Exploratory Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior.

Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. Our machine learning analysis extends previous research by showing that both adaptive and maladaptive traits are associated with passively sensed behavior providing initial evidence for the utility of this type of data to study personality and its pathology. The analysis also suggests directions for future confirmatory studies into the underlying behavior patterns that link adaptive and maladaptive variants consistent with contemporary models of personality pathology.

Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study.

BACKGROUND: Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression. OBJECTIVE: This study aims to identify links between productivity and biobehavioral rhythms modeled from passively collected mobile data streams. METHODS: In this study, we used a multimodal mobile sensing data set consisting of data collected from smartphones and Fitbits worn by 188 college students over a continuous period of 16 weeks. The participants reported their self-evaluated daily productivity score (ranging from 0 to 4) during weeks 1, 6, and 15. To analyze the data, we modeled cyclic human behavior patterns based on multimodal mobile sensing data gathered during weeks 1, 6, 15, and the adjacent weeks. Our methodology resulted in the creation of a rhythm model for each sensor feature. Additionally, we developed a correlation-based approach to identify connections between rhythm stability and high or low productivity levels. RESULTS: Differences exist in the biobehavioral rhythms of high- and low-productivity students, with those demonstrating greater rhythm stability also exhibiting higher productivity levels. Notably, a negative correlation (C=-0.16) was observed between productivity and the SE of the phase for the 24-hour period during week 1, with a higher SE indicative of lower rhythm stability. CONCLUSIONS: Modeling biobehavioral rhythms has the potential to quantify and forecast productivity. The findings have implications for building novel cyber-human systems that align with human beings' biobehavioral rhythms to improve health, well-being, and work performance.

Qing-Re-Xiao-Zheng-Yi-Qi formula relieves kidney damage and activates mitophagy in diabetic kidney disease.

Introduction: Qing-Re-Xiao-Zheng-Yi-Qi Formula is an effective prescription in diabetic kidney disease treatment, we have confirmed the efficacy of Qing-Re-Xiao-Zheng therapy in diabetic kidney disease through clinical trials. In this study, we investigated the mechanisms of Qing-Re-Xiao-Zheng-Yi-Qi Formula in the treatment of diabetic kidney disease. Methods: We used Vanquish UHPLCTM to analyze the chemical profiling of Qing-Re-Xiao-Zheng-Yi-Qi Formula freeze-dried powder. We constructed diabetic kidney disease rat models induced by unilateral nephrectomy and high-dose streptozocin injection. We examined blood urea nitrogen, serum creatinine, serum glucose, total cholesterol, triglyceride, serum total protein, albumin, alanine aminotransferase, aspartate aminotransferase and 24 h urinary total protein in diabetic kidney disease rats. The renal pathological changes were observed by HE, Masson, PAS stanning and transmission electron microscopy. The levels of fibrosis-related proteins and mitophagy-related proteins were detected by western blot analysis. We also conducted an immunofluorescence co-localization analysis on podocytes to further investigate the effect of Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment on mitophagy. Results: A total of 27 constituents in Qing-Re-Xiao-Zheng-Yi-Qi Formula were tentatively identified. We found PINK1/Parkin-mediated mitophagy was inhibited in diabetic kidney disease. Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment could raise body weight and reduce renal index, reduce proteinuria, improve glycolipid metabolic disorders, ameliorate renal fibrosis, and reduce the expression of Col Ⅳ and TGF-ß1 in diabetic kidney disease rats. Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment could also increase the expression of nephrin, activate mitophagy and protect podocytes in diabetic kidney disease rats and high glucose cultured podocytes. Conclusion: PINK1/Parkin-mediated mitophagy was inhibited in diabetic kidney disease, and Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment could not only ameliorate pathological damage, but also promote mitophagy to protect podocytes in diabetic kidney disease.

The Profile and Function of Gut Microbiota in Diabetic Nephropathy.

INTRODUCTION: Dysbiosis of gut microbiota impairs the homeostasis of immune and metabolic systems. Although previous studies have revealed the correlation between gut microbiota and various diseases, the function between gut microbiota and diabetic nephropathy (DN) has not been discovered distinctly. In this study, we tried to investigate the profile and function of gut microbiota in DN. METHODS: A total of 100 people were enrolled in this study. Twenty were healthy people, 20 were diabetes patients, and 60 were DN patients. The DN patients were divided into three stages including stage III, IV, and V. We conducted taxonomic analyses in different groups. The distributions of phyla, classes, orders, families, and genera in different groups and samples were investigated. We also evaluated the correlations between clinical parameters and gut microbiota in 60 DN patients. RESULTS: The gut microbiota in the healthy group, diabetes group, and DN group had 1764 operational taxonomic units (OTUs) in total. The healthy group had 1034 OTUs, the diabetes group had 899 OTUs, and the DN group had 1602 OTUs. The diversity of gut microbiota in the stage III DN group was smaller than that in the other groups. 24-h urinary protein was positively correlated with Alistipes and Subdoligranulum, cholesterol was positively correlated with Bacteroides and Lachnoclostridium, and estimated glomerular filtration rate was negatively correlated with Ruminococcus torques group. DISCUSSION: The gut microbiota might play an important role in the development and pathogenesis of DN. A change in gut microbiota diversity is correlated with disease progression. Some kinds of gut microbiota including Alistipes, Bacteroides, Subdoligranulum, Lachnoclostridium, and Ruminococcus torques group might be detrimental factors in DN.