Medical Research Research Articles

Serum proteomic profiling of physical activity reveals CD300LG as a novel exerkine with a potential causal link to glucose homeostasis.

Physical activity has been associated with preventing the development of type 2 diabetes and atherosclerotic cardiovascular disease. However, our understanding of the precise molecular mechanisms underlying these effects remains incomplete and good biomarkers to objectively assess physical activity are lacking. We analyzed 3072 serum proteins in 26 men, normal weight or overweight, undergoing 12 weeks of a combined strength and endurance exercise intervention. We estimated insulin sensitivity with hyperinsulinemic euglycemic clamp, maximum oxygen uptake, muscle strength, and used MRI/MRS to evaluate body composition and organ fat depots. Muscle and subcutaneous adipose tissue biopsies were used for mRNA sequencing. Additional association analyses were performed in samples from up to 47,747 individuals in the UK Biobank, as well as using two-sample Mendelian randomization and mice models. Following 12 weeks of exercise intervention, we observed significant changes in 283 serum proteins. Notably, 66 of these proteins were elevated in overweight men and positively associated with liver fat before the exercise regimen, but were normalized after exercise. Furthermore, for 19.7 and 12.1% of the exercise-responsive proteins, corresponding changes in mRNA expression levels in muscle and fat, respectively, were shown. The protein CD300LG displayed consistent alterations in blood, muscle, and fat. Serum CD300LG exhibited positive associations with insulin sensitivity, and to angiogenesis-related gene expression in both muscle and fat. Furthermore, serum CD300LG was positively associated with physical activity and negatively associated with glucose levels in the UK Biobank. In this sample, the association between serum CD300LG and physical activity was significantly stronger in men than in women. Mendelian randomization analysis suggested potential causal relationships between levels of serum CD300LG and fasting glucose, 2 hr glucose after an oral glucose tolerance test, and HbA1c. Additionally, Cd300lg responded to exercise in a mouse model, and we observed signs of impaired glucose tolerance in male, but not female, Cd300lg knockout mice. Our study identified several novel proteins in serum whose levels change in response to prolonged exercise and were significantly associated with body composition, liver fat, and glucose homeostasis. Serum CD300LG increased with physical activity and is a potential causal link to improved glucose levels. CD300LG may be a promising exercise biomarker and a therapeutic target in type 2 diabetes. South-Eastern Norway Regional Health Authority, Simon Fougners Fund, Diabetesforbundet, Johan Selmer Kvanes' legat til forskning og bekjempelse av sukkersyke. The UK Biobank resource reference 53641. Australian National Health and Medical Research Council Investigator Grant (APP2017942). Australian Research Council Discovery Early Career Award (DE220101226). Research Council of Norway (Project grant: 325640 and Mobility grant: 287198). The Medical Student Research Program at the University of Oslo. Novo Nordisk Fonden Excellence Emerging Grant in Endocrinology and Metabolism 2023 (NNF23OC0082123). clinicaltrials.gov: NCT01803568.

Two-fractionated stereotactic magnetic resonance-guided adaptive radiation therapy for patients with prostate cancer (SMART PRO trial): protocol for a confirmatory clinical trial

IntroductionIn an MRI-guided linear accelerator (MR-LINAC) system, the planned doses for organs at risk and for tumours are assessed by MR imaging and re-contouring at every treatment. This allows treatment...

An emerging discipline: brain-computer interfaces medicine

With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.

Bisindolylmethanes and Relative Compounds: Synthesis, Green Chemistry, Drug Discovery and Environmental Pollution Control

AbstractThe review discusses methods for the synthesis of bisidolylmethanes and the prospects for using these compounds in the drug design, as sensors in the biomedical research and in the environmental pollution control.

“Traitors to Their Profession”: Popular Media and the Repudiation of Sakaki Yasusaburō’s Rejuvenation Study in the 1920s

In 1921, Sakaki Yasusaburō, a professor of psychiatry at Kyushu Imperial University, introduced to Japanese audiences a rejuvenation method invented by the Austrian physiologist Eugen Steinach. Four years later, Sakaki was forced to resign from his position due to an embezzlement scandal, leading to the academic repudiation of his rejuvenation studies. This paper investigates the reasons behind this decline and identifies the role of popular media in shaping the professional identities of biomedical researchers in post-WWI Japan. It argues that a series of confrontations concerning the rules and norms of academic behavior that eventually led to Sakaki being labeled a “traitor” to his profession was as much influenced by the popular media as by considerations of scientific rigor. Highlighting the previously understudied interaction between popular media and its impact on the professionalization of biomedical research in modern Japan, this paper reveals the active and multiple roles that popular media plays in determining legitimate research topics and appropriate conduct for biomedical researchers.

A diffusion model multi-scale feature fusion network for imbalanced medical image classification research

Background and objectiveMedicine image classification are important methods of traditional medical image analysis, but the trainable data in medical image classification is highly imbalanced and the accuracy of medical image classification models is low. In view of the above two common problems in medical image classification. This study aims to: (i) effectively solve the problem of poor training effect caused by the imbalance of class imbalanced data sets. (ii) propose a network framework suitable for improving medical image classification results, which needs to be superior to existing methods. MethodsIn this paper, we put in the diffusion model multi-scale feature fusion network (DMSFF), which mainly uses the diffusion generation model to overcome imbalanced classes (DMOIC) on highly imbalanced medical image datasets. At the same time, it is processed according to the cropped image augmentation strategy through cropping (IASTC). Based on this, we use the new dataset to design a multi-scale feature fusion network (MSFF) that can fully utilize multiple hierarchical features. The DMSFF network can effectively solve the problems of small and imbalanced samples and low accuracy in medical image classification. ResultsWe evaluated the performance of the DMSFF network on highly imbalanced medical image classification datasets APTOS2019 and ISIC2018. Compared with other classification models, our proposed DMSFF network achieved significant improvements in classification accuracy and F1 score on two datasets, reaching 0.872, 0.731, and 0.906, 0.836, respectively. ConclusionsOur newly proposed DMSFF architecture outperforms existing methods on two datasets, and verifies the effectiveness of generative model inverse balance for imbalance class datasets and feature enhancement by multi-scale feature fusion. Further, the method can be applied to other class imbalanced data sets where the results will be improved.

Turn-table micro-CT scanner for dynamic perfusion imaging in mice: design, implementation, and evaluation

Objective.This study introduces a novel desktop micro-CT scanner designed for dynamic perfusion imaging in mice, aimed at enhancing preclinical imaging capabilities with high resolution and low radiation doses.Approach.The micro-CT system features a custom-built rotating table capable of both circular and helical scans, enabled by a small-bore slip ring for continuous rotation. Images were reconstructed with a temporal resolution of 3.125 s and an isotropic voxel size of 65µm, with potential for higher resolution scanning. The system's static performance was validated using standard quality assurance phantoms. Dynamic performance was assessed with a custom 3D-bioprinted tissue-mimetic phantom simulating single-compartment vascular flow. Flow measurements ranged from 1.51to 9 ml min-1, with perfusion metrics such as time-to-peak, mean transit time, and blood flow index calculated.In vivoexperiments involved mice with different genetic risk factors for Alzheimer's and cardiovascular diseases to showcase the system's capabilities for perfusion imaging.Main Results.The static performance validation confirmed that the system meets standard quality metrics, such as spatial resolution and uniformity. The dynamic evaluation with the 3D-bioprinted phantom demonstrated linearity in hemodynamic flow measurements and effective quantification of perfusion metrics.In vivoexperiments highlighted the system's potential to capture detailed perfusion maps of the brain, lungs, and kidneys. The observed differences in perfusion characteristics between genotypic mice illustrated the system's capability to detect physiological variations, though the small sample size precludes definitive conclusions.Significance.The turn-table micro-CT system represents a significant advancement in preclinical imaging, providing high-resolution, low-dose dynamic imaging for a range of biological and medical research applications. Future work will focus on improving temporal resolution, expanding spectral capabilities, and integrating deep learning techniques for enhanced image reconstruction and analysis.

Nano-Bio Interfaces: Pioneering Targeted Approaches for Disease Treatment and Prevention

Nanotechnology has emerged as a pivotal force in biomedical research, offering groundbreaking solutions for disease treatment and prevention through the integration of nanoscience with biology. This review explores the multifaceted applications of nano-bio interfaces in revolutionizing therapeutic interventions and public health strategies. At the forefront of innovation, nanomaterial-based drug delivery systems demonstrate unparalleled precision and efficacy in targeted drug delivery, minimizing systemic toxicity and maximizing therapeutic outcomes. Furthermore, the development of multifunctional nanoparticles capable of simultaneous drug delivery and diagnostic imaging enables real-time monitoring of treatment responses, heralding a new era of personalized medicine. Nano-bio interfaces also play a crucial role in advancing vaccine development and immunotherapies, with nanoparticle-based vaccine platforms offering enhanced antigen delivery and immune stimulation for robust and durable immune responses against infectious diseases and cancer. In the realm of disease prevention, antimicrobial nanomaterials provide effective strategies for infection control, while nanovaccines and prophylactic drug delivery systems offer tailored solutions for mitigating the spread of infectious agents. Through a comprehensive exploration of these pioneering approaches, this review, we believe, highlights the transformative potential of nano-bio interfaces in reshaping the landscape of disease treatment and prevention.

Effectiveness of digital Cognitive-Behavioural Therapy for Insomnia in patients with musculoskeletal complaints and insomnia in primary care physiotherapy: study protocol for a randomised controlled trial

IntroductionInsomnia is prevalent among patients visiting physiotherapists due to musculoskeletal complaints and associated with poorer pain prognosis. Cognitive-Behavioural Therapy for Insomnia (CBT-I) may be effective for improving sleep quality and...

HIV-1 subtype-specific drug resistance on dolutegravir-based antiretroviral therapy: protocol for a multicentre study (DTG RESIST)

IntroductionHIV drug resistance poses a challenge to the United Nation’s goal of ending the HIV/AIDS epidemic. The integrase strand transfer inhibitor (InSTI) dolutegravir, which has a higher resistance barrier, was...

Microbiome-driven IBS metabotypes influence response to the low FODMAP diet: insights from the faecal volatome

Irritable bowel syndrome (IBS) is a common and debilitating disorder manifesting with abdominal pain and bowel dysfunction. A mainstay of treatment is dietary modification, including restriction of FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides and polyols). A greater response to a low FODMAP diet has been reported in those with a distinct IBS microbiome termed IBS-P. We investigated whether this is linked to specific changes in the metabolome in IBS-P. Solid phase microextraction gas chromatography-mass spectrometry was used to examine the faecal headspace of 56 IBS cases (each paired with a non-IBS household control) at baseline, and after four-weeks of a low FODMAP diet (39 pairs). 50% cases had the IBS-P microbial subtype, while the others had a microbiome that more resembled healthy controls (termed IBS-H). Clinical response to restriction of FODMAPs was measured with the IBS-symptom severity scale, from which a pain sub score was calculated. Two distinct metabotypes were identified and mapped onto the microbial subtypes. IBS-P was characterised by a fermentative metabolic profile rich in short chain fatty acids (SCFAs). After FODMAP restriction significant reductions in SCFAs were observed in IBS-P. SCFA levels did not change significantly in the IBS-H group. The magnitude of pain and overall symptom improvement were significantly greater in IBS-P compared to IBS-H (p=0.016 and p=0.026, respectively). Using just five metabolites, a biomarker model could predict microbial subtype with accuracy (AUROC 0.797, sensitivity 78.6% (95% CI: 0.78-0.94), specificity 71.4% (95% CI: 0.55-0.88). A metabotype high in SCFAs can be manipulated by restricting fermentable carbohydrate, and is associated with an enhanced clinical response to this dietary restriction. This implies that SCFAs harbour pro-nociceptive potential when produced in a specific IBS niche. By ascertaining metabotype, microbial subtype can be predicted with accuracy. This could allow targeted FODMAP restriction in those seemingly primed to respond best. This research was co-funded by Addenbrooke's Charitable Trust, Cambridge University Hospitals and the Wellcome Sanger Institute, and supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014).

Systematic review of randomised controlled clinical trials examining effectiveness of contingent financial rewards for smoking cessation during pregnancy: intention-to-treat and causal effects on birthweight.

Objective: To examine birth weight change caused by adding financial rewards for smoking cessation compared to no rewards for pregnant women. To estimate the average expected birth weight change for those who quit because of rewards. This study updates a previous systematic review and refocuses the outcome from smoking cessation to birth weight.Eligibility Criteria: Trials with an experimental design allowing treatment effects to be attributed to rewards were included. Trials involving non-pregnant participants, or with no report of magnitude, treatment duration, timing or where most rewards were contingent on another behaviour (e.g., treatment attendance) were excluded.Information sources: Medline, PsycInfo, Embase, Cochrane (Central Register of Controlled Trials, Tobacco Addiction Group Specialised Register and Database of Systematic Reviews), and PubMed searched to 5th December 2023.Risk of bias: Risk of bias and certainty of evidence used Cochrane 'Risk of bias 2' and GRADE assessments.Synthesis of results: Primary analysis estimated Intention-To-Treat (ITT) mean birthweight difference when randomised to offer of rewards versus control. Within-trial estimates and standard errors were derived from mean, standard deviation, and sample size data provided, or from publications. Pooled ITT estimates used common (fixed) and random effects models. Secondary analyses used trial team supplied data to derive Complier Average Causal Effect (CACE) estimate of smoking cessation on birth weight, and a standard error. Estimates were pooled using common and random effects models. Similar analyses were applied to low birth weight (<2500g), birth weight for gestational age z-scores, and small for gestational age (<10 th percentile). Included studies: Primary analysis included 8 trials (2351 participants) from the UK (2 trials, 1475 participants); France (1 trial, 407 participants), and the US (6 trials, 469 participants). Secondary analysis included 7 trials as data retrieval from one US trial (51 participants) was not possible.Synthesis of results: Primary ITT analysis (2351 participants) estimated a mean 46.3g (95% CI: 0.0 to 92.6) birth weight increase when offered financial rewards for smoking cessation. Secondary CACE analysis (2239 participants) estimated a mean 206.0g (95% CI: -69.1 to 481.1) increase for smokers who quit because of rewards. There was no effect on low birth weight (<2500g), or birth weight adjusted for gestational age, though less babies were born small for gestational age, particularly if cessation was because of rewards (CACE risk difference -17.7%; 95% CI: -34.9% to -0.4%). Limitation of evidence: Sample size led to imprecision - maximum 2351 participants. A single trial of 3712 participants would give 80% power at 5% significance to show a 46g increase from 3.1kg to 3.146kg with 0.5kg standard deviation in both groups. Consistency - trials where smoking cessation increased (7 of 8) all showed a mean birth weight increase. In one trial smoking cessation fell as did birth weight. Bias is unlikely as 3 of 4 trials with no birth weight data showed increased cessation consistent with higher mean birth weight. Interpretation: Trials of contingent financial rewards for smoking cessation have previously been shown to more than double pregnancy quit rates. We have uncovered a significant (46g) population level increase in mean birth weight, driven by a clinically important mean increase (206g) for those who quit because of financial rewards associated with a reduction in Small for Gestational Age births. Funding: Review update - The U.S. National Institute of Health, National Institute of General Medical Sciences Center of Biomedical Research Excellence Award P30GM149331. Data retrieval, synthesis and analysis - Scottish Cot Death Trust.Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024494262.

Semi-supervised abdominal multi-organ segmentation by object-redrawing.

Multi-organ segmentation is a critical task in medical imaging, with wide-ranging applications in both clinical practice and research. Accurate delineation of organs from high-resolution 3D medical images, such as CT scans, is essential for radiation therapy planning, enhancing treatment outcomes, and minimizing radiation toxicity risks. Additionally, it plays a pivotal role in quantitative image analysis, supporting various medical research studies. Despite its significance, manual segmentation of multiple organs from 3D images is labor-intensive and prone to low reproducibility due to high interoperator variability. Recent advancements in deep learning have led to several automated segmentation methods, yet many rely heavily on labeled data and human anatomy expertise. In this study, our primary objective is to address the limitations of existing semi-supervised learning (SSL) methods for abdominal multi-organ segmentation. We aim to introduce a novel SSL approach that leverages unlabeled data to enhance the performance of deep neural networks in segmenting abdominal organs. Specifically, we propose a method that incorporates a redrawing network into the segmentation process to correct errors and improve accuracy. Our proposed method comprises three interconnected neural networks: a segmentation network for image segmentation, a teacher network for consistency regularization, and a redrawing network for object redrawing. During training, the segmentation network undergoes two rounds of optimization: basic training and readjustment. We adopt the Mean-Teacher model as our baseline SSL approach, utilizing labeled and unlabeled data. However, recognizing significant errors in abdominal multi-organ segmentation using this method alone, we introduce the redrawing network to generate redrawn images based on CT scans, preserving original anatomical information. Our approach is grounded in the generative process hypothesis, encompassing segmentation, drawing, and assembling stages. Correct segmentation is crucial for generating accurate images. In the basic training phase, the segmentation network is trained using both labeled and unlabeled data, incorporating consistency learning to ensure consistent predictions before and after perturbations. The readjustment phase focuses on reducing segmentation errors by optimizing the segmentation network parameters based on the differences between redrawn and original CT images. We evaluated our method using two publicly available datasets: the beyond the cranial vault (BTCV) segmentation dataset (training: 44, validation: 6) and the abdominal multi-organ segmentation (AMOS) challenge 2022 dataset (training:138, validation:16). Our results were compared with state-of-the-art SSL methods, including MT and dual-task consistency (DTC), using the Dice similarity coefficient (DSC) as an accuracy metric. On both datasets, our proposed SSL method consistently outperformed other methods, including supervised learning, achieving superior segmentation performance for various abdominal organs. These findings demonstrate the effectiveness of our approach, even with a limited number of labeled data. Our novel semi-supervised learning approach for abdominal multi-organ segmentation addresses the challenges associated with this task. By integrating a redrawing network and leveraging unlabeled data, we achieve remarkable improvements in accuracy. Our method demonstrates superior performance compared to existing SSL and supervised learning methods. This approach holds great promise in enhancing the precision and efficiency of multi-organ segmentation in medical imaging applications.

Comparable Real-World Patient-Reported Outcomes Data Across Health Conditions, Settings, and Countries: The PROMIS International Collaboration

The patient voice is critical to achieving value-based care, improving health outcomes, and advancing medical research. However, a key challenge is how to translate this “voice” into scientifically valid data that can inform evidence-based clinical decisions. One of the biggest barriers is the sheer variety of available patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs) and the associated challenges of translation, validation, implementation, and interpretation, making it difficult to obtain valid and comparable health outcomes. The authors present a harmonized global approach to international standardization of PROs and PROMs. This approach has the potential to accelerate patient-centered care by facilitating the collection of accurate and comparable real-world evidence on health outcomes that matter most to patients. This proposed approach consists of two elements: a data collection process based on a common set of PROs and a state-of-the-art measurement approach based on item response theory. First, there is growing evidence that outcomes such as pain, fatigue, anxiety, depression, sleep disturbance, physical function, and the ability to participate in social roles and activities are relevant for most people, irrespective of their health condition. Measuring these outcomes routinely in all patients could increase outcome comparability and utility for a range of stakeholders. Second, a measurement strategy based on a state-of-the-art psychometric approach — using item response theory (IRT)-based item banks — offers short, flexible, sustainable, and universally applicable PROMs with robust measurement properties and a common measurement scale. The unique integration of these two elements offers the potential to collect comparable PROM data across patients and providers to support shared decision-making, which may lead to better outcomes. The Patient-Reported Outcomes Measurement Information System (PROMIS) is a globally used example of such an approach. The PROMIS Profile measures serve as a resource for measuring a harmonized core set of PROs across medical conditions, languages, and countries. To meet the United Nations Sustainable Development Goal of ensuring healthy lives and promoting well-being for all, at all ages, a collaborative effort is needed to achieve consensus on international standardization of PROs and PROMs to accelerate patient-centered care across health conditions, settings, and countries. The authors propose to routinely measure a core set of broadly relevant PROs in all patients, regardless of their health condition, with universally applicable IRT-based PROMs.

Do Reasons Matter? Navigating Parents’ Reasons in Healthcare Decisions for Children

Bioethics has dedicated itself to exploring and defending both reasons for and against certain aspects of clinical care, biomedical research and health policy, including what decisions must be made, who should make them, and how they should be made. In pediatrics, it’s widely acknowledged that parents’ reasons may matter pragmatically; attending to parents’ reasons is important if we want to work with families. Yet the conventional view in pediatric ethics is that parents’ reasons are irrelevant to whether a decision is permissible or impermissible according to accepted ethical standards. In this paper, we explore whether parents’ reasons matter ethically and, if so, in what way and for whom. First, we clarify what we mean by ‘reasons.’ Second, we provide an overview of how reasons are typically treated in medical decision-making and pediatric ethics. Third, we analyze a hypothetical pediatric case to illustrate how changing reasons can transform ethical analyses, including by contributing to where and how clinicians and ethicists draw the boundaries intrinsic to common pediatric ethical frameworks. We push back against the conventional view and argue that parents’ reasons matter ethically in several ways. We call for further research on the role of parents’ reasons in clinical ethics deliberation.

Comparative assessment of projection and clustering method combinations in the analysis of biomedical data

BackgroundClustering on projected data is common in biomedical research analysis. Principal component analysis (PCA) is widely used for projection, focusing on data dispersion (variance), while clustering identifies data concentrations (neighborhood). These are conflicting aims. This study re-evaluates combinations of PCA and other projection methods with common clustering algorithms. MethodsSix projection methods (PCA, ICA, isomap, MDS, t-SNE, UMAP) were combined with five clustering algorithms (k-means, k-medoids, single link, Ward's method, average link). Projections and clusterings were evaluated using a numerical criterion for evaluating clustering performance and a visual criterion based on plotting the projected data on a Voronoi tessellation plane with class-wise coloring. Nine artificial and five real biomedical datasets were analyzed. ResultsNo combination consistently captured prior classifications in projections and clusters. Visual inspection proved essential. PCA was often but not always outperformed or equaled by neighborhood-based methods (UMAP, t-SNE) and manifold learning techniques (isomap). ConclusionsThe results dissaprove PCA as a standard projection method prior to clustering. Therefore, method selection should be data specific as a tailored approach to data projection and clustering in biomedical analysis. To aid this process, we propose a novel visualization technique that combines Voronoi tessellation with color coding.

Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1·97 million adults with 100 000 incident cases from 31 cohorts in 20 countries

Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches. This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity. Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7-15) years. Median meat consumption across cohorts was 0-110 g/day for unprocessed red meat, 0-49 g/day for processed meat, and 0-72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 1·10 (95% CI 1·06-1·15) per 100 g/day of unprocessed red meat (I2=61%), 1·15 (1·11-1·20) per 50 g/day of processed meat (I2=59%), and 1·08 (1·02-1·14) per 100 g/day of poultry (I2=68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes. The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines. The EU, the Medical Research Council, and the National Institute of Health Research Cambridge Biomedical Research Centre.

RXR agonist, 9-cis-13,14-dihydroretinoic acid (9CDHRA), reduces damage and protects from demyelination in transsynaptic degeneration model

Neurodegenerative and demyelinating disease, such as multiple sclerosis (MS) are at the forefront of medical research and the discovery of new drugs and therapeutics. One phenomenon of degeneration seen in these diseases is transsynaptic degeneration (TSD), where damage from one axon spreads to the other axons that are connected to it synaptically. It has previously been found that demyelination occurs prior to neuronal loss in an experimental form of induced TSD. Retinoid-x receptor (RXR) agonists have been shown to promote remyelination. Therefore, this study aimed to reveal the effects of a novel endogenous RXR-γ agonist, 9-cis-13,14-dihydroretinoic acid (9CDHRA), on preventing or restoring the effects of TSD. 9CDHRA was administered to mice following optic nerve crush (ONC) procedures, and electrophysiology (visual evoked potential, VEP) and histological (immunofluorescent) assessments were performed. It was found that 9CDHRA treatment effectively delayed glial activation and reduced the presence of apoptosis at the site of injury and further anterogradely in the visual system, including the lateral geniculate nucleus (LGN) and primary visual cortex (V1). Most notably, 9CDHRA was able to maintain myelin levels following ONC, and effectively protected from demyelination. This was corroborated by VEP recordings with improved P1 latency. The promising findings regarding the injury attenuating and myelin protecting properties of 9CDHRA necessitates further investigations into the potential therapeutic uses of this compound.

Comprehensive four-year disease progression assessment of myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is a heterogeneous neuromuscular disorder characterized by progressive muscle weakness and myotonia. This study investigates the progression of muscular strength and function over a four-year period. Patients with DM1 were examined at baseline and four years later. The following metrics were assessed over time: muscle strength (Medical Research Council-sumscore), hand-grip strength (Martin-Vigorimeter), hand-grip relaxation time (myotonia), and limitations in activities of daily living and (DM1ActivC questionnaire). A total of 648 patients entered the registry. Recruitment and follow-up is ongoing. In our manuscript, we focus on, 187 patients who were followed for 4 years. A significant decline in MRC sum score was observed, with distal muscles showing more deterioration. Hand-grip strength decreased significantly, with notable differences between sex and phenotype classified by disease onset. Surprisingly, an improvement of myotonia was observed. Follow-up analysis revealed a significant interaction between myotonia and grip-strength over time. Thus, the improvement in myotonia is likely explained by decreased in grip strength. Finally, there was a significant reduction in DM1ActivC score, indicating decreased activity and social participation. This study demonstrated variability in disease progression depending on sex, phenotype and disease status. This research demonstrates a nuanced pattern of disease progression, highlighting the need to combine different outcome measures to fully understand the complexity of DM1.

Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells.

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to discover a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging prior assumptions that this form of methylation is only biologically relevant to neurons in the brain.