Objective:To develop a nomogram model for predicting the risk of medication deviation in ischemic stroke patients after hospital discharge, providing insights for clinical nursing strategies.Methods:A convenience sampling method was used to select 440 ischemic stroke patients hospitalized in the Neurology Department of Nanyang Central Hospital from March 2022 to September 2023. Data on basic patient information and post-discharge medication deviation were collected using the medication deviation assessment tool eight weeks after discharge. Patients were categorized into a no medication deviation group and a medication deviation group. Univariate and binomial logistic regression analyses were conducted to identify independent risk factors for post-discharge medication deviation, and a nomogram prediction model was developed.Results:Data were successfully collected from 424 patients, with a response rate of 96.36% (424/440). The incidence of medication deviation was 58.02% (246/424). Binomial logistic regression analysis identified the absence of a fixed caregiver ( OR=2.113, P=0.001), large number of comorbid chronic diseases ( OR=1.773, P<0.001), medication non-adherence ( OR=2.443, P=0.003), and negative coping ( OR=2.255, P<0.001) as independent risk factors for medication deviation. A nomogram prediction model based on these four risk factors was constructed. The Hosmer-Lemeshow goodness-of-fit test indicated good calibration (χ 2=10.569, P=0.227), with calibration curve closely approximating the ideal line. The area under the receiver operating characteristic curve was 0.770 (95% CI: 0.584-0.866) . Conclusions:The absence of a fixed caregiver, medication non-adherence, negative coping, and large number of comorbid chronic diseases are independent risk factors for medication deviation in ischemic stroke patients. The nomogram model developed in this study demonstrates good discrimination and accuracy, providing a basis for formulating targeted nursing strategies.

Objective:To develop a nomogram model for predicting the risk of medication deviation in ischemic stroke patients after hospital discharge, providing insights for clinical nursing strategies.Methods:A convenience sampling method was used to select 440 ischemic stroke patients hospitalized in the Neurology Department of Nanyang Central Hospital from March 2022 to September 2023. Data on basic patient information and post-discharge medication deviation were collected using the medication deviation assessment tool eight weeks after discharge. Patients were categorized into a no medication deviation group and a medication deviation group. Univariate and binomial logistic regression analyses were conducted to identify independent risk factors for post-discharge medication deviation, and a nomogram prediction model was developed.Results:Data were successfully collected from 424 patients, with a response rate of 96.36% (424/440). The incidence of medication deviation was 58.02% (246/424). Binomial logistic regression analysis identified the absence of a fixed caregiver ( OR=2.113, P=0.001), large number of comorbid chronic diseases ( OR=1.773, P<0.001), medication non-adherence ( OR=2.443, P=0.003), and negative coping ( OR=2.255, P<0.001) as independent risk factors for medication deviation. A nomogram prediction model based on these four risk factors was constructed. The Hosmer-Lemeshow goodness-of-fit test indicated good calibration (χ 2=10.569, P=0.227), with calibration curve closely approximating the ideal line. The area under the receiver operating characteristic curve was 0.770 (95% CI: 0.584-0.866) . Conclusions:The absence of a fixed caregiver, medication non-adherence, negative coping, and large number of comorbid chronic diseases are independent risk factors for medication deviation in ischemic stroke patients. The nomogram model developed in this study demonstrates good discrimination and accuracy, providing a basis for formulating targeted nursing strategies.

Objective:To investigate gender measurement invariance of the Perceived Social Support Scale(PSSS)in people aged 50 years and older.Methods:A total of 1013 adults(50-96 years old)in Beijing, Hunan and Shandong were tested by using PSSS.The measurement invariance of PSSS between middle-aged and elderly males and females was analyzed.Differences in PSSS total scores and subscale scores between males and females were examined.Results:The equivalence test results of each item in the questionnaire met the requirements of the metrology(△CFI≥0.010, △TLI≥0.010, △RSMEA≤0.015), indicating that the hypotheses of morphological equivalence, weak equivalence, strong equivalence and strict equivalence of PSSS were all valid in the middle-aged and elderly population regardless of gender.In addition, middle-aged and elderly females had higher scores in family support, support from friends, support from other people and perceived social support than their male counterparts( P<0.05). Conclusions:PSSS has cross-gender equivalence in middle-aged and elderly people.Thus, differences in PSSS can reflect the perceived social support level in middle-aged and elderly people of different genders.

The binding characteristics of quinolone antibacterial agent, ofloxacin (OFLX) and ciprofloxacin(CPFX) with catalase(CAT) were studied by fluorescence spectroscopy in aqueous solution. It was showed that both OFLX and CPFX had a strong ability to quench the CAT intrinsic fluorescence through a static quenching procedure. The formation constant K and the number of bind ing site n were further calculated according to the fluorescence quenching r esults.

AIM　To predict skin permeability of drugs with theoretical parameters. METHODS　The semiempirical self-consistent field molecular calculation AM1 method is utilized to obtain the structural parameters of drug molecules. Stepwise multiple regression analysis or BP neural network is then utilized to establish the correlation between skin permeability of drugs and their structural parameters. RESULTS　The calculated human skin permeability coefficients (kp) of 22 model drugs in vitro or the R values (R=absorbed/unabsorbed) of 17 drugs in vivo are in good agreement with their observed values. CONCLUSION　Theoretical parameters can be used to predict skin permeability of drugs.