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  • Frontiers Media SA  (3)
  • 2020-2024  (3)
  • 2023  (3)
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  • Frontiers Media SA  (3)
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Language
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  • 2020-2024  (3)
Year
  • 2023  (3)
  • 1
    Online Resource
    Online Resource
    The analysis and optimization of thermal sensation of train drivers under occupational thermal exposure
    Frontiers Media SA ; 2023
    In:  Frontiers in Public Health Vol. 11 ( 2023-6-9)
    Details
    In: Frontiers in Public Health, Frontiers Media SA, Vol. 11 ( 2023-6-9)
    Abstract: Prolonged exposure of train drivers to thermal discomfort can lead to occupational safety and health (OSH) risks, causing physical and mental injuries. Traditional method of treating human skin as a wall surface fail to observe accurate skin temperature changes or obtain human thermal comfort that adapts to the thermal environment. Methods This study employs the Stolwijk human thermal regulation model to investigate and optimize the thermal comfort of train drivers. To minimize the time-consuming design optimization, a pointer optimization algorithm based on radial basis function (RBF) approximation was utilized to optimize the train cab ventilation system design and enhance drivers’ thermal comfort. The train driver thermal comfort model was developed using Star-CCM+ and 60 operating conditions were sampled using an Optimal Latin Hypercube Design (Opt LHD). Results and Discussion We analyzed the effects of air supply temperature, air supply volume, air supply angle, solar radiation intensity and solar altitude angle on the local thermal sensation vote (LTSV) and overall thermal sensation vote (OTSV) of the train driver. Finally, the study obtained the optimal air supply parameters for the Heating Ventilation and Air Conditioning (HVAC) in the train cabin under extreme summer conditions, effectively improving the thermal comfort of the driver.
    Type of Medium: Online Resource
    ISSN: 2296-2565
    URL: Article
    DOI: 10.3389/fpubh.2023.1164817
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2711781-9
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Table1.xlsx
    Frontiers Media SA ; 2023
    In:  Frontiers in Surgery Vol. 9 ( 2023-1-6)
    Details
    In: Frontiers in Surgery, Frontiers Media SA, Vol. 9 ( 2023-1-6)
    Abstract: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. Methods A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. Results The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. Conclusion We used ML methods to screen out the blood-specific factors—PDW, LYM, AST/ALT, BUN, and Na + —of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.
    Type of Medium: Online Resource
    ISSN: 2296-875X
    URL: Article
    URL: Article
    DOI: 10.3389/fsurg.2022.1031105
    DOI: 10.3389/fsurg.2022.1031105.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2773823-1
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  • 3
    Online Resource
    Online Resource
    Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer
    Frontiers Media SA ; 2023
    In:  Frontiers in Oncology Vol. 13 ( 2023-5-16)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-5-16)
    Abstract: With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is an important class of SPM, but remains poorly understood. Methods We retrospectively analyzed 3,322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables and analyze causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate-cancer-specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram. Results The median follow-up (interquartile range, IQR) time was 47 (24–75) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32 (16–57) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p & lt; 0.05), tumor–node–metastasis (TNM) stage (p & lt; 0.001), Gleason score (p & lt; 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI, 0.813–0.899) and 0.905 (95% CI, 0.941–0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM. Conclusion In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram, which performed well in predicting the 3-, 5-, and 10-year PCSM.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2023.918324
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2649216-7
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