Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Temperature-Regulated Flowering Locus T-Like Gene Coordinates the Spike Initiation in Phalaenopsis Orchid.

Phalaenopsis aphrodite can be induced to initiate spike growth and flowering by exposure to low ambient temperatures. However, the factors and mechanisms responsible for spike initiation in P. aphrodite remain largely unknown. In this study, we show that a repressor Flowing Locus T-like (FTL) gene, FTL, can act as a negative regulator of spike initiation in P. aphrodite. The mRNA transcripts of PaFTL are consistently high during high ambient temperature, thereby preventing premature spike initiation. However, during low ambient temperature, PaFTL expression falls while FT expression increases, allowing for spike initiation. Knock-down of PaFTL expression through virus-inducing gene silencing promoted spike initiation at 30/28°C. Moreover, PaFTL interacts with FLOWERING LOCUS D in a similar manner to FT to regulate downstream flowering initiation genes. Transgenic P. aphrodite plants exhibiting high expression of PaFTL do not undergo spike initiation, even when exposed to low ambient temperatures. These findings shed light on the flowering mechanisms in Phalaenopsis and provide new insights into how perennial plants govern spike initiation in response to temperature cues.

Accuracy of long-form data in the Taiwan cancer registry.

The Taiwan Cancer Registry (TCR) is a nationwide population-based registry that collects the data of patients with newly diagnosed cancer from hospitals with ≥50 beds. TCR data are high quality in terms of completeness and timeliness. However, accuracy is also a crucial quality indicator. This study evaluated the accuracy rates of selected 55 major items in the long-form TCR data between 2014 and 2016 with 700 reported cases randomly selected from 25 long-form-reporting hospitals. We calculated the accuracy rates of the reported data by employing a reabstracted chart review. Among the 55 items, the accuracy rates of 38 (69%) were at least 95%, those of 10 (18%) were between 90% and 95%, those of 5 (9%) were between 85% and 90%, and the remaining 2 (4%) were between 80% and 85%. This demonstrates a high degree of accuracy in the TCR long-form data.

Obatoclax, a Pan-BCL-2 Inhibitor, Downregulates Survivin to Induce Apoptosis in Human Colorectal Carcinoma Cells Via Suppressing WNT/ß-catenin Signaling.

Colorectal cancer (CRC) is a highly prevailing cancer and the fourth leading cause of cancer mortality worldwide. Aberrant expression of antiapoptotic BCL-2 family proteins is closely linked to neoplastic progression and chemoresistance. Obatoclax is a clinically developed drug, which binds antiapoptotic BCL-2, BCL-xL, and MCL-1 for inhibition to elicit apoptosis. Survivin is an antiapoptotic protein, whose upregulation correlates with pathogenesis, therapeutic resistance, and poor prognosis in CRC. Herein, we provide the first evidence delineating the functional linkage between Obatoclax and survivin in the context of human CRC cells. In detail, Obatoclax was found to markedly downregulate survivin. This downregulation was mainly achieved via transcriptional repression, as Obatoclax lowered the levels of both survivin mRNA and promoter activity, while blocking proteasomal degradation failed to prevent survivin from downregulation by Obatoclax. Notably, ectopic survivin expression curtailed Obatoclax-induced apoptosis and cytotoxicity, confirming an essential role of survivin downregulation in Obatoclax-elicited anti-CRC effect. Moreover, Obatoclax was found to repress hyperactive WNT/ß-catenin signaling activity commonly present in human CRC cells, and, markedly, ectopic expression of dominant-active ß-catenin mutant rescued the levels of survivin along with elevated cell viability. We further revealed that, depending on the cell context, Obatoclax suppresses WNT/ß-catenin signaling in HCT 116 cells likely via inducing ß-catenin destabilization, or by downregulating LEF1 in DLD-1 cells. Collectively, we for the first time define survivin downregulation as a novel, pro-apoptotic mechanism of Obatoclax as a consequence of Obatocalx acting as an antagonist to WNT/ß-catenin signaling.