A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach.

The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S.

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area.

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends.

Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria.

Background: Women in low- and middle-income countries (LMIC) remain at high risk of developing cervical cancer and have limited access to screening programs. The limits include geographical barriers related to road network characteristics and travel behaviors but these have neither been well studied in LMIC nor have methods to overcome them been incorporated into cervical cancer screening delivery programs.

Methods: To identify and evaluate spatial barriers to cervical cancer prevention services in Ondo State, Nigeria, we applied a Multi-Mode Enhanced Two-Step Floating Catchment Area model to create a spatial access index for cervical cancer screening services in Ondo City and the surrounding region.

Component analyses of urinary nanocrystallites of uric acid stone formers by combination of high-resolution transmission electron microscopy, fast Fourier transformation, energy dispersive X-ray spectroscopy, X-ray diffraction and Fourier transform infrared spectroscopy.

This study aimed to analyse the components of nanocrystallites in urines of patients with uric acid (UA) stones. X-ray diffraction (XRD), Fourier transform infrared spectroscopy, high-resolution transmission electron microscopy (HRTEM), fast Fourier transformation (FFT) of HRTEM, and energy dispersive X-ray spectroscopy (EDS) were performed to analyse the components of these nanocrystallites. XRD and FFT showed that the main component of urinary nanocrystallites was UA, which contains a small amount of calcium oxalate monohydrate and phosphates.

Changes in urinary nanocrystallites in calcium oxalate stone formers before and after potassium citrate intake.

The property changes of urinary nanocrystallites in 13 patients with calcium oxalate (CaOx) stones were studied before and after ingestion of potassium citrate (K3cit), a therapeutic drug for stones. The analytical techniques included nanoparticle size analysis, transmission electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The studied properties included the components, morphologies, zeta potentials, particle size distributions, light intensity autocorrelation curves, and polydispersity indices (PDIs) of the nanocrystallites.

[The property changes of urine crystallites of calcium oxalate stone formers before and after taking medicine].

The property changes of urine crystallites of six cases of calcium oxalate stone formers before and after taking medicine were comparatively studied using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, nanoparticle size analyzer, and transmission electron microscopy (TEM). The results showed that the urinary pH increases after taking medicine (before taking 5.87 +/- 0.