Bone mineral density changes after parathyroidectomy are dependent on biochemical profile

Background

Bone mineral density (BMD) has been found to improve after parathyroidectomy (PTX) in patients with primary hyperparathyroidism. There are few data on the effect of PTX on BMD in normocalcemic and normohormonal primary hyperparathyroidism.

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

Ad-CD40L mobilizes CD4 T cells for the treatment of brainstem tumors

BackgroundDiffuse midline glioma, formerly DIPG (diffuse intrinsic pontine glioma), is the deadliest pediatric brainstem tumor with median survival of less than one year. Here, we investigated (i) whether direct delivery of adenovirus-expressing cluster of differentiation (CD)40 ligand (Ad-CD40L) to brainstem tumors would induce immune-mediated tumor clearance and (ii) if so, whether therapy would be associated with a manageable toxicity due to immune-mediated inflammation in the brainstem.MethodsSyngeneic gliomas in the brainstems of immunocompetent mice were treated with Ad-CD40L and survival, toxicity, and immune profiles determined. A clinically translatable vector, whose replication would be tightly restricted to tumor cells, rAd-Δ24-CD40L, was tested in human patient–derived diffuse midline gliomas and immunocompetent models.ResultsExpression of Ad-CD40L restricted to brainstem gliomas by pre-infection induced complete rejection, associated with immune cell infiltration, of which CD4+ T cells were critical for therapy. Direct intratumoral injection of Ad-CD40L into established brainstem tumors improved survival and induced some complete cures but with some acute toxicity. RNA-sequencing analysis showed that Ad-CD40L therapy induced neuroinflammatory immune responses associated with interleukin (IL)-6, IL-1β, and tumor necrosis factor α. Therefore, to generate a vector whose replication, and transgene expression, would be tightly restricted to tumor cells, we constructed rAd-Δ24-CD40L, the backbone of which has already entered clinical trials for diffuse midline gliomas. Direct intratumoral injection of rAd-Δ24-CD40L, with systemic blockade of IL-6 and IL-1β, generated significant numbers of cures with readily manageable toxicity.ConclusionsVirus-mediated delivery of CD40L has the potential to be effective in treating diffuse midline gliomas without obligatory neuroinflammation-associated toxicity.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.