoperative IOPNCT change(△IOPNCT)and intraoperative cutting corneal thickness(△CCT)and corneal curvature at 1 mo was Y=-2.252+0.008X1+0.587X2(Y represents △IOPNCT, X1 stands for the △CCT, X2 represents the corneal curvature change value), with statistical significant difference in the equation(F=17.550, P<0.001); the regression equation for 3 mo after surgery was: Y=-2.168+0.024X1+0.281X2(Y represents △IOPNCT, X1 represents △CCT, X2 indicates the corneal curvature change values), with statistical significant difference in the equation(F=16.030, P<0.001).CONCLUSION: After SMILE and FS-LASIK surgery, the IOPNCT value of patients was mainly affected by CCT compared with preoperative surgery, and the short-term use of hormone eye drops, fluorometholone, did not cause a significant increase in IOP; both the IOP correction formula at 1 and 3 mo postoperatively can be used clinically to evaluate and correct actual IOP in patients after SMILE.

Collagen is a major structural protein in the matrix of animal cells and the most widely distributed and abundant functional protein in mammals. Collagen’s good biocompatibility, biodegradability and biological activity make it a very valuable biomaterial. According to the source of collagen, it can be broadly categorized into two types: one is animal collagen; the other is recombinant collagen. Animal collagen is mainly extracted and purified from animal connective tissues by chemical methods, such as acid, alkali and enzyme methods, etc. Recombinant collagen refers to collagen produced by gene splicing technology, where the amino acid sequence is first designed and improved according to one’s own needs, and the gene sequence of improved recombinant collagen is highly consistent with that of human beings, and then the designed gene sequence is cloned into the appropriate vector, and then transferred to the appropriate expression vector. The designed gene sequence is cloned into a suitable vector, and then transferred to a suitable expression system for full expression, and finally the target protein is obtained by extraction and purification technology. Recombinant collagen has excellent histocompatibility and water solubility, can be directly absorbed by the human body and participate in the construction of collagen, remodeling of the extracellular matrix, cell growth, wound healing and site filling, etc., which has demonstrated significant effects, and has become the focus of the development of modern biomedical materials. This paper firstly elaborates the structure, type, and tissue distribution of human collagen, as well as the associated genetic diseases of different types of collagen, then introduces the specific process of producing animal source collagen and recombinant collagen, explains the advantages of recombinant collagen production method, and then introduces the various systems of expressing recombinant collagen, as well as their advantages and disadvantages, and finally briefly introduces the application of animal collagen, focusing on the use of animal collagen in the development of biopharmaceutical materials. In terms of application, it focuses on the use of animal disease models exploring the application effects of recombinant collagen in wound hemostasis, wound repair, corneal therapy, female pelvic floor dysfunction (FPFD), vaginal atrophy (VA) and vaginal dryness, thin endometritis (TE), chronic endometritis (CE), bone tissue regeneration in vivo, cardiovascular diseases, breast cancer (BC) and anti-aging. The mechanism of action of recombinant collagen in the treatment of FPFD and CE was introduced, and the clinical application and curative effect of recombinant collagen in skin burn, skin wound, dermatitis, acne and menopausal urogenital syndrome (GSM) were summarized. From the exploratory studies and clinical applications, it is evident that recombinant collagen has demonstrated surprising effects in the treatment of all types of diseases, such as reducing inflammation, promoting cell proliferation, migration and adhesion, increasing collagen deposition, and remodeling the extracellular matrix. At the end of the review, the challenges faced by recombinant collagen are summarized: to develop new recombinant collagen types and dosage forms, to explore the mechanism of action of recombinant collagen, and to provide an outlook for the future development and application of recombinant collagen.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.