Causal association between psoriasis vulgaris and bullous pemphigoid: a two-sample bidirectional Mendelian randomization study.

Background: The association between psoriasis vulgaris and bullous pemphigoid (BP) remains largely unknown. Objectives: To investigate whether there is a causal effect between psoriasis vulgaris and BP. Methods: Two-sample bidirectional Mendelian randomization (MR) analyses were conducted using publicly released genome-wide association studies (GWAS) summary statistics. The GWAS summary statistics for BP were downloaded online from FinnGen Biobank Documentation of the R12 release, which includes 219 BP cases and 218,066 controls. The GWAS data for psoriasis vulgaris were extracted from Sakaue et al., which comprises 5072 cases and 478,102 controls. Single-nucleotide polymorphisms (SNPs) associated with exposure were selected as instrumental variables by performing additional quality control steps. The inverse-variance-weighted (IVW) method was used for the primary MR analyses, and the MR-Egger regression, weighted mode method, weighted median method, and simple mode were employed for sensitivity analyses. The MR-Egger intercept test and "leave-one-out" sensitivity analysis were performed to evaluate the horizontal pleiotropy and the potentially influential SNPs, respectively. Results: Genetically determined log odds of psoriasis vulgaris were associated with an increased risk of BP (IVW: odds ratio (OR) = 1.263, 95% confidence interval (CI): 1.013-1.575, P=0.038). Sensitivity analyses by the weighted mode (OR=1.255, 95%CI: 0.973-1.618, P=0.106), MR Egger (OR=1.315, 95%CI: 0.951-1.817, P=0.126), simple mode (OR=1.414, 95%CI: 0.823-2.429, P=0.234) and weighted median method (OR=1.177, 95%CI: 0.889-1.559, P=0.254) derived directionally consistent relationship between the genetically predicted log odds of psoriasis vulgaris and risks of developing BP. On the contrary, we found that genetically predicted BP had no significant effect on psoriasis vulgaris (IVW: OR=0.996, P= 0.707), indicating the unidirectionality of the relationship. MR-Egger intercept tests showed no evidence of horizontal pleiotropy. No influential SNP driving the results was detected by the leave-one-out sensitivity analysis. Conclusions: Our results suggested that psoriasis vulgaris causally increases the risk of BP, highlighting the need for potential strategies for the prevention and early diagnosis of comorbid BP in patients with psoriasis vulgaris. Further researches into this association and underlying mechanisms are warranted.

A Prognostic Model Based on Clinical Biomarkers for Heart Failure in Adult Patients Following Allogeneic Hematopoietic Stem Cell Transplantation.

Heart failure (HF) is an uncommon but serious cardiovascular complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Unfortunately, knowledge about early mortality prognostic factors in patients with HF after allo-HSCT is limited, and an easy-to-use prognostic model is not available. This study aimed to develop and validate a clinical-biomarker prognostic model capable of predicting HF mortality following allo-HSCT that uses a combination of variables readily available in clinical practice. To investigate this issue, we conducted a retrospective analysis at our center with 154 HF patients who underwent allo-HSCT between 2008 and 2021. The patients were separated according to the time of transplantation, with 100 patients composing the derivation cohort and the other 54 patients composing the external validation cohort. We first calculated the univariable association for each variable with 2-month mortality in the derivation cohort. We then included the variables with a P value <.1 in univariate analysis as candidate predictors in the multivariate analysis using a backward stepwise logistic regression model. Variables remaining in the final model were identified as independent prognostic factors. To predict the prognosis of HF, a scoring system was established, and scores were assigned to the prognostic factors based on the regression coefficient. Finally, 4 strongly significant independent prognostic factors for 2-month mortality from HF were identified using multivariable logistic regression methods with stepwise variable selection: pulmonary infection (P = .005), grade III to IV acute graft-versus-host disease (severe aGVHD; P = .033), lactate dehydrogenase (LDH) >426 U/L (P = .049), and brain natriuretic peptide (BNP) >1799 pg/mL (P = .026). A risk grading model termed the BLIPS score (for BNP, LDH, cardiac troponin I, pulmonary infection, and severe aGVHD) was constructed according to the regression coefficients. The validated internal C-statistic was .870 (95% confidence interval [CI], .798 to .942), and the external C-statistic was .882 (95% CI, .791-.973). According to the calibration plots, the model-predicted probability correlated well with the actual observed frequencies. The clinical use of the prognostic model, according to decision curve analysis, could benefit HF patients. The BLIPS model in our study can serve to identify HF patients at higher risk for mortality early, which might aid designing timely targeted therapies and eventually improving patients' survival and prognosis.

Machine learning algorithm as a prognostic tool for venous thromboembolism in allogeneic transplant patients.

As a serious complication after allogenic hematopoietic stem cell transplantation (allo-HSCT), venous thromboembolism (VTE) is significantly related to increased nonrelapse mortality. Therefore distinguishing patients at high risk of death who should receive specific therapeutic management is key to improving survival. This study aimed to establish a machine learning-based prognostic model for the identification of post-transplantation VTE patients who have a high risk of death. We retrospectively evaluated 256 consecutive VTE patients who underwent allo-HSCT at our center between 2008 and 2019. These patients were further randomly divided into (1) a derivation (80%) cohort of 205 patients and (2) a test (20%) cohort of 51 patients. The least absolute shrinkage and selection operator (LASSO) approach was used to choose the potential predictors from the primary dataset. Eight machine learning classifiers were used to produce 8 candidate models. A 10-fold cross-validation procedure was used to internally evaluate the models and to select the best-performing model for external assessment using the test cohort. In total, 256 of 7238 patients were diagnosed with VTE after transplantation. Among them, 118 patients (46.1%) had catheter-related venous thrombosis, 107 (41.8%) had isolated deep-vein thrombosis (DVT), 20 (7.8%) had isolated pulmonary embolism (PE), and 11 (4.3%) had concomitant DVT and PE. The 2-year overall survival (OS) rate of patients with VTE was 68.8%. Using LASSO regression, 8 potential features were selected from the 54 candidate variables. The best-performing algorithm based on the 10-fold cross-validation runs was a logistic regression classifier. Therefore a prognostic model named BRIDGE was then established to predict the 2-year OS rate. The areas under the curves of the BRIDGE model were 0.883, 0.871, and 0.858 for the training, validation, and test cohorts, respectively. The Hosmer-Lemeshow goodness-of-fit test showed a high agreement between the predicted and observed outcomes. Decision curve analysis indicated that VTE patients could benefit from the clinical application of the prognostic model. A BRIDGE risk score calculator for predicting the study result is available online (47.94.162.105:8080/bridge/). We established the BRIDGE model to precisely predict the risk for all-cause death in VTE patients after allo-HSCT. Identifying VTE patients who have a high risk of death can help physicians treat these patients in advance, which will improve patient survival.

Two smart coumarin-based fluorescent probes with AIE effect for sensing ClO- and imaging in living cells.

It's worth noting that detect effective methods for tracking ClO- could help us uncover the function of ClO- in living systems. Here, two coumarin-based probes, named (E)-3-(1-hydrazonoethyl)-2H-chromen-2-one (1A) and 3-((E)-1-(((E)-(2,3-dihydro-1H-imidazol-4-yl)methylene)-hydrazono)ethyl)- 2H-chromen-2-one (1B) with aggregation-induced emission (AIE) effect in Tris-HCl (pH = 7.2) buffer solution were synthesized and used for sensing ClO- selectivity. 1A and 1B responded to ClO- through the oxidation hydrolysis effect. The mechanism was further verified by HR-MS and DFT calculation. Cell imaging indicated that 1A and 1B were good membrane permeability with low toxicity to HEK293T, and expected to be used to detect ClO- in cells.

A simple "turn-on" fluorescent sensor for reversible recognition of aluminum ion in living cell.

A simple and reliable "turn-on" fluorescent sensor (E)-1-[((2-hydroxyethyl)imino) methyl] naphthalen-2-ol (HNP) has been designed, synthesized, and characterized by 1H-NMR, 13C-NMR, FT-IR, and EI-MS analysis. The binding property of HNP was examined employing UV-Vis and fluorescence spectroscopy. HNP exhibited high selectivity towards Al3+ among other cations and anions. The fluorescence titration experiment has established binding stoichiometry of HNP with Al3+ is 2:1, which can be further verified by HR-MS. The detection limit of HNP is 2.9 µM, and it can be reversible five-to-seven times to detect Al3+ without losing much efficiency which indicates that it can be a reliable probe for Al3+. Additionally, HNP was successfully applied for the detection of Al3+ in living cells. To achieve the detection of aluminum ion across a simple, reliable, and precise method, we have investigated the reversible detection (which can reversible response to Al3+ for five-to-seven times) of Al3+ through an extremely simple (requires only one-step reaction) "turn-on" fluorescent probe which enables us to visualize and analyze Al3+ with low detection limit (2.9 µM) and high selectivity in living cell without interference from the high abundant small biological molecules.