Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources.

Objectives: This study aimed to prospectively validate an application that automates the detection of broad categories of hospital adverse events (AEs) extracted from a basic hospital information system, and to efficiently mobilize resources to reduce the level of acquired patient harm.

Methods: Data were collected from an internally designed software, extracting results from 14 triggers indicative of patient harm, querying clinical and administrative databases including all inpatient admissions (n = 8760) from October 2019 to June 2020. Representative samples of the triggered cases were clinically validated using chart review by a consensus expert panel.

The Use of Medical Imaging Request Forms as Trigger Tools to Detect Intra-Hospital Adverse Events: A Pilot Study.

Aim: To evaluate the contribution of medical imaging request forms as trigger tools to detect patient adverse event (AE) occurring during hospitalization.

Material And Methods: This is a retrospective study in a single institution. Between January and June 2019, the hospital information system (HIS) was fetched for request forms of radiological examinations performed for inpatients >48 hours after the admission date.

Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations.

Objective: The aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients.

Design: This is a retrospective single-institution study. All consecutive adult patients' cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included.

Comparison of Machine Learning Algorithms for Classifying Adverse-Event Related 30-Day Hospital Readmissions: Potential Implications for Patient Safety.

Studies in the last decade have focused on identifying patients at risk of readmission using predictive models, in an objective to decrease costs to the healthcare system. However, real-time models specifically identifying readmissions related to hospital adverse-events are still to be elaborated. A supervised learning approach was adopted using different machine learning algorithms based on features available directly from the hospital information system and on a validated dataset elaborated by a multidisciplinary expert consensus panel.

Evaluation of a Broad-Spectrum Partially Automated Adverse Event Surveillance System: A Potential Tool for Patient Safety Improvement in Hospitals With Limited Resources.

Objective: The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources.

Methods: Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107).

Antitumor activities of the synthetic retinoid ST1926 in two-dimensional and three-dimensional human breast cancer models.

Despite recent advances in chemotherapy, aggressive and metastatic breast cancers remain refractory to targeted therapy and the development of novel drugs is urgently needed. Retinoids are crucial regulators of cellular proliferation, differentiation, and cell death, and have shown potent chemotherapeutic and chemopreventive properties. The major drawback of the use of all-trans retinoic acid (ATRA) in cancer therapy is disease relapse.

Inhibition of tumor promotion by parthenolide: epigenetic modulation of p21.

The promotion stage in the multistep process of epidermal tumorigenesis is NF-кB-dependent, epigenetically regulated, and reversible, thus, a suitable target for chemoprevention. We investigated whether the NF-кB inhibitor, parthenolide, currently in cancer clinical trials, attenuates tumor promotion by modulating the epigenetically regulated NF-кB target genes, p21 and cyclin D1. Parthenolide selectively inhibited the growth of neoplastic keratinocytes while sparing normal ones.

Sesquiterpene lactones isolated from indigenous Middle Eastern plants inhibit tumor promoter-induced transformation of JB6 cells.

Background: Sesquiterpene lactones (SL) are plant secondary metabolites that are known for their anti-fungal, anti-bacterial, anti-inflammatory, and anti-tumor properties. Considering that several SL-derived drugs are currently in cancer clinical trials, we have tested two SL molecules, 3-β-methoxy-iso-seco-tanapartholide (β-tan) isolated from Achillea falcata and salograviolide A (Sal A) isolated from Centaurea ainetensis, for their anti-tumor properties. We used the mouse epidermal JB6P + cells as a model for tumor promotion and cellular transformation.

Epigenetic mechanisms of plant-derived anticancer drugs.

Epigenetic mechanisms are essential for normal development and maintenance of adult life. Disruption of epigenetic processes results in deregulated gene expression and leads to life-threatening diseases, in particular, cancer. Global epigenetic alterations are a hallmark of cancer.