Putting the "mi" in omics: discovering miRNA biomarkers for pediatric precision care

Current perspectives on perinatal mental health and neurobehavioral development: focus on regulation, coregulation and self-regulation

PURPOSE OF REVIEW

Perinatal mental health research provides an important perspective on neurobehavioral development. Here, we aim to review the association of maternal perinatal health with offspring neurodevelopment, providing an update on (self-)regulation problems, hypothesized mechanistic pathways, progress and challenges, and implications for mental health.

RECENT FINDINGS

(1) Meta-analyses confirm that maternal perinatal mental distress is associated with (self-)regulation problems which constitute cognitive, behavioral, and affective social-emotional problems, while exposure to positive parental mental health has a positive impact. However, effect sizes are small. (2) Hypothesized mechanistic pathways underlying this association are complex. Interactive and compensatory mechanisms across developmental time are neglected topics. (3) Progress has been made in multiexposure studies. However, challenges remain and these are shared by clinical, translational and public health sciences. (4) From a mental healthcare perspective, a multidisciplinary and system level approach employing developmentally-sensitive measures and timely treatment of (self-)regulation and coregulation problems in a dyadic caregiver-child and family level approach seems needed. The existing evidence-base is sparse.

SUMMARY

During the perinatal period, addressing vulnerable contexts and building resilient systems may promote neurobehavioral development. A pluralistic approach to research, taking a multidisciplinary approach to theoretical models and empirical investigation needs to be fostered.

Artificial Intelligence in Pediatrics: Learning to Walk Together

In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.

Microfluidics for Profiling miRNA Biomarker Panels in AI-Assisted Cancer Diagnosis and Prognosis

Early detection of cancers and their precise subtyping are essential to patient stratification and effective cancer management. Data-driven identification of expression biomarkers coupled with microfluidics-based detection shows promise to revolutionize cancer diagnosis and prognosis. MicroRNAs play key roles in cancers and afford detection in tissue and liquid biopsies. In this review, we focus on the microfluidics-based detection of miRNA biomarkers in AI-based models for early-stage cancer subtyping and prognosis. We describe various subclasses of miRNA biomarkers that could be useful in machine-based predictive modeling of cancer staging and progression. Strategies for optimizing the feature space of miRNA biomarkers are necessary to obtain a robust signature panel. This is followed by a discussion of the issues in model construction and validation towards producing Software-as-Medical-Devices (SaMDs). Microfluidic devices could facilitate the multiplexed detection of miRNA biomarker panels, and an overview of the different strategies for designing such microfluidic systems is presented here, with an outline of the detection principles used and the corresponding performance measures. Microfluidics-based profiling of miRNAs coupled with SaMD represent high-performance point-of-care solutions that would aid clinical decision-making and pave the way for accessible precision personalized medicine.