Predictive Modeling to Identify Children With Complex Health Needs At Risk for Hospitalization.

BACKGROUND: Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tools available for identifying high-risk CCHN. METHODS: This retrospective cohort study included children aged 0 to 20 years with established care within a single health system. The model development/validation cohort included 33 months (January 1, 2016-September 30, 2018) and the testing cohort included 18 months (October 1, 2018-March 31, 2020) of EHR data. Machine learning methods generated a model that predicted probability (0%-100%) for hospitalization within 6 months. Model performance measures included sensitivity, positive predictive value, area under receiver-operator curve, and area under precision-recall curve. Three CDS rules for identifying high-risk CCHN were compared: (1) hospitalization probability ≥10% (model-predicted); (2) complex chronic disease classification (using Pediatric Medical Complexity Algorithm [PMCA]); and (3) previous high hospital utilization. RESULTS: Model development and testing cohorts included 116 799 and 27 087 patients, respectively. The model demonstrated area under receiver-operator curve = 0.79 and area under precision-recall curve = 0.13. PMCA had the highest sensitivity (52.4%) and classified the most children as high risk (17.3%). Positive predictive value of the model-based CDS rule (19%) was higher than CDS based on the PMCA (1.9%) and previous hospital utilization (15%). CONCLUSIONS: A novel EHR-based predictive model was developed and validated as a population-level CDS tool for identifying CCHN at high risk for future hospitalization.

Covalent chemistry on nanostructured substrates enables noninvasive quantification of gene rearrangements in circulating tumor cells.

Well-preserved mRNA in circulating tumor cells (CTCs) offers an ideal material for conducting molecular profiling of tumors, thereby providing a noninvasive diagnostic solution for guiding treatment intervention and monitoring disease progression. However, it is technically challenging to purify CTCs while retaining high-quality mRNA.Here, we demonstrate a covalent chemistry-based nanostructured silicon substrate ("Click Chip") for CTC purification that leverages bioorthogonal ligation-mediated CTC capture and disulfide cleavage-driven CTC release. This platform is ideal for CTC mRNA assays because of its efficient, specific, and rapid purification of pooled CTCs, enabling downstream molecular quantification using reverse transcription Droplet Digital polymerase chain reaction. Rearrangements of ALK/ROS1 were quantified using CTC mRNA and matched with those identified in biopsy specimens from 12 patients with late-stage non-small cell lung cancer. Moreover, CTC counts and copy numbers of ALK/ROS1 rearrangements could be used together for evaluating treatment responses and disease progression.

Bio-Inspired NanoVilli Chips for Enhanced Capture of Tumor-Derived Extracellular Vesicles: Toward Non-Invasive Detection of Gene Alterations in Non-Small Cell Lung Cancer.

Tumor-derived extracellular vesicles (EVs) present in bodily fluids are emerging liquid biopsy markers for non-invasive cancer diagnosis and treatment monitoring. Because the majority of EVs in circulation are not of tumor origin, it is critical to develop new platforms capable of enriching tumor-derived EVs from the blood. Herein, we introduce a biostructure-inspired NanoVilli Chip, capable of highly efficient and reproducible immunoaffinity capture of tumor-derived EVs from blood plasma samples. Anti-EpCAM-grafted silicon nanowire arrays were engineered to mimic the distinctive structures of intestinal microvilli, dramatically increasing surface area and enhancing tumor-derived EV capture. RNA in the captured EVs can be recovered for downstream molecular analyses by reverse transcription Droplet Digital PCR. We demonstrate that this assay can be applied to monitor the dynamic changes of ROS1 rearrangements and epidermal growth factor receptor T790M mutations that predict treatment responses and disease progression in non-small cell lung cancer patients.

PARP9 is overexpressed in human breast cancer and promotes cancer cell migration.

Poly(ADP-Ribose) polymerase family member 9 (PARP9) promotes the proliferation, survival and chemotherapy resistance in lymphoma and prostate cancer. The expression and function of PARP9 in human breast cancer remains unknown. In the present study, it was demonstrated that PARP9 is frequently overexpressed in human breast cancer. In 57 normal breast tissues, the expression of PARP9 was not detected in 43 cases (75.4%), but low levels of PARP9 were detected in 13 cases (22.8%), and modest levels of PARP9 (PARP9/GAPDH ratio ~1:1) were detected in only 1 case (1.7%). In contrast, the expression of PARP9 was detected in all 57 breast cancer tissues, in which the levels of PARP9 were higher than that in paired normal breast tissues. In addition, high levels of PARP9 were detected in 43.8% of breast cancer tissues. Overexpression of PARP9 was negatively associated with estrogen receptor expression, and positively associated with axillary lymph node metastasis. However, PARP9 expression was not associated with other clinicopathological parameters, including age, HER-2 and tumor size. Furthermore, PARP9-knockdown inhibited breast cancer cell migration. These data indicate that PARP9 may promote breast cancer progression.

[Establishment of a mice model with liver-specific AMP-activated protein kinase gene knockout].

AMP-activated protein kinase (AMPK) is involved in the development and progression of tumors including hepatocellular carcinoma (HCC). However, studies on AMPK and tumorigenesis were largely based on experiments in vitro or tumor xenografts model. Here, we introduce a liver-specific AMPKα1 knockout mice model, which is achieved by Alb-Cre recombinase system. The expression of AMPKα1 in the liver of AMPKα1 -/--Alb-Cre mice is absent. AMPKα1 knockout in the liver does not affect the growth and histological structure of mouse liver. This model provides a favorable tool to the study of the roles of AMPKα1 in liver metabolism or tumorigenesis.

[Research on characteristic of tobaccos from different ecological environments base on near infrared spectroscopy].

Four hundred ninety five samples from 3 parts of tobacco were collected from 11 different ecological environments in 2010 to analyze the characteristics of tobaccos in different ecological environments with near infrared spectroscopy. The results indicate that the differences are remarkable both between lower and upper parts and between lower and middle parts of tobacco, while negligible between upper and middle parts of tobacco under the same ecological environment. Good stability and consistency were showed by the similarity of characteristics of tobaccos under different ecological environments, and the projection correlation coefficient of analysis set and validation set is over 98%. This article also proposes a method of measuring the similarity between characteristics of tobaccos from different ecological environments. The results can provide reference data to quantify tobacco's planting programming, production processing and quality management.