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\"Growing up as the daughter of a dedicated surgeon, Elizabeth L. Silver felt an unquestioned faith in medicine. When her six-week-old daughter, Abby, was rushed to the Neonatal Intensive Care Unit with sudden seizures, and scans revealed a serious brain bleed, her relationship to medicine began to change. The Tincture of Time is Silver's gorgeous and haunting chronicle of Abby's first year. It's a year of unending tests, doctors' opinions, sleepless nights, promising signs and steps backward, and above all, uncertainty : The mysterious circumstances of Abby's hospitalization attract dozens of specialists, none of whom can offer a conclusive answer about what went wrong or what the future holds. As Silver explores what it means to cope with uncertainty as a patient and parent and seeks peace in the reality that Abby's injury may never be fully understood, she looks beyond her own story for comfort, probing literature and religion, examining the practice of medicine throughout history, and reporting the experiences of doctors, patients, and fellow caretakers. The result is a brilliant blend of personal narrative and cultural analysis, at once a poignant snapshot of a parent's struggle and a wise meditation on the reality of uncertainty, in and out of medicine, and the hard-won truth that time is often its only cure. Heart-wrenching, unflinchingly honest, and beautifully written, The Tincture of Time is a powerful story of parenthood, an astute investigation of the boundaries of medicine, and an inspiring reminder of life's precariousness\"-- Provided by publisher.

AbstractObjectiveTo quantify the risk of overall and type specific cardiovascular and cerebrovascular diseases as well as venous thromboembolism in women with a history of gestational diabetes mellitus.DesignSystematic review and meta-analyses.Data sourcesPubMed, Embase, and the Cochrane Library from inception to 1 November 2021 and updated on 26 May 2022.Review methodsObservational studies reporting the association between gestational diabetes mellitus and incident cardiovascular and cerebrovascular diseases were eligible. Data, pooled by random effects models, are presented as risk ratios (95% confidence intervals). Certainty of evidence was appraised by the Grading of Recommendations, Assessment, Development, and Evaluations.Results15 studies rated as moderate or serious risk of bias were included. Of 513 324 women with gestational diabetes mellitus, 9507 had cardiovascular and cerebrovascular disease. Of more than eight million control women without gestational diabetes, 78 895 had cardiovascular and cerebrovascular disease. Compared with women without gestational diabetes mellitus, women with a history of gestational diabetes mellitus showed a 45% increased risk of overall cardiovascular and cerebrovascular diseases (risk ratio 1.45, 95% confidence interval 1.36 to 1.53), 72% for cardiovascular diseases (1.72, 1.40 to 2.11), and 40% for cerebrovascular diseases (1.40, 1.29 to 1.51). Women with gestational diabetes mellitus showed increased risks of incident coronary artery diseases (1.40, 1.18 to 1.65), myocardial infarction (1.74, 1.37 to 2.20), heart failure (1.62, 1.29 to 2.05), angina pectoris (2.27, 1.79 to 2.87), cardiovascular procedures (1.87, 1.34 to 2.62), stroke (1.45, 1.29 to 1.63), and ischaemic stroke (1.49, 1.29 to 1.71). The risk of venous thromboembolism was observed to increase by 28% in women with previous gestational diabetes mellitus (1.28, 1.13 to 1.46). Subgroup analyses of cardiovascular and cerebrovascular disease outcomes stratified by study characteristics and adjustments showed significant differences by region (P=0.078), study design (P=0.02), source of data (P=0.005), and study quality (P=0.04), adjustment for smoking (P=0.03), body mass index (P=0.01), and socioeconomic status (P=0.006), and comorbidities (P=0.05). The risk of cardiovascular and cerebrovascular diseases was, however, attenuated but remained significant when restricted to women who did not develop subsequent overt diabetes (all gestational diabetes mellitus: 1.45, 1.33 to 1.59, gestational diabetes mellitus without subsequent diabetes: 1.09, 1.06 to 1.13). Certainty of evidence was judged as low or very low quality.ConclusionsGestational diabetes mellitus is associated with increased risks of overall and type specific cardiovascular and cerebrovascular diseases that cannot be solely attributed to conventional cardiovascular risk factors or subsequent diabetes.

Riding the Cyclone, the world famous Coney Island rollercoaster was supposed to be the highlight of twelve-year-old Nora's summer, but right after they disembark, Nora's thirteen-year-old cousin Riley falls to the ground and into a coma that Nora thinks is her fault.

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults ( N  = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort ( N  = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets. Genomic analyses of large population-based cohorts uncover the genetic determinants of perivascular space burden, an MRI marker of cerebral small vessel disease, across the lifespan, and reveal potential pathways implicated in the etiology of stroke and dementia.

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net-is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies.

Abstract Context Chronic obesity is associated with several complications, including cognitive impairment and dementia. However, we have only piecemeal knowledge of the mechanisms linking obesity to central nervous system damage. Among candidate mechanisms are other elements of obesity-associated metabolic syndrome, such as hypertension, dyslipidemia, and diabetes, but also systemic inflammation. While there have been several neuroimaging studies linking adiposity to changes in brain morphometry, a comprehensive investigation of the relationship has so far not been done. Objective To identify links between adiposity and cognitive dysfunction. Methods This observational cohort study (UK Biobank), with an 8-year follow-up, included more than 20 000 participants from the general community, with a mean age of 63 years. Only participants with data available on both baseline and follow-up timepoints were included. The main outcome measures were cognitive performance and mediator variables: hypertension, diabetes, systemic inflammation, dyslipidemia, gray matter measures, and cerebrovascular disease (volume of white matter hyperintensities on magnetic resonance imaging). Results Using structural equation modeling, we found that body mass index, waist-to-hip ratio, and body fat percentage were positively related to higher plasma C-reactive protein, dyslipidemia, hypertension, and diabetes. In turn, hypertension and diabetes were related to cerebrovascular disease. Finally, cerebrovascular disease was associated with lower cortical thickness and volume and higher subcortical volumes, but also cognitive deficits (largest significant pcorrected = 0.02). Conclusions We show that adiposity is related to poor cognition, with metabolic consequences of obesity and cerebrovascular disease as potential mediators. The outcomes have clinical implications, supporting a role for the management of adiposity in the prevention of late-life dementia and cognitive decline.

Background Epidemiological studies have found an association between air pollution and cardiovascular and cerebrovascular diseases (CACD) and its subtypes. However, there is a lack of individual-level data to explore the associations of air pollutants on CACD and its subtypes, the interaction among them, and the potential mechanism. Methods This study employed a two-stage design, combining a time-stratified case-crossover study with a cohort study, analyzing data from one million individuals from China and half million from the UK. The study assessed the impact of air pollutants on CACD and its subtypes, while also examining the mediating effects of inflammation. Distributed lag non-linear models were used to analyze the lagged effects of pollutants, and mediation analysis was conducted to evaluate the role of inflammatory markers (SII, SIRI, AISI) in the relationship between air pollution and CACD. Results A total of 829,135 CSDs patients were recorded in this study. An interquartile range (IQR) increase in concentrations of PM 2.5 , PM 10 , NO 2 , SO 2 , CO, and O 3 was associated with increases of 11.3% [95% confidence interval (CI) 9.5%-13.2%], 10.5% (95% CI 8.6%-12.3%), 3% (95% CI 1%-5%), 15.2% (95% CI 13.3%-17.1%), 15.5% (95% CI 11.6%-19.5%), and 2.8% (95% CI 2.2%-3.4%) in CSDs, respectively. A similar positive association was also observed for cardiovascular and ischemic heart diseases. A significant synergistic interaction between PM 2.5 and NO 2 and CO for CSDs. Approximately 64.75%, 21.13%, 32.2%, 2.31%, 43.7% and 43.7% of the effects of PM 2.5 , PM 10 , NO 2 , SO 2 , CO, and O 3 on CSDs were significantly mediated by SII. Conclusions This study provides robust evidence that short-term exposure to common air pollutants significantly increases the risk of CACD and its subtypes, with inflammation playing a crucial mediating role. The findings underscore the importance of coordinated air pollution control strategies and public health interventions to mitigate the cardiovascular risks associated with air pollution.

AimStroke is the leading cause of disability and death in China. Ischaemic stroke accounts for about 60%–80% of all strokes. It is of considerable significance to carry out multidimensional management of ischaemic cerebrovascular diseases. This evidence-based guideline aims to provide the latest detailed and comprehensive recommendations on the diagnosis, treatment and secondary prevention of ischaemic cerebrovascular diseases.MethodsWe had performed comprehensive searches of MEDLINE (via PubMed) (before 30 June 2019), and integrated the relevant information into charts and distributed to the writing group. Writing group members discussed and determined the recommendations through teleconference. We used the level of evidence grading algorithm of Chinese Stroke Association to grade each recommendation. The draft was reviewed by the Guideline Writing Committee of Chinese Stroke Association Stroke and finalised. This guideline is fully updated every 3 years.ResultsThis evidence-based guideline is based on the treatment, care and prevention of ischaemic cerebrovascular diseases, which emphasises on pathogenesis evaluation, intravenous thrombolysis, endovascular therapy, antiplatelet therapy, prevention and treatment of complications, and risk factor management.ConclusionsThis updated guideline presents a framework for the management of ischaemic cerebrovascular diseases. Timely first-aid measures, professional care in the acute stage, and proactive secondary prevention will be helpful to patients.

Cerebrovascular disorders are underlain by perturbations in cerebral blood flow and abnormalities in blood vessel structure. Here, we provide an overview of the current knowledge of select cerebrovascular disorders that are associated with genetic lesions and connect genomic findings with analyses aiming to elucidate the cellular and molecular mechanisms of disease pathogenesis. We argue that a mechanistic understanding of genetic (familial) forms of cerebrovascular disease is a prerequisite for the development of rational therapeutic approaches, and has wider implications for treatment of sporadic (non-familial) forms, which are usually more common.

Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with acute cerebrovascular disease. This study was a retrospective study based on two different cohorts. Five machine learning methods were used to develop AKI risk prediction models. We used six popular metrics (AUROC, F2-Score, accuracy, sensitivity, specificity and precision) to evaluate the performance of these models. We identified 2935 patients in the MIMIC-III database and 499 patients in our local database to develop and validate the AKI risk prediction model. The incidence of AKI in these two different cohorts was 18.3% and 61.7%, respectively. Analysis showed that several laboratory parameters (serum creatinine, hemoglobin, white blood cell count, bicarbonate, blood urea nitrogen, sodium, albumin, and platelet count), age, and length of hospital stay, were the top ten important factors associated with AKI. The analysis demonstrated that the XGBoost had higher AUROC (0.880, 95%CI: 0.831-0.929), indicating that the XGBoost model was better at predicting AKI risk in patients with acute cerebrovascular disease than other models. This study developed machine learning methods to identify critically ill patients with acute cerebrovascular disease who are at a high risk of developing AKI. This result suggested that machine learning techniques had the potential to improve the prediction of AKI risk models in critical care.