The prediction incidence of the three most common cancers among Iranian military community during 2007-2019: a time series analysis

Comparative performance of hybrid model based on discrete wavelet transform and ARIMA models in prediction incidence of COVID-19

Objective

Public health surveillance is an important aspect of outbreak early warning based on prediction models. The present study compares a hybrid model based on discrete wavelet transform (DWT) and ARIMA (Autoregressive Integrated Moving Average) for predicting incidence cases due to COVID-19.

Methods

In the current cross-sectional stuady based on time-series data, the incidence data for confirmed daily cases of COVID-19 from February 26, 2019, to April 25, 2022, were used. A hybrid model based on DWT and ARIMA and a pure ARIMA model were used to predict the trend. All analyzes were performed by MATLAB 2018, stata 2015, and Excel 2013 computer software.

Results

Compared to the ARIMA model, the prediction results of the hybrid model were closer to the actual number of incident cases. The correlation between predicted values by the hybrid model with real data was higher than the correlation between predicted values by the ARIMA model with actual data.

Conclusions

Discreet Wavelet decomposition of the dataset was combined with an ARIMA model and showed better performance in predicting the future trend.

Diagnostic utility of a-methylacyl COA racemase in prostate cancer of the Iranian population

Background:

Considering the great variations in the reported prevalence of prostate cancer across the world possibly due to different genetic and environmental backgrounds, we aimed to determine the expression pattern and the diagnostic utility of α-methylacyl coenzyme A racemase (AMACR) among Iranian patients with prostate adenocarcinoma.

Materials and Methods:

In this cross-sectional study, formalin-fixed paraffin-embedded tissues of 58 patients with a definitive pathologic diagnosis of prostatic adenocarcinoma were evaluated. The expression of AMACR, intensity, and extensity of its staining was determined in selected samples by immunohistochemical technique.

Results:

AMACR expression was significantly higher in neoplastic compared to normal tissue (P < 0.05). The expression of AMACR was significantly associated with the age of the patients (P = 0.04). The intensity of the staining was associated with the grade of the prostate adenocarcinoma (P = 0.04). There was no significant relationship between AMACR expression and perineural invasion. The sensitivity, specificity, positive predictive value, and negative predictive value of AMACR were 90%, 96%, 96%, and 90%, respectively.

Conclusion:

Findings from our study indicate that AMACR could be used as a diagnostic tool for the diagnosis of prostate adenocarcinoma. However, due to false-positive staining in the mimicker of prostatic adenocarcinoma, it is recommended to use it in combination with basal cell markers.

Molecular Examination of Trichomonas vaginalis Infection and Risk of Prostate Cancer in the Biopsy of Patients with Different Prostate Lesions

Background

Trichomoniasis is a sexually transmitted infectious disease caused by a flagellated protozoa, Trichomonas vaginalis (T.vaginalis) and is often asymptomatic in men. Benign prostatic hyperplasia (BPH) and prostate cancer (PCA) are the most common urological diseases in the elderly. Scientists have proposed various factors which trigger prostate cancer, including sexually transmitted diseases. Thus, this study aimed to evaluate the potential role of T. vaginalis as a risk factor for various prostate lesions such as hyperplasia and prostate cancer.

Methods

A total of 250 paraffin-embedded of different prostate lesion biopsies were analyzed by Polymerase Chain Reaction (PCR) using the beta-tubulin gene for identifying T. vaginalis.

Result

All 250 pathologic specimens were negative for this parasite by using PCR technique.

Conclusion

It seems that T. vaginalis may have not had a causative role for different prostate lesions and it seems proposed PCR technique is an insufficient method to find the parasite in paraffin-embedded tissues. Therefore, other diagnostic techniques to identify the parasite in biopsy samples are suggested.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.