Objective:To establish an early prediction model with multiple indicators to predict the risk of severe acute pancreatitis (SAP) in hyperlipidemic acute pancreatitis (HLAP).Methods:The clinical data of 92 patients with HLAP admitted to the Emergency Department of our hospital from March 2018 to February 2020 were analyzed retrospectively. Among them, 29 cases deteriorated to SAP and 63 cases did not. Univariate analysis was used to screen predictive indicators related to hyperlipidemic severe acute pancreatitis (HL-SAP), and logistic regression analysis was used to screen independent predictive indicators related to HL-SAP. Then a prediction model was established. The area under (AUC) the receiver operating curve (ROC) was used to evaluate the predictive ability of each predictive indicator and the model for HL-SAP. Bootstrap resampling technology was used to validate the predictive ability of the model.Results:Univariate analysis showed that procalcitonin, D-dimer, C-reactive protein, albumin, cholesterol and CT grade had influence on the progression of HLAP to SAP ( P<0.05). Logistic regression analysis showed that D-dimer ( OR=2.112, 95% CI: 1.022-4.366; P<0.05), CT grade ( OR=5.818, 95% CI: 2.481-13.643; P<0.01) and cholesterol ( OR=1.146, 95% CI: 1.004-1.308; P<0.05) were independent risk factor of HL-SAP. The AUC of D-dimer, CT grade, cholesterol and the model were 0.802, 0.875, 0.665 and 0.927, respectively. Internal validation of the predictive ability of the model showed that the C-index was 0.927. Conclusions:In the early phase, application of the prediction model that composes D-dimer, CT grade and cholesterol has a good predictive effect on HL-SAP.

Objective:To study the problems found in the implementation of key clinical specialty programs and come up with feasibility suggestions.Methods:November 2020 to January 2021, by means of quota sampling and snowball sampling, 22 depth interviews were made with principal-investigators, project team elites, leaders of functional departments, and experts of independent examination teams. The interview data were subject to theme analysis.Results:Six(27%) of the interviewees confused the concepts of " key specialty" and " key discipline" ; 9(41%) of them held that at specific stage of the program development, the connotation and implementation emphasis of specialty development and those of key disciplines were overlapped to some extent. Twenty(88%)of them held that functional management process and responsibility should be further refined, 19(86%) said that lack of professional financial knowledge hindered their program implementation, 16(73%) reported difficulties in information statistics, management and sharing, 19(86%)presented disputes in understanding the program examination criteria.Conclusions:In the implementation of key clinical specialty program, management at various levels should work in alignment with program teams, provide accurate definition and guidance to key steps and working mechanisms, refine the management process, provide accurate financial management, and build information platforms for big data collection and sharing.

The Spt-Ada-Gcn5-acetyltransferase (SAGA) is an ancillary transcription initiation complex which is highly conserved. The ADA1 (alteration/deficiency in activation 1, also called histone H2A functional interactor 1, HFI1) is a subunit in the core module of the SAGA protein complex. ADA1 plays an important role in plant growth and development as well as stress resistance. In this paper, we performed genome-wide identification of banana ADA1 gene family members based on banana genomic data, and analyzed the basic physicochemical properties, evolutionary relationships, selection pressure, promoter cis-acting elements, and its expression profiles under biotic and abiotic stresses. The results showed that there were 10, 6, and 7 family members in Musa acuminata, Musa balbisiana and Musa itinerans. The members were all unstable and hydrophilic proteins, and only contained the conservative SAGA-Tad1 domain. Both MaADA1 and MbADA1 have interactive relationship with Sgf11 (SAGA-associated factor 11) of core module in SAGA. Phylogenetic analysis revealed that banana ADA1 gene family members could be divided into 3 classes. The evolution of ADA1 gene family members was mostly influenced by purifying selection. There were large differences among the gene structure of banana ADA1 gene family members. ADA1 gene family members contained plenty of hormonal elements. MaADA1-1 may play a prominent role in the resistance of banana to cold stress, while MaADA1 may respond to the Panama disease of banana. In conclusion, this study suggested ADA1 gene family members are highly conserved in banana, and may respond to biotic and abiotic stress.
Musa/genetics* ; Phylogeny ; Fungal Proteins ; Cell Nucleus ; Histones ; Stress, Physiological/genetics*