Predictive models for personalized precision medical intervention in spontaneous regression stages of cervical precancerous lesions.

BACKGROUND: During the prolonged period from Human Papillomavirus (HPV) infection to cervical cancer development, Low-Grade Squamous Intraepithelial Lesion (LSIL) stage provides a critical opportunity for cervical cancer prevention, giving the high potential for reversal in this stage. However, there is few research and a lack of clear guidelines on appropriate intervention strategies at this stage, underscoring the need for real-time prognostic predictions and personalized treatments to promote lesion reversal. METHODS: We have established a prospective cohort. Since 2018, we have been collecting clinical data and pathological images of HPV-infected patients, followed by tracking the progression of their cervical lesions. In constructing our predictive models, we applied logistic regression and six machine learning models, evaluating each model's predictive performance using metrics such as the Area Under the Curve (AUC). We also employed the SHAP method for interpretative analysis of the prediction results. Additionally, the model identifies key factors influencing the progression of the lesions. RESULTS: Model comparisons highlighted the superior performance of Random Forests (RF) and Support Vector Machines (SVM), both in clinical parameter and pathological image-based predictions. Notably, the RF model, which integrates pathological images and clinical multi-parameters, achieved the highest AUC of 0.866. Another significant finding was the substantial impact of sleep quality on the spontaneous clearance of HPV and regression of LSIL. CONCLUSIONS: In contrast to current cervical cancer prediction models, our model's prognostic capabilities extend to the spontaneous regression stage of cervical cancer. This model aids clinicians in real-time monitoring of lesions and in developing personalized treatment or follow-up plans by assessing individual risk factors, thus fostering lesion spontaneous reversal and aiding in cervical cancer prevention and reduction.

Heterlimnius luyashanensis sp. n. and Zaitzevia triangularis sp. n. from Shanxi Province, China (Coleoptera: Elmidae).

Two new species, Heterlimnius luyashanensis sp. n. and Zaitzevia triangularis sp. n. are described from Shanxi Province, China. The genus Heterlimnius Hinton, 1935 and Zaitzevia Champion, 1923 are reported from Shanxi Province for the first time. Heterlimnius luyashanensis sp. n. belonging to the Heterlimnius trachys species group has the following characteristics: 1. anterior margin of pronotum strongly produced anteriad; 2. median longitudinal sulcus of pronotum extends from basal 0.3 to 0.8. Zaitzevia triangularis sp. n. has a larger body size and a triangular apex of penis.

An Inhibitor-Loaded LDH- and MOF-Based Bilayer Hybrid System for Active Corrosion Protection of Aluminum Alloys.

A novel inhibitor-loaded bilayer hybrid system based on the LDH inner layer and MOF outer layer is designed on an aluminum alloy 2A12 surface to improve corrosion performance. The hybrid film system covers the inherent cavities and intercrystalline defects of the LDH film using the affinity between the LDH and the MOF compounds. The results demonstrate that the LDH-inhI precursor film is entirely covered by new Zn-based MOF microrods. The LDH-inhI precursor film is partially dissolved and recrystallized in favor of MOF crystal growth to strengthen the binding adhesion between LDH and MOF films. The LDH-inhI/MOF-inhII bilayer film shows significantly enhanced corrosion resistance through the synergistic action of LDH and MOF nanocontainers doped with different corrosion inhibitors (vanadates, 2,5-furandicarboxylic acid, and benzotriazoles). Due to the multiple loadings of the MOF film and the sustained-release of the LDH film, this method provides an effective approach to developing new anticorrosion systems and enhancing both the barrier ability and active corrosion protection performance of LDH-based conversion treatments.

The mediating effect of 24-h time-use behaviors on the relationship between depression and mortality: A compositional mediation analysis for survival outcomes.

BACKGROUND: A compositional mediation model of survival outcomes was established to explore whether 24-h time-use behaviors mediate the relationship between depression and mortality. METHODS: 4137 adults from the National Health and Nutrition Examination Survey (NHANES 2005-2006) were followed up to 2019. Cox proportional hazards regression model was used to estimate the total effect of depression on mortality. Compositional data analysis was used to examine the relationship between 24-h time-use compositions and mortality. Furthermore, we constructed a compositional mediation model for survival outcomes to investigate the mediating effect of 24-h time-use behaviors on depression and mortality. RESULTS: Compared with participants without depression, depressive patients had a significantly higher risk of overall mortality (HR = 1.49, 95 % CI: 1.25,1.79), cardiovascular disease -specific mortality (HR =1.89, 95 % CI: (1.37,2.63)) and mortality from causes other than cardiovascular disease or cancer (HR = 1.62, 95 % CI: (1.25,2.08)). Physical activity, especially moderate-to-vigorous physical activity, significantly mediated the relationship between depression and all-cause and CVD-specific mortality. LIMITATIONS: Despite being a cohort study, the exposure and mediatiors were measured at the baseline. Further research is necessary to require a temporal order between the exposure and mediating variables. CONCLUSIONS: Our findings indicate that 24-h time-use behaviors link depression to mortality. In particular, increasing the time spent on physical activity can reduce the risk of death in patients with depression. This finding provides potential interventions for reducing the risk of death in patients with depression.