Attention deficit and hyperactive disorder （ADHD） is the most common neurodevelopmental disorder of childhood. It seriously impairs academic achievement， social interaction， and vocational development， and increases the risk of accidental injury and substance abuse. In some cases， the symptoms may also exert an indirect disruptive effect on public order. Its aetiology involves interactions among genetic， perinatal environmental， and psychosocial factors that cannot be fully disentangled by single clinical studies. Therefore， a systematic evaluation of existing animal models is essential for revealing pathophysiology and developing novel therapies. Using the keywords "attention deficit and hyperactive disorder"， "models， animal"， "validity"， and their English equivalents， we systematically searched PubMed， Web of Science， CNKI， and Wanfang for publications from 2000 to 2025 （retrieving 328 publications） and added further references by citation tracking. Eighty-six rodent ADHD models that provided detailed construction protocols， behavioural assessments， neurobiological mechanisms， or pharmacological data were included and classified into spontaneous genetic， genetically engineered， and environmentally induced paradigms. Their face， construct， and predictive validity were compared. Among spontaneous genetic models， spontaneously hypertensive rats reproduce hyperactivity， impulsivity， and stimulant responses well， yet hypertension and sex differences limit specificity. Acallosal mouse strains link corpus callosum absence to ADHD-like behaviours， but neurotransmitter studies remain scarce. Genetically engineered rodents—including dopamine transporter， neurokinin-1 receptor and mediator complex subunit 23 knockout or conditional gene knockout lines—precisely dissect dopaminergic， noradrenergic， synaptic， or epigenetic pathways， yet generally lack full phenotypic coverage， social-deficit modelling， and comorbidity representation， and are accompanied by adverse effects such as growth retardation or ocular defects. Environmentally induced models employ lead， polychlorinated biphenyls， alcohol， nicotine exposures， 6-hydroxydopamine lesions， neonatal hypoxia， early social isolation， or maternal stress to recapitulate core symptoms. However， dose-schedule standardisation is lacking. Behavioural reversibility diverges from clinical persistence， and non-specific phenotypes such as anxiety or depression are common. Overall， no single paradigm simultaneously achieves high validity across all three dimensions. Currently， ADHD models have progressed from single-factor simulations to multidimensional evaluation， yet significant gaps remain in genetic-background standardisation， sex differences， cross-species translation， and syndrome-differentiation modelling under traditional Chinese medicine. Future directions should integrate genetic， environmental， and epigenetic interactions， establish life-span validation systems， and incorporate computational neuroscience alongside integrative Chinese-Western strategies to enhance clinical relevance and translational utility， thereby providing robust evidence-based support for mechanistic elucidation， drug screening and precision intervention in ADHD.

Animal experimentation constitutes a critical link between basic research and clinical application, making its research quality and translational efficiency paramount. Although considerable progress has been made in standardizing operational procedures and ethical guidelines, the standardization of outcome evaluation systems has significantly lagged, creating a key bottleneck that constrains the quality of biomedical research and evidence synthesis. This deficiency is manifested by pronounced heterogeneity in outcome selection across similar studies, incomplete methodological reporting, and disparate criteria for result interpretation, which severely impairs the comparability of findings and the evidence integration. To cope with this challenge, this paper systematically introduces a mature methodological tool from clinical research–the core outcome set (COS)–and explores its construction strategies and application potential in the field of animal experimentation. Given the extensive diversity of animal experiments, a pragmatic strategy of "focusing on key areas, implementing phased pilots, and promoting gradual expansion" should be adopted. This approach prioritizes the development of domain-specific COS for disease areas characterized by high research volume, urgent translational needs, and well-established animal models. A multi-source integration pathway for COS development is detailed, comprising systematic literature searches, methodological appraisals, and expert consensus, with the feasibility of leveraging artificial intelligence (AI) to enhance efficiency also being examined. The development and promotion of such COS are not intended to restrict scientific exploration; rather, they aim to establish a new, tiered evaluation paradigm consisting of "core outcomes" (mandatory), "recommended outcomes" (encouraged), and "exploratory outcomes" (optional). This framework is expected not only to enhance research quality through standardization and to adhere to the "3R" principles but also to accelerate the accumulation of high-quality evidence. This, in turn, provides a solid foundation for higher-level evidence synthesis, ultimately facilitating the effective translation of basic research findings into clinical practice and providing an essential methodological framework for scientific advancement in relevant disciplines.

The linear ubiquitin chain assembly complex (LUBAC), composed of heme-oxidized IRP2 ubiquitin ligase 1-interacting protein (HOIP), haem-oxidized IRP2 ubiquitin ligase-1(HOIL-1), and SHANK-associated RBCK1 homology-domain-interacting protein (SHARPIN), and its specific deubiquitinating enzyme OTULIN, regulates the dynamic balance of Met1-linked linear ubiquitination and maintains ubiquitin signaling homeostasis. Their precise interaction plays a central role in the regulation of nuclear factor-κB (NF-κB) and type I interferon signaling, inflammatory response, and cell survival and death. Dysregulation of the LUBAC-OTULIN axis can lead to aberrant immune and inflammatory signaling, immunodeficiency, and dysregulation of receptor-interacting protein kinase 1-mediated apoptosis or necroptosis. Genetic defects in OTULIN and LUBAC are associated with rare autoinflammatory diseases such as OTULIN-related autoinflammatory syndrome (ORAS) and HOIP/HOIL-1/SHARPIN deficiency, respectively, with complex clinical phenotypes and gene dose correlation. Current treatments primarily rely on glucocorticoids and tumor necrosis factor inhibitors, and hematopoietic stem cell transplantation has shown potential curative value in some patients with severe ORAS. This review summarizes the molecular composition, interaction mechanisms, and pathogenic roles of the LUBAC-OTULIN axis in rare autoinflammatory diseases, providing reference for the development of targeted therapeutic strategies.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Objective To explore the molecular mechanism of Jianfu mixture in the treatment of erectile dysfunction （ED） by network pharmacology and molecular docking techniques, and validate its core targets and mechanisms through in vitro experiments. Methods The active components and corresponding molecular targets of Jianfu mixture were searched by searching TCMSP and Batman-TCM databases, and the disease targets of ED were searched by using GeneCards database. Find the intersection of drug ingredient target and disease target. The interaction between intersected targets was described and analyzed by String database, and the analysis results were visualized by Cytoscape software to determine the core target and the corresponding active components. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for intersection targets; the core target within the intersection were found through MCODE plug-in on Cytoscape software and molecular docking was performed with the corresponding active ingredients. An endothelial dysfunction model was established by transfecting HUVECs with si-eNOS. Intervene with different concentrations of the Jianfu mixture for the model cells for 24 h. QPCR was used to detect mRNA expression of core targets （MAPK1, MAPK3, JUN, ESR1, MAPK8）; Western blot was used to analyze protein expression （eNOS, JUN, p-JUN, MAPK, p-MAPK） and phosphorylation levels. Results 144 effective active components and 168 active components target-disease targe intersection of Jianfu mixture were obtained. GO analysis revealed 200 5 biological processes, 151 molecular functions, and 63 cellular components. KEGG analysis yielded 181 pathways. 5 core targets including MAPK1, MAPK3, JUN, ESR1 and MAPK8 were screened out. The active components such as β-sitosterol, kaempferol, astapterocarpan had good binding affinity with the core target. In vitro experiments confirmed successful construction of the endothelial dysfunction model （eNOS expression significantly decreased after si-eNOS transfection）. Jianfu mixture dose-dependently inhibited mRNA expression of MAPK1, MAPK3, JUN, ESR1, and MAPK8. Additionally, it reduced phosphorylation levels of JUN and MAPK, indicating inhibition of the JNK/c-Jun and ERK/MAPK signaling pathways to improve endothelial function. Conclusion Jianfu mixture treats ED by suppressing abnormal activation of multi-target signaling pathways （MAPK/JUN/ESR1）, reducing endothelial apoptosis, and promoting NO synthesis. This mechanism aligns with the traditional Chinese medicine principle of “activating blood circulation, resolving stasis, tonifying Qi, and strengthening cardiovascular function.” The study provided molecular-level evidence for the therapeutic efficacy of Jianfu mixture in ED management.

Objective To explore the molecular mechanism of Jianfu mixture in the treatment of erectile dysfunction （ED） by network pharmacology and molecular docking techniques, and validate its core targets and mechanisms through in vitro experiments. Methods The active components and corresponding molecular targets of Jianfu mixture were searched by searching TCMSP and Batman-TCM databases, and the disease targets of ED were searched by using GeneCards database. Find the intersection of drug ingredient target and disease target. The interaction between intersected targets was described and analyzed by String database, and the analysis results were visualized by Cytoscape software to determine the core target and the corresponding active components. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for intersection targets; the core target within the intersection were found through MCODE plug-in on Cytoscape software and molecular docking was performed with the corresponding active ingredients. An endothelial dysfunction model was established by transfecting HUVECs with si-eNOS. Intervene with different concentrations of the Jianfu mixture for the model cells for 24 h. QPCR was used to detect mRNA expression of core targets （MAPK1, MAPK3, JUN, ESR1, MAPK8）; Western blot was used to analyze protein expression （eNOS, JUN, p-JUN, MAPK, p-MAPK） and phosphorylation levels. Results 144 effective active components and 168 active components target-disease targe intersection of Jianfu mixture were obtained. GO analysis revealed 200 5 biological processes, 151 molecular functions, and 63 cellular components. KEGG analysis yielded 181 pathways. 5 core targets including MAPK1, MAPK3, JUN, ESR1 and MAPK8 were screened out. The active components such as β-sitosterol, kaempferol, astapterocarpan had good binding affinity with the core target. In vitro experiments confirmed successful construction of the endothelial dysfunction model （eNOS expression significantly decreased after si-eNOS transfection）. Jianfu mixture dose-dependently inhibited mRNA expression of MAPK1, MAPK3, JUN, ESR1, and MAPK8. Additionally, it reduced phosphorylation levels of JUN and MAPK, indicating inhibition of the JNK/c-Jun and ERK/MAPK signaling pathways to improve endothelial function. Conclusion Jianfu mixture treats ED by suppressing abnormal activation of multi-target signaling pathways （MAPK/JUN/ESR1）, reducing endothelial apoptosis, and promoting NO synthesis. This mechanism aligns with the traditional Chinese medicine principle of “activating blood circulation, resolving stasis, tonifying Qi, and strengthening cardiovascular function.” The study provided molecular-level evidence for the therapeutic efficacy of Jianfu mixture in ED management.

ObjectiveStroke poses a heavy burden due to its high mortality and morbidity rates. Accurate and real-time detection of lesions is pivotal for prompt clinical intervention and favorable prognosis. Electrical impedance tomography (EIT) and microwave tomography (MWT) have emerged as compelling alternatives for stroke screening, owing to their non-ionizing, non-invasive and portable nature. EIT provides information on tissue conductivity, and MWT offers high sensitivity to changes in dielectric properties. However, single-modality imaging is inherently limited, EIT suffers from low sensitivity to deep-seated tissues and severe ill-posedness of inverse problems, whereas MWT is challenged by strong nonlinearity in inverse scattering and susceptibility to modeling errors. Consequently, the clinical utility of standalone EIT or MWT for stroke diagnosis remains constrained by poor spatial resolution and imaging artifacts. To improve the accuracy and robustness of stroke imaging, a dual-modality fusion conditional denoising diffusion probabilistic model (DM-DDPM) was proposed for high-precision dual-modality image reconstruction. MethodsA dual-encoder network with a symmetric architecture and independently trained parameters was constructed to extract heterogeneous features separately from EIT boundary voltage measurements and MWT scattered field signals. Attentional feature fusion (AFF) is employed to integrate complementary information from the two modalities adaptively, generating robust fused priors that suppress redundant noise while preserving key physical characteristics. Subsequently, the fused priors are embedded into a Transformer-based diffusion model via a cross attention mechanism to guide the reverse denoising process. This approach effectively reduces artifacts and enhances the stability of conductivity distribution reconstruction. Time step embedding is introduced to enable the network to perceive the diffusion stage and further improve the accuracy of noise prediction. ResultsSimulated experiments demonstrated that DM-DDPM significantly outperforms single-modality and multi-modality networks under various noise levels. A head model simulation dataset was constructed based on COMSOL Multiphysics, and tests were carried out under 50 dB, 40 dB and 30 dB signal-to-noise ratio levels. At 30 dB, the average relative error (RE) was below 0.20, while the structural similarity index measure (SSIM) and correlation coefficient (CC) remained above 0.90 and 0.89, respectively. Compared with single-modality and multi-modality networks, artifacts were significantly reduced, lesion edges were clearer, and localization was more accurate. The model maintains high reconstruction quality and strong robustness for single, double, and triple lesions simultaneously. Furthermore, physical experiments were conducted using a 16-electrode EIT system and a 16-antenna MWT system with asynchronous data acquisition. These experiments confirmed the feasibility of the method in real-world scenarios and demonstrated that it can robustly reconstruct simulated lesions despite environmental interference and measurement noise, validating its reliability for practical clinical applications. ConclusionThe proposed method effectively combines complementary dual-modality information with a conditional diffusion model. Low accuracy and poor noise resistance in single-modality imaging were effectively addressed, while the noise amplification issue caused by direct multimodal data fusion was avoided. The proposed algorithm exhibits strong anti-noise interference ability and high imaging stability in both simulation and physical experiments. Precise localization of stroke lesions with different quantities was achieved, providing a high-precision, and practical technical support for clinical stroke detection.

Osteoporosis is a systemic disease, and epidemiological projections indicate that by 2050, approximately 23.43% of the Chinese population over 50 years of age will be affected. Given the poor prognosis associated with osteoporosis, the exploration of safe and effective natural products is of considerable significance. Studies investigating the chemical constituents of traditional Chinese medicine in cellular and/or animal models have demonstrated bone-protective effects. Although most of these compounds lack clinical data, they hold considerable potential as lead candidates for drug development. In-depth study of the structure-activity relationship of these natural products not only contributes to elucidating the mechanisms of action but also provides a theoretical basis for the development of novel antiosteoporosis therapies. This review summarizes natural products with potential antiosteoporotic effects reported between 2020 and 2024. Overall, plant-derived natural compounds exhibit antiosteoporotic effects by regulating bone remodeling, inflammation, and oxidative stress, highlighting their promise as multitarget therapeutic candidates.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.