ObjectiveTo improve the therapeutic effect of Buyang Huanwutang（BYHW） on diabetic kidney disease （DKD） and explore new methods for developing new Chinese medicine decoctions，we utilized 5-hydroxymethylcytosine （5hmC）-Seal sequencing technology and network pharmacology to modify BYHW. MethodsWe selected 14 diabetes mellitus （DM） patients and 15 DKD patients hospitalized in the Department of Endocrinology of Peking University Third Hospital in 2021. Circulating free DNA （cfDNA） in the patients’ plasma was sequenced. After data processing and screening， we performed temporal clustering analysis to select a DKD 5hmC gene set， which was then cross-validated with a DKD database gene set to obtain the DKD gene set. We retrieved target genes of the seven herbal components of BYHW from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）， and performed cross-analysis with the DKD gene set to identify common genes shared by the disease and the Chinese medicines. A protein-protein interaction （PPI） network was constructed for the common genes to screen out the key genes. Chinese medicines targeting these key genes were searched against ETCM to identify removable Chinese medicines. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed on non-common DKD genes， and key genes in DKD-related pathways were selected based on machine learning. The GSE30529 dataset was used to verify the expression trends of 5hmC-modified genes and the feasibility of target genes as drug targets. TCMBank was used to search for target genes and obtain compounds targeting these genes and the corresponding Chinese medicines to construct a "key target-compound-Chinese medicine" network. Molecular docking was employed to verify the binding affinity of compounds with key targets. TCMSP and ETCM were used to search and count the candidate Chinese medicines targeting DKD-related genes， and a new decoction was formed by adding the selected Chinese medicines. A mouse model of DKD was established to examine the efficacy of the new decoction based on the mouse body mass， random blood glucose， urinary microalbumin （mALB）， serum creatinine （Scr）， and blood urea nitrogen （BUN） and by hematoxylin-eosin staining， periodic acid-Schiff staining， Masson staining， immunofluorescence assay， and Real-time PCR. ResultsThe cross-analysis results showed that the DKD gene set included 507 genes， of which 30 were target genes of BYHW. The PPI analysis indicated that the top 15% target genes regarding the degree were interleukin-6 （IL-6）， Toll-like receptor 4 （TLR4）， lactotransferrin （LTF）， lipoprotein lipase （LPL）， and sterol regulatory element-binding transcription factor 1 （SREBF1）. Persicae Semen and Pheretima in BYHW were unrelated to key genes and removed. Machine learning identified 10 potential target genes， among which TBC1 domain family member 5 （TBC1D5）， RAD51 paralog B （RAD51B）， and proteasome 20S subunit alpha 6 （PSMA6） had expression trends consistent with the GSE30529 dataset and could serve as drug targets. The "key target-compound-Chinese medicine" network and molecular docking results indicated that the compounds with good binding affinity to target proteins were arginine， glycine， myristicin， serine， and tyrosine， corresponding to 121 Chinese medicines. The top 10 Chinese medicines targeting DKD-related genes were Lycii Fructus， Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Isatidis Radix， Glehniae Radix， Ophiopogonis Radix， Allii Sativi Bulbus， Isatidis Folium， and Bolbostemmatis Rhizoma. Based on traditional Chinese medicine theory， the new decoction was obtained after removal of Persicae Semen and Pheretima and addition of Rehmanniae Radix Praeparata and Dioscoreae Rhizoma. Animal experiment results indicated that the modified BYHW improved the kidney function and inhibited renal fibrosis in DKD mice， with better effects than the original decoction. ConclusionThe BYHW modified based on 5hmC-Seal sequencing demonstrates better performance in inhibiting fibrosis and ameliorating DKD than the original decoction. This elucidates the biomedical theory behind the epigenetic modification of traditional Chinese medicine prescriptions， potentially offering new perspectives for the exploration of these prescriptions

ObjectiveTo improve the therapeutic effect of Buyang Huanwutang（BYHW） on diabetic kidney disease （DKD） and explore new methods for developing new Chinese medicine decoctions，we utilized 5-hydroxymethylcytosine （5hmC）-Seal sequencing technology and network pharmacology to modify BYHW. MethodsWe selected 14 diabetes mellitus （DM） patients and 15 DKD patients hospitalized in the Department of Endocrinology of Peking University Third Hospital in 2021. Circulating free DNA （cfDNA） in the patients’ plasma was sequenced. After data processing and screening， we performed temporal clustering analysis to select a DKD 5hmC gene set， which was then cross-validated with a DKD database gene set to obtain the DKD gene set. We retrieved target genes of the seven herbal components of BYHW from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）， and performed cross-analysis with the DKD gene set to identify common genes shared by the disease and the Chinese medicines. A protein-protein interaction （PPI） network was constructed for the common genes to screen out the key genes. Chinese medicines targeting these key genes were searched against ETCM to identify removable Chinese medicines. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed on non-common DKD genes， and key genes in DKD-related pathways were selected based on machine learning. The GSE30529 dataset was used to verify the expression trends of 5hmC-modified genes and the feasibility of target genes as drug targets. TCMBank was used to search for target genes and obtain compounds targeting these genes and the corresponding Chinese medicines to construct a "key target-compound-Chinese medicine" network. Molecular docking was employed to verify the binding affinity of compounds with key targets. TCMSP and ETCM were used to search and count the candidate Chinese medicines targeting DKD-related genes， and a new decoction was formed by adding the selected Chinese medicines. A mouse model of DKD was established to examine the efficacy of the new decoction based on the mouse body mass， random blood glucose， urinary microalbumin （mALB）， serum creatinine （Scr）， and blood urea nitrogen （BUN） and by hematoxylin-eosin staining， periodic acid-Schiff staining， Masson staining， immunofluorescence assay， and Real-time PCR. ResultsThe cross-analysis results showed that the DKD gene set included 507 genes， of which 30 were target genes of BYHW. The PPI analysis indicated that the top 15% target genes regarding the degree were interleukin-6 （IL-6）， Toll-like receptor 4 （TLR4）， lactotransferrin （LTF）， lipoprotein lipase （LPL）， and sterol regulatory element-binding transcription factor 1 （SREBF1）. Persicae Semen and Pheretima in BYHW were unrelated to key genes and removed. Machine learning identified 10 potential target genes， among which TBC1 domain family member 5 （TBC1D5）， RAD51 paralog B （RAD51B）， and proteasome 20S subunit alpha 6 （PSMA6） had expression trends consistent with the GSE30529 dataset and could serve as drug targets. The "key target-compound-Chinese medicine" network and molecular docking results indicated that the compounds with good binding affinity to target proteins were arginine， glycine， myristicin， serine， and tyrosine， corresponding to 121 Chinese medicines. The top 10 Chinese medicines targeting DKD-related genes were Lycii Fructus， Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Isatidis Radix， Glehniae Radix， Ophiopogonis Radix， Allii Sativi Bulbus， Isatidis Folium， and Bolbostemmatis Rhizoma. Based on traditional Chinese medicine theory， the new decoction was obtained after removal of Persicae Semen and Pheretima and addition of Rehmanniae Radix Praeparata and Dioscoreae Rhizoma. Animal experiment results indicated that the modified BYHW improved the kidney function and inhibited renal fibrosis in DKD mice， with better effects than the original decoction. ConclusionThe BYHW modified based on 5hmC-Seal sequencing demonstrates better performance in inhibiting fibrosis and ameliorating DKD than the original decoction. This elucidates the biomedical theory behind the epigenetic modification of traditional Chinese medicine prescriptions， potentially offering new perspectives for the exploration of these prescriptions

Objective:To investigate the relationship between serum levels of interleukin-17A (IL-17A) and chemokine ligand 19 (CCL19) and disease activity in patients with lupus nephritis.Methods:A total of 100 patients with lupus nephritis admitted to Affiliated Hospital of Jining Medical College from June 2020 to February 2023 were collected as the disease group, according to the disease activity index, patients were grouped into inactive group (32 cases), mild active group (21 cases), moderate active group (29 cases), and severe active group (18 cases); another 100 healthy individuals who underwent physical examinations in our hospital during the same period were collected as the control group. Enzyme linked immunosorbent assay (ELISA) was applied to detect the expression levels of IL-17A and CCL19 in serum; Pearson method was applied to analyze the correlation between serum IL-17A, CCL19 and routine indicators in patients with lupus nephritis; receiver operating characteristic curve was applied to analyze the diagnostic value of serum IL-17A and CCL19 for moderate/severe lupus nephritis disease activity.Results:The expression levels of IL-17A and CCL19 in the serum of the disease group were obviously higher than those of the control group: (252.63 ± 64.47) ng/L vs. (123.27 ± 25.12) ng/L and (566.98 ± 73.36) ng/L vs. (275.63 ± 50.48) ng/L ( t = 18.70 and 32.72, P<0.05); the serum levels of IL-17A and CCL19 in the severe active, moderate active, and mild active groups were higher than those in the inactive group: (331.42 ± 87.46), (278.50 ± 74.19) and (232.34 ± 59.16) ng/L vs. (198.18 ± 46.22) ng/L; (662.33 ± 89.57), (606.14 ± 79.25) and (552.84 ± 68.36) ng/L vs. (487.13 ± 62.19) ng/L, and with the increase of disease activity, the levels of serum IL-17A and CCL19 gradually increased ( F = 17.86 and 25.35, P<0.05); the glomerular filtration rate, albumin, complement C 3 and complement C 4 in the active group were obviously lower than those in the inactive group: (69.17 ± 13.25) ml/(min·1.73 m 2) vs. (86.18 ± 14.16) ml/(min·1.73 m 2), (24.18 ± 5.11) g/L vs. (31.25 ± 6.35) g/L, (432.35 ± 95.22) mg/L vs. (675.42 ± 125.16) mg/L, (76.58 ± 17.51) mg/L vs. (121.42 ± 27.18) mg/L, while blood creatinine, urine protein and erythrocyte sedimentation rate were obviously higher than those in the inactive group: (92.34 ± 16.24) μmoI/L vs. (53.21 ± 9.17) μmoI/L, (3.43 ± 0.82) g/24 h vs. (1.26 ± 0.23) g/24 h, (66.37 ± 12.28) mm/1 h vs. (35.62 ± 8.67) mm/1 h ( t = 5.86, 5.97, 10.74, 9.93, 12.70, 14.67 and 12.74; P<0.05); serum IL-17A and CCL19 in patients with lupus nephritis were negatively correlated with glomerular filtration rate, albumin, complement C 3, and complement C 4, while positively correlated with blood creatinine, urine protein, and ESR ( P<0.05); the area under the curve (AUC) of the combined diagnosis of serum IL-17A and CCL19 for lupus nephritis disease activity was 0.961, which was superior to their respective individual diagnoses ( Z = 2.24 and 3.16, P = 0.025 and 0.002). Conclusions:The expression levels of IL-17A and CCL19 in serum gradually increase with the increase of disease activity in patients with lupus nephritis. The combined detection of the two has good diagnostic value for disease activity in lupus nephritis.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.