Prediction of 6-month poststroke spasticity in patients with acute first-ever stroke by machine learning.

OBJECTIVE: Poststroke spasticity (PSS) reduces arm function and leads to low levels of independence. This study suggested applying machine learning (ML) from routinely available data to support the clinical management of PSS. DESIGN: 172 patients with acute first-ever stroke were included in this prospective cohort study. Twenty clinical information and rehabilitation assessments were obtained to train various ML algorithms for predicting 6-month PSS defined by a modified Ashworth scale (MAS) score ≥ 1. Factors significantly relevant were also defined. RESULTS: The study results indicated that multivariate adaptive regression spline (area under the curve (AUC) value: 0.916; 95% confidence interval (CI): 0.906-0.923), adaptive boosting (AUC: 0.962; 95% CI: 0.952-0.973), random forest (RF) (AUC: 0.975; 95% CI: 0.968-0.981), support vector machine (SVM) (AUC: 0.980; 95% CI: 0.970-0.989) outperformed the traditional logistic model (AUC: 0.897; 95% CI: 0.884-0.910) (P < 0.05). Among all of the algorithms, the RF and SVM models outperformed the others (P < 0.05). FMA score, days in hospital, age, stroke location, and paretic side were the most important features. CONCLUSION: These findings suggest that ML algorithms can help augment clinical decision-making processes for the assessment of PSS occurrence, which may enhance the efficacy of management for patients with PSS in the future.

Research on Public Environmental Perception of Emotion, Taking Haze as an Example.

Ecological and environmental problems have become increasingly prominent in recent years. Environmental problems represented by haze have become a topic that affects the harmonious ecology of human beings. The trend of this topic is on the rise. People's perception of the environment after the impact of haze has also changed. A real-time grasp of the dynamic public environment perception of emotions is often an important basis for environmental management departments to effectively solve environmental problems through public opinion. This article focuses on the problem of the public perception of emotional changes, which is caused by fog and hazy weather, proposes an environmental emotion perception model, using Weibo comment data about fog and haze as environmental perception data, and analyzes the impact of fog and haze on the public in four seasonal time dimensions. The post-environment perception of emotion changes: the results show that in spring, the public's environmental perception of emotions is mainly negative emotions at the beginning of the season; in summer, positive emotions become dominant emotions; in autumn, the public's environmental perception of emotions is dominated by negative emotions that increase substantially; and in winter, the dominant environmental perception of emotions of the public is still negative. This theory provides support for research on social emotions and public opinion behavior.

Public Perception of Haze Weather Based on Weibo Comments.

In China, haze weather has become a major public concern and is frantically discussed by the public. Many people express their views, opinions, or complaints on social media. Effectively extracting this useful information may help to improve our understanding of how the public perceive and respond to haze, and could potentially contribute to environmental policy-making. In this paper, we study how the public perceive haze during haze weather and how this perception changes with seasons based on comment data from a major social media platform in China, Weibo, and using several social network methods, including perceptual keyword cloud mapping, complex network topology characteristics, and social perception analysis. The results showed that the public's perception was focused on the causes of haze in spring, enjoyment of life and travel in summer, measures to prevent haze in autumn, and the adverse effects of haze on human health in winter.

High IFIT1 expression predicts improved clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

Interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) plays a key role in growth suppression and apoptosis promotion in cancer cells. Interferon was reported to induce the expression of IFIT1 and inhibit the expression of O-6-methylguanine-DNA methyltransferase (MGMT).This study aimed to investigate the expression of IFIT1, the correlation between IFIT1 and MGMT, and their impact on the clinical outcome in newly diagnosed glioblastoma. The expression of IFIT1 and MGMT and their correlation were investigated in the tumor tissues from 70 patients with newly diagnosed glioblastoma. The effects on progression-free survival and overall survival were evaluated. Of 70 cases, 57 (81.4%) tissue samples showed high expression of IFIT1 by immunostaining. The χ(2) test indicated that the expression of IFIT1 and MGMT was negatively correlated (r = -0.288, P = .016). Univariate and multivariate analyses confirmed high IFIT1 expression as a favorable prognostic indicator for progression-free survival (P = .005 and .017) and overall survival (P = .001 and .001), respectively. Patients with 2 favorable factors (high IFIT1 and low MGMT) had an improved prognosis as compared with others. The results demonstrated significantly increased expression of IFIT1 in newly diagnosed glioblastoma tissue. The negative correlation between IFIT1 and MGMT expression may be triggered by interferon. High IFIT1 can be a predictive biomarker of favorable clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.