Major Stressful Life Events and the Risk of Pancreatic, Head and Neck Cancers: A Case-Control Study.

BACKGROUND: Major stressful life events have been shown to be associated with an increased risk of lung cancer, breast cancer and the development of various chronic illnesses. The stress response generated by our body results in a variety of physiological and metabolic changes which can affect the immune system and have been shown to be associated with tumor progression. In this study, we aim to determine if major stressful life events are associated with the incidence of head and neck or pancreatic cancer (HNPC). METHODS: This is a matched case-control study. Cases (CAs) were HNPC patients diagnosed within the previous 12 months. Controls (COs) were patients without a prior history of malignancy. Basic demographic data information on major stressful life events was collected using the modified Holmes-Rahe stress scale. A total sample of 280 was needed (79 cases, 201 controls) to achieve at least 80% power to detect odds ratios (ORs) of 2.00 or higher at the 5% level of significance. RESULTS: From 1 January 2018 to 31 August 2021, 280 patients were enrolled (CA = 79, CO = 201) in this study. In a multivariable logistic regression analysis after controlling for potential confounding variables (including sex, age, race, education, marital status, smoking history), there was no difference between the lifetime prevalence of major stressful event in cases and controls. However, patients with HNPC were significantly more likely to report a major stressful life event within the preceding 5 years when compared to COs ( CONCLUSIONS: Patients with head, neck and pancreatic cancers are significantly associated with having a major stressful life event within 5 years of their diagnosis. This study highlights the potential need to recognize stressful life events as risk factors for developing malignancies

Metabolomic Profiles in Jamaican Children With and Without Autism Spectrum Disorder

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, the underlying metabolic perturbations associated with ASD which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls. OBJECTIVE: To elucidate potential metabolomic signatures associated with ASD in children and identify specific metabolites that may serve as biomarkers for the disorder. METHODS: We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica (ERAJ-2), which was a 1:1 age (±6 months)-and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and imputation of missing values, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child\u27s parish of birth. RESULTS: Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three of these metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally, the amino acid sarcosine exhibited a significant association with ASD. CONCLUSIONS: These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions

Fungi: Friend or Foe? A Mycobiome Evaluation in Children with Autism and Gastrointestinal Symptoms

Gastrointestinal (GI) symptoms often affect children with autism spectrum disorders (ASD) and GI symptoms have been associated with an abnormal fecal microbiome. There is limited evidence of Candida species being more prevalent in children with ASD. We enrolled 20 children with ASD and GI symptoms (ASD + GI), 10 children with ASD but no GI symptoms (ASD - GI), and 20 from typically developing (TD) children in this pilot study. Fecal mycobiome taxa were analyzed by Internal Transcribed Spacer sequencing. GI symptoms (GI Severity Index [GSI]), behavioral symptoms (Social Responsiveness Scale -2 [SRS-2]), inflammation and fungal immunity (fecal calprotectin and serum dectin-1 [ELISA]) were evaluated. We observed no changes in the abundance of total fungal species (alpha diversity) between groups. Samples with identifiable Candida spp. were present in 4 of 19 (21%) ASD + GI, in 5 of 9 (56%) ASD - GI, and in 4 of 16 (25%) TD children (overall P = 0.18). The presence of Candida spp. did not correlate with behavioral or GI symptoms (P = 0.38, P = 0.5, respectively). Fecal calprotectin was normal in all but one child. Finally, there was no significance in serum dectin-1 levels, suggesting no increased fungal immunity in children with ASD. Our data suggest that fungi are present at normal levels in the stool of children with ASD and are not associated with gut inflammation

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Large-scale Unit Commitment under uncertainty

International audienceThe Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

Large-scale Unit Commitment under uncertainty: a literature survey

The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an “unreasonably” small time; recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, (stochastic, ro-bust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

Opioid analgesic use in patients with ankylosing spondylitis: An analysis of the prospective study of outcomes in an ankylosing spondylitis cohort

OBJECTIVE: Opioid analgesics may be prescribed to ankylosing spondylitis (AS) patients with pain that is unresponsive to antirheumatic treatment. Our study assessed factors associated with opioid usage in AS. METHODS: A prospective cohort of 706 patients with AS meeting modified New York criteria followed at least 2 years underwent comprehensive clinical evaluation of disease activity and functional impairment. These were assessed by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Bath Ankylosing Spondylitis Functional Index (BASFI). Radiographic severity was assessed by the Bath Ankylosing Spondylitis Radiology Index and modified Stokes Ankylosing Spondylitis Scoring System. Medications taken concurrently with opioids, as well as C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR), were determined at each study visit, performed every 6 months. Analyses were carried out at baseline, and longitudinal multivariable models were developed to identify factors independently associated with chronic and intermittent opioid usage over time. RESULTS: Factors significantly associated with opioid usage, especially chronic opioid use, included longer disease duration, smoking, lack of exercise, higher disease activity (BASDAI) and functional impairment (BASFI), depression, radiographic severity, and cardiovascular disease. Patients taking opioids were more likely to be using anxiolytic, hypnotic, antidepressant, and muscle relaxant medications. Multivariable analysis underscored the association with smoking, older age, antitumor necrosis factor agent use, and psychoactive drugs, as well as with subjective but not objective determinants of disease activity. CONCLUSION: Opioid usage was more likely to be associated with subjective measures (depression, BASDAI, BASFI) than objective measures (CRP, ESR), suggesting that pain in AS may derive from sources other than spinal inflammation alone

Harmonization, data management, and statistical issues related to prospective multicenter studies in Ankylosing spondylitis (AS): experience from the Prospective Study Of Ankylosing Spondylitis (PSOAS) cohort

Ankylosing spondylitis (AS) is characterized by inflammation of the spine and sacroiliac joints causing pain and stiffness and, in some patients, ultimately new bone formation, and progressive joint ankyloses. The classical definition of AS is based on the modified New York (mNY) criteria. Limited data have been reported regarding data quality assurance procedure for multicenter or multisite prospective cohort of patients with AS. Since 2002, 1272 qualified AS patients have been enrolled from five sites (4 US sites and 1 Australian site) in the Prospective Study Of Ankylosing Spondylitis (PSOAS). In 2012, a Data Management and Statistical Core (DMSC) was added to the PSOAS team to assist in study design, establish a systematic approach to data management and data quality, and develop and apply appropriate statistical analysis of data. With assistance from the PSOAS investigators, DMSC modified Case Report Forms and developed database in Research Electronic Data Capture (REDCap). DMSC also developed additional data quality assurance procedure to assure data quality. The error rate for various forms in PSOAS databases ranged from 0.07% for medications data to 1.1% for arthritis activity questionnaire-Global pain. Furthermore, based on data from a sub study of 48 patients with AS, we showed a strong level (90.0%) of agreement between the two readers of X-rays with respect to modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). This paper not only could serve as reference for future publications from PSOAS cohort but also could serve as a basic guide to ensuring data quality for multicenter clinical studies