Development and validation of a short-form suboptimal health status questionnaire

Sun, Shuyu; Liu, Hongzhi; Guo, Zheng; Guan, Qihua; Wang, Yinghao; Wang, Jie; Qi, Yan; Yan, Yuxiang; Wang, Youxin; Wen, Jun; Hou, Haifeng; ,

doi:10.1007/s13167-023-00339-z

Cited by 7 publications

(1 citation statement)

References 48 publications

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“…For factor analysis, we chose 208 molecular property descriptors from the computational chemistry software RDKit 2023.9.5 (https://www.rdkit.org, accessed on 12 December 2023). The component matrix was generated by choosing the right number of common elements, and the component matrix score was computed after passing the KMO and Bartlett's spherical test [31]. Selecting descriptors for the component matrix that had factor loading values higher than 0.75 allowed us to determine if the distributions of A and B differed noticeably.…”

Section: Factor Analysis and Difference Analysismentioning

confidence: 99%

Mlp4green: A Binary Classification Approach Specifically for Green Odor

Yang,

Qian,

et al. 2024

IJMS

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Fresh green leaves give off a smell known as “green odor.” It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization.

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Section: Factor Analysis and Difference Analysismentioning

confidence: 99%

Mlp4green: A Binary Classification Approach Specifically for Green Odor

Yang,

Qian,

et al. 2024

IJMS

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The caregiving role influences Suboptimal Health Status and psychological symptoms in unpaid carers

Garcia,

Guo,

Zheng

et al. 2024

EPMA Journal

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Background Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM). Methods A cross-sectional study was conducted among 368 participants who were enrolled from Australia, including 203 unpaid carers as cases and 165 individuals from the general population as controls. SHS scores were measured using SHSQ-25 (Suboptimal Health Status Questionnaire-25), whilst psychological symptoms were measured by DASS-21 (Depression, Anxiety and Stress Scale-21). Chi-square was used to measure SHS and psychological symptom prevalence. Spearman correlation analysis was utilised to identify the relationship between SHSQ-25 and DASS-21 scores. Logistic regression analysis was used for multivariate analysis. Results The prevalence of SHS in carers was 43.0% (98/203), significantly higher than the prevalence 12.7% (21/165) in the general population (p < 0.001). In addition, suboptimal health prevalence was higher in female carers (50.3%; 95/189) than females in the general population (12.4%; 18/145). Logistic regression showed that the caregiving role influenced SHS, with carers 6.4 times more likely to suffer from SHS than their non-caring counterparts (aOR = 6.400, 95% CI = 3.751–10.919). Conclusions Unpaid carers in Australia have a significantly higher prevalence of SHS than that in the general population and experience poorer health. The SHSQ-25 is a powerful tool that can be utilised to screen at-risk individuals to predict their risk of chronic disease development, an essential pillar for shifting the paradigm change from reactive medicine to that of predictive, preventive and personalised medicine (PPPM).

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