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Background: The geriatric nutritional risk index (GNRI) is a commonly used method to assess nutritional risk for predicting potential surgical site infections (SSI) in cancer patients. This study aims to create and verify a simple nomogram and a dynamic web-based calculator for predicting the risk of SSI among gynecologic oncology patients. Methods: A retrospective evaluation was conducted on patients who were admitted into a tertiary hospital in China with confirmed diagnosis of gynecologic cancer between 01 August 2017 and 30 November 2021. A two-piecewise linear regression model with a smoothing function was used to investigate the non-linear association between GNRI and SSI to determine the ideal cut-off point. Three models were developed on the basis of different variables to predict SSI in gynecologic oncology patients. Through a nomogram the concordance index (C-index), the Akaike information criterion (AIC), and the integrated discrimination index (IDI) were used to determine the final model. Finally, the performance of the nomogram was validated using the 1,000-bootstrap resamples method and analyzed using C-index, GiViTI calibration belts, and decision curve. Also, a user-friendly dynamic web-based calculator was developed. Results: A total of 1,221 patients were included in the analysis. A non-linear association could be observed between GNRI and SSI risk with a GNRI cut-off value of 101.7. After adding GNRI to Model 2 (which comprised Morse Fall Scale score, preoperative length of stay, operation time, and estimated blood loss), the AIC value decreased, the C-index value increased and IDI increased significantly. The nomogram C-index in the development cohort and internal validation cohort demonstrates a moderate-high degree of discrimination. The GiViTI calibrated belt showed a good agreement between the observed and predicted probabilities of SSI. The decision curve validates the clinical feasibility of the nomogram with a threshold value between 0 and 49%. Conclusion: The GNRI cut-off value of 101.7 allowed for appropriate stratification of patients into distinct SSI risk groups. This study found that including GNRI in the above nomogram (Model 2) would enhance its potential to predict SSI in gynecologic oncology patients.
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Accurately identifying important areas of biodiversity is one of the key issues in ecology and biodiversity research, as well as an important basis for the delineation of the red line for ecologi-cal protection and territorial spatial planning. With China's typical plateau mountainous area (Yunnan Province) as a research case, we used the net primary productivity (NPP) quantitative index method, InVEST model and InVEST model focusing on topographic relief to identify biodiversity important areas. The results showed that NPP quantitative index method was not suitable for the plateau mountainous areas with obvious vertical zonal development. The identified area contained only 26.1% of the protected areas. The InVEST model had higher identification accuracy than the NPP quantitative index method in Yunnan Province. The identified area covered 49.4% of the protected natural areas. Fragmentation was obvious in northwest Yunnan. The InVEST model focusing on topographic relief improved the identification accuracy of important areas of biodiversity, including 71.7% of nature reserves. The deficiency of NPP quantitative index method in water area identification was made up and the fragmentation problem of InVEST model was solved. The area of biodiversity important areas was 119466.94 km2, accounting for 30.3% of the total land area of Yunnan Province. The spatial distribution showed a pattern of "three barriers, two zones and one region for multi-point development".
Assuntos
Biodiversidade , Ecossistema , China , Conservação dos Recursos Naturais , EcologiaRESUMO
This study was aimed to investigate the specific anti-leukemia cell effect of cytotoxic T lymphocytes (CTLs) induced by HL-60 or K562 cell-sensitized dendritic cells (DCs) from umbilical cord blood. 12 units of human umbilical cord blood (UCB) were collected and the mononuclear cells (MNCs) were isolated from UCB, then cultured with granulocyte monocyte colony- stimulating factor (GM-CSF), interleukin 3 (IL-3), recombinant human stem cell factor (SCF) and EPO for 3 - 4 weeks. Flow cytometry was used to determine the number of DCs and cell surface antigens before and after culture with monoclonal antibodies including CD83, CD1a, CD11c and CDw123. HL-60 and K562 were frozen-thawed, and released their tumor antigen peptides (TAP). The CTLs were produced by sensitizing T lymphocytes with DC-loaded HL-60 and K562 cell antigens. The test of (3)H-TdR incorporation was used to detect the immunostimulation activity of DCs. MTT assay was applied to evaluate specific cytotoxicity of CTL on leukaemia cells. The results indicated that the MNCs of UCBs cultured with GM-CSF, IL-3, EPO and SCF were shown to differentiate into CD1a(+) CD11c(+) CD83(+) CDw123(+) DCs. Numbers of DCs from UCBs remarkably increased in 2 - 4 weeks and then decreased. After culture with cytokines DCs increased (10.6 - 28.2) x 10(5)/ml in actual numbers. The CTL induced by DC pulsed with HL-60, K562 frozen-thawed lysates were effective to kill HL-60 and K562. Cytotoxicity of CTL to HL60 and K562 were (42.04 +/- 8.46)% and (31.25 +/- 11.07)% respectively. It is concluded that the MNCs of UCBs cultured with cytokines of GM-CSF, SCF, EPO and IL-3 can differentiate into CD1a(+), CD83(+), CD11c(+) and CDw123(+) DCs. The CTL induced by DCs pulsed with HL-60, K562 frozen-thawed lysates can effectively kill HL-60 and K562. These DCs as antigen presenting cells play an important role in cancer immunotherapy.
