Intracerebral hemorrhage (ICH) is a devastating form of stroke. Misoprostol, a synthetic PGE1 analog and PGE2 receptor agonist, has shown protection against cerebral ischemia. In this study, we tested the efficacy of misoprostol in 12-month-old mice subjected to one of two complementary ICH models, the collagenase model (primary study) and blood model (secondary study, performed in an independent laboratory). We also investigated its potential mechanism of action. Misoprostol post-treatment decreased brain lesion volume, edema, and brain atrophy and improved long-term functional outcomes. In the collagenase-induced ICH model, misoprostol decreased cellular inflammatory response; attenuated oxidative brain damage and gelatinolytic activity; and decreased HMGB1 expression, Src kinase activity, and interleukin-1β expression without affecting cyclooxygenase-2 expression. Further, HMGB1 inhibition with glycyrrhizin decreased Src kinase activity, gelatinolytic activity, neuronal death, and brain lesion volume. Src kinase inhibition with PP2 decreased gelatinolytic activity and brain edema and improved neurologic function, but did not decrease HMGB1 protein level. These results indicate that misoprostol protects brain against ICH injury through mechanisms that may involve the HMGB1, Src kinase, and MMP-2/9 pathway.

Background and objectivePrevious studies have suggested a positive link between serum uric acid (UA) and bone mineral density (BMD). In this study, we re-examined the association between UA and BMD and further explored whether this was mediated by skeletal muscle mass in a general Chinese population.MethodThis community-based cross-sectional study was conducted among 3079 (963 men and 2116 women) Chinese adults aged 40–75 years. Face-to-face interviews and laboratory analyses were performed to determine serum UA and various covariates. Dual-energy X-ray absorptiometry was used to assess the BMD and appendicular skeletal muscle mass. The skeletal muscle mass index (SMI = ASM/Height2, kg/m2) for the total limbs, arms, and legs was then calculated.ResultsThe serum UA was graded and, in general, was significantly and positively associated with the BMD and muscle mass, after adjustment for multiple covariates in the total sample. Compared with participants in lowest quartile of UA, those participants in highest quartile showed a 2.3%(whole body), 4.1%(lumbar spine), 2.4%(total hip), and 2.0% (femoral neck) greater BMDs. The mean SMIs in the highest (vs. lowest) quartile increased by 2.7% (total), 2.5% (arm), 2.7% (leg) respectively. In addition, path analysis suggested that the favorable association between UA and BMD might be mediated by increasing SMI.ConclusionThe elevated serum UA was associated with a higher BMD and a greater muscle mass in a middle-aged and elderly Chinese population and the UA-BMD association was partly mediated by muscle mass.

Abstract. Eosinophilic pancreatitis (EP) is a rare form of chronic pancreatitis characterized by localized or diffuse eosinophilic infiltration of the pancreas and elevated serum immunoglobulin E levels. EP is difficult to distinguish from pancreatic cancer on the basis of clinical symptoms and the results of auxiliary examination alone. A retrospective analysis of the clinicopathological characteristics and laboratory, imaging, and pathology results of 3 patients with EP, who were initially diagnosed with pancreatic malignancy, was performed. EP is an allergic disease with non-specific clinical manifestations that is difficult to distinguish from pancreatic cancer based exclusively on clinical symptoms and auxiliary examination, resulting in the need for invasive procedures to confirm the diagnosis. An increase in the eosinophil count in the peripheral blood and pathological examination are essential for the diagnosis of EP.

The COVID-19 pandemic has dramatically affected everyone's work and daily life, and many employees are talking with their coworkers about this widespread pandemic on a regular basis. In this research, we examine how talking about crises such as COVID-19 at the team level affects team dynamics and behaviors. Drawing upon cultural tightness-looseness theory, we propose that talking about the COVID-19 crisis among team members is positively associated with team cultural tightness, which in turn benefits teams by decreasing team deviance but hurts teams by decreasing team creativity. Furthermore, we suggest that team virtuality moderates and weakens these indirect effects because face-to-face communication about COVID-19 is more powerful in influencing team cultural tightness than virtual communication. Results from a multisource, three-wave field study during the pandemic lend substantial support to these hypotheses. We discuss the theoretical and practical implications of these findings and directions for future research.

ObjectivesThe microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study.DesignThe deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals.ResultsThe deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists.ConclusionsThe deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists’ performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations.