Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.

OBJECTIVES

The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.

MATERIALS AND METHODS

In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).

RESULTS

Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.

CONCLUSIONS

Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Distribution and clinical characteristics of patients with chronic hepatitis B virus infection in the grey zone

A substantial proportion of patients with chronic hepatitis B (CHB) who do not fit into any of the usual immune states are considered to be in the 'grey zone (GZ)'. We aimed to investigate the distribution and characteristics of GZ in a large cohort of CHB patients. Four thousand seven hundred and fifty-nine consecutive treatment-naïve CHB patients were enrolled. The immune states were defined based on AASLD 2018 Hepatitis B Guidance. GZ CHB patients were classified into four groups: HBeAg positive, normal ALT levels and serum HBV DNA ≤10⁶  IU/ml (GZ-A); HBeAg positive, elevated ALT levels and serum HBV DNA ≤2 × 10⁴  IU/ml (GZ-B); HBeAg negative, normal ALT levels and serum HBV DNA ≥2 × 10³  IU/ml (GZ-C); HBeAg negative, elevated ALT levels and serum HBV DNA ≤2 × 10³  IU/ml (GZ-D). The distributions of different immune states were: 233 (4.90%) patients in immune-tolerant phase, 941 (19.77%) patients in HBeAg-positive immune active phase, 1,717 (36.08%) patients in inactive phase and 546 (11.47%) patients in HBeAg-negative immune active phase. Of note, 1,322 (27.78%) patients did not fit into any of above phases and were defined as the GZ. A high proportion of patients in GZ-B had advanced fibrosis (33.3%) or cirrhosis (25.8%). Older age, HBeAg-positive status and higher ALT levels were independently risk factors of advanced disease in GZ CHB patients. Therefore, our results revealed that more than a quarter of CHB patients were classified into the GZ and a high proportion of patients in GZ-B had advanced fibrosis or even cirrhosis.

[Imaging Findings of Pulmonary Cryptococcosis]

Pulmonary cryptococcosis(PC)is a fungal infection that can be easily misdiagnosed due to its non-specific clinical features and imaging findings.This article reviews the imaging findings of PC,their relationships with pathology and immune status,and differential diagnosis of PC with other disease,so as to improve the clinical management of PC.