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Introduction. The presence and influence of physician review websites (PRW) have increased significantly in the field of medicine. This study aims to better understand determinants of patient satisfaction and the sentiment of ophthalmologists using natural language processing of Healthgrades reviews. Methods. Healthgrades is a PRW where patients submit verified reviews, containing a star rating and a narrative review, of US-based ophthalmologists. This was a quantitative observational study conducted on May 23, 2022. We identified associations between physician demographics and both the sentiment analysis scores of narrative reviews and star ratings using the Student’s t-tests and one-way ANOVA tests. After natural language processing the reviews, a logistic regression explored the impacts of the most frequent words on the positivity of a given review. Results. This study examined a total of 16700 reviews of 1125 ophthalmologists. Ophthalmologists of younger age and male gender received statistically significantly higher star ratings and sentiment analysis scores; analysis of location of practice did not affect scores. Textual analysis revealed that words describing the physician’s personality, such as “friendly” and “caring,” increased the likelihood of reviews being positive more than descriptors of the visit’s effectiveness, such as “results” and “efficient.” Conclusion. Younger and male ophthalmologists received higher star ratings and sentiment analysis scores. Additionally, results indicated that words describing the ophthalmologist’s pleasant personality and the visit’s effectiveness most positively impacted a review, whereas descriptors of a wait or an unpleasant personality most negatively impacted a review.

Lipid droplets (LDs) are distinct morphological markers of hepatic stellate cells (HSCs). They are composed of a core of predominantly retinyl esters and triacylglycerols surrounded by a phospholipid layer; the latter harbors perilipins 2, 3, and 5, which help control LD lipolysis. Electron microscopy distinguishes between Types I and II LDs. Type I LDs are surrounded by acid phosphatase-positive lysosomes, which likely digest LDs. LD count and retinoid concentration are modulated by vitamin A intake. Alcohol consumption depletes hepatic retinoids and HSC LDs, with concomitant transformation of HSCs to fibrogenic myofibroblast-like cells. LD loss and accompanying HSC activation occur in HSC cell culture models. Loss of LDs is a consequence of and not a prerequisite for HSC activation. LDs are endowed with enzymes for synthesizing retinyl esters and triacylglycerols as well as neutral lipases and lysosomal acid lipase for breaking down LDs. HSCs have two distinct metabolic LD pools: an "original" pool in quiescent HSCs and a "new" pool emerging in HSC activation; this two-pool model provides a platform for analyzing LD dynamics in HSC activation. Besides lipolysis, LDs are degraded by lipophagy; however, the coordination between and relative contributions of these two pathways to LD removal are unclear. While induction of autophagy accelerates LD loss in quiescent HSCs and promotes HSC activation, blocking autophagy impairs LD degradation and inhibits HSC activation and fibrosis. This article is a critique of five decades of investigations into the morphology, molecular structure, synthesis, and degradation of LDs associated with HSC activation and fibrosis.

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to “noise”. Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.