The Proctor's reporting guideline for implementation strategies represents a landmark framework in the field of implementation science, aiming to address the issue of inconsistent reporting in implementation research by standardizing the naming, definition, and operationalization of implementation strategies, thereby enhancing the credibility and utility of research findings. This paper provides an in-depth interpretation of the core connotations of this reporting guideline and illustrates its application in developing interview outlines and specifying implementation strategies, using a brief smoking cessation intervention project as a case study. Through this reporting guideline, abstract recommendations for implementation are systematically transformed into clear, multidimensional operational guides, significantly improving the transparency of strategy connotations and the replicability of actual execution. Meanwhile, the case study highlights the flexibility of the guideline, which allows researchers to adapt the content and format of strategies based on local resources and cultural contexts, thus enhancing practical adaptability while maintaining scientific rigor. However, the application of Proctor's reporting guideline still faces challenges, primarily manifested in the potential confusion surrounding the constructs of temporality and dose in practice, as well as the challenges that the inherent flexibility of the guideline may pose to the assessment of fidelity and effectiveness. Despite these limitations, the reporting guideline remains a vital tool for implementation research; future efforts should focus on optimizing its application—through refining operational guidelines, standardizing flexible adaptations, and involving stakeholders—to better guide implementation studies and continuously promote high-quality development in the field.

ObjectiveTo evaluate the therapeutic effect of Huazhuo SanJie Chubi presciption （HSCD） on chronic gouty arthritis （CGA） rats with low-grade inflammation and to explore the underlying mechanism with a focus on macrophage polarization. MethodsThe 41 male 6-week-old SD rats were randomly allocated， using the random number table， to a normal group （n=8） and a model group （n =33）. CGA with low-grade inflammation was induced in the model group by daily gavage of potassium oxonate （250 mg·kg^-1·d^-1） and hypoxanthine （300 mg·kg^-1·d^-1）， combined with intra-articular injection of a monosodium urate （MSU） crystal suspension （50 μL， 25 g·L^-¹） into the left ankle twice weekly. After 4 weeks of modeling， 3 rats were randomly selected from each group for model validation. The remaining successfully modeled rats were randomly divided into a model group， an HSCD group （10.35 g·kg^-1·d^-1， gavage once daily）， an M1 polarization agonist group （L-methionine sulfoximine， 300 mg·kg^-1， subcutaneous injection every other day）， an M1 polarization agonist + HSCD group， an M2 polarization inhibitor group （PD0325901， 10 mg·kg^-1·d^-1， gavage once daily）， and M2 polarization inhibitor + HSCD group. The corresponding drug or drug combination was administered according to group assignment， whereas rats in the normal and model groups received 0.5% carboxymethyl cellulose sodium （CMC-Na） vehicle （10.35 g·kg^-1·d^-1， gavage once daily）. All interventions were continued for four weeks. During the intervention period， except for the normal group， potassium oxonate （250 mg·kg⁻¹） and hypoxanthine （300 mg·kg^-1） were co-administered by gavage every other day to maintain the model. At the end of treatment， serum uric acid （SUA）， ankle joint diameter and joint swelling index were measured. The levels of high-sensitivity C-reactive protein （hs-CRP）， interleukin-1β （IL-1β）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， chemokine C-C motif ligand 2 （CCL2）， S100 calcium-binding protein A8/A9 （S100A8/A9）， interleukin-10 （IL-10） and arginase-1 （Arg-1） in serum and joint fluid were determined by enzyme-linked immunosorbent assay （ELISA）. High-frequency ultrasound was used to assess MSU deposition in the ankle joint. Hematoxylin-eosin （HE） staining was performed to evaluate synovial histopathological changes. Quantitative Real-time PCR and immunofluorescence were used to detect the mRNA and protein expression of the M1 macrophage polarization markers inducible nitric oxide synthase （iNOS） and the M2 macrophage polarization marker scavenger receptor cysteine-rich type 1 protein M130 （CD163） in synovial tissue. ResultsCompared with the normal group， the model group showed significantly elevated SUA level and joint swelling index， and increased levels of pro-inflammatory cytokines， CCL2， and S100A8/A9 in both serum and joint fluid （P<0.05）， accompanied by MSU deposition and synovial inflammation in the ankle joint. The mRNA and protein expression levels of macrophage polarization M1/M2 markers iNOS and CD163 in synovial tissues were also significantly up-regulated （P<0.05）. Compared with model group， rats in HSCD group had significantly lower SUA levels， attenuated joint swelling， reduced serum levels of pro-inflammatory cytokines， and decreased levels of CCL2 and S100A8/A9 in both serum and joint fluid， accompanied with alleviated MSU deposition and synovial inflammation （P<0.05）. HSCD markedly downregulated the mRNA and protein expression of M1 marker iNOS （P<0.05）， whereas it had no significant effect on the expression of M2 marker CD163. Compared with the M1 polarization agonist group， the M1 polarization agonist + HSCD group showed significantly reduced joint swelling， lower serum levels of pro-inflammatory cytokines， and decreased levels of CCL2 and S100A8/A9 in joint fluid （P<0.05）. In addition， synovial inflammatory cell infiltration and angiogenesis were attenuated， and iNOS mRNA and protein expression levels were significantly reduced （P<0.05）. Compared with the M2 polarization inhibitor group， the M2 polarization inhibitor + HSCD group exhibited reduced joint swelling， decreased levels of CCL2 and S100A8/A9 in joint fluid and ameliorated synovial inflammation （P<0.05）， whereas the levels of anti-inflammatory mediators （IL-10， Arg-1） and CD163 mRNA and protein expression were not significantly increased. ConclusionHSCD alleviates low-grade inflammation in CGA rats， at least in part， by inhibiting macrophage polarization toward the M1 phenotype.

ObjectiveTo explore the interaction and competitive binding of Homo sapiens long intergenic non-protein-coding RNA 1134 （LINC01134） to CCCTC-binding factor CTCF， affecting the transcription of cyclin-dependent kinase inhibitor （p21） and influencing the proliferation of A549 cells， in order to investigate the possible mechanism of Astragali Radix and Hedyotis diffusa （A-H） in inhibiting A549 proliferation by regulating this axis. MethodsRNA-binding protein immunoprecipitation （RIP） assays were conducted to examine the interaction between LINC01134 and CTCF， and chromatin immunoprecipitation （ChIP） assays were used to study the effect of LINC01134 overexpression on the interaction between CTCF and p21. Stable A549 cell lines （oe-NC and oe-LINC01134） were established using lentiviral transfection， and each group was treated with 10% A-H drug-containing serum. Real-time PCR and Western blot analyses were performed to detect the effects of A-H on the expression of LINC01134， CTCF， and p21 in A549 cells. Cell counting kit-8 （CCK-8） and colony formation assays were used to assess the effects of A-H on A549 cell proliferation via LINC01134. Flow cytometry was employed to evaluate the effects of A-H on the A549 cell cycle through LINC01134， and Western blot was used to detect changes in cell cycle proteins. ResultsCompared with the IgG group， the oe-CTCF group showed a significantly increased abundance of LINC01134 aggregates （P0.01）. Compared with the oe-Vector group， p21 abundance in CTCF complexes was significantly reduced in the oe-LINC01134 group （P0.01）. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of LINC01134 and p21 （P0.05）， but had no significant regulatory effect on CTCF. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed cell viability at 72 h （P0.05）， inhibited malignant proliferation （P0.05）， and reversed the proportions of cells in the G₀/G₁ and S phases （P0.01）. Furthermore， compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of Cyclin D₁， CDK4， Cyclin E， CDK2， phosphorylated retinoblastoma protein （p-Rb）， and E2F transcription factor 3 （E2F3） （P0.01）. ConclusionA-H regulates the LINC01134-CTCF-p21 axis to block the G₁/S phase transition of A549 cell cycle， accelerate cellular senescence， and inhibit malignant proliferation.

ObjectiveTo explore the interaction and competitive binding of Homo sapiens long intergenic non-protein-coding RNA 1134 （LINC01134） to CCCTC-binding factor CTCF， affecting the transcription of cyclin-dependent kinase inhibitor （p21） and influencing the proliferation of A549 cells， in order to investigate the possible mechanism of Astragali Radix and Hedyotis diffusa （A-H） in inhibiting A549 proliferation by regulating this axis. MethodsRNA-binding protein immunoprecipitation （RIP） assays were conducted to examine the interaction between LINC01134 and CTCF， and chromatin immunoprecipitation （ChIP） assays were used to study the effect of LINC01134 overexpression on the interaction between CTCF and p21. Stable A549 cell lines （oe-NC and oe-LINC01134） were established using lentiviral transfection， and each group was treated with 10% A-H drug-containing serum. Real-time PCR and Western blot analyses were performed to detect the effects of A-H on the expression of LINC01134， CTCF， and p21 in A549 cells. Cell counting kit-8 （CCK-8） and colony formation assays were used to assess the effects of A-H on A549 cell proliferation via LINC01134. Flow cytometry was employed to evaluate the effects of A-H on the A549 cell cycle through LINC01134， and Western blot was used to detect changes in cell cycle proteins. ResultsCompared with the IgG group， the oe-CTCF group showed a significantly increased abundance of LINC01134 aggregates （P0.01）. Compared with the oe-Vector group， p21 abundance in CTCF complexes was significantly reduced in the oe-LINC01134 group （P0.01）. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of LINC01134 and p21 （P0.05）， but had no significant regulatory effect on CTCF. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed cell viability at 72 h （P0.05）， inhibited malignant proliferation （P0.05）， and reversed the proportions of cells in the G₀/G₁ and S phases （P0.01）. Furthermore， compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of Cyclin D₁， CDK4， Cyclin E， CDK2， phosphorylated retinoblastoma protein （p-Rb）， and E2F transcription factor 3 （E2F3） （P0.01）. ConclusionA-H regulates the LINC01134-CTCF-p21 axis to block the G₁/S phase transition of A549 cell cycle， accelerate cellular senescence， and inhibit malignant proliferation.

ObjectiveTo systematically evaluate the content differences of 4 components in Leonuri Herba granules， reveal the quality fluctuation patterns of products from the same and different manufacturers， providing scientific basis for the optimization of production process and quality control. MethodsHigh performance liquid chromatography-diode array detector-charged aerosol detector（HPLC-DAD-CAD） was employed to determine the contents of 4 components（syringic acid， leonurine hydrochloride， ferulic acid， and stachydrine hydrochloride） in samples from 19 manufacturers（53 batches， 159 boxes）. Additionally， fingerprint profiles were constructed， and the fingerprint dissimilarity（P_S） and relative standard deviation（RSD） of different samples from the same manufacturer were calculated. A principal component analysis（PCA） model was established with P_S and the RSD values of the 4 components as variables to classify the manufacturers. Finally， samples from 5 manufacturers（M1-M5） covering three consistency groups were selected to calculate three quality consistency parameters， namely intra-batch consistency（P_A）， inter-batch consistency（P_B）， and P_S. Then， PCA was performed with P_A， P_B， and P_S of these 5 manufacturers as variables. ResultsThe average total content of the 4 index components per bag across the 19 manufacturers ranged from 41.10 mg to 97.54 mg. Among them， the content of stachydrine hydrochloride（a pharmacopoeial quality control component） was 32.46-72.70 mg per bag， all meeting the requirements of the 2025 edition of the Pharmacopoeia of the People's Republic of China， with RSD of 1.7%-17.1%. The content ranges of the other 3 components were as follows：syringic acid of 1.43-41.92 mg per bag， leonurine hydrochloride of 0.67-11.85 mg per bag， and ferulic acid of 0.11-3.81 mg per bag. Notably， leonurine hydrochloride exhibited the most significant content fluctuation among samples from the same manufacturer（RSD of 4.8%-59.2%）. PCA results showed that the 19 manufacturers could be classified into 3 categories. Samples from 8 manufacturers（M2， M6， M7， M8， M10， M15， M17， M18） demonstrated relatively high consistency， five manufacturers（M3， M9， M12， M13， M14） showed moderate consistency， six manufacturers（M1， M4， M5， M11， M16， M19） exhibited low consistency. The two methods yielded consistent classification results for the 5 representative manufacturers， verifying the reliability of the proposed method. Among these， manufacturer M2 showed the best quality consistency and the highest total content of indicator components among M1-M5. ConclusionThe HPLC-DAD-CAD multi-detector hyphenation technology established in this study enables the accurate detection of 4 components in Leonuri Herba granules. Significant differences in the total content of these four components are observed among products from 19 manufacturers. The application of 2 consistency evaluation methods combined with PCA can effectively classify their consistency into 3 categories， and the classification results of the 2 methods are highly consistent. This study provides scientific basis for the process optimization and quality standard improvement of Leonuri Herba granules.

ObjectiveTo systematically evaluate the content differences of 4 components in Leonuri Herba granules， reveal the quality fluctuation patterns of products from the same and different manufacturers， providing scientific basis for the optimization of production process and quality control. MethodsHigh performance liquid chromatography-diode array detector-charged aerosol detector（HPLC-DAD-CAD） was employed to determine the contents of 4 components（syringic acid， leonurine hydrochloride， ferulic acid， and stachydrine hydrochloride） in samples from 19 manufacturers（53 batches， 159 boxes）. Additionally， fingerprint profiles were constructed， and the fingerprint dissimilarity（P_S） and relative standard deviation（RSD） of different samples from the same manufacturer were calculated. A principal component analysis（PCA） model was established with P_S and the RSD values of the 4 components as variables to classify the manufacturers. Finally， samples from 5 manufacturers（M1-M5） covering three consistency groups were selected to calculate three quality consistency parameters， namely intra-batch consistency（P_A）， inter-batch consistency（P_B）， and P_S. Then， PCA was performed with P_A， P_B， and P_S of these 5 manufacturers as variables. ResultsThe average total content of the 4 index components per bag across the 19 manufacturers ranged from 41.10 mg to 97.54 mg. Among them， the content of stachydrine hydrochloride（a pharmacopoeial quality control component） was 32.46-72.70 mg per bag， all meeting the requirements of the 2025 edition of the Pharmacopoeia of the People's Republic of China， with RSD of 1.7%-17.1%. The content ranges of the other 3 components were as follows：syringic acid of 1.43-41.92 mg per bag， leonurine hydrochloride of 0.67-11.85 mg per bag， and ferulic acid of 0.11-3.81 mg per bag. Notably， leonurine hydrochloride exhibited the most significant content fluctuation among samples from the same manufacturer（RSD of 4.8%-59.2%）. PCA results showed that the 19 manufacturers could be classified into 3 categories. Samples from 8 manufacturers（M2， M6， M7， M8， M10， M15， M17， M18） demonstrated relatively high consistency， five manufacturers（M3， M9， M12， M13， M14） showed moderate consistency， six manufacturers（M1， M4， M5， M11， M16， M19） exhibited low consistency. The two methods yielded consistent classification results for the 5 representative manufacturers， verifying the reliability of the proposed method. Among these， manufacturer M2 showed the best quality consistency and the highest total content of indicator components among M1-M5. ConclusionThe HPLC-DAD-CAD multi-detector hyphenation technology established in this study enables the accurate detection of 4 components in Leonuri Herba granules. Significant differences in the total content of these four components are observed among products from 19 manufacturers. The application of 2 consistency evaluation methods combined with PCA can effectively classify their consistency into 3 categories， and the classification results of the 2 methods are highly consistent. This study provides scientific basis for the process optimization and quality standard improvement of Leonuri Herba granules.

BackgroundEmotional disorders in adolescents have emerged as a prominent issue in recent years. Current mainstream clinical assessment approaches for such conditions predominantly rely on interviews and rating scales， which are limited by inherent drawbacks such as high subjectivity and recall bias. Accordingly， there exists an urgent clinical need for the development of objective， quantifiable auxiliary diagnostic tools.In previous studies， frontal electroencephalography （EEG） has demonstrated significant value in assessing depressive and anxiety. However， the lack of standardized quantitative metrics and intuitive visual analytical approaches has severely restricted clinical interpretability of EEG data and diminished patient engagement. To address these key limitations， the present study proposes an innovative analytical framework that converts frontal EEG signals into quantifiable visual metrics to enhance clinical comprehension and acceptance. ObjectiveTo explore the value of frontal EEG in assessing anxiety， depression， and sleep quality in adolescents with emotional disorders， with the aim of providing objective auxiliary tools for clinical diagnosis and assessment of adolescents with emotional disorders. MethodsThis cross-sectional study recruited 105 adolescents aged 12-18 years who visited the outpatient department of a specialized mental hospital in Beijing from April 2023 to April 2024. All participants met the diagnostic criteria for mood （affective） disorders or anxiety disorders in the International Classification of Diseases， tenth edition （ICD-10）. Frontal EEG signals were collected within a big data analytics-driven framework and further processed by EEG system to generate six quantitative cerebral function indices， namely brain load， tension and excitement， emotional stress， sleepiness index， cerebral vitality， and cerebral fatigue. In addition， validated standardized scales， including the Self-rating Anxiety Scale （SAS）， the Self-rating Depression Scale （SDS）， and the Pittsburgh Sleep Quality Index （PSQI）， were administered for anxiety， depressive symptoms， and sleep quality， respectively. ResultsIn adolescent patients with emotional disorders， the SAS score exhibited significant positive correlations with brain load （r_s=0.328， P<0.01）， emotional stress （r_s=0.341， P<0.01）， and cerebral fatigue （r_s=0.286， P<0.01）. The SDS score was positively correlated with brain load （r_s=0.275， P<0.01）， emotional stress （r_s=0.241， P<0.05）， and cerebral fatigue （r_s=0.311， P<0.01）， while showing a significant negative correlation with cerebral vitality （r_s=-0.212， P<0.05）. Additionally， the PSQI total score demonstrated positive correlations with brain load （r_s=0.340， P<0.01）， emotional stress （r_s=0.322， P<0.01）， and cerebral fatigue （r_s=0.229， P<0.05）. ConclusionFrontal EEG-derived indices， including brain load， emotional stress， cerebral fatigue and cerebral vitality， may serve as objective markers for reflecting anxiety， depression， and sleep quality in adolescents with emotional disorders. ［Funded by Beijing High level Innovation and Entrepreneurship Talent Support Program （number， 202504841041）； Horizontal Joint Project］

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

OBJECTIVE To explore the influence of human cytomegalovirus(HCMV)infection on transforming growth factor β(TGF-β)/Smad signaling pathway and helper T cell(Th)1/Th2 cytokines of the patients with di-abetic retinopathy(DR)so as to provide guidance for clinical treatment of the DR patients with HCMV infection.METHODS Totally 100 DR patients complicated with HCMV infection who were treated in Xinyu People's Hospi-tal from Jan.2021 to Dec.2023 were assigned as the HCMV infection group and were divided into the non-prolif-erative DR group(NPDR group)and the proliferative DR group(the PDR group)according to the degree of path-ological change of retina.Meanwhile,100 DR patients without HCMV infection were chosen as the no HCMV in-fection group.The levels of serum TGF-β,interferon-γ(IFN-γ)and interleukin(IL)-4 as well as the relative ex-pression levels of Smad2 and Smad3 mRNA were observed.RESULTS The levels of serum TGF-β,IFN-γ and IL-4 of the HCMV infection group were respectively 1.41±0.43,(92.68±15.86)ng/L and(593.91±84.67)ng/L,higher than those of the no HCMV infection group;the relative expression levels of Smad2 mRNA and Smad3 mRNA of the HCMV infection group were respectively(1.54±0.44)and(1.68±0.50),higher than those of the no HCMV infection group(P＜0.05);the levels of the above indexes of the PDR group were higher than those of the NPDR group(P＜0.05).Receiver operating characteristic(ROC)curve analysis showed that the area under the curve(AUC)of the joint detection of TGF-β,IFN-γ,IL-4,Smad2 and Smad3 was 0.961 in prediction of HC-MV infection in the DR patients,with the sensitivity 90.70％,the specificity 85.10％.CONCLUSION The HC-MV infection may aggravate the degree of retinopathy,which may be associated with the regulation of TGF-β/Smad signaling pathways and imbalance of Th1/Th2 function.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.