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1.
Chin J Acad Radiol ; 6(1): 47-56, 2023.
Article in English | MEDLINE | ID: mdl-36741827

ABSTRACT

Background: Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information. Methods: The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared. Results: Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, P < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, P = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, P = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815). Conclusions: The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-023-00116-x.

2.
Health Phys ; 115(3): 375-381, 2018 09.
Article in English | MEDLINE | ID: mdl-30045118

ABSTRACT

To achieve real-time monitoring of gross beta radioactivity in drinking water, a scintillating fiber array system was developed. Using this system, the gross beta radioactivity in tap water at Tsinghua University campus in Beijing was monitored, and the same water samples were measured using the evaporation concentration method as a comparison experiment. Finally, the annual committed effective doses to children and adults who drank the tap water for a long time were estimated. The results showed that the gross beta radioactivity in tap water was 0.09 ± 0.03 Bq L using the scintillating fiber array system and 0.076 ± 0.009 Bq L using the evaporation concentration method. The annual committed effective dose values were less than the dose reference level of 0.1 mSv y, as suggested by the World Health Organization. The scintillating fiber array system can be used for measuring gross beta radioactivity in drinking water and protecting public health.


Subject(s)
Beta Particles , Drinking Water , Radiation Dosage , Radiation Monitoring , Water Pollutants, Radioactive/analysis , Radioactivity , Time Factors , Volatilization
3.
Appl Radiat Isot ; 137: 161-166, 2018 Jul.
Article in English | MEDLINE | ID: mdl-29649685

ABSTRACT

A scintillating fiber array detector for measuring gross beta counting is developed to monitor the real-time radioactivity in drinking water. The detector, placed in a stainless-steel tank, consists of 1096 scintillating fibers, both sides of which are connected to a photomultiplier tube. The detector parameters, including working voltage, background counting rate and stability, are tested, and the detection efficiency is calibrated using standard potassium chloride solution. Water samples are measured with the detector and the results are compared with those by evaporation method. The results show consistency with those by evaporation method. The background counting rate of the detector is 38.131 ±â€¯0.005 cps, and the detection efficiency for ß particles is 0.37 ±â€¯0.01 cps/(Bq/l). The MDAC of this system can be less than 1.0 Bq/l for ß particles in 120 min without pre-concentration.


Subject(s)
Drinking Water/analysis , Radiation Monitoring/instrumentation , Scintillation Counting/instrumentation , Water Pollutants, Radioactive/analysis , Beta Particles , Computer Systems , Equipment Design , Humans , Online Systems , Radiation Monitoring/statistics & numerical data , Scintillation Counting/statistics & numerical data , Water Pollution, Radioactive/analysis , Water Pollution, Radioactive/statistics & numerical data , Water Quality
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