The role of kidney biopsy in Immune Checkpoint Inhibitor-associated AKI

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Comparison of effects of medicinal cannabis or standard palliative care on quality of life of patients with cholangiocarcinoma in Northeast Thailand

Background: Cholangiocarcinoma (CCA) has a poor prognosis and is a major cause of mortality and suffering in Thailand’s Northeastern (Isaan) Region. Methods: This prospective cohort study aimed to compare the health-related quality of life (HRQoL) among 72 newly diagnosed CCA patients; 42 patients who received cannabis treatment (CT) and 30 patients who received a standard palliative care treatment (ST). The study was carried out between 1st September 2019 to 31st October 2020. Data were collected from patients from oncology clinics of six hospitals in five provinces of northeast Thailand. The HRQoL was measured at baseline, and at 2 and 4 months after diagnosis by the European Organisation for Research and Treatment of Cancer (EORTC) Core Quality of Life questionnaires QLQ-C30, and QLQ-BIL21. The Mann-Whitney U-test was performed to compare quality of life scores between the two patient groups and Wilcoxon signed rank test was performed to compare within groups QoL scores at pre-treatment, and 2 and 4 month follow-ups. Results: Global health status and functional scales, for both groups were high at pre-treatment. At 2 and 4 month follow-up, CT group patients had consistent statistically significantly better Palliative Performance Scale (PPS), and QoL scores, and many symptom scores than the ST group. Conclusions: Medicinal cannabis may increase QoL for advanced CCA patients. Our findings support the importance of early access to palliative cannabis care clinic before the terminal and acceleration phase close to death.

Repurposing of Medicines in the EU: Launch of a Pilot Framework

Repurposing of authorised medicines has been under discussion for a long time. Drug repurposing is the process of identifying a new use for an existing medicine in an indication outside the scope of the original approved indication. Indeed, the COVID-19 health crisis has brought the concept to the frontline by proving the usefulness of this practise in favour of patients for an early access to treatment. Under the umbrella of the Pharmaceutical Committee and as a result of the discussions at the European Commission Expert Group on Safe and Timely Access to Medicines for Patients (STAMP) a virtual Repurposing Observatory Group (RepOG) was set up in 2019 to define and test the practical aspects of a pilot project thought to provide support to “not-for-profit” stakeholders generating or gathering data for a new therapeutic use for an authorised medicine. The group's initial plan was impacted by the outbreak of the SARS-CoV-2 pandemic and the launch of the pilot needed to be postponed. This article describes the progress and the activities conducted by the group during this past and yet extraordinary 2020–2021 to keep the project alive and explores on the background of this topic together with the obvious opportunities this health crisis has brought up in terms of repurposing of medicines.

Do PPIs cause CKD and progression of CKD? Commentary

This is an Early Access article. Please select the PDF button, above, to view it. Be sure to also read the PRO: 10.34067/KID.0007622021 and the CON: 10.34067/KID.0005852021

Do PPIs cause CKD and progression of CKD? PRO

This is an Early Access article. Please select the PDF button, above, to view it. Be sure to also read the CON: 10.34067/KID.0005852021 and the COMMENTARY: 10.34067/KID.0008302021

Do PPIs cause CKD and progression of CKD? CON

This is an Early Access article. Please select the PDF button, above, to view it. Be sure to also read the PRO: 10.34067/KID.0007622021 and the COMMENTARY: 10.34067/KID.0008302021

Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes

In this chapter, we provide the main elements for implementing deep neural networks in Keras for binary, categorical, and mixed outcomes under feedforward networks as well as the main practical issues involved in implementing deep learning models with binary response variables. The same practical issues are provided for implementing deep neural networks with categorical and count traits under a univariate framework. We follow with a detailed assessment of information for implementing multivariate deep learning models for continuous, binary, categorical, count, and mixed outcomes. In all the examples given, the data came from plant breeding experiments including genomic data. The training process for binary, ordinal, count, and multivariate outcomes is similar to fitting DNN models with univariate continuous outcomes, since once we have the data to be trained, we need to (a) define the DNN model in Keras, (b) configure and compile the model, (c) fit the model, and finally, (d) evaluate the prediction performance in the testing set. In the next section, we provide illustrative examples of training DNN for binary outcomes in Keras R (Chollet and Allaire, Deep learning with R. Manning Publications, Manning Early Access Program (MEA), 2017; Allaire and Chollet, Keras: R interface to Keras’, 2019).

Drug-induced osmotic nephropathy: Add SGLT2- inhibitors to the list?

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