Nucleophilic aromatic substitution (S
Ar) is one of the most widely applied reaction classes in pharmaceutical and chemical research, providing a broadly useful platform for the modification of ...aromatic ring scaffolds. The generally accepted mechanism for S
Ar reactions involves a two-step addition-elimination sequence via a discrete, non-aromatic Meisenheimer complex. Here we use
C/
C kinetic isotope effect (KIE) studies and computational analyses to provide evidence that prototypical S
Ar reactions in fact proceed through concerted mechanisms. The KIE measurements were made possible by a new technique that leverages the high sensitivity of
F as an NMR nucleus to quantitate the degree of isotopic fractionation. This sensitive technique permits the measurement of KIEs on 10 mg of natural abundance material in one overnight acquisition. As a result, it provides a practical tool for performing detailed mechanistic analyses of reactions that form or break C-F bonds.
The receptor for advanced glycation end products (RAGE) is highly expressed in various cancers and is correlated with poorer outcome in breast and other cancers. Here we tested the role of targeting ...RAGE by multiple approaches in the tumor and tumor microenvironment, to inhibit the metastatic process. We first tested how RAGE impacts tumor cell-intrinsic mechanisms using either RAGE overexpression or knockdown with short hairpin RNAs (shRNAs). RAGE ectopic overexpression in breast cancer cells increased MEK-EMT (MEK-epithelial-to-mesenchymal transition) signaling, transwell invasion and soft agar colony formation, and in vivo promoted lung metastasis independent of tumor growth. RAGE knockdown with multiple independent shRNAs in breast cancer cells led to decreased transwell invasion and soft agar colony formation, without affecting proliferation. In vivo, targeting RAGE shRNA knockdown in human and mouse breast cancer cells, decreased orthotopic tumor growth, reduced tumor angiogenesis and recruitment of inflammatory cells, and markedly decreased metastasis to the lung and liver in multiple xenograft and syngeneic mouse models. To test the non-tumor cell microenvironment role of RAGE, we performed syngeneic studies with orthotopically injected breast cancer cells in wild-type and RAGE-knockout C57BL6 mice. RAGE-knockout mice displayed striking impairment of tumor cell growth compared with wild-type mice, along with decreased mitogen-activated protein kinase signaling, tumor angiogenesis and inflammatory cell recruitment. To test the combined inhibition of RAGE in both tumor cell-intrinsic and non-tumor cells of the microenvironment, we performed in vivo treatment of xenografted tumors with FPS-ZM1 (1 mg/kg, two times per week). Compared with vehicle, FPS-ZM1 inhibited primary tumor growth, inhibited tumor angiogenesis and inflammatory cell recruitment and, most importantly, prevented metastasis to the lung and liver. These data demonstrate that RAGE drives tumor progression and metastasis through distinct tumor cell-intrinsic and -extrinsic mechanisms, and may represent a novel and therapeutically viable approach for treating metastatic cancers.
p27 restrains normal cell growth, but PI3K-dependent C-terminal phosphorylation of p27 at threonine 157 (T157) and T198 promotes cancer cell invasion. Here, we describe an oncogenic feedforward loop ...in which p27pT157pT198 binds Janus kinase 2 (JAK2) promoting STAT3 (signal transducer and activator of transcription 3) recruitment and activation. STAT3 induces TWIST1 to drive a p27-dependent epithelial-mesenchymal transition (EMT) and further activates AKT contributing to acquisition and maintenance of metastatic potential. p27 knockdown in highly metastatic PI3K-activated cells reduces STAT3 binding to the TWIST1 promoter, TWIST1 promoter activity and TWIST1 expression, reverts EMT and impairs metastasis, whereas activated STAT3 rescues p27 knockdown. Cell cycle-defective phosphomimetic p27T157DT198D (p27CK-DD) activates STAT3 to induce a TWIST1-dependent EMT in human mammary epithelial cells and increases breast and bladder cancer invasion and metastasis. Data support a mechanism in which PI3K-deregulated p27 binds JAK2, to drive STAT3 activation and EMT through STAT3-mediated TWIST1 induction. Furthermore, STAT3, once activated, feeds forward to further activate AKT.
We sought to: (1) determine the agreement in cardiovascular magnetic resonance (CMR) and speckle tracking echocardiography (STE) derived strain measurements, (2) compare their reproducibility, (3) ...determine which approach is best related to CMR late gadolinium enhancement (LGE).
While STE-derived strain is routinely used to assess left ventricular (LV) function, CMR strain measurements are not yet standardized. Strain can be measured using dedicated pulse sequences (strain-encoding, SENC), or post-processing of cine images (feature tracking, FT). It is unclear whether these measurements are interchangeable, and whether strain can be used as an alternative to LGE.
Fifty patients underwent 2D echocardiography and 1.5 T CMR. Global longitudinal strain (GLS) was measured by STE (Epsilon), FT (NeoSoft) and SENC (Myocardial Solutions) and circumferential strain (GCS) by FT and SENC.
GLS showed good inter-modality agreement (r-values: 0.71-0.75), small biases (< 1%) but considerable limits of agreement (- 7 to 8%). The agreement between the CMR techniques was better for GLS than GCS (r = 0.81 vs 0.67; smaller bias). Repeated measurements showed low intra- and inter-observer variability for both GLS and GCS (intraclass correlations 0.86-0.99; coefficients of variation 3-13%). LGE was present in 22 (44%) of patients. Both SENC- and FT-derived GLS and GCS were associated with LGE, while STE-GLS was not. Irrespective of CMR technique, this association was stronger for GCS (AUC 0.77-0.78) than GLS (AUC 0.67-0.72) and STE-GLS (AUC = 0.58).
There is good inter-technique agreement in strain measurements, which were highly reproducible, irrespective of modality or analysis technique. GCS may better reflect the presence of underlying LGE than GLS.
The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully ...automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert.
Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images.
Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events.
Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF.
This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.
The HOSPITAL Risk Score (HRS) predicts 30-day hospital readmissions and is internationally validated. Social determinants of health (SDOH) such as low socioeconomic status (SES) affect health ...outcomes and have been postulated to affect readmission rates. We hypothesized that adding SDOH to the HRS could improve its predictive accuracy.
Records of 37,105 inpatient admissions at the University of Chicago Medical Center were reviewed. HRS was calculated for each patient. Census tract-level SDOH then were combined with the HRS and the performance of the resultant "Social HRS" was compared against the HRS. Patients then were assigned to 1 of 7 typologies defined by their SDOH and a balanced dataset of 14,235 admissions was sampled from the larger dataset to avoid over-representation by any 1 sociodemographic group. Principal component analysis and multivariable linear regression then were performed to determine the effect of SDOH on the HRS.
The c-statistic for the HRS predicting 30-day readmission was 0.74, consistent with published values. However, the addition of SDOH to the HRS did not improve the c-statistic (0.71). Patients with unfavorable SDOH (no high-school, limited English, crowded housing, disabilities, and age > 65 yrs) had significantly higher HRS (p < 0.05 for all). Overall, SDOH explained 0.2% of the HRS.
At an urban tertiary care center, the addition of census tract-level SDOH to the HRS did not improve its predictive power. Rather, the effects of SDOH are already reflected in the HRS.
Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, ...which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms.
To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance.
The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training.
Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.
•We compared the accuracy of machine learning and deep learning techniques for cardiac magnetic resonance view classification.•Both techniques can accurately classify cardiac views Deep learning outperformed machine learning when classifying images with complex heart anatomy.
Celiac disease (CeD) is a widespread, gluten-induced, autoimmune disorder that lacks any medicinal therapy. Towards the goal of developing non-dietary treatments for CeD, research has focused on ...elucidating its molecular and cellular etiology. A model of pathogenesis has emerged centered on interactions between three molecular families: specific class II MHC proteins on antigen-presenting cells (APCs), deamidated gluten-derived peptides, and T cell receptors (TCRs) on inflammatory CD4
T cells. Growing evidence suggests that this pathogenic axis can be pharmacologically targeted to protect patients from some of the adverse effects of dietary gluten. Further studies have revealed the existence of additional host and environmental contributors to disease initiation and tissue damage. This review summarizes our current understanding of CeD pathogenesis and how it is being harnessed for therapeutic design and development.
Lipoprotein (a) Lp(a) is a robust predictor of coronary heart disease outcomes, with targeted therapies currently under investigation. We aimed to evaluate the association of high Lp(a) with standard ...modifiable risk factors (SMuRFs) for incident first acute myocardial infarction (AMI).
This retrospective study used the Mass General Brigham Lp(a) Registry, which included patients aged ≥18 years with an Lp(a) measurement between 2000 and 2019. Exclusion criteria were severe kidney dysfunction, malignant neoplasm, and prior known atherosclerotic cardiovascular disease. Diabetes, dyslipidemia, hypertension, and smoking were considered SMuRFs. High Lp(a) was defined as >90th percentile, and low Lp(a) was defined as <50th percentile. The primary outcome was fatal or nonfatal AMI. A combination of natural language processing algorithms,
(
) codes, and laboratory data was used to identify the outcome and covariates. A total of 6238 patients met the eligibility criteria. The median age was 54 (interquartile range, 43-65) years, and 45% were women. Overall, 23.7% had no SMuRFs, and 17.8% had ≥3 SMuRFs. Over a median follow-up of 8.8 (interquartile range, 4.2-12.8) years, the incidence of AMI increased gradually, with higher number of SMuRFs among patients with high (log-rank
=0.031) and low Lp(a) (log-rank
<0.001). Across all SMuRF subgroups, the incidence of AMI was significantly higher for patients with high Lp(a) versus low Lp(a). The risk of high Lp(a) was similar to having 2 SMuRFs. Following adjustment for confounders and number of SMuRFs, high Lp(a) remained significantly associated with the primary outcome (hazard ratio, 2.9 95% CI, 2.0-4.3;
<0.001).
Among patients with no prior atherosclerotic cardiovascular disease, high Lp(a) is associated with significantly higher risk for first AMI regardless of the number of SMuRFs.
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