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OBJECTIVE: We investigated how diagnosis and injury location on neonatal brain MRI following onset of acute provoked seizures was associated with short term outcome. STUDY DESIGN: A multicenter cohort of neonates with acute provoked seizures enrolled in the Neonatal Seizure Registry. MRIs were centrally evaluated by a neuroradiologist for location of injury and radiologic diagnosis. Clinical outcomes were determined by chart review. Multivariate logistic regression was used to examine the association between MRI findings and outcomes. RESULTS: Among 236 newborns with MRI at median age 4 days (IQR 3-8), 91% had abnormal MRI. Radiologic diagnoses of intracranial hemorrhage (OR 3.2 [1.6-6.5], p < 0.001) and hypoxic-ischemic encephalopathy (OR 2.7 [1.4-5.4], p < 0.003) were associated with high seizure burden. Radiologic signs of intracranial infection were associated with abnormal neurologic examination at discharge (OR 3.9 [1.3-11.6], p < 0.01). CONCLUSION: Findings on initial MRI can help with expectant counseling on short-term outcomes following acute provoked neonatal seizures.

Prednisolone given universally for COPD exacerbations causes harm without any benefit. Patients deserve blood eosinophil-guided prednisolone treatment for COPD exacerbations.

The purpose of this qualitative phenomenological study will be to describe long-term recovery (LTR) from drug addiction for African American (AA) recovering addicts (RAs) who have ten or more years in recovery and are members of Narcotics Anonymous (NA). The theory guiding this study is the Transtheoretical Model (TTM) (Stages of Change) (Prochaska & DiClemente, 1977) as it describes the process of how individuals change their habitual behavior/s by transitioning through five stages of change (precontemplation, contemplation, preparation, action, maintenance,). TTM indicates that positive behavioral change is an ongoing process which is significant to understanding these factors in relation to LTR as a continuing commitment to change one’s thinking, behaviors, and spiritual purpose to find a new way to approach life with the disease of addiction (DOA) without the use of drugs. Therefore, the purpose of this study will be threefold: (1) to describe and understand the turning point or initial surrender of RA’s active addiction and why they stop using drugs, (2) to delineate and understand the meaning of LTR from the perspective of AA RA’s lived experience/s as members of NA and, (3) to delineate and understand AA RA’s daily maintenance tools of recovery capital (social supports, spiritual practices, and 12-step affiliation) or sustainable practices of LTR. This phenomenological study will seek to understand the lived experience of AA RAs while delineating the factors that influenced them to seek recovery and the daily preservation practices that they apply to their lives to stay in LTR from a Christian worldview.

The traditional ‘central dogma’ describes the flow of genetic information from DNA to RNA to protein. This process highlights the critical role of genes in living organisms. Nonetheless, ongoing immunological research coming to realize that emerging disciplines like glycomics and epigenetics are challenging the traditional viewpoint and extending the boundaries of the ‘central dogma’. . . .

Exercise has emerged as fundamental therapeutic medicine in the management of cancer. Exercise improves health-related outcomes, including quality of life, neuromuscular strength, physical function, and body composition, and it is associated with a lower risk of disease recurrence and increased survival. Moreover, exercise during or post cancer treatments is safe, can ameliorate treatment-related side effects, and may enhance the effectiveness of chemotherapy and radiation therapy. To date, traditional resistance training (RT) is the most used RT modality in exercise oncology. However, alternative training modes, such as eccentric, cluster set, and blood flow restriction are gaining increased attention. These training modalities have been extensively investigated in both athletic and clinical populations (e.g., age-related frailty, cardiovascular disease, type 2 diabetes), showing considerable benefits in terms of neuromuscular strength, hypertrophy, body composition, and physical function. However, these training modes have only been partially or not at all investigated in cancer populations. Thus, this study outlines the benefits of these alternative RT methods in patients with cancer. Where evidence in cancer populations is sparse, we provide a robust rationale for the possible implementation of certain RT methods that have shown positive results in other clinical populations. Finally, we provide clinical insights for research that may guide future RT investigations in patients with cancer and suggest clear practical applications for targeted cancer populations and related benefits.

Purpose: The purpose of this study was to estimate the incidence and mortality of conjunctival melanoma in Australia from 1982 to 2014. Methods: De-identified unit data for all cases of ocular melanoma were extracted from the Australian Cancer Database from 1982 to 2014. Conjunctival melanoma cases were extracted, and the incidence and mortality were analyzed. Incidence rates were age-standardized against the 2001 Australian Standard Population. Mortality was assessed using log-rank and Cox regression. Results: From 1982 to 2014, there were 299 cases of conjunctival melanoma. The age-standardized incidence rate was 0.48 (95% confidence interval [CI] = 0.41 to 0.54) per million per year. Women (0.52, 95% CI = 0.42 to 0.62) had a higher incidence than men (0.42, 95% CI = 0.33 to 0.51). The incidence of conjunctival melanoma increased in men (+1.46%) and significantly women (+1.41%, P = 0.023) over the study period. The mean 5-, 10-, and 15-year disease-specific survival were 90%, 82%, and 80%, respectively, during the 33-year interval. Comparisons of survival among age, sex, and state revealed no significant differences when tested using log-rank or Cox regression. Conclusions: In conclusion, we found an increase in the rate of conjunctival melanoma diagnoses in Australia from 1982 to 2014. Over the same period, disease survival remained unchanged at a mean of 90%.

Background: Lupus nephritis (LN) is a crucial complication of systemic lupus erythematosus (SLE) and has important clinical implications in guiding treatment. N-glycosylation of immunoglobulin G (IgG) plays a key role in the development of SLE by affecting the balance of anti-inflammatory and proinflammatory responses. This study aimed to evaluate the performance of IgG N-glycosylation for diagnosing LN in a sample of female SLE patients. Methods: This case-control study recruited 188 women with SLE, including 94 patients with LN and 94 age-matched patients without LN. The profiles of plasma IgG N-glycans were detected by hydrophilic interaction chromatography with ultra-performance liquid chromatography (HILIC-UPLC). A multivariate logistic regression model was used to explore the associations between IgG N-glycans and LN. A diagnostic model was developed using the significant glycans as well as demographic factors. The performance of IgG N-glycans in the diagnosis of LN was evaluated by receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) and its 95% confidence interval (CI) were calculated. Results: There were significant differences in 9 initial glycans (GP2, GP4, GP6, GP8, GP10, GP14, GP16, GP18 and GP23) between women with SLE with and without LN (P < 0.05). The levels of sialylated, galactosylated and fucosylated glycans were significantly lower in the LN patients than in the control group, while bisected N-acetylglucosamine (GlcNAc) glycans were increased in LN patients (P < 0.05). GP8, GP10, GP18, and anemia were included in our diagnostic model, which performed well in differentiating female SLE patients with LN from those without LN (AUC = 0.792, 95% CI: 0.727 to 0.858). Conclusion: Our findings indicate that decreased sialylation, galactosylation, and core fucosylation and increased bisecting GlcNAc might play a role in the development of LN by upregulating the proinflammatory response of IgG. IgG N-glycans can serve

Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long-term prognosis. AAC can be assessed on dual-energy X-ray absorptiometry (DXA)-derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24-point scoring method (AAC-24). Previous studies have identified robust associations between AAC-24 score, incident falls, and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine-learning algorithm for assessing AAC-24 scores (ML-AAC24). In this prospective study, we evaluated the association between ML-AAC24 and long-term incident falls and fractures in 1023 community-dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow-up, 253 (24.7%) women experienced a clinical fracture identified via self-report every 4–6 months and verified by X-ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML-AAC24 ≥ 2) had a greater risk of clinical fractures (hazard ratio [HR] 1.42; 95% confidence interval [CI], 1.10–1.85) and fall-related hospitalization (HR 1.35; 95% CI, 1.09–1.66), compared to those with low AAC (ML-AAC24 ≤ 1). Similar to manually assessed AAC-24, ML-AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC-24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associati

The causality between atherosclerosis and dementia remains unclear. This study aimed to explore the causal effect of atherosclerosis related indicators on dementia risk based on two-sample Mendelian randomization (MR) using summary statistics of genome-wide association studies (GWASs). The inverse variance weighted (IVW) method was performed as the main analysis, supplemented by different sensitivity analyses. Suggestive evidence indicated that peripheral arterial disease (PAD) (odds ratio (OR): 0.864, 95% confidence interval (CI): 0.797–0.937), coronary atherosclerosis (CoAS) (OR: 0.927, 95% CI: 0.860–0.998) and atherosclerosis, excluding cerebral, coronary, and PAD (ATHSCLE) (OR: 0.812, 95% CI: 0.725–0.909) were inversely associated with the risk of AD. The sensitivity analysis confirmed a suggestive reverse effect of ATHSCLE on the risk of frontotemporal dementia (FTD) (OR, 0.812, 95% CI, 0.725–0.909). Findings provide suggestive evidence that PAD, CoAS, and ATHSCLE might be associated with the risk of AD or FTD, which requires further exploration in larger samples.

Background: The Mitogen-activated protein kinase 1 (MAPK1) has both independent functions of phosphorylating histones as a kinase and directly binding the promoter regions of genes to regulate gene expression as a transcription factor. Previous studies have identified elevated expression of MAPK1 in human gastric cancer, which is associated with its role as a kinase, facilitating the migration and invasion of gastric cancer cells. However, how MAPK1 binds to its target genes as a transcription factor and whether it modulates related gene expressions in gastric cancer remains unclear. Results: Here, we integrated biochemical assays (protein interactions and chromatin immunoprecipitation (ChIP)), cellular analysis assays (cell proliferation and migration), RNA sequencing, ChIP sequencing, and clinical analysis to investigate the potential genomic recognition patterns of MAPK1 in a human gastric adenocarcinoma cell-line (AGS) and to uncover its regulatory effect on gastric cancer progression. We confirmed that MAPK1 promotes AGS cells invasion and migration by regulating the target genes in different directions, up-regulating seven target genes (KRT13, KRT6A, KRT81, MYH15, STARD4, SYTL4, and TMEM267) and down-regulating one gene (FGG). Among them, five genes (FGG, MYH15, STARD4, SYTL4, and TMEM267) were first associated with cancer procession, while the other three (KRT81, KRT6A, and KRT13) have previously been confirmed to be related to cancer metastasis and migration. Conclusion: Our data showed that MAPK1 can bind to the promoter regions of these target genes to control their transcription as a bidirectional transcription factor, promoting AGS cell motility and invasion. Our research has expanded the understanding of the regulatory roles of MAPK1, enriched our knowledge of transcription factors, and provided novel candidates for cancer therapeutics.

Objective: A range of neuropathological changes occur in the brains of individuals with adult Niemann-Pick type C disease (NPC), a recessive disorder of cholesterol trafficking that results in accumulation of cholesterol and gangliosides in lysosomes, particularly in neurons. One of the most significant regions of grey matter loss occurs in the thalami, which abut the midline. What is not known is whether these are neurodevelopmental in origin well prior to symptomatic onset. We aimed to examine other markers of midline developmental anomalies in adults with NPC. Method: We examined the size of adhesio interthalamica (AI) and cavum septum pellucidum (CSP) (if present) in nine individuals diagnosed with NPC and nine healthy comparison subjects, matched for age and gender, using a 3T magnetic resonance volumetric sequence and measured the length of the AI and CSP in mm. Results: We found that 5/9 NPC patients and 0/9 controls had a missing AI. AI length was significantly shorter in the patient group. No subject in other group had a large CSP, and CSP length did not differ. Duration of illness showed a trend to a negative correlation with AI length in patients. Conclusions: Our findings suggest that adult NPC patients show some markers of early neurodevelopmental disturbance, matching findings seen in psychotic disorders. The differences in AI, but not CSP, suggest neurodevelopmental change may occur early in gestation rather than post-partum. The relationship with duration of illness suggests that there may be atrophy over time in these structures, consistent with prior analyses of grey matter regions in NPC.

Externalizing traits have been related with the outcomes of coronavirus disease 2019 (COVID-19) and Alzheimer's dementia (AD); however, whether these associations are causal remains unknown. We used the two-sample Mendelian randomization (MR) approach with more than 200 single-nucleotide polymorphisms (SNPs) for externalizing traits to explore the causal associations of externalizing traits with the risk of COVID-19 (infected COVID-19, hospitalized COVID-19, and severe COVID-19) or AD based on the summary data. The inverse variance–weighted method (IVW) was used to estimate the main effect, followed by several sensitivity analyses. IVW analysis showed significant associations of externalizing traits with COVID-19 infection (odds ratio [OR] = 1.456, 95% confidence interval [95% CI] = 1.224–1.731), hospitalized COVID-19 (OR = 1.970, 95% CI = 1.374–2.826), and AD (OR = 1.077, 95% CI = 1.037–1.119). The results were consistent using weighted median (WM), penalized weighted median (PWM), MR-robust adjusted profile score (MR-RAPS), and leave-one-out sensitivity analyses. Our findings assist in exploring the causal effect of externalizing traits on the pathophysiology of infection and severe infection of COVID-19 and AD. Furthermore, our study provides evidence that shared externalizing traits underpin the two diseases.

Cancer-related fatigue is one of the most prevalent and distressing symptoms experienced by people diagnosed with cancer [1]. Effective fatigue management requires cancer survivors and caregivers to adopt a range of self-management behaviours; therefore, the provision of quality self-management support is critical [2]. Clinicians report a lack of clear guidance on self-management practices, which hinders their ability to provide optimal supportive care for people experiencing cancer-related fatigue. . . .

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors., CC BY 4.0 DEED© 2023, The Author(s)Correspondence Address: A. Javeed; Department of Health, Blekinge Institute of Technology, Karlskrona, Valhallavägen 1, Blekinge, 37179, Sweden; email: ajv@bth.seOpen access funding provided by Blekinge Institute of Technology.

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors., CC BY 4.0 DEED© 2023, The Author(s)Correspondence Address: A. Javeed; Department of Health, Blekinge Institute of Technology, Karlskrona, Valhallavägen 1, Blekinge, 37179, Sweden; email: ajv@bth.seOpen access funding provided by Blekinge Institute of Technology.

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. © 2023, The Author(s)., NEAR - National E-Infrastructure for Aging Research

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. © 2023, The Author(s)., NEAR - National E-Infrastructure for Aging Research

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. © 2023, The Author(s)., NEAR - National E-Infrastructure for Aging Research

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. © 2023, The Author(s)., NEAR - National E-Infrastructure for Aging Research

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors., CC BY 4.0 DEED© 2023, The Author(s)Correspondence Address: A. Javeed; Department of Health, Blekinge Institute of Technology, Karlskrona, Valhallavägen 1, Blekinge, 37179, Sweden; email: ajv@bth.seOpen access funding provided by Blekinge Institute of Technology.

Background: Metformin is a commonly prescribed and well-tolerated medication. In laboratory studies, metformin suppresses BRAF wild-type melanoma cells but accelerates the growth of BRAF-mutated cells. This study investigated the prognostic and predictive value of metformin, including with respect to BRAF mutation status, in the European Organisation for Research and Treatment of Cancer 1325/KEYNOTE-054 randomised controlled trial. Methods: Patients with resected high-risk stage IIIA, IIIB, or IIIC melanoma received 200 mg of pembrolizumab (n = 514) or placebo (n = 505) every 3 weeks for twelve months. Pembrolizumab prolonged recurrence-free survival (RFS) and distant metastasis-free survival (DMFS) at approximately 42 months median follow-up (Eggermont et al., TLO, 2021). Multivariable Cox regression was used to estimate associations of metformin with RFS and DMFS. Interaction terms were used to model effect modification by treatment and BRAF mutation. Results: Fifty-four patients (0.5%) used metformin at baseline. Metformin was not significantly associated with RFS (hazard ratio [HR] 0.87, 95% confidence interval [CI] 0.52–1.45) and DMFS (HR 0.82, 95% CI 0.47–1.44). The interaction between metformin and the treatment arm was not significant for either RFS (p = 0.92) or DMFS (p = 0.93). Among patients with mutated BRAF, the association of metformin with RFS (HR 0.70, 95% CI 0.37–1.33) was greater in magnitude though not significantly different to those without mutated BRAF (HR 0.98, 95% CI 0.56–1.69). Conclusions: There was no significant impact of metformin use on pembrolizumab efficacy in resected high-risk stage III melanoma. However, larger studies or pooled analyses are needed, particularly to explore a possible effect of metformin in BRAF-mutated melanoma. © 2023

A prominent toxicity of androgen suppression in patients with prostate cancer (PCa) is loss of skeletal muscle. Exercise may induce tumor suppression through the endocrinal function of skeletal muscle; however, this is currently unknown. In this review, we summarize our work demonstrating the acute and chronic myokine response to exercise and the tumor-suppressive effect of circulatory milieu alteration in PCa patients. © Lippincott Williams & Wilkins.

Dear Editors: Abboud et al 1 have written an intriguing and fascinating descriptive account of the rise of pancreatic cancer incidence in the United States from 2001 to 2018, which is curiously more marked in females. . . .

It appears that nowadays in life every human being believe in medication given from hospitals meanwhile all diseases can be cured by healing trees that we discovered in Bushbuckridge region To achieve the aim of this proposal we will use an interview method, content analysis, textual analysis together with an unstructured interview. These methods will also be supported by an open-ended questionnaires whereby anyone can be questioned to give their point of view about healing trees. The study will review to people that not only the doctor`s medicine is able to heal the diseases but the study of healing trees and research can also come to rescue.

Chronic migraine (CM) is a neurological disease characterized by frequent migraine attacks that prevent affected individuals from performing daily activities of living, significantly diminish quality of life, and increase familial burden. Before onabotulinumtoxinA was approved for CM, there were few treatment options for these seriously disabled patients and none had regulatory approval. The terminology and recognition of CM evolved in parallel with the onabotulinumtoxinA clinical development program. Because there were no globally accepted classification criteria for CM when onabotulinumtoxinA was in development, the patient populations for the trials conducted by Allergan were determined by the Allergan migraine team in collaboration with headache scientists and clinicians. These trials and collaborations ultimately led to improvements in CM classifications. In 2010, onabotulinumtoxinA became the first medication and first biologic approved specifically to prevent headaches in patients with CM. Approval was based on 2 similarly designed phase 3, double-blind, randomized, placebo-controlled, multicenter clinical studies. Both studies showed significantly greater improvements in mean change from baseline in headache-day frequency in patients with CM receiving onabotulinumtoxinA compared with those receiving placebo. The safety and effectiveness of onabotulinumtoxinA have been established globally in >5000 patients with CM with or without medication overuse treated in clinical and observational studies. Benefits also include improvements in quality of life, fewer psychiatric comorbidities, and reduced healthcare resource utilization. Across studies, onabotulinumtoxinA was well tolerated; adverse events tended to be mild or moderate in severity and to decline over subsequent treatment cycles.

BACKGROUND: Phospholipase-associated neurodegeneration (PLAN) caused by mutations in the PLA2G6 gene is a rare neurodegenerative disorder that presents with four sub-groups. Infantile neuroaxonal dystrophy (INAD) and PLA2G6-related dystonia-parkinsonism are the main two subtypes. In this cohort, we reviewed clinical, imaging, and genetic features of 25 adult and pediatric patients harboring variants in the PLA2G6. METHODS: An extensive review of the patients' data was carried out. Infantile Neuroaxonal Dystrophy Rating Scale (INAD-RS) was used for evaluating the severity and progression of INAD patients. Whole-exome sequencing was used to determine the disease's underlying etiology followed by co-segregation analysis using Sanger sequencing. In silico prediction analysis based on the ACMG recommendation was used to assess the pathogenicity of genetic variants. We aimed to survey a genotype-genotype correlation in PLA2G6 considering all reported disease-causing variants in addition to our patients using the HGMD database and the chi-square statistical approach. RESULTS: Eighteen cases of INAD and 7 cases of late-onset PLAN were enrolled. Among 18 patients with INAD, gross motor regression was the most common presenting symptom. Considering the INAD-RS total score, the mean rate of progression was 0.58 points per month of symptoms (Standard error 0.22, lower 95% - 1.10, and upper 95% - 0.15). Sixty percent of the maximum potential loss in the INAD-RS had occurred within 60 months of symptom onset in INAD patients. Among seven adult cases of PLAN, hypokinesia, tremor, ataxic gate, and cognitive impairment were the most frequent clinical features. Various brain imaging abnormalities were also observed in 26 imaging series of these patients with cerebellar atrophy being the most common finding in more than 50%. Twenty unique variants in 25 patients with PLAN were detected including nine novel variants. Altogether, 107 distinct disease-causing variants from 87 patient wer

The European honey bee (Apis mellifera) is one of the most important livestock animals in New Zealand. Their value comes from a combination of pollination services and the production of honey for export, notably mānuka honey. American Foulbrood (AFB) is a disease of honey bee larvae and pupae and is caused by the bacterial pathogen, Paenibacillus larvae. AFB is the most serious disease that infects honey bees and is present in almost all countries where honey bees are found. AFB has been present in New Zealand since 1877 and spread to all parts of the country within 10 years. Unlike other countries, the use of antibiotics in hives infected with P. larvae is prohibited under New Zealand law and infected hives must be destroyed immediately. Bacteriophages (phages) are a well-studied alternative to antibiotics. Phages are simple viruses that kill specific bacteria and are highly abundant in the environment with an estimated 1031 globally. Phages have been shown to work effectively as a prophylactic to infection from certain diseases. With the growing antimicrobial resistance crisis, phages are becoming a well-studied and promising alternative to antibiotics. The aim of this research was to investigate the use of phages as a preventative measure against AFB. Previous work undertaken in other laboratories around the world has shown that phages can be isolated from healthy hives and nearby soil and that AFB pathogens are susceptible to destruction by these phages. In this work, we collected soil samples using citizen led science from hives throughout New Zealand. From soil samples provided we isolated 26 novel phages that are destructive to P. larvae. Selected phages were combined into a cocktail and tested against vegetative forms of P. larvae in in-lab testing. All phages were also sequenced and annotated and compared to other P. larvae phages that have been isolated around the world. This project: ABAtE (Active Bacteriophages for AFB Elimination), provides the groundwo

Due to the increasing demand for poultry products from traditional farming systems, the growing trend for keeping chickens in alternative production systems has also been observed worldwide. Different types of extensive systems such are organic production, low-input production systems, and free-range systems have experienced significant growth in response to this trend in our country. The Government of the Republic of Serbia, within the framework of the Program for the Preservation of Autochthonous Genotypes, supports the development of extensive rearing systems for poultry in the form of significant annual subsidies. We have three types of backyard farming systems: permanent confinement, free-range, and mixed confinement. Mixed confinement is the most frequent system and implies, the birds scavenge freely in the courtyard of the farmer’s house during the day, and at night they are kept in an improvised house (hennery or chicken coop). These rearing systems often consist of free, native, unselected breeds of various ages, mixed with different poultry/bird species in the flocks. They are less standardized than intensive-rearing systems. In addition, not enough attention is paid to the need to protect backyard poultry. Most of these birds are kept outdoors where the birds forage around the house or in the backyard during the day. Access to the outdoors brings poultry into contact with wild birds and other animals that can carry various diseases. The results from our laboratories showed that due to poor or absent disease control strategies and inadequate hygiene, in the last 7 years, the most frequent cause of outbreaks and high mortalities of backyard poultry in Serbia were pathogens such as bacteria (E. coli, Clostridium perfringens), viruses (Marek's disease virus, Avian leucosis virus), fungi (Aspergillus spp.) and parasites (Ascaridia galli, Eimeria spp, Dermanyssus gallinae). Vaccination against Marek's disease virus is not legally binding; therefore, this diseas

ObjectiveDevelop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning.Study designModels using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center.ResultsDiscrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882-0.908) for survival and 0.867 (95% CI 0.857-0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741-0.893 and 0.804, CI 0.770-0.837, respectively).ConclusionOur successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets.

Outcomes of neonatal encephalopathy (NE) have improved since the widespread implementation of therapeutic hypothermia (TH) in high-resource settings. While TH for NE in term and near-term infants has proven beneficial, 30-50% of infants with moderate-to-severe NE treated with TH still suffer death or significant impairments. There is therefore a critical need to find additional pharmacological and non-pharmacological interventions that improve the outcomes for these children. There are many potential candidates; however, it is unclear whether these interventions have additional benefits when used with TH. Although primary and delayed (secondary) brain injury starting in the latent phase after HI are major contributors to neurodisability, the very late evolving effects of tertiary brain injury likely require different interventions targeting neurorestoration. Clinical trials of seizure management and neuroprotection bundles are needed, in addition to current trials combining erythropoietin, stem cells, and melatonin with TH. IMPACT: The widespread use of therapeutic hypothermia (TH) in the treatment of neonatal encephalopathy (NE) has reduced the associated morbidity and mortality. However, 30-50% of infants with moderate-to-severe NE treated with TH still suffer death or significant impairments. This review details the pathophysiology of NE along with the evidence for the use of TH and other beneficial neuroprotective strategies used in term infants. We also discuss treatment strategies undergoing evaluation at present as potential adjuvant treatments to TH in NE.

Introduction: Neonatal mortality remains a major public health problem worldwide, particularly in developing countries. Most neonatal deaths are attributable to preventable complications, such as premature birth, neonatal sepsis, and birth asphyxia. Objective: To determine the clinical characteristics of neonatal mortality in a tertiary-level hospital in Paraguay through a retrospective observational study. Methods: This observational, descriptive, and retrospective study was conducted at the Hospital de Clínicas in Paraguay. This design allowed information to be obtained from the medical records of newborns who attended the hospital over a period of five years. Results: Between 2018 and 2022, 121 cases of neonatal mortality were recorded at the Hospital de Clínicas del Paraguay. The mean birth weight was 2013.51 ± 1021 kg and mean gestational age was 32.87 ± 4.6 weeks. The most frequent diagnoses of neonatal death were sepsis (37 cases), cardiac involvement (61 cases), and genetic syndromes (39 cases). Discussion: The data obtained from this study suggest that maternal and neonatal risk factors are associated with neonatal mortality in the Hospital de Clínicas of Paraguay. Further studies are needed to analyze these factors and develop preventive strategies to reduce the neonatal mortality rate in the country, Introducción: La mortalidad neonatal sigue siendo un importante problema de salud pública en todo el mundo, especialmente en países en desarrollo. La mayoría de las muertes neonatales son atribuibles a complicaciones prevenibles, como la prematuridad, la sepsis neonatal y la asfixia al nacer. Objetivo: Determinar las características clínicas de la mortalidad neonatal en un hospital de tercer nivel del Paraguay mediante un estudio observacional retrospectivo. Metodología: El presente estudio es de diseño observacional, descriptivo y retrospectivo, se realizó en el Hospital de Clínicas de Paraguay. Este diseño permitió obtener información de los registros médicos de los recién nacidos atendidos en el hospital durante un período de cinco años. Resultados: En el periodo comprendido entre el 2018 y el 2022, se registraron un total de 131 casos de mortalidad neonatal en el Hospital de Clínicas del Paraguay. El peso medio al nacer de 2009,5 ± 991,4 kilogramos y una edad gestacional media de 32,87 ± 4,5 semanas. Los diagnósticos de óbito más frecuentes en los neonatos fueron sepsis (37 casos), afectación cardiaca (61 casos) y síndrome genético (39 casos). Discusión: Los datos obtenidos del estudio sugieren que existen factores de riesgo maternos y neonatales que están vinculados con la mortalidad neonatal en el Hospital de Clínicas del Paraguay. Se necesitan estudios posteriores que permitan profundizar en el análisis de estos factores y que permitan el desarrollo de estrategias preventivas para disminuir la tasa de mortalidad neonatal en el país.

Health and religion are strongly connected. This study aims primarily to compile the diseases described in the Bible and Quran with a secondary aim to group the diseases in punishment or blessing. Diseases mentioned in the Bible and Quran were compiled by manual review and grouped as punishment if imposed by the deity as penance; blessing if cured by grace or neutral. The results show difference among the books in the distribution of the diseases as associated to punishment (more prevalent in the first testament), blessing (more prevalent in the second testament) or neutral (more prevalent in the Quran).

Health and religion are strongly connected. This study aims primarily to compile the diseases described in the Bible and Quran with a secondary aim to group the diseases in punishment or blessing. Diseases mentioned in the Bible and Quran were compiled by manual review and grouped as punishment if imposed by the deity as penance; blessing if cured by grace or neutral. The results show difference among the books in the distribution of the diseases as associated to punishment (more prevalent in the first testament), blessing (more prevalent in the second testament) or neutral (more prevalent in the Quran).

Background: The growing burden of chronic disease (CD) in Ireland represents the greatest challenge to Irish Health Services. The role of physical activity/exercise training (PAET) is well established as an adjunct therapy in the management of CD. An integrated multi-disease, community-based exercise programme (MCEP) has the potential to offer a resource-efficient strategy to provide exercise in the community for either those with CD following completion of medically supervised hospital-based programmes or for individuals who do not require medical supervision. The purpose of this PhD was to assess the adherence to, efficacy of and patient perspective of a MCEP. A total of 118 patients with coronary artery disease (CAD) who had completed hospital-based cardiac rehabilitation, along with individuals with other CD (OCD), were referred by health care professionals to a newly established MCEP. CAD patients who could not attend the MCEP were assigned to a control group (CAD-CG) that received usual care advice. Study 1 evaluated baseline characteristics of individuals with CAD (96, 73%M) to those with OCD (98, 47%M) and a CAD-CG (22, 77%M) and compared rates and predictors of adherence to the MCEP. Outcome measures were socioeconomic and health-based demographics, health indices, functional capacity, and health-related quality of life (HRQoL). There was a significant difference in age, gender, number of comorbidities, and certain medications/conditions between the groups. Individuals with CAD had significantly better functional capacity and HRQoL scores than individuals with OCD. Females and individuals with lower mental wellbeing were less likely to attend and more likely to drop out. Study 2 evaluated the efficacy of participation in a MCEP for 10-weeks on selected health indices, functional capacity, and HRQoL. Lower body muscle strength increased (p < 0.005) in both CAD and OCD compared to CAD-CG. Within group improvements were found in aerobic fitness (p < 0.005) and

At the end of 2020, The Global Health Observatory estimated that globally there are 37.7 million people living with HIV (WHO, 2022). In Ireland, in 2021 over 6,000 people were estimated to be living with HIV, however this figure has already increased in 2022 with 293 notified cases of HIV in the first six months of 2022, compared to 145 for the same period of 2021 (Health Protection Surveillance Centre 2022). The HPSC 2021 statistics reveal that in 2% of HIV cases, intravenous drug use behaviour was the source of transmission. Research studies and health strategies emphasize the importance of adherence to HAART (Highly Active Antiretroviral Therapy) to avoid viral replication and personal/public health consequences. However, it is evident that addiction and homelessness can contribute to non-adherence to medical and pharmacological treatment for a range of diseases. Explanations have been put forward in the research including, side effects, stigma/shame, lack of structure/routine, cognitive impairment caused by drug use, lack of motivation/self-worth, lack of facilities to store and take medication. Adherence to HIV treatment for people who are homeless and in addiction, is further complicated by the required drug regimen which can be complex and difficult to tolerate. This study explores the lived experience of adhering to HIV medication in the context of drug addiction and homelessness, using a qualitative research approach, involving interviews with six people experiencing homelessness and addiction and who are prescribed HIV medication. Individual participant narratives are gathered and analyzed using Riesman’s (2008) narrative analysis framework which enables a deep exploration of the stories people tell about their lived experiences. The study findings illuminate how HIV, addiction and homelessness impact on adherence to HAART particularly in relation to diagnosis experiences, stigma and shame as well as the challenges of managing treatment in the context of h

At the end of 2020, The Global Health Observatory estimated that globally there are 37.7 million people living with HIV (WHO, 2022). In Ireland, in 2021 over 6,000 people were estimated to be living with HIV, however this figure has already increased in 2022 with 293 notified cases of HIV in the first six months of 2022, compared to 145 for the same period of 2021 (Health Protection Surveillance Centre 2022). The HPSC 2021 statistics reveal that in 2% of HIV cases, intravenous drug use behaviour was the source of transmission. Research studies and health strategies emphasize the importance of adherence to HAART (Highly Active Antiretroviral Therapy) to avoid viral replication and personal/public health consequences. However, it is evident that addiction and homelessness can contribute to non-adherence to medical and pharmacological treatment for a range of diseases. Explanations have been put forward in the research including, side effects, stigma/shame, lack of structure/routine, cognitive impairment caused by drug use, lack of motivation/self-worth, lack of facilities to store and take medication. Adherence to HIV treatment for people who are homeless and in addiction, is further complicated by the required drug regimen which can be complex and difficult to tolerate. This study explores the lived experience of adhering to HIV medication in the context of drug addiction and homelessness, using a qualitative research approach, involving interviews with six people experiencing homelessness and addiction and who are prescribed HIV medication. Individual participant narratives are gathered and analyzed using Riesman’s (2008) narrative analysis framework which enables a deep exploration of the stories people tell about their lived experiences. The study findings illuminate how HIV, addiction and homelessness impact on adherence to HAART particularly in relation to diagnosis experiences, stigma and shame as well as the challenges of managing treatment in the context of h

Background: The growing burden of chronic disease (CD) in Ireland represents the greatest challenge to Irish Health Services. The role of physical activity/exercise training (PAET) is well established as an adjunct therapy in the management of CD. An integrated multi-disease, community-based exercise programme (MCEP) has the potential to offer a resource-efficient strategy to provide exercise in the community for either those with CD following completion of medically supervised hospital-based programmes or for individuals who do not require medical supervision. The purpose of this PhD was to assess the adherence to, efficacy of and patient perspective of a MCEP. A total of 118 patients with coronary artery disease (CAD) who had completed hospital-based cardiac rehabilitation, along with individuals with other CD (OCD), were referred by health care professionals to a newly established MCEP. CAD patients who could not attend the MCEP were assigned to a control group (CAD-CG) that received usual care advice. Study 1 evaluated baseline characteristics of individuals with CAD (96, 73%M) to those with OCD (98, 47%M) and a CAD-CG (22, 77%M) and compared rates and predictors of adherence to the MCEP. Outcome measures were socioeconomic and health-based demographics, health indices, functional capacity, and health-related quality of life (HRQoL). There was a significant difference in age, gender, number of comorbidities, and certain medications/conditions between the groups. Individuals with CAD had significantly better functional capacity and HRQoL scores than individuals with OCD. Females and individuals with lower mental wellbeing were less likely to attend and more likely to drop out. Study 2 evaluated the efficacy of participation in a MCEP for 10-weeks on selected health indices, functional capacity, and HRQoL. Lower body muscle strength increased (p < 0.005) in both CAD and OCD compared to CAD-CG. Within group improvements were found in aerobic fitness (p < 0.005) and

Background: The growing burden of chronic disease (CD) in Ireland represents the greatest challenge to Irish Health Services. The role of physical activity/exercise training (PAET) is well established as an adjunct therapy in the management of CD. An integrated multi-disease, community-based exercise programme (MCEP) has the potential to offer a resource-efficient strategy to provide exercise in the community for either those with CD following completion of medically supervised hospital-based programmes or for individuals who do not require medical supervision. The purpose of this PhD was to assess the adherence to, efficacy of and patient perspective of a MCEP. A total of 118 patients with coronary artery disease (CAD) who had completed hospital-based cardiac rehabilitation, along with individuals with other CD (OCD), were referred by health care professionals to a newly established MCEP. CAD patients who could not attend the MCEP were assigned to a control group (CAD-CG) that received usual care advice. Study 1 evaluated baseline characteristics of individuals with CAD (96, 73%M) to those with OCD (98, 47%M) and a CAD-CG (22, 77%M) and compared rates and predictors of adherence to the MCEP. Outcome measures were socioeconomic and health-based demographics, health indices, functional capacity, and health-related quality of life (HRQoL). There was a significant difference in age, gender, number of comorbidities, and certain medications/conditions between the groups. Individuals with CAD had significantly better functional capacity and HRQoL scores than individuals with OCD. Females and individuals with lower mental wellbeing were less likely to attend and more likely to drop out. Study 2 evaluated the efficacy of participation in a MCEP for 10-weeks on selected health indices, functional capacity, and HRQoL. Lower body muscle strength increased (p < 0.005) in both CAD and OCD compared to CAD-CG. Within group improvements were found in aerobic fitness (p < 0.005) and

In recent years, the microbiota and the gut-brain axis have become one of the most exciting areas of research, since the constant communication of this axis has been evidenced, in which the intestinal microbiota also participates. The intestinal microbiota and its microbiome (microbiota genome) are fundamental elements for the balance of health and act as key regulators of different functions of the human organism, including the relationship of the microbiota-gut-brain axis characterized by their actions on brain development and physiology. When the microbiota is altered, it enters a state of dysbiosis. These alterations are present in many diseases. Consequently, the present investigation has the objective of capturing the generalities around the gut-brain-microbiota axis and its relationship or impact on human health. From the review, it can be deduced that the relationship between the gut-brain-microbiota axis and some diseases has been demonstrated in multiple investigations. Among the main ones are: behavioral disorders, autism spectrum disorder, attention deficit/hyperactivity disorder, anxiety, depression, Alzheimer's disease, obesity, asthma, type 1 diabetes, multiple sclerosis, Crohn's disease, food allergy, among other. Despite the evidence found in favor of the relationship between the microbiota and the central nervous system, they do not establish their role in the origin of diseases. New studies will be necessary to clear up the question: does the gut-brain-microbiota axis impact the origin of the disease or does the disease impact the relationship of this axis?.

In this paper, we investigate if external resonances created at a spherical tumor model can be utilized to increase the contrast between the tumor and background field in a simple microwave breast imaging scenario. Numerical simulations are being performed for two cases, low-adipose and high-adipose average breast tissue, exhibiting high and low permittivity and dielectric losses, respectively. The electric field is probed along the breast surface with and without a spherical tumor model present, and the relative tumor signal with respect to the background field is separated into a surface-normal and surface-parallel component. It is shown that in the high-adipose case, an external spherical resonance is created at the tumor, whereas no clear resonance occurs in the low-adipose case. In both cases is the contrast between the tumor signal and the background field stronger in the surface-normal than in the surface-parallel component. The external tumor resonance could directly be observed in the high-adipose case as a decrease of field strength close to the tumor if placed in close proximity to the breast surface.

BACKGROUND: DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction. OBJECTIVE: Testing long-term predictive ability ( > 15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers. METHODS: EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points. RESULTS: EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022- < 0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOE 4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for 4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.

Background: Plant-based diets have been associated with a lower risk of several chronic diseases, but the relationship with PD is unknown. Objectives: We examined the association of three different plant-based diets with PD incidence in the UK Biobank cohort. Methods: We conducted a prospective study among 126,283 participants from the UK Biobank cohort. Three plant-based diet indices (overall plant-based diet index, PDI; healthful plant-based diet index, hPDI; and unhealthful plant-based diet index, uPDI) were derived from 24-hour dietary recalls based on 17 food groups. Multivariable Cox regression models were used to estimate the risk of PD across quartiles of the PDIs and for each of the food groups that constituted the score. Further analyses were carried out to assess potential heterogeneity in associations between hPDI and PD across strata of some hypothesized effect modifiers. Results: During 11.8 years of follow-up (1,490,139 person-years), 577 cases of PD incidence were reported. After multivariable adjustment, participants in the highest hPDI and overall PDI quartile had lower risk of PD (22% and 18%, respectively), whereas a higher uPDI was associated with a 38% higher PD risk. In food-based analyses, higher intakes of vegetables, nuts, and tea were associated with a lower risk of PD (28%, 31% and 25%, respectively). Stratifying by Polygenic Risk Score (PRS), results were significant only for those with a lower PRS for PD. Conclusions: Following a healthful plant-based diet and in particular the inclusion of readily achievable intakes of vegetables, nuts and tea in the habitual diet are associated with a lower risk of PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The aim of this paper is to explore what we have learned during the COVID-19 pandemic in the field of Physical Education in three different countries: Argentina, Spain and Sweden. Data were generated through semi-structured interviews, and the concept of field agency is used to make sense of the data. Differences were found among the three countries, regarding the content of the classes, the use of resources, the emotions of teachers, and the use of physical contact. This was also a result of the regulations and resources in place. The COVID-19 pandemic has demonstrated that the nature of the field of PE had been momentarily disrupted during the pandemic in these contexts. The habitus of both, teachers and students was challenged, and the economic capital of each context determined what was possible to do ‘as PE’. Particular new discourses, such as the risk of getting the COVID-19 virus, interacted through agents to re-shape the field of PE and modify agents’ habitus. The participating teachers enacted agency and expressed their capacity to make practical and normative judgements among alternative possible trajectories of action for their classes, in response to the emerging demands, dilemmas and ambiguities of the COVID-19 pandemic.

Sirtuin-1 (Sirt1), encoded by the SIRT1 gene, is a conserved Nicotinamide adenine dinucleotide (NAD+) dependent deacetylase enzyme, considered as the master regulator of metabolism in humans. Sirt1 contributes to a wide range of biological pathways via several mechanisms influenced by lifestyle, such as diet and exercise. The importance of a healthy lifestyle is of relevance to highly prevalent modern chronic diseases, such as Alzheimer's disease (AD). There is growing evidence at multiple levels for a role of Sirt1/SIRT1 in AD pathological mechanisms. As such, this review will explore the relevance of Sirt1 to AD pathological mechanisms, by describing the involvement of Sirt1/SIRT1 in the development of AD pathological hallmarks, through its impact on the metabolism of amyloid- and degradation of phosphorylated tau. We then explore the involvement of Sirt1/SIRT1 across different AD-relevant biological processes, including cholesterol metabolism, inflammation, circadian rhythm, and gut microbiome, before discussing the interplay between Sirt1 and AD-related lifestyle factors, such as diet, physical activity, and smoking, as well as depression, a common comorbidity. Genome-wide association studies have explored potential associations between SIRT1 and AD, as well as AD risk factors and co-morbidities. We summarize this evidence at the genetic level to highlight links between SIRT1 and AD, particularly associations with AD-related risk factors, such as heart disease. Finally, we review the current literature of potential interactions between SIRT1 genetic variants and lifestyle factors and how this evidence supports the need for further research to determine the relevance of these interactions with respect to AD and dementia.

Epigenetic mechanisms such as DNA methylation have been implicated in a number of diseases including cancer, heart disease, autoimmune disorders, and neurodegenerative diseases. While it is recognized that DNA methylation is tissue-specific, a limitation for many studies is the ability to sample the tissue of interest, which is why there is a need for a proxy tissue such as blood, that is reflective of the methylation state of the target tissue. In the last decade, DNA methylation has been utilized in the design of epigenetic clocks, which aim to predict an individual’s biological age based on an algorithmically defined set of CpGs. A number of studies have found associations between disease and/or disease risk with increased biological age, adding weight to the theory of increased biological age being linked with disease processes. Hence, this review takes a closer look at the utility of DNA methylation as a biomarker in aging and disease, with a particular focus on Alzheimer’s disease.

Clostridioides difficile poses an ongoing threat as a cause of gastrointestinal disease in humans and animals. Traditionally considered a human healthcare-related disease, increases in community-associated C. difficile infection (CDI) and growing evidence of inter-species transmission suggest a wider perspective is required for CDI control. In horses, C. difficile is a major cause of diarrhoea and life-threatening colitis. This study aimed to better understand the epidemiology of CDI in Australian horses and provide insights into the relationships between horse, human and environmental strains. A total of 752 faecal samples from 387 Western Australian horses were collected. C. difficile was isolated from 104 (30.9%) horses without gastrointestinal signs and 19 (37.8%) with gastrointestinal signs. Of these, 68 (55.3%) harboured one or more toxigenic strains, including C. difficile PCR ribotypes (RTs) 012 (n = 14), 014/020 (n = 10) and 087 (n = 7), all prominent in human infection. Whole-genome analysis of 45 strains identified a phylogenetic cluster of 10 closely related C. difficile RT 012 strains of equine, human and environmental origin (0–62 SNP differences; average 23), indicating recent shared ancestry. Evidence of possible clonal inter-species transmission or common-source exposure was identified for a subgroup of three horse and one human isolates, highlighting the need for a One Health approach to C. difficile surveillance.

Alzheimer’s disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes—aggregation of the amyloid- (A ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)—are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of A plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with A plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than A and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with A and tau.

In recent years, serum-based tests for early detection and detection of tissue of origin are being developed. Circulating microRNA has been shown to be a potential source of diagnostic information that can be collected non-invasively. In this study, we investigate circulating microRNAs as predictors of cancer stage. Specifically, we predict whether a sample stems from a patient with early stage (0-II) or late stage cancer (III-IV). We trained five machine learning algorithms on a data set of cancers from twelve different primary sites. The results showed that cancer stage can be predicted from circulating microRNA with a sensitivity of 71.73%, specificity of 79.97%, as well as positive and negative predictive value of 54.81% and 89.29%, respectively. Furthermore, we compared the best pan-cancer model with models specialized on individual cancers and found no statistically significant difference. Finally, in the best performing pan-cancer model 185 microRNAs were significant. Comparing the five most relevant circulating microRNAs in the best performing model with the current literature showed some known associations to various cancers. In conclusion, the study showed the potential of circulating microRNA and machine learning algorithms to predict cancer stage and thus suggests that further research into its potential as a non-invasive clinical test is warranted., Part of the book sub series: Lecture Notes in Bioinformatics (LNBI) Electronic ISSN 2366-6331 Print ISSN 2366-6323© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.This work was supported by the University of Skövde, Swede nunder grants from the Knowledge Foundation (20170302, 20200014). The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at Chalmers University of Technology partially funded by the Swedish Research Council through grant agreement no. 2022–06725.

Cardiovascular diseases (CVDs) is considered a serious public health problem due to the uncertainty of its onset. Consuming wearable devices have increasing popularities for healthcare monitoring, and many of them are capable of continuous monitoring and early detection of CVDs. This paper proposes a framework for heart sound detection that can be considered for deployment on smart wearable devices to screen CVDs conveniently. A dual-stream convolutional neural network (DS-CNN) is developed to detect abnormal ones from short-term heart sound recordings. Preprocessing module is first employed for noise filtering and amplitude normalization. Then short-time Fourier transform and higher-order spectral are introduced for feature extraction, whose products are subsequently fed into the DS-CNN for screening abnormal heart sound signals. Two open accessible datasets are employed for performance evaluation. The results well demonstrate the classification accuracy of the proposed DS-CNN, and also indicate its advantages for adapting to heart sound recordings collected by different equipments. IEEE, Export Date: 13 April 2023; Article; CODEN: ITCED