Tag: nevin manimala

JACC CardioOncol. 2020 Aug 28;2(3):400-410. doi: 10.1016/j.jaccao.2020.07.001. eCollection 2020 Sep.

ABSTRACT

BACKGROUND: Cardiovascular disease (CVD) and cancer share many common risk factors; patients with CVD also may be at risk of developing cancer.

OBJECTIVES: The aim of this study was to derive and externally validate prediction models for the estimation of lifetime and 10-year risk for total, colorectal, and lung cancer in patients with established CVD.

METHODS: Data from patients with established CVD from the UCC-SMART cohort (N = 7,280) were used for model development, and from the CANTOS trial (N = 9,322) for model validation. Predictors were selected based on previously published cancer risk scores, clinical availability, and presence in the derivation dataset. Fine and Gray competing risk-adjusted lifetime models were developed for the outcomes total, colorectal, and lung cancer.

RESULTS: Selected predictors were age, sex, smoking, weight, height, alcohol use, antiplatelet use, diabetes, and C-reactive protein. External calibration for the 4-year risk of lung, colorectal, and total cancer was reasonable in our models, as was discrimination with C-statistics of 0.74, 0.64, and 0.63, respectively. Median predicted lifetime and 10-year risks in CANTOS were 26% (range 1% to 52%) and 13% (range 1% to 31%) for total cancer; 4% (range 0% to 13%) and 2% (range 0% to 6%) for colorectal cancer; and 5% (range 0% to 37%) and 2% (range 0% to 24%) for lung cancer.

CONCLUSIONS: Lifetime and 10-year risk of total, colorectal, and lung cancer can be estimated reasonably well in patients with established CVD with readily available clinical predictors. With additional study, these tools could be used in clinical practice to further aid in the emphasis of healthy lifestyle changes and to guide thresholds for targeted diagnostics and screening.

PMID:34396248 | PMC:PMC8352343 | DOI:10.1016/j.jaccao.2020.07.001

JACC CardioOncol. 2020 Jul 6;2(3):428-440. doi: 10.1016/j.jaccao.2020.06.001. eCollection 2020 Sep.

ABSTRACT

BACKGROUND: Many patients with cancer have a hypercoagulable state and an increased risk of developing venous thromboembolism (VTE), arterial occlusion, and pulmonary emboli. Patients with cancer may also have an increased risk of bleeding with anticoagulant treatment. Recent trials have reported that direct oral anticoagulants (DOACs) are noninferior to the low-molecular-weight heparin, dalteparin, in preventing VTE, but have a higher bleeding rate.

OBJECTIVES: This study compared the efficacy and risks of DOACs versus dalteparin in patients with cancer-related VTEs across all randomized controlled trials (RCTs).

METHODS: This study performed a systematic analysis of RCTs published in PubMed, SCOPUS, and Google Scholar from September 1, 2007 through March 31, 2020 that reported clinical outcomes of treatment with DOACs versus dalteparin in patients with cancer with acute VTE. Two investigators independently performed study selection and data extraction. Extracted data were recorded and exported to statistical software for all analyses (OpenMetaAnalyst).

RESULTS: This study included 4 randomized trials (N = 2,907). Compared with DOACs, dalteparin was associated with higher VTE recurrence (risk ratio [RR]: 1.55; 95% confidence interval [CI]: 1.19 to 2.03; p = 0.001), whereas clinically relevant nonmajor bleeding (CRNMB) was significantly less frequent with dalteparin than that with DOACs (RR: 0.68; 95% CI: 0.54 to 0.86; p = 0.001). The risk of CRNMB was largely observed with patients with gastrointestinal malignancies. No significant differences were observed in major bleeding (RR: 0.74; 95% CI: 0.52 to 1.06; p = 0.11).

CONCLUSIONS: DOACs were noninferior to dalteparin in preventing VTE recurrence in patients with cancer without a significantly increased risk of major bleeding. However, DOACs were associated with higher rates of CRNMB compared with dalteparin, primarily in patients with gastrointestinal malignancies.

PMID:34396250 | PMC:PMC8352218 | DOI:10.1016/j.jaccao.2020.06.001

Brain Commun. 2021 Jul 11;3(3):fcab156. doi: 10.1093/braincomms/fcab156. eCollection 2021.

ABSTRACT

Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.

PMID:34396112 | PMC:PMC8361393 | DOI:10.1093/braincomms/fcab156

Brain Commun. 2021 Jul 16;3(3):fcab164. doi: 10.1093/braincomms/fcab164. eCollection 2021.

ABSTRACT

Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model’s C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities (P-value = 0.02). Adding quantitative MRI data increases the performance of a model developed to predict post-operative seizure outcomes. The distribution of the regions of interest included in the final model supports the notion that focal epilepsies are network disorders and that subtle cortical volume loss outside the surgical site influences seizure outcome.

PMID:34396113 | PMC:PMC8361423 | DOI:10.1093/braincomms/fcab164

Mach Learn Sci Technol. 2021 Sep;2(3):035015. doi: 10.1088/2632-2153/abe6d6. Epub 2021 May 13.

ABSTRACT

Allostery is considered important in regulating protein’s activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN), to predict allosteric sites. Our model can learn physical properties and topology without any prior information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

PMID:34396127 | PMC:PMC8360383 | DOI:10.1088/2632-2153/abe6d6

Brain Commun. 2021 Aug 6;3(3):fcab146. doi: 10.1093/braincomms/fcab146. eCollection 2021.

ABSTRACT

Identify preoperative imaging findings in hemifacial spasm patients that predict the post-surgical success following microvascular decompression. This is a retrospective study of patients who were diagnosed with hemifacial spasm, had a dedicated cranial nerve MRI, and underwent microvascular decompression for hemifacial spasm. Bilateral facial nerves were interrogated for neurovascular compression. If neurovascular compression was identified, we recorded whether the offending vessel was an artery, a vein or both. The location of the neurovascular compression (proximal nerve versus distal nerve) was noted. The severity of the neurovascular compression was categorized as contact versus deformity of the nerve. Patients were contacted to determine their post-operative spasm status. The relationships between imaging findings and post-surgical outcome were assessed by Chi-square tests, and odds ratios were calculated to quantify the degree of association. The study included 212 patients. Upon follow up, 192 patients were spasm free (90.57%). Imaging findings on the symptomatic side were as follows: arterial neurovascular compression was seen in 207 patients (97.64%), venous only neurovascular compression in two patients (0.94%), and no neurovascular compression in three patients (1.42%). Arterial neurovascular compression along the proximal, susceptible segment of the nerve was observed in 202 patients (95.28%); deformity was observed more commonly than contact alone. Arterial neurovascular compression along the distal segment only of the nerve was observed in five patients (2.36%). In patients with arterial neurovascular compression of the proximal and distal portions of the nerve, 93.07% and 60.0% of patients were spasm-free respectively. If venous neurovascular compression only was observed on imaging, 0% of patients were spasm-free. Patients with arterial neurovascular compression of the susceptible segment are much more likely to be spasm free than patients without this imaging finding, [odds ratio 20.14 (CI 5.08, 79.81), P-value <0.0001]. When comparing the two groups of arterial neurovascular compression (deformity versus contact), no statistically significant difference in outcomes was observed. In patients with hemifacial spasm undergoing microvascular decompression, imaging findings do predict surgical outcome. Patients with arterial neurovascular compression of the proximal, susceptible portion of the nerve are much more likely to be spasm free after surgery than those without this imaging finding. The imaging findings inform the risk benefit analysis and discussion with patients before they undergo microvascular decompression for hemifacial spasm.

PMID:34396106 | PMC:PMC8361424 | DOI:10.1093/braincomms/fcab146

Brain Commun. 2021 Jul 2;3(3):fcab145. doi: 10.1093/braincomms/fcab145. eCollection 2021.

ABSTRACT

Little is known about the characteristics and causes of early-onset cognitive impairment. Responders to the 2001 New York World Trade Center disaster represent an ageing population that was recently shown to have an excess prevalence of cognitive impairment. Neuroimaging and molecular data demonstrate that a subgroup of affected responders may have a unique form of parietal-dominant Alzheimer’s Disease. Recent neuropsychological testing and artificial intelligence approaches have emerged as methods that can be used to identify and monitor subtypes of cognitive impairment. We utilized data from World Trade Center responders participating in a health monitoring program and applied a deep learning approach to evaluate neuropsychological and neuroimaging data to generate a cortical atrophy risk score. We examined risk factors associated with the prevalence and incidence of high risk for brain atrophy in responders who are now at midlife. Training was conducted in a randomly selected two-thirds sample (N = 99) enrolled using of the results of a structural neuroimaging study. Testing accuracy was estimated for each training cycle in the remaining third subsample. After training was completed, the scoring methodology that was generated was applied to longitudinal data from 1441 World Trade Center responders. The artificial neural network provided accurate classifications of these responders in both the testing (Area Under the Receiver Operating Curve, 0.91) and validation samples (Area Under the Receiver Operating Curve, 0.87). At baseline and follow-up, responders identified as having a high risk of atrophy (n = 378) showed poorer cognitive functioning, most notably in domains that included memory, throughput, and variability as compared to their counterparts at low risk for atrophy (n = 1063). Factors associated with atrophy risk included older age [adjusted hazard ratio, 1.045 (95% confidence interval = 1.027-1.065)], increased duration of exposure at the WTC site [adjusted hazard ratio, 2.815 (1.781-4.449)], and a higher prevalence of post-traumatic stress disorder [aHR, 2.072 (1.408-3.050)]. High atrophy risk was associated with an increased risk of all-cause mortality [adjusted risk ratio, 3.19 (1.13-9.00)]. In sum, the high atrophy risk group displayed higher levels of previously identified risk factors and characteristics of cognitive impairment, including advanced age, symptoms of post-traumatic stress disorder, and prolonged duration of exposure to particulate matter. Thus, this study suggests that a high risk of brain atrophy may be accurately monitored using cognitive data.

PMID:34396105 | PMC:PMC8361422 | DOI:10.1093/braincomms/fcab145

Brain Commun. 2021 Jul 2;3(3):fcab139. doi: 10.1093/braincomms/fcab139. eCollection 2021.

ABSTRACT

Metabolomics in the Alzheimer’s Disease Neuroimaging Initiative cohort provides a powerful tool for mapping biochemical changes in Alzheimer’s disease, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in Alzheimer’s disease. We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition and conversion from mild cognitive impairment to Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative. Processed [¹⁸F] Florbetapir PET images were used to perform a voxel-wise statistical analysis of the effect of metabolite levels on amyloid-β accumulation across the whole brain. We performed a multivariable regression analysis using age, sex, body mass index, apolipoprotein E ε4 status and study phase as covariates. We identified nine metabolites as significantly associated with amyloid-β deposition after multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation and higher memory scores. However, higher levels of seven phosphatidylcholines (lysoPC a C18:2, PC aa C42:0, PC ae C42:3, PC ae C44:3, PC ae C44:4, PC ae C44:5 and PC ae C44:6) were associated with increased brain amyloid-β deposition. In addition, higher levels of PC ae C44:4 were significantly associated with lower memory and executive function scores and conversion from mild cognitive impairment to Alzheimer’s disease dementia. Our findings suggest that dysregulation of peripheral phosphatidylcholine metabolism is associated with earlier pathological changes noted in Alzheimer’s disease as measured by brain amyloid-β deposition as well as later clinical features including changes in memory and executive functioning. Perturbations in phosphatidylcholine metabolism may point to issues with membrane restructuring leading to the accumulation of amyloid-β in the brain. Additional studies are needed to explore whether these metabolites play a causal role in the pathogenesis of Alzheimer’s disease or if they are biomarkers for systemic changes during preclinical phases of the disease.

PMID:34396103 | PMC:PMC8361396 | DOI:10.1093/braincomms/fcab139

Brain Commun. 2021 Jun 26;3(3):fcab143. doi: 10.1093/braincomms/fcab143. eCollection 2021.

ABSTRACT

Since amyotrophic lateral sclerosis cases exhibit significant heterogeneity, we aim to investigate the association of lipid composition of plasma and CSF with amyotrophic lateral sclerosis diagnosis, its progression and clinical characteristics. Lipidome analyses would help to stratify patients on a molecular basis. For this reason, we have analysed the lipid composition of paired plasma and CSF samples from amyotrophic lateral sclerosis cases and age-matched non-amyotrophic lateral sclerosis individuals (controls) by comprehensive liquid chromatography coupled to mass spectrometry. The concentrations of neurofilament light chain-an index of neuronal damage-were also quantified in CSF samples and plasma. Amyotrophic lateral sclerosis versus control comparison, in a moderate stringency mode, showed that plasma from cases contains more differential lipids (n = 122 for raw P < 0.05; n = 27 for P < 0.01) than CSF (n = 17 for raw P < 0.05; n = 4 for P < 0.01), with almost no overlapping differential species, mainly characterized by an increased content of triacylglyceride species in plasma and decreased in CSF. Of note, false discovery rate correction indicated that one of the CSF lipids (monoacylglycerol 18:0) had high statistic robustness (false discovery rate-P < 0.01). Plasma lipidomes also varied significantly with the main involvement at onset (bulbar, spinal or respiratory). Notably, faster progression cases showed particular lipidome fingerprints, featured by decreased triacylclycerides and specific phospholipids in plasma, with 11 lipids with false discovery rate-P < 0.1 (n = 56 lipids in plasma for raw P < 0.01). Lipid species associated with progression rate clustered in a relatively low number of metabolic pathways, mainly triacylglyceride metabolism and glycerophospholipid and sphingolipid biosynthesis. A specific triacylglyceride (68:12), correlated with neurofilament content (r = 0.8, P < 0.008). Thus, the present findings suggest that systemic hypermetabolism-potentially sustained by increased triacylglyceride content-and CNS alterations of specific lipid pathways could be associated as modifiers of disease progression. Furthermore, these results confirm biochemical lipid heterogeneity in amyotrophic lateral sclerosis with different presentations and progression, suggesting the use of specific lipid species as potential disease classifiers.

PMID:34396104 | PMC:PMC8361390 | DOI:10.1093/braincomms/fcab143

NAR Genom Bioinform. 2021 Aug 11;3(3):lqab072. doi: 10.1093/nargab/lqab072. eCollection 2021 Sep.

ABSTRACT

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

PMID:34396096 | PMC:PMC8356963 | DOI:10.1093/nargab/lqab072