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.

Effect of chitosan on blood profile, inflammatory cytokines by activating TLR4/NF-κB signaling pathway in intestine of heat stressed mice.

Heat stress can significantly affect the immune function of the animal body. Heat stress stimulates oxidative stress in intestinal tissue and suppresses the immune responses of mice. The protecting effects of chitosan on heat stress induced colitis have not been reported. Therefore, the aim of this study was to investigate the protective effects of chitosan on immune function in heat stressed mice. Mice were exposed to heat stress (40 °C per day for 4 h) for 14 consecutive days. The mice (C57BL/6J), were randomly divided into three groups including: control group, heat stress, Chitosan group (LD: group 300 mg/kg/day, MD: 600 mg/kg/day, HD: 1000 mg/kg/day). The results showed that tissue histology was improved in chitosan groups than heat stress group. The current study showed that the mice with oral administration of chitosan groups had improved body performance as compared with the heat stress group. The results also showed that in chitosan treated groups the production of HSP70, TLR4, p65, TNF-α, and IL-10 was suppressed on day 1, 7, and 14 as compared to the heat stress group. In addition Claudin-2, and Occludin mRNA levels were upregulated in mice receiving chitosan on day 1, 7, and 14 of heat stress. Furthermore, the IL-6, IL-10, and TNF-α plasma levels were down-regulated on day 1, 7, and 14 of heat stress in mice receiving the oral administration of chitosan. In conclusion, the results showed that chitosan has an anti-inflammatory ability to tolerate hot environmental conditions.

Terpinen4-ol inhibits heat stress induced inflammation in colonic tissue by Activating Occludin, Claudin-2 and TLR4/NF-κB signaling pathway.

Heat stress has severe implications on the health of mice involving intestinal mucosal barrier damage and dysregulated mucosal immune response. This study was designed with long-term heat stress to detect the protective effect of terpinen4-ol on body weight, colon length, organ index, morphological structure, inflammatory cytokines expression, Claudin-2, Occludin, and TLR4 signaling pathway of colonic tissue in mice under heat stress. A study found that oral administration of terpinen4-ol helped against mortality and intestinal inflammation in a mouse model of acute colitis induced by heat stress (40 °C per day for 4 h) exposed for 14 consecutive days. The mice were divided into five groups including control, heat stress, terpinen4-ol low dose (TER LD: 5 mg/kg), medium dose (TER MD: 10 mg/kg), and high dose (TER HD: 20 mg/kg) group. Our study showed that the heat-stress terpinen4-ol group had improved body weight, colon length, and organ index, the number of white blood cells, lymphocytes, and neutrophils in the blood as compared to the heat stress group. In addition, results showed that heat stress upregulated the expression of TLR4, p65, TNF-α, and IL-10. While, in mice receiving the oral administration of terpinen4-ol, the production of TNF-α, IL-10, TLR4, and p65 was suppressed on day 1, 7, and 14 of heat stress. In addition Claudin-2, Occludin mRNA levels were upregulated in mice receiving terpinen4-ol on day 1, 7, and 14 of heat stress. Furthermore, the IL-6, IL-10, TNF-α serum levels were also upregulated in mice under heat stress, but in mice receiving the oral administration of terpinen4-ol, the IL-6, IL-10, TNF-α level was down-regulated on day 1, 7, and 14 of heat stress. Histomorphological examination found that as compared to the control group, the muscle layer thickness and villi height of mice in the heat stress group were significantly reduced, while the changes of the above indicators in the terpinene4-ol groups were improved than those in the heat stress group. In conclusion, the terpinen4-ol has a protective effect on colonic tissue damage induced by heat stress.

Artificial intelligence reveals environmental constraints on colour diversity in insects.

Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species' mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.

Chiral phosphoric acid catalyzed highly enantioselective Friedel-Crafts alkylation reaction of C3-substituted indoles to ß,γ-unsaturated α-ketimino esters.

A highly enantioselective C2 Friedel-Crafts alkylation reaction of 3-substituted indoles to ß,γ-unsaturated α-ketimino esters has been developed. This reaction was efficiently catalyzed by a chiral phosphoric acid catalyst. The corresponding C2-substituted indole derivatives, bearing an α-ketimino ester motif, were obtained in moderate to high yields (up to 93%) and with high enantioselectivities (up to >99% ee).

Degradation of crystal violet by an FeGAC/H2O2 process.

Because of the growing concern over highly contaminated crystal violet (CV) wastewater, an FeGAC/H(2)O(2) process was employed in this research to treat CV-contaminated wastewater. The experimental results indicated that the presence of iron oxide-coated granular activated carbon (FeGAC) greatly improved the oxidative ability of H(2)O(2) for the removal of CV. For instance, the removal efficiencies of H(2)O(2), GAC, FeGAC, GAC/H(2)O(2) and FeGAC/H(2)O(2) processes were 10%, 44%, 40%, 43% and 71%, respectively, at test conditions of pH 3 and 7.4mM H(2)O(2). FeGAC/H(2)O(2) combined both the advantages of FeGAC and H(2)O(2). FeGAC had a good CV adsorption ability and could effectively catalyze the hydrogen peroxide oxidation reaction. Factors (including pH, FeGAC dosage and H(2)O(2) dosage) affecting the removal of CV by FeGAC/H(2)O(2) were investigated in this research as well. In addition, the reaction intermediates were separated and identified using HPLC-ESI-MS. The N-demethylation step might be the main reaction pathway for the removal of CV. The reaction mechanisms for the process proposed in this research might be useful for future application of this technology to the removal of triphenylmethane (TPM) dyes.