Objective: To systematically evaluate risk factors for cardiometabolic multimorbidity (CMM), so as to provide the evidence for formulating CMM prevention and control strategies. Methods: Publications pertaining to the risk factors for CMM were retrieved from databases, including SinoMed, CNKI, Wanfang Data, VIP, PubMed and Cochrane Library from inception to March 31, 2023. Meta-analysis was performed using the software RevMan 5.4 and Stata 16.0, and sensitivity analysis was performed using the leave-one-out method. The publication bias was evaluated using Egger's test. Results: Totally 494 publications were screened, and 20 publications were included in the final analysis, including 13 cohort studies (covering 1 940 000 participants) and 7 cross-sectional studies (covering 13 000 000 participants). Meta-analysis revealed that female (OR=1.54, 95%CI: 1.40-1.71), middle age (OR=3.80, 95%CI: 3.33-4.34), elderly (OR=2.82, 95%CI: 1.48-5.37), urban resident (OR=1.41, 95%CI: 1.27-1.57), higher education level (OR=2.01, 95%CI: 1.35-3.01), higher economic level (OR=1.21, 95%CI: 1.16-1.25), overweight (OR=1.92, 95%CI: 1.64-2.26), obesity (OR=3.01, 95%CI: 2.30-3.93), central obesity (OR=1.70, 95%CI: 1.12-2.56), smoking (OR=1.27, 95%CI: 1.07-1.51), alcohol consumption (OR=1.27, 95%CI: 1.01-1.59), irregular diet (OR=1.10, 95%CI: 1.02-1.18), insufficient intake of vegetables and fruits (OR=1.12, 95%CI: 1.07-1.17), lack of sleep at night (OR=1.17, 95%CI: 1.08-1.27), and depression (OR=1.50, 95%CI: 1.33-1.69) were risk factors for CMM. Sensitivity analysis of effects of central obesity and alcohol consumption were not robust. No publication bias was examined by Egger's test. Conclusions Female, middle age, elderly, urban resident, higher education level, higher economic level, overweight, obesity, central obesity, smoking, alcohol consumption, irregular diet, insufficient intake of vegetables and fruits, lack of sleep at night and depression are risk factors for CMM.

BACKGROUND: Dialysis-related amyloid may occur during long-term dialysis for patients with uraemia, of which the main evocator is β_2-microglobulin (β_22M); therefore, how to eliminate 132M from blood is always the focus of research. OBJECTIVE: To observe ability of removal of β_2-microglobulin (β_2M) from serum using two kinds of polyethersulfone (PES) membrane materials with various degrees of sulfonation.METHODS: These materials were incubated in radio-labeled β_2M (~(125)Ⅰ-β_2M) solution and human serum respectively at appointed time at 37 ℃, and then the amounts of ~(125)Ⅰ-β_2M and serumβ_2M adsorbed by materials were measured by radio immunoassay. RESULTS AND CONCLUSION: In the ~(125)Ⅰ-β_2M system, amounts of ~(125)Ⅰ-β_2M adsorbed by the materials decreased in the following sequence PES with high degree of sulfonation > PES with low degree of sulfonation > PES, whatever the source of PES was. In the serum system, amounts of β_2M adsorbed reached maximums at 30 minutes and the final adsorptions decreased in sequence of PES with high degree of sulfonation > PES with low degree of sulfonaUon > PES. Sulfonated PES removed β_2M more than PES did and the adsorption of β_2M increases with the increase in the degree of sulfonation. Its ability to remove significant amount of β_2M may result in less β_2M available for incorporation into amyloid. The use of sulfonated PES membranes may lessen the likelihood of development of dialysis-related amyloidosis, which remains a major source of morbidity for patients treated with long-term hemodialysis.

Objective:To investigate the mechanism of histone deacetylase (HDAC) inhibitor in down-regulating the expression of HER-2 in breast cancer cells and to provide an innovative therapeutic option to overcome the disadvantages of anti-HER-2 therapy. Meth-ods:HER-2-positive breast cell lines were treated with HDAC inhibitors. The changes in the gene and protein levels of HER-2 were de-tected by qPCR and Western blot. MiRNA microarray was used to identify the HDAC inhibitors, whereas qPCR was used to verify the miRNA expression. Results:In vitro cell experiments confirmed that the HDAC inhibitors TSA and SAHA can down-regulate the expres-sion of HER-2 in breast cancer cell lines. TSA can down-regulate the expression of HER-2 gene in BT474 and decrease the concentra-tions of 100 nmol by 10.7%and 200 nmol by 38.9%(P<0.05). TSA had no effect on the primary cells. The expression of HER-2 gene of BT474 was down-regulated by 93.9%(P<0.05) in the 5μmol/L group but not in the 1μmol/L group. SAHA significantly affected the pri-mary cells at a concentration of 1μmol/L and reduced the cells at 87.1%at a concentration of 5μmol/L. Seven miRNAs were identified from the miRNA microarray. MiR-762 was used as a basis to identify the changes in miRNA. The miRNA sputum identified by miRNA microarray and qPCR may be associated with the down-regulation of HER-2 by HDAC inhibitors. Conclusion: HDAC inhibitors may down-regulate the expression of HER-2 in breast cancer cells by changing some miRNAs.

Cancer is a serious threat to human health at present,so it is an important task in the medical field to overcome it.The first step is to recognize cancer.Metaphor is a universal cognitive mode.This paper analyzed the metaphorical phenomenon which the tumor growth process was described as plant growth.The results showed that in modern medicine,growth pattern,tumor shape characteristics,growth velocity and diffusion were using plant metaphors to illustrate.Tumor growth is the growth of stems and leaves of plants,plant root growth,plant fruit growth,plant growth in four seasons,special types of plant growth.The tumor metastasis is the seed spreading of the plant.The result implied that tumor in modern medicine was a metaphorical cognition of plants.This metaphor showed the vitality of tumor.

Objective: To systematically evaluate the incidence and influencing factors of depression in family caregivers of Alzheimer's disease (AD) patients, so as to provide the basis for the prevention and treatment of depression among the family caregivers of AD patients. Methods: Publications pertaining to depression in family caregivers of AD patients were retrieved from CNKI, Wanfang Data, PubMed and other databases from the time of their establishment to June 15, 2023. The evaluation criteria recommended by the Agency for Healthcare Research and Quality (AHRQ) and the Newcastle-Ottawa Scale were used to assess the quality of cross-sectional and cohort studies, respectively. Stata 16.0 and Revman 5.4 softwares were used to conduct a meta-analysis on the incidence and influencing factors of depression in family caregivers of AD patients. Sensitivity analysis and publication bias assessment were also performed on the results. Results: A total of 2 324 articles were retrieved, and ultimately 14 articles were included, with a total sample size of 8 313 individuals. There were 6 high-quality articles and 8 moderate-quality articles. Meta-analysis showed that the incidence of depression in family caregivers of AD patients was 37.5% (95%CI: 30.2%-45.1%). Factors associated with depression included patients' high degree of dementia (OR=1.718, 95%CI: 1.059-2.789), patients' low scores on Activities of Daily Living Scale (OR=1.344, 95%CI: 1.059-1.706), patients' psychobehavioral abnormalities (OR=1.248, 95%CI: 1.155-1.348), long duration of caregiving (OR=1.998, 95%CI: 1.637-2.437), less involvement of other family members in caregiving (OR=1.597, 95%CI: 1.237-2.061), low educational level (OR=1.191, 95%CI: 1.044-1.359), poor caregiving skills (OR=3.060, 95%CI: 2.257-4.149), poor self-rated health (OR=2.536, 95%CI: 1.114-5.771) and social support (OR=0.424, 95%CI: 0.232-0.774). The results of depression incidence demonstrated good stability with no significant publication bias. However, publication bias was observed in the influencing factors for depression, which were patients' high degree of dementia and patients' low scores on Activities of Daily Living Scale. Conclusions The incidence of depression in family caregivers of AD patients ranges from 30.2% to 45.1%. It is primarily influenced by the severity of patients' symptoms and ability to perform daily activities, and caregivers' educational level, caregiving skills, health status, caregiving duration and social support.

Objective: To conduct a scoping review on prognostic prediction models for patients with comorbidity of chronic diseases, and understand modeling methods, predictive factors and predictive effect of the models, so as to provide the reference for prognostic evaluation on patients with comorbidity of chronic diseases. Methods: Literature on prognostic prediction models for patients with comorbidity of chronic diseases was collected through SinoMed, CNKI, Wanfang Data, VIP, PubMed, Embase, Cochrane Library and Web of Science published from the time of their establishment to November 1, 2023. The quality of literature was assessed using prediction model risk of bias assessment tool (PROBAST), then modeling methods, predictive factors and predictive effects were reviewed. Results: Totally 2 130 publications were retrieved, and nine publications were finally enrolled, with an overall high risk of bias. Thirteen models were involved, with three established using machine learning methods and ten established using logistic regression. The prediction results of four models were death, with main predictive factors being age, gender, body mass index (BMI), Barthel index and pressure ulcers; the prediction results of nine models were rehospitalization, with main predictive factors being age, BMI, hospitalization frequency, duration of hospital stay and hospitalization costs. Eleven models reported the area under the receiver operating characteristic curve (AUC), ranging from 0.663 to 0.991 6; two models reported the C-index, ranging from 0.64 to 0.70. Eight models performed internal validation, one model performed external validation, and four models did not reported verification methods. Conclusions The prognostic prediction models for patients with comorbidity of chronic diseases are established by logistic regression and machine learning methods with common nursing evaluation indicators, and perform well. Laboratory indicators should be considered to add in the models to further improve the predictive effects.

To calculate resting energy expenditure (REE) in patients receiving invasive mechanical ventilation and compare different predictive equations with indirect calorimetry(IC).A total of 60 patients in intensive care unit(ICU) were enrolled. Measure calculating daily REE in the first week included IC, Harris-Benedict formula, Penn State formula and Swinamer formula. Daily REE did not exhibit significant difference in the first week of mechanical ventilation by IC (all P>0.05).All patients' REE values by IC were higher than those by Harris-Benedict formula (all P<0.01). By Penn State formula, REE in day l, 2, and 5 were comparable (all P>0.05) with those by IC, whereas the consistency between the two methods was poor. Similarly, daily REE by Swinamer formula calculation in the first week did not show significant difference (all P>0.05), with acceptable consistency as IC. Resting energy expenditure in patients receiving invasive mechanical ventilation dose not significantly change during the first week. Swinamer formula is more accurate than other equations when IC is considered as the standard method.

Objective To observe the effect of early bundle therapy on prognosis of patients with sepsis/septic shock and analyze the risk factors for death.Methods A retrospective cohort study was conducted to select patients with sepsis/septic shock at the Second Soochow University Hospital betweenJanuary 1,2016,and December 31,2016.Data pertaining to demographic variables,compliance rate of bundle therapy,and incidence of organ failure were collected.Patients were categorized into the nonsurvivor or survivor groups based on 28-day mortality.Logistic regression analysis was used to identify risk factors for 28-day mortality.Results Totally 118 sepsis/septic shock patients were included in the analysis;28-day mortality was 32.2％.Compared to the survivor group,patients in the non-survivor group were more likely to have chronic heart dysfunction and cerebrovascular disease,lower lactate clearance,lower 6-h compliance rate of bundle therapy and higher incidence of failure of one or ＞2 organs.Age,leukocyte,blood urea nitrogen,creatinine,brain natriuretic peptide,sequential organ failure score and acute physiological and chronic health scores Ⅱ on admission,and lactate after bundle therapy were higher than that of the survivor group.Logistical regression analysis showed that age ≥ 75 years [odds ratio (OR)1.012],6-h lactate clearance ＜30％ (OR=1.122),chronic heart failure (OR=1.741),failure of ＞2 organs (OR=1.769),and 6-h compliance rate of bundle therapy (OR=1.958) were independent risk factors for 28-day mortality.Conclusions Patients with sepsis/septic shock need early diagnosis and resuscitation to improve the compliance rate of bundle therapy and reduce the mortality.

ObjectiveThis study is aimed to investigate the association of human leukocyte antigen (HLA)-DRB1 with rheumatoid arthritis (RA) in Chinese Han population.MethodsA total of 281 Chinese Han patients with RA and 202 healthy controls were recruited.DNA was extracted from PBMC and HLA typing was performed by sequence based typing and PCR-Sequence Specific Primer.The frequency of HLADRB1 was compared between patients and controls using x2 test with continuity correction.ResultsThe susceptible HLA-DRB1 alleles were * 0101,* 0102,*0404,* 0405,and * 0410 which belonged to QRRAA.DRRAA and DERAA were protective alleles.At genotypic level,The association of S3P and S3D was detected.However,the protective effect of S3D was shown to be in a recessive mode.ConclusionOur results have shown that there are racial differences in RA susceptibility between Chinese Han population and Caucasians.

Objective:To compare the accuracy and generalized robustness of three predictive models of knowledge-based treatment strategies for radiotherapy for optimized model selection.Methods:The clinical radiotherapy plans of 45 prostate cancer (PC) cases and 25 nasopharyngeal cancer (NPC) cases were collected, and analyzed using three models (Z, L and S model), proposed by Zhu et al, Appenzoller et al and Shiraishi et al, respectively, to predict the dose-volume histogram (DVH) of bladder and rectum on PC cases and that of left and right parotid on NPC cases. The prediction error was measured by the difference of area under the predicted DVH and the clinical DVH curves (|V (pre_DVH)-V (clin_DVH)|), where a smaller prediction error implies a greater prediction accuracy. The accuracies of these three models were compared on the single organ at risk (OAR), and the generalized robustness of models was evaluated and compared by calculating the standard deviation of the prediction accuracy on different OAR. Results:For bladder and rectum, the prediction error of L model (0.114 and 0.163, respectively) was significantly higher than those values of Z and S models (≤0.071, P<0.05); for left parotid gland, the predicted error of S model (0.033) did not present significant difference from those values of Z and L models (≤0.025, P>0.05); for right parotid gland, S model (0.033) demonstrated significantly higher prediction error than those of Z and L models (≤0.028, P<0.05). Regarding different OAR, S model showed a lower standard deviation of prediction accuracy when comparing to Z and L models (0.016, 0.018 and 0.060, respectively). Conclusions:In the prediction of DVH in bladder and rectum of PC, Z and S models were more accurate than L model. In contrast, Z and L models demonstrated higher accuracy than S model in the prediction of left and right parotid glands of NPC. In respect to different OAR, the generalized robustness of S model was superior than the other two models.