Objective To construct recombinant plasmids containing HPV18E7 gene ,and explore the optimization condition of its expression in Escherichia coli .Methods The genomic DNA extracted from HeLa cell line which served as a template to the HPV18 E7 gene was amplified using PCR method ;and the amplified product of HPV18E7 gene was connected to the pET-32a(+ ) vector ,which composed the pET-32a(+ )-HPV18E7 recombinant plasmid ;the positive recombinant plasmids were transformed into BL21-DE3-pLysS competent cells and the optimized expression condition was explored in order to obtain a large amount of HPV18E7 oncogenic protein .Results The fragment length of PCR products of HeLa cell genomic DNA was consistent with that of HPV18 E7 gene .In LB medium ,the expression level of the target protein was not high under such conditions as different concentra-tion of IPTG and lactose ,different temperatures and different induction starting amount .Therefore the ZYM-5052 auto-induction medium was tried in this experiment ,and the expression amount of the fusion protein was much higher than that induced with IPTG and lactose .Conclusion The amount of HPV18E7 fusion protein in ZYM-5052 automatic induction medium is much higher than that induced with IPTG and lactose .

Objective:To investigate the status of evidence-based nursing competence among clinical nurses in tertiary grade A hospitals of Anhui province, and analyze the influencing factors based on random forest model, so as to provide reference for improving the evidence-based nursing ability of clinical nurses and formulating intervention strategies.Methods:The convenience sampling method was used to select 543 clinical nurses from 4 tertiary grade A hospitals in Anhui Province from October to December 2022. The general data questionnaire, Evidence-based Nursing Competence Scale, Information Literacy Scale, and Nurse Innovation Ability Scale were used to investigate. The random forest model was used to evaluate the importance of the influencing factors. The Lasso regression analysis was used to complete the screening of the influencing factors. The influencing factors of the evidence-based nursing competence among clinical nurses were explored by multiple linear regression analysis.Results:A total of 543 valid questionnaires were retrieved. Among 543 clinical nurses, 55 males and 388 females, aged (32.34 ± 6.93) years old. Evidence-based nursing competence scored (45.49 ± 21.18) points, information literacy scored (73.50 ± 10.47) points, innovation ability scored (126.78 ± 21.99) points. The random forest model and Lasso regression analysis showed that the model achieved the best fit with 8 variables. In order of importance, the top 8 variables were information literacy (25.78%), innovation ability (22.37%), night shift per month (9.91%), educational background (9.19%), English proficiency (8.44%), professional title (6.71%), scientific research and innovation experience (5.17%), and professional attitude (4.50%). Multiple linear regression analysis showed that information literacy, innovation ability, and English proficiency were the influencing factors of evidence-based nursing competence among clinical nurses ( t=9.17, 7.31, 2.52, all P<0.05). Conclusions:The level of evidence-based nursing ability among clinical nurses in tertiary grade A hospitals of Anhui province needs to be improved. From the perspective of improving the information literacy among clinical nurses, nursing managers can increase the training of information retrieval ability and English ability, enhance their English literature reading skills, pay attention to the cultivation of nurses' innovation ability, stimulate the innovative consciousness and thinking among clinical nurses, formulate and implement targeted interventions, so as to gradually improve the level of evidence-based nursing ability among clinical nurses.

Objective To investigate the influencing factors of malnutrition in hospitalized elder-ly patients with comorbidities of chronic diseases,and to construct a predictive model.Methods A convenience sampling method was used to select 426 elderly patients with comorbidities of chronic dis-eases admitted to the Department of Geriatrics of Suzhou Ninth People's Hospital Affiliated to Soochow University from January 2023 to February 2024.Based on a Mini-nutritional Assessment-Short Form(MNA-SF)score＜8 and either an albumin level＜34.0 g/L or a prealbumin level＜200 mg/L as reference of malnutrition,patients were classified into malnutrition group and non-malnutrition group.General characteristics,oral status[assessed using the Oral Health Assessment Tool(OHAT)],diet-ary inflammatory index(DII,evaluated through a food frequency questionnaire),and activities of daily living[assessed using the Barthel Index(BI)]were compared between the two groups.Multiva-riable Logistic regression analysis was employed to explore the influencing factors of malnutrition in elderly patients with comorbidities of chronic diseases and to construct a model formula.A gradient boosting machine(GBM)algorithm was implemented using R software to build a GBM predictive mod-el.Receiver Operating Characteristic(ROC)curves were utilized to analyze the predictive performance of both models,and the Delong test was applied to compare the difference of the area under the curve(AUC).Results Ninety-two patients were diagnosed with malnutrition(malnutrition group),while 334 patients had no malnutrition(non-malnutrition group).Statistically significant differences were observed between the malnutrition and non-malnutrition groups in terms of age,the number of chronic comorbidities,the number of medication taken,OHAT scores,DII,and BI scores(P＜0.05).Advanced age,a higher number of chronic comorbidities,a greater number of medication taken,higher OHAT scores,higher DII,and lower BI scores were all influencing factors of malnu-trition in elderly patients with comorbidities of chronic diseases(P＜0.05).The ROC curve analy-sis revealed an AUC of GBM model was 0.901 and 0.874 for the Logistic regression model.The De-long test indicated that the predictive performance of the GBM model was superior to that of the Lo-gistic regression model(P＜0.05).Conclusion Malnutrition in hospitalized elderly patients with chronic multimorbidity is associated with age,the number of chronic comorbidities,the number of medications taken,OHAT scores,DII,and BI scores.The constructed GBM model can effectively assess the risk of malnutrition in these patients.

Objective To investigate the influencing factors of malnutrition in hospitalized elder-ly patients with comorbidities of chronic diseases,and to construct a predictive model.Methods A convenience sampling method was used to select 426 elderly patients with comorbidities of chronic dis-eases admitted to the Department of Geriatrics of Suzhou Ninth People's Hospital Affiliated to Soochow University from January 2023 to February 2024.Based on a Mini-nutritional Assessment-Short Form(MNA-SF)score＜8 and either an albumin level＜34.0 g/L or a prealbumin level＜200 mg/L as reference of malnutrition,patients were classified into malnutrition group and non-malnutrition group.General characteristics,oral status[assessed using the Oral Health Assessment Tool(OHAT)],diet-ary inflammatory index(DII,evaluated through a food frequency questionnaire),and activities of daily living[assessed using the Barthel Index(BI)]were compared between the two groups.Multiva-riable Logistic regression analysis was employed to explore the influencing factors of malnutrition in elderly patients with comorbidities of chronic diseases and to construct a model formula.A gradient boosting machine(GBM)algorithm was implemented using R software to build a GBM predictive mod-el.Receiver Operating Characteristic(ROC)curves were utilized to analyze the predictive performance of both models,and the Delong test was applied to compare the difference of the area under the curve(AUC).Results Ninety-two patients were diagnosed with malnutrition(malnutrition group),while 334 patients had no malnutrition(non-malnutrition group).Statistically significant differences were observed between the malnutrition and non-malnutrition groups in terms of age,the number of chronic comorbidities,the number of medication taken,OHAT scores,DII,and BI scores(P＜0.05).Advanced age,a higher number of chronic comorbidities,a greater number of medication taken,higher OHAT scores,higher DII,and lower BI scores were all influencing factors of malnu-trition in elderly patients with comorbidities of chronic diseases(P＜0.05).The ROC curve analy-sis revealed an AUC of GBM model was 0.901 and 0.874 for the Logistic regression model.The De-long test indicated that the predictive performance of the GBM model was superior to that of the Lo-gistic regression model(P＜0.05).Conclusion Malnutrition in hospitalized elderly patients with chronic multimorbidity is associated with age,the number of chronic comorbidities,the number of medications taken,OHAT scores,DII,and BI scores.The constructed GBM model can effectively assess the risk of malnutrition in these patients.

In the original publication of the article the keywords are incorrectly online published. The correct keywords should read as α-Conotoxin; Nicotinc acetylcholine receptor; Acetylcholine binding protein; X-ray crystallography".

The α3* nAChRs, which are considered to be promising drug targets for problems such as pain, addiction, cardiovascular function, cognitive disorders etc., are found throughout the central and peripheral nervous system. The α-conotoxin (α-CTx) LvIA has been identified as the most selective inhibitor of α3β2 nAChRs known to date, and it can distinguish the α3β2 nAChR subtype from the α6/α3β2β3 and α3β4 nAChR subtypes. However, the mechanism of its selectivity towards α3β2, α6/α3β2β3, and α3β4 nAChRs remains elusive. Here we report the co-crystal structure of LvIA in complex with Aplysia californica acetylcholine binding protein (Ac-AChBP) at a resolution of 3.4 Å. Based on the structure of this complex, together with homology modeling based on other nAChR subtypes and binding affinity assays, we conclude that Asp-11 of LvIA plays an important role in the selectivity of LvIA towards α3β2 and α3/α6β2β3 nAChRs by making a salt bridge with Lys-155 of the rat α3 subunit. Asn-9 lies within a hydrophobic pocket that is formed by Met-36, Thr-59, and Phe-119 of the rat β2 subunit in the α3β2 nAChR model, revealing the reason for its more potent selectivity towards the α3β2 nAChR subtype. These results provide molecular insights that can be used to design ligands that selectively target α3β2 nAChRs, with significant implications for the design of new therapeutic α-CTxs.
Animals ; Aplysia ; Binding Sites ; Conotoxins ; chemistry ; Crystallography, X-Ray ; Humans ; Protein Structure, Quaternary ; Receptors, Nicotinic ; chemistry