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Objective:To establish a predictive model for the progression of acute kidney injury (AKI) to stage 3 AKI (renal failure) in the intensive care unit (ICU), so as to assist physicians to make early and timely decisions on whether to intervene in advance.Methods:A retrospective analysis was conducted. Thirty-eight patients with AKI admitted to the intensive care medicine of the Third People's Hospital of Henan Province from January 2018 to May 2023 were enrolled. Patient data including acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) upon admission, serum creatinine (SCr), blood urea nitrogen (BUN), daily urine output during hospitalization, and the timing of continuous renal replacement therapy (CRRT) intervention were recorded. Based on clinically collected pathological data, standardized creatinine value ratio mean polynomial fitting models were established as the first criterion for judging the progression to stage 3 AKI after data cleansing, screening, and normalization. Additionally, standardized creatinine value ratio index fitting models were established as the second criterion for predicting progression to stage 3 AKI.Results:A total of 38 AKI patients were included, including 25 males and 13 females. The average age was (58.45±12.94) years old. The APACHEⅡ score was 24.13±4.17 at admission. The intervention node was (4.42±0.95) days. Using a dual regression model approach, statistical modeling was performed with a relatively small sample size of statistical data samples, yielding a scatter index non-linear regression model for standardized creatinine value ratio data relative to day " n", with y = 1.246?2 x1.164?9 and an R2 of 0.860?1, indicating reasonable statistical fitting. Additionally, a quadratic non-linear regression model was obtained for the mean standardized creatinine value ratio relative to day " n", with y = -0.260?6 x2+3.010?7 x-1.612 and an R2 of 0.998?9, indicating an excellent statistical fit. For example, using a baseline SCr value of 66 μmol/L for a healthy individual, the dual regression model predicted that the patient would progress to stage 3 AKI within 3-5 days. This prediction was consistent when applied to other early intervention renal injury patients. Conclusion:The established model effectively predicts the time interval of the progression of AKI to stage 3 AKI (renal failure), which assist intensive care physicians to intervene AKI as early as possible to prevent disease progression.
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Objective:To explore the causes of high noise exposure in PICU, and to provide reference for formulating effective intervention measures to improve the status of noise exposure.Methods:With phenomenological method, eleven nurses of Xinhua Hospital, Shanghai Jiaotong University School of Medicine from August to September 2020 were enrolled by purposive sampling method, and joined face-to-face and semi-structured interviews with researchers. Interview data were analyzed by Colaizzi 7-step analysis method.Results:Four themes were extracted, including the characteristics of PICU, difficulty in equipment alarm management, the crying of children which was difficult to appease, and the low level of knowledge, attitude and practice of noise management of medical staff.Conclusions:High noise exposure of PICU is caused by many reasons. Researchers and managers can optimize the equipment alarm management, implement ideal analgesia and sedation and humanistic care, and carry out noise management training to improve the noise exposure in PICU and the quality of clinical nursing.
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After the stratification of the normal glucose tolerance, the changes of insulin resistance and βcell function in the development of type 2 diabetes mellitus were investigated. A retrospective analysis on data of 275 cases with oral glucose insulin releasing tests. The area under the insulin curve (AUCINS ) 108. 43 mU/ L was taken as the critical value of diagnosis. Normal glucose tolerance subjects were divided into the NGT-a group(AUCINS<108. 43 mU/ L) and the NGT-b group(AUCINS≥108. 43 mU/ L). The plasma glucose, insulin, insulin sensitivity, and β cell function were compared among the 4 groups: NGT-a group (n=96), NGT-b group (n=49), prediabetes group (n=71), and type 2 diabetes mellitus group ( n = 59). Among the fasting insulin, 2 h insulin, AUCINS , early-phase insulin secretion index(△I30 / △G30), the ratio of total insulin area under curve, and total glucose area under curve, disposition index, homeostasis model assessment for insulin resistance, and Matsuda insulin sensitivity index, the relationship as follows: NGT-b group>prediabetes group>NGT-a group>type 2 diabetes mellitus group. The NGT-b group was always the highest, prediabetes group was lower, NGT-a group and type 2 diabetes mellitus group were the lowest, there were significant differences (all P<0. 05). Making the NGT-a group as the basic state, in the NGT-b group, β cell function has begun to appear compensation and insulin resistance, and β cell function compensation reached the peak, the β cell function in the prediabetes group was beginning to compensate for the deficiency, the function of β cell in type 2 diabetes mellitus group decreased further. These findings suggest that the development process of type 2 diabetes mellitus could be the following four stages according to the function of β cell: β cell function normal, β cell functional compensation, β cell function loss of compensation, and finally β cell function failure.
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After the stratification of the normal glucose tolerance, the changes of insulin resistance and βcell function in the development of type 2 diabetes mellitus were investigated. A retrospective analysis on data of 275 cases with oral glucose insulin releasing tests. The area under the insulin curve (AUCINS ) 108. 43 mU/ L was taken as the critical value of diagnosis. Normal glucose tolerance subjects were divided into the NGT-a group(AUCINS<108. 43 mU/ L) and the NGT-b group(AUCINS≥108. 43 mU/ L). The plasma glucose, insulin, insulin sensitivity, and β cell function were compared among the 4 groups: NGT-a group (n=96), NGT-b group (n=49), prediabetes group (n=71), and type 2 diabetes mellitus group ( n = 59). Among the fasting insulin, 2 h insulin, AUCINS , early-phase insulin secretion index(△I30 / △G30), the ratio of total insulin area under curve, and total glucose area under curve, disposition index, homeostasis model assessment for insulin resistance, and Matsuda insulin sensitivity index, the relationship as follows: NGT-b group>prediabetes group>NGT-a group>type 2 diabetes mellitus group. The NGT-b group was always the highest, prediabetes group was lower, NGT-a group and type 2 diabetes mellitus group were the lowest, there were significant differences (all P<0. 05). Making the NGT-a group as the basic state, in the NGT-b group, β cell function has begun to appear compensation and insulin resistance, and β cell function compensation reached the peak, the β cell function in the prediabetes group was beginning to compensate for the deficiency, the function of β cell in type 2 diabetes mellitus group decreased further. These findings suggest that the development process of type 2 diabetes mellitus could be the following four stages according to the function of β cell: β cell function normal, β cell functional compensation, β cell function loss of compensation, and finally β cell function failure.
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Computational epidemiology is a fast-developing and interdisciplinary research area .Through comprehensive computation-analysis of multi-uncertain factors affecting the epidemic process , this method may add to our knowledge about epidemic patterns and help design reasonable response plans and emergency strategies .This article briefly summarizes the idea and theory of computational epidemiology based on related researches in the recent years , introduces the application of this method in case of smallpox bioterrorism and influenza pandemic , and predicts the development of this area .
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The use of the Internet big data to discriminate the early symptoms of unexpected biological events has be-come a frontier research area in biosurveillance .This paper mainly introduces the principle , method, practical applications of big data based biosurveillance , and the future development of this new research field .
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Modeling biosecurity events based on mathematical theories to reveal the inherent mechanism can shed light on the development of biosecurity events and offer accurate scientific support to emergency preparation and response , con-tributing much to national biodefense capability .This paper reviews the applications of mathematical theories as a research and analytic tool in biosecurity and discusses the trends of development in this field .
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To explore the transmission patterns and advise public health departments , this paper establishes a dynamic model on the peculiarity of avian influenza A (H7N9) virus and analyzes the dynamic feature of this type of bird flu trans-mission between humans and birds .Results show that this type of bird flu can be controled , and that closing down live-bird markets is the most effective method .
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Human behavior significantly affects the spreading dynamics of infectious diseases in large populations .The study of the interplay between the adaptive behavior and the epidemic dynamics underlies the professional information release and psychological counseling mechanism and is conducive to disease control and social panic elimination .This paper aims at the investigation of the effect of adaptive behaviors on the spreading dynamics of epidemics in structured populations .We analyzed the empirical data on adaptive behavior from several large epidemics after the outbreak of SARS in 2003 and confirmed the induction effect of two main information sources on adaptive behavior , e.g., public available information and local perceived information .Then we investigated the effect of adaptive behavior on epidemic dynamics in a structured population based on two primary models , e.g., health-belief model and network model .The results showed that the individual adaptive behavior had significantly decreased the chance of infection and thus mitigated the epidemics . Human adaptive behavior has a significant effect on spreading dynamics of epidemics .An effective information release mechanism will induce human adaptive behavior and is thus conducive to control of epidemics .
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Emerging large-scale epidemics not only cause heavy casualties , but have a huge impact on economy .This paper analyzes the characteristics and manners of the economic impact of emerging large -scale epidemics .Economic losses and the national economic impact of emerging large-scale epidemics are analyzed at three levels: microscopic , supply and demand , and macroscopic .Then the relationship between the these levels is discussed .This research is expected to play an important role in the construction of an appropriate economic model for analysis and calculation of economic losses of emerging large-scale epidemics , and in the formulation of the best cost-effective ratio for preventin and control measures against emerging large-scale epidemics .
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Syndromic surveillance , based on non-specific pre-diagnosis and other information , is capable of nearly real-time detection and early warning of potential bioterrorism and emerging infectious threats .Developed countries , led by the U.S., have conducted in-depth and front research in this area while China has just started syndromic surveillance re -search in the exploratory trial stage .The Chinese army is facing an urgent need for syndromic surveillance .This paper makes a case study of foreign military syndromic surveillance practices , typically the U.S.representative system ESSENSE , which was developed by the U .S.Department of Defense and deployed in all military treatment facilities .Some suggestions for the Chinese army are also discussed .
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Objective To develop a nursing standard of baccalaureate program for Shanghai Jiaotong University to guide and improve the training quality of nursing students.Methods On the basis of literature review,the nominal group technique and Delphi survey technique were used to develop the standard.Results The training objective and the training plan included attitude,knowledge and ability constituted the standard.Conclusions The deficient of dominating personalities or obedience to authority which existed in traditional group discussion can be diminished in this study.The nominal group technique is a scientific and professional method for the construction and evaluation of nursing program.
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Objective To identify the most common fungal pathogens and their antifungal drug resistance in autoimmune disease (AD) patients with fungal infection , for guiding the rational use of antifungal therapy. Methods The identification were analyzed by ATB Expression automatic microbiology analytical instrument system. The antifungal susceptibility test was done by ATB FUNGUS strip. Results Of 428 patients studied,36(8.4%) had fungal infections. The most commonly infective sites were lower respiratory tract(53.5%),urinary tract(20.9%), intestinal tract(11.6%). A total of 43 fungi strains were isolated, the most common fungi were Candida albicans (65.1%), Candida glabrata (11.6%), Candida tropicalis (7.0%),and Candida parapsilosis (4.7%).Resistance rates of Candida albicans against ketoconazole, miconazole and econazole were 35. 7% , 46.4% and 32.1% , while resistance rates to fluconazole, amphotericin B, nystain and flucytosine were much lower, being 0, 14.3% , 14.3% and 7.1% . The resistance rates of other fungi were similar to Candida albicans. Conclusion The main pathogens causing fungal infection in patients with AD is Candida albicans. It should as early as possible process the clinical antifungal therapy under the result of antifungal susceptibility test and supportive measure.