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  1. Fulltext Testosterone Level in Testicular Cancer Patients after Chemotherapy
    Sarfraz M, Ashraf Y, Sajid S, Ashraf MA
    West Indian Med J, 2015 12;64(5):487-494.
    PMID: 27399795 DOI: 10.7727/wimj.2016.060
    Background: The use of chemotherapy for the treatment of cancer began at the start of the 20(th) century in an attempt to narrow the universe of chemicals that affect the disease. Metastatic testicular cancer has always been sensitive to chemotherapy.

    Subjects and Method: A retrospective and prospective study was performed of patients who had undergone testicular cancer from 2011-2013. The overall age of the testicular cancer patients at the time of diagnosis, their marital status, stage of disease and treatment strategies, testosterone level etc were analysed using linear regression and t-test.

    Results: Most of the patients had seminoma tumour. A greater number of patients were diagnosed in the later stages of the disease. Before chemotherapy, testosterone level was normal and decreased during chemotherapy but after completion, it returned to normal level.

    Conclusion: There is an early onset of testicular cancer in the Pakistani population. There is no effect of chemotherapy on testosterone production in late survivors.

  2. Fulltext Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression
    Ahmad M, Al-Zubi MA, Kubińska-Jabcoń E, Majdi A, Al-Mansob RA, Sabri MMS, et al.
    Sci Rep, 2023 Aug 21;13(1):13593.
    PMID: 37604957 DOI: 10.1038/s41598-023-40903-1
    The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA-a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.
  3. Fulltext Author Correction: Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression
    Ahmad M, Al-Zubi MA, Kubińska-Jabcoń E, Majdi A, Al-Mansob RA, Sabri MMS, et al.
    Sci Rep, 2023 Sep 01;13(1):14376.
    PMID: 37658150 DOI: 10.1038/s41598-023-41737-7
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