Extracorporeal membrane oxygenation (ECMO) technology is an important life support method for critically ill patients. A large number of studies have found that ECMO can change the pharmacokinetic (PK) parameters of critically ill patients, thereby affecting the drug effect in vivo. However, there is still a lack of recommendations for the adjustment of commonly used drugs during ECMO support in China, and the selection or dosage adjustment of drugs during ECMO support is not clear. Therefore, a multidisciplinary group of domestic experts in clinical pharmacy and critical care medicine was established by Critical Care Medicine Committee of China International Exchange and Promotive Association for Medical and Health Care, and Hospital Pharmacy Committee of China Pharmaceutical Association, to develop the Expert consensus on drug adjustment during extracorporeal membrane oxygenation support (2025). Eight clinical issues of drug adjustment during ECMO support were discussed in this consensus: (1) Why does the patient's demand for drug dosage change during ECMO support? (2) What factors are related to the degree of drug loss during ECMO support? (3) Considering the features of drugs, which types of drugs may need to be adjusted during ECMO support? (4) How to adjust the dosage when using antibacterial drugs during ECMO support? (5) How to adjust antifungal drugs during ECMO support? (6) Does ECMO support change patients' dosage requirements for antiviral drugs? (7) How to adjust sedative and analgesic drugs during ECMO support? (8) Does ECMO support affect the dosage requirement of vasoactive agents? Eighteen consensus are elaborated based on the latest clinical evidence, aiming to provide recommendations for drug adjustment in critically ill patients receiving ECMO support to ensure the safety and effectiveness of medication.
Humans ; Critical Illness ; Extracorporeal Membrane Oxygenation ; Consensus ; Candidiasis/drug therapy* ; Intraabdominal Infections/therapy*

Objective To investigate the effects of Aconite decoction （AD） on the viability and polarization of murine RAW264.7 macrophages induced by lipopolysaccharide （LPS） or interleukin-4 （IL-4）. Methods Cytotoxicity of AD was assessed by the CCK-8 assay. RAW264.7 cells were polarized toward M1 phenotype by LPS or M2 phenotype by IL-4, followed by treatment with varying concentrations of AD. Macrophage polarization was analyzed by flow cytometry. Quantitative PCR was performed to measure mRNA expression of polarization-associated markers （IL-6, iNOS, Arg1, and Ym1）. ELISA was used to quantify secreted cytokines （TNF-α and IL-10）in the supernatant. Results At non-toxic concentrations, IL-6 and iNOS mRNA levels in LPS-stimulated cells were significantly upregulated while Arg1 and Ym1 expression in IL-4-treated groups were downregulated by AD. Concurrently, TNF-α secretion in LPS-induced M1 polarization was enhanced but IL-10 production in IL-4-induced M2 polarization was suppressed by AD. Conclusion AD could promote macrophage proliferation and viability, augments LPS-driven M1 polarization, and inhibit IL-4-mediated M2 polarization, which provided experimental evidence for the potential application of AD in tumor immunotherapy.

Objective To analyze the computed tomography(CT)imaging features of urachal carcinoma and evaluate its diagnostic value.Methods The clinical data of 20 patients with urachal carcinoma confirmed by surgery and pathology,who were admitted to The First Affiliated Hospital of Naval Medical University from Dec.2012 to Dec.2022,were collected.Seventeen of the 20 patients underwent enhanced CT urography and 3 underwent pelvic CT plain scan+enhanced scan.After scanning,multiplanar reconstruction was performed on the post-processing workstation.The general data,clinical symptoms,CT imaging findings,pathological data,and prognosis of the patients were analyzed and summarized.Results The patients included 16 males and 4 females,aged 27 to 75 years old,with a median age of 61.50(41.50,71.25)years old.The tumors were all located in the anterior wall of the bladder,along the urachus,with a maximum diameter of 1.72-5.55 cm and a median maximum diameter of 3.34(2.48,3.71)cm.Fourteen cases had cystic-solid lesions and 6 had solid lesions.In the cystic-solid lesions,9 cases showed the"upper cystic and lower solid"sign on the sagittal plane.Calcification was noted in 17 cases.After enhanced scanning,18 cases showed progressive enhancement,and 2 cases showed"fast in and fast out"enhancement.Tumor invasion extended beyond the urachus and/or bladder muscle layer in 19 cases.At the end of follow-up,3 cases had recurrence,2 had metastasis,5 had no recurrence after surgery,3 died,and 7 were lost to follow-up.Conclusion Urachal carcinoma has certain characteristic manifestations on CT imaging.Reconstructing the sagittal plane with enhanced CT scanning and multiplanner reformation can help preoperative diagnosis and prognostic evaluation of urachal carcinoma.

Objective:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

Objective:To explore the effect of hairy and enhancer of split related protein 2 (Hey2) on osteoclast differentiation through the activation of nuclear factor of activated T cells cytoplasmic 1 (NFATc1).Methods:RAW264.7 cells were induced with receptor activator of NF-κB ligand (RANKL) to differentiate into osteoclasts. Experimental groups were divided by different concentrations of RANKL (0, 10, 20, 50 μg/L) and different processing time (0, 3, 5, 7 days). Hey2 overexpression experiment was grouped as follows: blank control group, RANKL group, empty plasmid vector control group (Hey2-NC+RANKL), Hey2 overexpression group (Hey2-OE+RANKL); similarly, groups in Hey2 knockdown experiment were as follows: blank control group, RANKL group, negative control group (si-NC+RANKL), Hey2 knockdown group (si-Hey2+RANKL). Chromatin immunoprecipitation experiment groups were divided as non-specific IgG control group (IgG control group), non-specific IgG group (IgG RANKL group), Hey2-specific antibody control group (anti-Hey2 control group), Hey2-specific antibody group (anti-Hey2-RANKL group). For the different RANKL concentration groups and different induction time groups, real-time fluorescent quantitative PCR (RT-qPCR) was used to detect the mRNA expressions of nuclear factor of NFATc1, cathepsin K (CTSK), and cellular feline osteosarcoma oncogene (c-Fos) and tartrate-resistant acid phosphatase (TRAP) staining was used to assess the formation of multinucleated osteoclasts. After Hey2 overexpression or knockdown, RT-qPCR and Western blotting were used to detect the gene and protein expressions of NFATc1, c-Fos, and CTSK. TRAP staining was used to evaluate the formation of multinucleated osteoclasts. Bioinformatics prediction (NCBI, JASPAR) and chromatin immunoprecipitation (ChIP) assay were used to validate the binding of Hey2 to the NFATc1 promoter region.Results:During the osteoclastic differentiation of RAW 264.7 cells induced by RANKL, the expression of Hey2 could be detected, and the expression level of Hey2 decreased with the increase of RANKL concentration and induction time. In the 50 μg/L RANKL group, the expression levels of Hey2 gene (0.18±0.00) and protein (0.22±0.02) were significantly lower than those in the control group (1.00±0.00, 0.52±0.01) ( t=41.67, 12.88; both P<0.001). In the 50 μg/L RANKL group inducted for 5 days, the expression levels of Hey2 gene (0.27±0.02) and protein (0.79±0.01) were significantly lower than those in the control group (1.00±0.00, 1.15±0.02) ( t=11.47, 108.60; both P<0.001). Hey2 overexpression significantly reduced the gene and protein expressions of NFATc1, c-Fos, and CTSK, as well as the production of TRAP-positive cells (all P<0.05). Hey2 knockdown significantly increased the gene and protein expressions of NFATc1, c-Fos, and CTSK, as well as the production of TRAP-positive cells (all P<0.05). After inducing RAW264.7 cells with 50 μg/L RANKL for 1 day, ChIP results showed that among the two sample groups treated with Hey2 antibody, the detection level of the NFATc1 promoter region (-400 to -200 bp) in the anti-Hey2-RANKL group (18.06±0.06) was significantly higher than that in the anti-Hey2 control group (13.37±0.36) ( t=12.56, P<0.001). Conclusions:Hey2 can bind to the downstream target gene NFATc1 at -400 to -200 bp region of the promoter. As a transcriptional repressor, Hey2 inhibits osteoclast differentiation.

Objective To understand the clinical applicability and implementation of expert consensus on the implementation and removal of protective restraints in psychiatry,and to provide references for promoting the standardized practice of psychiatric protective restraints and updating the consensus.Methods By the convenience sampling method,a questionnaire survey was conducted among nurses from 480 hospitals in 30 provinces from June 15 to July 15,2024.The survey was conducted using the instrument for evaluating clinical applicability of guide-lines(version 2.0)and a self-compiled questionnaire on the clinical implementation of the restraint consensus.Results A total of 7,844 valid questionnaires were collected,with a valid questionnaire recovery rate of 93.78％.The results of clinical applicability scoring showed that the consensus had the lowest availability score(64.72％)and the highest acceptability score(76.74％).The results showed that nurses' receiving training and the level of their hospitals were the main influencing factors for scores in various dimensions(P＜0.05).4,774 participants(87.42％)believed that the application of consensus could enhance the standardization of nurses' restraint operations.The safety rate of the restraint consensus was 79.51％,and the economic ratio was 76.87％.Among the evaluators,1,739(22.17％)believed that there were implementation obstacles in the consensus.Conclusion The clinical applicability of the consensus is relatively good,and the application of the consensus helps to improve the standardization of clinical operations.In the future,efforts should be made to strengthen the promotion and training of the consensus,develop hierarchical promotion strategies according to the characteristics of medical institutions,and improve the quality of evidence for the consensus,so as to further enhance the clinical application effect of the consensus.

Objective:To explore the effect of hairy and enhancer of split related protein 2 (Hey2) on osteoclast differentiation through the activation of nuclear factor of activated T cells cytoplasmic 1 (NFATc1).Methods:RAW264.7 cells were induced with receptor activator of NF-κB ligand (RANKL) to differentiate into osteoclasts. Experimental groups were divided by different concentrations of RANKL (0, 10, 20, 50 μg/L) and different processing time (0, 3, 5, 7 days). Hey2 overexpression experiment was grouped as follows: blank control group, RANKL group, empty plasmid vector control group (Hey2-NC+RANKL), Hey2 overexpression group (Hey2-OE+RANKL); similarly, groups in Hey2 knockdown experiment were as follows: blank control group, RANKL group, negative control group (si-NC+RANKL), Hey2 knockdown group (si-Hey2+RANKL). Chromatin immunoprecipitation experiment groups were divided as non-specific IgG control group (IgG control group), non-specific IgG group (IgG RANKL group), Hey2-specific antibody control group (anti-Hey2 control group), Hey2-specific antibody group (anti-Hey2-RANKL group). For the different RANKL concentration groups and different induction time groups, real-time fluorescent quantitative PCR (RT-qPCR) was used to detect the mRNA expressions of nuclear factor of NFATc1, cathepsin K (CTSK), and cellular feline osteosarcoma oncogene (c-Fos) and tartrate-resistant acid phosphatase (TRAP) staining was used to assess the formation of multinucleated osteoclasts. After Hey2 overexpression or knockdown, RT-qPCR and Western blotting were used to detect the gene and protein expressions of NFATc1, c-Fos, and CTSK. TRAP staining was used to evaluate the formation of multinucleated osteoclasts. Bioinformatics prediction (NCBI, JASPAR) and chromatin immunoprecipitation (ChIP) assay were used to validate the binding of Hey2 to the NFATc1 promoter region.Results:During the osteoclastic differentiation of RAW 264.7 cells induced by RANKL, the expression of Hey2 could be detected, and the expression level of Hey2 decreased with the increase of RANKL concentration and induction time. In the 50 μg/L RANKL group, the expression levels of Hey2 gene (0.18±0.00) and protein (0.22±0.02) were significantly lower than those in the control group (1.00±0.00, 0.52±0.01) ( t=41.67, 12.88; both P<0.001). In the 50 μg/L RANKL group inducted for 5 days, the expression levels of Hey2 gene (0.27±0.02) and protein (0.79±0.01) were significantly lower than those in the control group (1.00±0.00, 1.15±0.02) ( t=11.47, 108.60; both P<0.001). Hey2 overexpression significantly reduced the gene and protein expressions of NFATc1, c-Fos, and CTSK, as well as the production of TRAP-positive cells (all P<0.05). Hey2 knockdown significantly increased the gene and protein expressions of NFATc1, c-Fos, and CTSK, as well as the production of TRAP-positive cells (all P<0.05). After inducing RAW264.7 cells with 50 μg/L RANKL for 1 day, ChIP results showed that among the two sample groups treated with Hey2 antibody, the detection level of the NFATc1 promoter region (-400 to -200 bp) in the anti-Hey2-RANKL group (18.06±0.06) was significantly higher than that in the anti-Hey2 control group (13.37±0.36) ( t=12.56, P<0.001). Conclusions:Hey2 can bind to the downstream target gene NFATc1 at -400 to -200 bp region of the promoter. As a transcriptional repressor, Hey2 inhibits osteoclast differentiation.

Objective To understand the clinical applicability and implementation of expert consensus on the implementation and removal of protective restraints in psychiatry,and to provide references for promoting the standardized practice of psychiatric protective restraints and updating the consensus.Methods By the convenience sampling method,a questionnaire survey was conducted among nurses from 480 hospitals in 30 provinces from June 15 to July 15,2024.The survey was conducted using the instrument for evaluating clinical applicability of guide-lines(version 2.0)and a self-compiled questionnaire on the clinical implementation of the restraint consensus.Results A total of 7,844 valid questionnaires were collected,with a valid questionnaire recovery rate of 93.78％.The results of clinical applicability scoring showed that the consensus had the lowest availability score(64.72％)and the highest acceptability score(76.74％).The results showed that nurses' receiving training and the level of their hospitals were the main influencing factors for scores in various dimensions(P＜0.05).4,774 participants(87.42％)believed that the application of consensus could enhance the standardization of nurses' restraint operations.The safety rate of the restraint consensus was 79.51％,and the economic ratio was 76.87％.Among the evaluators,1,739(22.17％)believed that there were implementation obstacles in the consensus.Conclusion The clinical applicability of the consensus is relatively good,and the application of the consensus helps to improve the standardization of clinical operations.In the future,efforts should be made to strengthen the promotion and training of the consensus,develop hierarchical promotion strategies according to the characteristics of medical institutions,and improve the quality of evidence for the consensus,so as to further enhance the clinical application effect of the consensus.

Objective:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

DNMT3A encodes a DNA methyltransferase involved in development, cell differentiation, and gene transcription, which is mutated and aberrant-expressed in cancers. Here, we revealed that loss of DNMT3A promotes malignant phenotypes in lung cancer. Based on the epigenetic inhibitor library synthetic lethal screening, we found that small-molecule HDAC6 inhibitors selectively killed DNMT3A-defective NSCLC cells. Knockdown of HDAC6 by siRNAs reduced cell growth and induced apoptosis in DNMT3A-defective NSCLC cells. However, sensitive cells became resistant when DNMT3A was rescued. Furthermore, the selectivity to HDAC6 inhibition was recapitulated in mice, where an HDAC6 inhibitor retarded tumor growth established from DNMT3A-defective but not DNMT3A parental NSCLC cells. Mechanistically, DNMT3A loss resulted in the upregulation of HDAC6 through decreasing its promoter CpG methylation and enhancing transcription factor RUNX1 binding. Notably, our results indicated that HIF-1 pathway was activated in DNMT3A-defective cells whereas inactivated by HDAC6 inhibition. Knockout of HIF-1 contributed to the elimination of synthetic lethality between DNMT3A and HDAC6. Interestingly, HIF-1 pathway inhibitors could mimic the selective efficacy of HDAC6 inhibition in DNMT3A-defective cells. These results demonstrated HDAC6 as a HIF-1-dependent vulnerability of DNMT3A-defective cancers. Together, our findings identify HDAC6 as a potential HIF-1-dependent therapeutic target for the treatment of DNMT3A-defective cancers like NSCLC.