Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease (ILD). Pulmonary fibroblasts play an important role in the development of IPF. Emerging evidence indicates that pulmonary endothelial cells could be the source of pulmonary fibroblasts through endothelial mesenchymal transition (EndoMT), which contributes to pulmonary fibrosis. EndoMT is a complex process in which endothelial cells lose their expression of endothelial markers and give rise to the characteristics of mesenchymal cells, including morphological fibroblast-like change and the expression of mesenchymal markers, which result in cardiac, renal, and dermal fibroses. Furthermore, EndoMT inhibition attenuates pulmonary fibrosis. Herein, we demonstrate that nintedanib, a tyrosine kinase receptor inhibitor, ameliorated murine bleomycin (BLM)-induced pulmonary fibrosis and suppressed the in vivo and in vitro models of EndoMT. We demonstrated that the activity of focal adhesion kinase (FAK), a key EndoMT regulator, increased in murine lung tissues and human pulmonary microvascular endothelial cells after BLM stimulation. Nintedanib treatment inhibited BLM-induced FAK activation and thus suppressed both in vivo and in vitro BLM-induced EndoMT. Importantly, we found that the VEGF/FAK signaling pathway was involved in nintedanib regulating EndoMT. These novel findings help us understand the mechanism and signaling pathway of EndoMT to further develop more efficacious drugs for IPF treatment.

Background: Mental illness diagnostic criteria are made based on assumptions. This pilot study aims to assess the public’s perspectives on mental illness diagnoses and these assumptions.Methods: An anonymous survey with 30 questions was made available online in 2021. Participants were recruited via social media, and no personal information was collected. Ten questions focused on participants’ perceptions regarding mental illness diagnoses, and 20 questions related to the assumptions of mental illness diagnoses. The participants’ perspectives on these assumptions held by professionals were assessed.Results: Among 14 survey participants, 4 correctly answered the relationships of 6 symptom pairs (28.57%). Two participants could not correctly conduct the calculations involved in mood disorder diagnoses (14.29%). Eleven (78.57%) correctly indicated that 2 or more sets of criteria were available for single diagnoses of mental illnesses. Only 1 (7.14%) correctly answered that the associations between symptoms and diagnoses were supported by including symptoms in the diagnostic criteria of the diagnoses. Nine (64.29%) correctly answered that the diagnosis variances were not fully explained by their symptoms. The confidence of participants in the major depressive disorder diagnosis and the willingness to take medications for this diagnosis were the same (mean = 5.50, standard deviation [SD] = 2.31). However, the confidence of participants in the symptom-based diagnosis of non-solid brain tumor was significantly lower (mean = 1.62, SD = 2.33, p < 0.001).Conclusion: Our study found that mental illness diagnoses are wrong from the perspectives of the public because our participants did not agree with all the assumptions professionals make about mental illness diagnoses. Only a minority of our participants obtained correct answers to the calculations involved in mental illness diagnoses. In the literature, neither patients nor the public have been engaged in formulating the diagnostic criteria of mental illnesses.

Neutrophils are involved in the alveolitis of idiopathic pulmonary fibrosis (IPF). However, their pathogenic mechanisms are still poorly understood. Nintedanib has antifibrotic and anti-inflammatory activity in IPF. This study aimed to investigate the regulatory mechanism of nintedanib on neutrophil chemotaxis in bleomycin (BLM)-induced pulmonary fibrosis. Nintedanib was administered via oral gavage to male C57BL/6 mice 24 h after a bleomycin intratracheal injection (1.5 U/kg). Lung histopathological findings, the expression of cytokines, and the regulatory signaling pathways of neutrophil chemotaxis were analyzed. The effect of nintedanib was also investigated in a mouse model with adoptive neutrophil transfer in vivo. Nintedanib significantly decreased the histopathological changes and neutrophil recruitment in BLM-induced pulmonary fibrosis. Nintedanib mediated a downregulation of chemokine (C-X-C motif) receptor 2 (CXCR2) and very late antigen 4 (VLA-4) expression, as well as an upregulation of G protein-coupled receptor kinase 2 (GRK2) activity in peripheral blood neutrophils in BLM-induced pulmonary fibrosis. Nintedanib also decreased the activation of endothelial cells by the decreased expression of vascular cell adhesion molecule 1 (VCAM-1). The effect of nintedanib on regulating neutrophil chemotaxis was also confirmed by a mouse model with adoptive neutrophil transfer in vivo. In conclusion, nintedanib reduces neutrophil chemotaxis and endothelial cell activation to regulate the severity of BLM-induced pulmonary fibrosis. These effects are associated with an enhancement of GRK2 activity and a reduction in CXCR2 and VLA-4 expression on neutrophils and a decrease in VCAM-1 expression on endothelial cells.

Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality. Three FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality. We found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs. FIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

Trend analysis summarizes patterns over time in the data to show the direction of change and can be used to investigate uncertainties in different time points and associations with other factors. However, this approach is not widely applied to national surveys and only selected outcomes are investigated. This study demonstrates a research framework to conduct trend analysis for all variables in a national survey, the Canadian Health Measures Survey (CHMS). The CHMS cycle 1 to 4 was implemented between 2007 and 2015. The characteristics of all variables were screened and associated to the weight variables. Missing values were identified and cleaned according to the User Guide. The characteristics of all variables were extracted and used to guide data cleaning. Trend analysis examined the statistical significance of candidate predictors: the cycles, age, sex, education, household income and body mass index (BMI). R (v3.2) and RStudio (v0.98.113) were used to develop the framework. There were 26557 variables in 79 data files from four cycles. There were 1055 variables significantly associated with the CHMS cycles and 2154 associated with the BMI after controlling for other predictors. The trend of blood pressure was similar to those published. Trend analysis for all variables in the CHMS is feasible and is a systematic approach to understand the data. Because of trend analysis, we have detected data errors and identified several environmental biomarkers with extreme rates of change across cycles. The impact of these biomarkers has not been well studied by Statistics Canada or others. This framework can be extended to other surveys, especially the Canadian Community Health Survey.