Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer.

BACKGROUND: Primary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential. METHODS: This study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis. RESULTS: In this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p < 0.0053). On the other hand, we validated the diagnostic efficiency of the four ultrasound image features in clinical data from 112 patients with PPCWOM and 150 patients with PPCWM, obtaining a combined diagnostic AUC of 0.904. In summary, using ultrasound imaging features for predicting whether prostate cancer is metastatic has many applications. CONCLUSION: The above study reveals noninvasive ultrasound image biomarkers and their underlying biological significance, which provide a basis for early diagnosis, treatment, and prognosis of primary prostate cancer with metastasis.

Proteomics reveals MRPL4 as a high-risk factor and a potential diagnostic biomarker for prostate cancer.

Through digital rectal examinations (DRE) and routine prostate-specific antigen (PSA) screening, early prostate cancer (PC) treatment has become possible. However, PC is a complex and heterogeneous disease. In vivo, cancer cells can invade adjacent tissues and metastasize to other tissues resulting in hard cures. Therefore, the key to improving PC patients' survival time is preventing cancer cells' metastasis. We used mass spectrometry to profile primary PC in patients with versus without metastatic PC. We named these two groups of PC patients as high-risk primary PC (n = 11) and low-risk primary PC (n = 7), respectively. At the same time, patients with benign prostatic hyperplasia (BPH, n = 6) were used as controls to explore the possible factors driving PC metastasis. Based on comprehensive mass spectrometry analysis and biological validation, we found significant upregulation of MRPL4 expression in high-risk primary PC relative to low-risk primary PC and BPH. Further, through research of the extensive clinical cohort data in the database, we discovered that MRPL4 could be a high-risk factor for PC and serve as a potential diagnostic biomarker. The MRPL4 might be used as an auxiliary indicator for clinical status/stage of primary PC to predict patient survival time.

Rehabilitation Tracking of Athletes Post Anterior Cruciate Ligament Reconstruction (ACL-R) Surgery Through Causal Analysis of Gait Data & Computational Modeling.

Early identification of motion disparities in Anterior Cruciate Ligament reconstructed (ACL-R) athletes may better post-operative decision making when returning athletes to sport. Existing return to play assessments consist of assessments of muscle strength, functional tasks, patient-reported outcomes, and 3D coordinate tracking. However, these methods primarily depend on the medical provider's intuition to release them to participate in an unrestricted activity after ACL-R that may cause reinjury or long-term impacts. This study proposes a wearable sensor-based system that helps track athlete rehabilitation progress and return to sport decision making. For this, we capture gait data from 89 ACL-R athletes during their walking and jogging trials. The raw gyroscope data collected from this system is used to extract causal features based on Nolte's phase slope index. Features extracted from this study are used to develop computational models that classify ACL-R athletes based on their reconstructed knee during two visits (3-6 months & 9 months) post ACL-R surgery. The classifier's performance degradation in detecting ACL-R athletes injured knee during multiple visits supports athletic trainers and physicians' decision-making process to confirm an athlete's safe return to sport.Clinical Relevance- This study develops computational models based on causal analysis of gait data to support athletic trainers and medical practitioners' decision to return athletes to sport post ACL-R surgery.

Theory-Guided Randomized Neural Networks for Decoding Medication-Taking Behavior.

Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness.

Artificial Intelligence in Health Care: Bibliometric Analysis.

BACKGROUND: As a critical driving power to promote health care, the health care-related artificial intelligence (AI) literature is growing rapidly. OBJECTIVE: The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care-related AI publications. METHODS: The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. RESULTS: The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. CONCLUSIONS: This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care-related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

A Social Cognitive Theory-based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors.

Poor adherence to long-term therapies for chronic diseases, such as cancer, compromises effectiveness of treatment and increases the likelihood of disease progression, making medication adherence a critical issue in population health. While the field has documented many eers to adherence to medication, it has also come up with few efficacious solutions to medication adherence, indicating that new and innovative approaches are needed. In this paper, we evaluate medication-taking behaviors based on social cognitive theory (SCT), presenting patterns of adherence stratified across SCT constructs in 33 breast cancer survivors over an 8-month period. Findings indicate that medication adherence is a very personal experience influenced by many simultaneously interacting factors, and a deeper contextual understanding is needed to understand and develop interventions targeting non-adherence.

Thermal stability of optical fiber metal organic framework based on graphene oxide and nickel and its hydrogen adsorption application.

The electrochemistry (EC) method was used to synthesize graphene oxide-nickel (GO-Ni) metal organic framework (MOF) that has the thickness of µm-level. The MOF's thermal stability and hydrogen adsorption and desorption capacity were measured by using an optical fiber Mach-Zehnder interferometer (MZI) sensor. This MZI was fabricated by core-offset fusion splicing one section of single mode fiber (SMF) between two SMFs. Experimental results showed that the GO-Ni MOF could be stabilized, even as the environmental temperature reached 125 °C. The MOF showed good hydrogen adsorption ability for the the MOF and hydrogen molecules's interactions.

High autophagic flux guards ESC identity through coordinating autophagy machinery gene program by FOXO1.

Although much is known about transcriptional networks that control embryonic stem cell (ESC) self-renewal and differentiation, the metabolic regulation of ESC is less clear. Autophagy is a catabolic process that is activated under both stress and normal conditions to degrade damaged organelles and aggregated proteins, and thus plays pivotal roles in somatic and adult stem cell function. However, if and how ESCs harness autophagy to regulate stemness remains largely unknown. Recently, we have defined that autophagy is essential for mitochondrial homeostasis regulation in pluripotency acquirement and maintenance. Here we identified high autophagic flux as an essential mechanism to maintain ESC identity. We show that mouse ESCs exhibit a high autophagic flux that is maintained by coordinating expression of autophagy core molecular machinery genes through FOXO1, a forkhead family transcription factor. Tapering autophagic flux by manipulating either Atg3 or Foxo1 expression compromised ESC self-renewal, pluripotency, and differentiation that could be restored by gain of wild-type but not function-deficient Atg3 or Foxo1 mutants, respectively. Our results define a newly recognized role of autophagic flux in mouse ESC identity maintenance that links cellular catabolism to ESC fate regulation.

Immunogenicity and functional evaluation of iPSC-derived organs for transplantation.

Whether physiologically induced pluripotent stem cell (iPSC)-derived organs are immunogenic and can be used for transplantation is unclear. Here, we generated iPSC-derived skin, islet, and heart representing three germ layers of the body through 4n complementation and evaluated their immunogenicity and therapeutic efficacy. Upon transplantation into recipient mice, iPSC-derived skin successfully survived and repaired local tissue wounds. In diabetic mouse models, explanted iPSC-derived islets effectively produced insulin and lowered blood glucose to basal levels. iPSC-derived heart grafts maintained normal beating for more than 3 months in syngeneic recipients. Importantly, no obvious immune rejection responses against iPSC-derived organs were detected long after transplantation. Our study not only demonstrates the fundamental immunogenicity and function of iPSC derivatives, but also provides preclinical evidence to support the feasibility of using iPSC-derived skin, islet, and heart for therapeutic use.