bluuebunny/crossref_metadata_2025 huggingface dataset:
Elevated serum levels of anti-collagen type I antibodies in patients with spontaneous cervical artery dissection and ischemic stroke: a prospective multicenter study
Authors: Zimmermann Silke , Weißenfels Markus , Krümmer Norma , Härtig Wolfgang , Weise Gesa , Branzan Daniela , Michalski Dominik , Pelz Johann Otto
Published: May 2024

Introduction

Spontaneous cervical artery dissection (sCAD) is a rare vasculopathy whose trigger is still unknown. We hypothesized that autoimmunity against components of the vascular wall might play a critical role in sCAD and examined anti-collagen type I antibodies in patients with sCAD, acute ischemic stroke, patients with thromboendarterectomy, and controls.

Methods

Fifty-seven patients with sCAD (age 45.7 ± 10.2 years, female 18 (31.6%)) were prospectively enrolled in four German stroke centers. Blood samples were collected at baseline, at day 10 ± 3, and after 6 ± 1 months. Patients with ischemic stroke not related to CAD (n=54, age 56.7 ± 13.7 years, female 15 (27.8%)), healthy probands (n=80, age 57.4 ± 12.9 years, female 56 (70%)), and patients undergoing thromboendarterectomy of the carotid artery (n=9, age 70.7 ± 9.3 years, female 2 (22.2%)) served as controls. Anti-collagen type I antibodies were determined by enzyme-linked immunosorbent assays (ELISAs).

Results

Patients with acute sCAD had higher serum levels of anti-collagen type I antibodies (33.9 ± 24.6 µg/ml) than probands (18.5 ± 11.0 µg/ml; p <0.001) but lower levels than patients with ischemic stroke not related to sCAD (47.8 ± 28.4 µg/ml; p=0.003). In patients with sCAD, serum levels of anti-collagen type I antibodies were similar in the acute, subacute, and chronic phase. Levels of anti-collagen type I antibodies significantly correlated with circulating collagen type I (rho=0.207, p=0.003).

Conclusion

Anti-collagen type I antibodies seem not to represent a trigger for acute sCAD or ischemic stroke but may rather be linked to the metabolism and turnover of collagen type I.
Clinical relevance of SCN and CyN induced by ELANE mutations: a systematic review
Authors: Xiao Yufan , Wang Nandi , Jin Xinghao , Liu Anna , Zhang Zhiyong
Published: May 2024

Introduction

According to the PRISMA criteria, a systematic review has been conducted to investigate the clinical relevance between patients with severe congenital neutropenia (SCN) and cyclic congenital neutropenia (CyN) induced by ELANE mutations.

Methods

We have searched PubMed, EMBASE, Web of Science, Scopus, Cochrane, CNKI, Wanfang Medicine, and VIP for ELANE mutation related literature published from 1997 to 2022. Using Microsoft Excel collect and organize data, SPSS 25, GraphPad Prism 8.0.1, and Omap analyze and plot statistical. Compare the gender, age, geography, mutation sites, infection characteristics, treatment, and other factors of SCN and CyN patients induced by ELANE mutations, with a focus on exploring the relationship between genotype and clinical characteristics, genotype and prognosis.

Results

This study has included a total of 467 patients with SCN and 90 patients with CyN. The onset age of SCN and CyN are both less than 1 year old, and the onset and diagnosis age of SCN are both younger than CyN. The mutation of ELANE gene is mainly missense mutation, and hot spot mutations include S126L, P139L, G214R, c.597+1G>A. The high-frequency mutations with severe outcomes are A57V, L121H, L121P, c.597+1G>A, c.597+1G>T, S126L, C151Y, C151S, G214R, C223X. Respiratory tract, skin and mucosa are the most common infection sites, Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli are the most common.

Discussion

Patients with refractory G-CSF are more likely to develop severe outcomes. The commonly used pre-treatment schemes for transplantation are Bu-Cy-ATG and Flu-Bu-ATG. The prognosis of transplantation is mostly good, but the risk of GVHD is high.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/. PROSPERO, identifier CRD42023434656.
Identifying hub genes in response to ustekinumab and the impact of ustekinumab treatment on fibrosis in Crohn’s disease
Authors: Xu Ying , Wang Shu , Ye Ziping , Zhang Hongjie
Published: May 2024

Introduction

Crohn’s disease (CD) is a chronic inflammatory disease. Approximately 50% of patients with CD progressed from inflammation to fibrosis. Currently, there are no effective drugs for treating intestinal fibrosis. Biologic therapies for CD such as ustekinumab have benefited patients; however, up to 30% of patients with CD have no response to initial treatment, and the effect of ustekinumab on intestinal fibrosis is still uncertain. Therefore, it is of great significance to explore the predictive factors of ustekinumab treatment response and the effect of ustekinumab on intestinal fibrosis.

Materials and methods

Public datasets—GSE207465 (blood samples) and GSE112366 and GSE207022 (intestinal samples)—were downloaded and analyzed individually (unmerged) based on the treatment response. Differentially expressed genes (DEGs) were identified by the “limma” R package and changes in immune cell infiltration were determined by the “CIBERSORT” R package in both blood and intestinal samples at week 0 (before treatment). To find predictive factors of ustekinumab treatment response, the weighted gene co-expression network analysis (WGCNA) R package was used to identify hub genes in GSE112366. Hub genes were then verified in GSE207022, and a prediction model was built by random forest algorithm. Furthermore, fibrosis-related gene changes were analyzed in ileal samples before and after treatment with ustekinumab.

Results

(1) Our analysis found that MUC1, DUOX2, LCN2, and PDZK1IP1 were hub genes in GSE112366. GSE207022 revealed that MUC1 (AUC:0.761), LCN2 (AUC:0.79), and PDZK1IP1 (AUC:0.731) were also lower in the response group. Moreover, the random forest model was shown to have strong predictive capabilities in identifying responders (AUC = 0.875). To explore the relationship between intestinal tissue and blood, we found that ITGA4 had lower expression in the intestinal and blood samples of responders. The expression of IL18R1 is also lower in responders’ intestines. IL18, the ligand of IL18R1, was also found to have lower expression in the blood samples from responders vs. non-responders. (2) GSE112366 revealed a significant decrease in fibrosis-related module genes (COL4A1, TUBB6, IFITM2, SERPING1, DRAM1, NAMPT, MMP1, ZEB2, ICAM1, PFKFB3, and ACTA2) and fibrosis-related pathways (ECM–receptor interaction and PI3K-AKT pathways) after ustekinumab treatment.

Conclusion

MUC1, LCN2, and PDZK1IP1 were identified as hub genes in intestinal samples, with lower expression indicating a positive prediction of ustekinumab treatment response. Moreover, ITGA4 and IL18/IL18R1 may be involved in the treatment response in blood and intestinal samples. Finally, ustekinumab treatment was shown to significantly alter fibrotic genes and pathways.
Future distribution of demersal species in a warming Mediterranean sub-basin
Authors: Panzeri Diego , Reale Marco , Cossarini Gianpiero , Salon Stefano , Carlucci Roberto , Spedicato Maria Teresa , Zupa Walter , Vrgoč Nedo , Libralato Simone
Published: May 2024

Predicting range shifts of marine species under different CO2 emission scenarios is of paramount importance to understand spatial potential changes in a context of climate change and to ensure appropriate management, in particular in areas where resources are critical to fisheries. Important tools which use environmental variables to infer range limits and species habitat suitability are the species distribution models or SDMs. In this work, we develop an ensemble species distribution model (e-SDM) to assess past, present and future distributions under Representative Concentration Pathway (RCP) 8.5 of nine demersal species and hotspot areas for their two life stages (adult and juvenile) in the Adriatic and Western Ionian Seas in four time windows (1999-2003, 2014-2018, 2031-2035 and 2046-2050). The e-SDM has been developed using three different models (and sub-models), i.e. (i) generalized additive models (GAM), (ii) generalized linear mixed model (GLMM), (iii) gradient boosting machine (GBM), through the combination of density data in terms of numbers of individuals km2 and environmental variables. Then, we have determined the changes in the aggregation hotspots and distributions. Finally, we assess gains and losses areas (i.e. occupation area) in the future climate change scenario as new potential range shifts for the nine species and their life stages. The results show that densities of some key commercial species, such as Merluccius merluccius (European hake), Mullus barbatus (red mullet), and Lophius budegassa (anglerfish) will be shifting northwards.
Fit for purpose? Evaluating climate change adaptation laws and policies for marine aquaculture in Chile
Authors: Engler Cecilia
Published: May 2024

This research article describes Chile’s climate change adaptation policies and plans for marine aquaculture in Chile, with a focus on the nationally important salmon farming industry, and assesses whether they have adequately addressed legal barriers to adaptation and the need for legal transformation. The article first outlines Chile’s climate change law, policies, and institutional framework, as reflected in the 2022 Framework Act on Climate Change, the 2020 updated Nationally Determined Contribution, the 2022 Strengthening of the Nationally Determined Contribution, and the 2022 Long-Term Climate Strategy. The article highlights the special attention given to the ocean-climate nexus in both international and national policy agendas. It then summarizes and assesses the adaptation policies and plans for the aquaculture sector. Three main shortcomings are identified: the lack of implementation of committed activities, the lack of a strategic vision for the role of aquaculture in a changing climate and oceans, and the lack of attention to the limits of adaptation resulting from existing regulatory frameworks. The article then strengthens this assessment with a legal analysis of the adaptive capacity of aquaculture planning and leasing frameworks. This assessment concludes that mainstreaming climate change into existing planning instruments is an ineffective adaptation measure due to the fragmented, rigid, and inefficient legal framework for the planning of aquaculture and other uses of the coastal zone. In turn, the leasing system is too rigid to allow for effective adaptation. Various mechanisms to introduce flexibility are suggested. The article concludes by highlighting an unprecedented window of opportunity to advance strategic, coherent, long-term, and transformative adaptation, resulting from concurrent initiatives to reform or update aquaculture law, policy, and adaptation planning and the principled approach to climate action embedded in the Framework Act on Climate Change.
Improving the quality of neonatal health care in Ethiopia: a systematic review
Authors: Belay Demeke Mesfin , Erku Daniel , Bayih Wubet Alebachew , Kassie Yohannes Tesfahun , Minuye Birhane Binyam , Assefa Yibeltal
Published: May 2024

Background

Ensuring high-quality healthcare for newborns is essential for improving their chances of survival within Ethiopia's healthcare system. Although various intervention approaches have been implemented, neonatal mortality rates remain stable. Therefore, the present review seeks to identify initiatives for enhancing healthcare quality, their effects on neonatal wellbeing, and the factors hindering or supporting these Quality Improvement (QI) efforts' success in Ethiopia.

Methods

We searched for original research studies up to June 23, 2023, using PubMed/Medline, WHO-Global Health Library, Cochrane, Clinical Trials.gov, and Hinari. After selecting eligible studies, we assessed their quality using a mixed-method appraisal tool. Quality of care refers to how healthcare services effectively improve desired outcomes for individuals and patient populations. It encompasses vital principles such as safety, effectiveness, timeliness, efficiency, equity, and patient-centeredness.

Results

We found 3,027 publication records and included 13 studies during our search. All these interventions primarily aimed to provide safe healthcare, with a strong focus on Domain One, which deals with the evidence-based routine upkeep and handling of complications, and Domain Seven, which revolves around ensuring staff competency, emerged as a frequent target for intervention. Many interventions aimed at improving quality also concentrate on essential quality measure elements such as processes, focusing on the activities that occur during care delivery, and quality planning, involving distributing resources, such as basic medicine and equipment, and improving infrastructure. Moreover, little about the facilitators and barriers to QI interventions is investigated.

Conclusions

This review highlights the significance of introducing QI initiatives in Ethiopia, enhancing the healthcare system's capabilities, engaging the community, offering financial incentives, and leveraging mobile health technologies. Implementing QI interventions in Ethiopia poses difficulties due to resource constraints, insufficient infrastructure, and medical equipment and supplies shortages. It necessitates persistent endeavors to improve neonatal care quality, involving ongoing training, infrastructure enhancement, the establishment of standardized protocols, and continuous outcome monitoring. These efforts are crucial to achieving the optimal outcomes for newborns and their families.
From obese to lean curriculum: exploring students’ experiences about developing competencies in medical education
Authors: Karami Morteza , Hashemi Nooriyah , Van Merrienboer Jeroen
Published: May 2024

Introduction

Since the beginning of the 21st century, competency-based education has been proposed as an approach to education in many disciplines including the medical sciences and it has become a dominant approach in many countries. We aimed to explore the lived experiences of general medical students about developing competencies in the academic curriculum.

Methods

We conducted a phenomenology method to study lived experiences of general medical students through selecting participants via a purposeful sampling strategy. Snowballing and maximum variation samplings were also applied to recruit additional participants. The study was conducted at a Medical School in Iran. Three successive phases of qualitative data analysis, namely, data reduction by coding, data structuring by categorization, and data interpretation by discussion were applied to analyze the interviews.

Results

The results of the research showed that students’ lived experiences fall under 4 main themes with 9 subthemes. The main themes show that (1) the compartmentalized curriculum in basic courses is experienced as the missing parts in a puzzle, (2) the physiopathology curriculum is experienced as swimming on land, (3) the externship is experienced as touring a mysterious land, (4) the internship is experienced as unleashed arrows.

Discussion

Our findings reveal that despite the changes already made in the curriculum, its compartmentalization is still a main obstacle to achieving competency-based medical education. A strict requirement for leaving the discipline-based curriculum behind is to use an integrated approach, in which basic science courses are connected with clinical cases, and physiopathology courses are connected with externships and internships.
Association of clinical, imaging and laboratory parameters with adverse effects of glucocorticoid therapy in patients with giant cell arteritis
Authors: Schweiger Leyla , Hafner Franz , Meinitzer Andreas , Brodmann Marianne , Dejaco Christian , Jud Philipp
Published: May 2024

Background

Giant cell arteritis (GCA) is characterized by inflammation of large and medium vessels. First-line therapy for the treatment of GCA are glucocorticoids, which are effective while potential adverse effects should be considered, especially during long-term use. The aim was to investigate the incidence of glucocorticoids’ adverse effects and potential predictors for them.

Materials and methods

138 GCA patients were retrospectively evaluated for newly developed glucocorticoid adverse effects in 2020. Potential predictors, defined as initial glucocorticoid pulse therapy, relapse of GCA and concomitant polymyalgia rheumatica as well as parameters of inflammation and endothelial dysfunction, including pulse-wave velocity and intima-media-thickness, were measured in 2012.

Results

Potential new glucocorticoid adverse effects per patient was 1 (25th-75th 0–3) of which chronic kidney disease progression (29%), bone fractures (23.2%), cataracts (18.1%), dementia, and arterial hypertension (each at 12.3%) were most commonly recorded. Significant associations were found between occurrence of any relapse and new diabetes mellitus and between initial glucocorticoid pulse therapy and new dementia (all with p < 0.05). In multivariate regression analysis, any relapse was a predictor for developing diabetes mellitus (OR 9.23 [95% CI 1.33–64.05], p = 0.025). However, no correlations were observed between endothelial dysfunction or inflammatory parameters and development of new glucocorticoid adverse effects.

Conclusion

GCA relapses may be associated for development of diabetes mellitus potentially by increasing glucocorticoid doses. Parameters of inflammation and endothelial dysfunction are not suited predictors for glucocorticoid adverse effects.
GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation
Authors: Zhou Qingqing , Liu Can , Duan Yuchen , Sun Kaijie , Li Yu , Kan Hongxing , Gu Zongyun , Shu Jianhua , Hu Jili
Published: May 2024

Introduction

Large Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases.

Methods

We fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI’s Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named “GastroBot,” which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information.

Results

When evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results.

Discussion

Research findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.
Artificial intelligence in drug repurposing for rare diseases: a mini-review
Authors: Cortial Lucas , Montero Vincent , Tourlet Sébastien , Del Bano Joanie , Blin Olivier
Published: May 2024

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.
Low-level viremia episodes appear to affect the provirus composition of the circulating cellular HIV reservoir during antiretroviral therapy
Authors: Sun Xiao , Zhang Hui , Kong Xiangchen , Li Nan , Zhang Tong , An Minghui , Ding Haibo , Shang Hong , Han Xiaoxu
Published: May 2024

Low-level viremia (LLV) ranging from 50 to 1,000 copies/ml is common in most HIV-1-infected patients receiving antiretroviral therapy (ART). However, the source of LLV and the impact of LLV on the HIV-1 reservoir during ART remain uncertain. We hypothesized that LLV may arise from the HIV reservoir and its occurrence affect the composition of the reservoir after LLV episodes. Accordingly, we investigated the genetic linkage of sequences obtained from plasma at LLV and pre-ART time points and from peripheral blood mononuclear cells (PBMCs) at pre-ART, pre-LLV, LLV, and post-LLV time points. We found that LLV sequences were populated with a predominant viral quasispecies that accounted for 67.29%∼100% of all sequences. Two episodes of LLV in subject 1, spaced 6 months apart, appeared to have originated from the stochastic reactivation of latently HIV-1-infected cells. Moreover, 3.77% of pre-ART plasma sequences were identical to 67.29% of LLV-3 plasma sequences in subject 1, suggesting that LLV may have arisen from a subset of cells that were infected before ART was initiated. No direct evidence of sequence linkage was found between LLV viruses and circulating cellular reservoirs in all subjects. The reservoir size, diversity, and divergence of the PBMC DNA did not differ significantly between the pre- and post-LLV sampling points (P > 0.05), but the composition of viral reservoir quasispecies shifted markedly before and after LLV episodes. Indeed, subjects with LLV had a higher total PBMC DNA level, greater viral diversity, a lower proportion of variants with identical sequences detected at two or more time points, and a shorter variant duration during ART compared with subjects without LLV. Overall, our findings suggested that LLV viruses may stem from an unidentified source other than circulating cellular reservoirs. LLV episodes may introduce great complexity into the HIV reservoir, which brings challenges to the development of treatment strategies.
Connectivity of the olfactory tubercle: inputs, outputs, and their plasticity
Authors: Yamaguchi Masahiro
Published: May 2024

The olfactory tubercle (OT) is a unique part of the olfactory cortex of the mammal brain in that it is also a component of the ventral striatum. It is crucially involved in motivational behaviors, particularly in adaptive olfactory learning. This review introduces the basic properties of the OT, its synaptic connectivity with other brain areas, and the plasticity of the connectivity associated with learning behavior. The adaptive properties of olfactory behavior are discussed further based on the characteristics of OT neuronal circuits.
Multi-sequence generative adversarial network: better generation for enhanced magnetic resonance imaging images
Authors: Li Leizi , Yu Jingchun , Li Yijin , Wei Jinbo , Fan Ruifang , Wu Dieen , Ye Yufeng
Published: May 2024

Introduction

MRI is one of the commonly used diagnostic methods in clinical practice, especially in brain diseases. There are many sequences in MRI, but T1CE images can only be obtained by using contrast agents. Many patients (such as cancer patients) must undergo alignment of multiple MRI sequences for diagnosis, especially the contrast-enhanced magnetic resonance sequence. However, some patients such as pregnant women, children, etc. find it difficult to use contrast agents to obtain enhanced sequences, and contrast agents have many adverse reactions, which can pose a significant risk. With the continuous development of deep learning, the emergence of generative adversarial networks makes it possible to extract features from one type of image to generate another type of image.

Methods

We propose a generative adversarial network model with multimodal inputs and end-to-end decoding based on the pix2pix model. For the pix2pix model, we used four evaluation metrics: NMSE, RMSE, SSIM, and PNSR to assess the effectiveness of our generated model.

Results

Through statistical analysis, we compared our proposed new model with pix2pix and found significant differences between the two. Our model outperformed pix2pix, with higher SSIM and PNSR, lower NMSE and RMSE. We also found that the input of T1W images and T2W images had better effects than other combinations, providing new ideas for subsequent work on generating magnetic resonance enhancement sequence images. By using our model, it is possible to generate magnetic resonance enhanced sequence images based on magnetic resonance non-enhanced sequence images.

Discussion

This has significant implications as it can greatly reduce the use of contrast agents to protect populations such as pregnant women and children who are contraindicated for contrast agents. Additionally, contrast agents are relatively expensive, and this generation method may bring about substantial economic benefits.
Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study
Authors: Liu Wei , Jia Longbin , Xu Lina , Yang Fengbing , Guo Zixuan , Li Jinna , Zhang Dandan , Liu Yan , Xiang Han , Cheng Hongjiang , Hou Jing , Li Shifang , Li Huimin
Published: May 2024

Background

Early neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors.

Methods

This retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery. We included demographic characteristics, clinical features, laboratory data, and imaging variables. Recursive feature elimination with cross-validation (RFECV) was performed to identify critical features. Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. We compared the accuracy of these models in predicting outcomes. Additionally, SHapley Additive exPlanations (SHAP) values were introduced to interpret the optimal model and assess the significance of input features.

Results

The study enrolled 1,020 PAI patients with a mean age of 60.46 (range 49.11–71.81) years. Of these, 30.39% were women, and 129 (12.65%) experienced END. RFECV selected 13 critical features, including blood urea nitrogen (BUN), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), atrial fibrillation, loading dual antiplatelet therapy (DAPT), single antiplatelet therapy (SAPT), argatroban, the basal ganglia, the thalamus, the posterior choroidal arteries, maximal axial infarct diameter (measured at < 15 mm), and stroke subtype. The gradient-boosting decision tree had the highest area under the curve (0.914) among the seven ML algorithms. The SHAP analysis identified apoB as the most significant variable for END.

Conclusion

Our results suggest that ML algorithms, especially the gradient-boosting decision tree, are effective in predicting the occurrence of END in PAI patients.
Impulse control disorders and use of dopamine agonists in early onset Parkinson’s disease
Authors: Turcano Pierpaolo , Jacobson Jessie , Ghoniem Khaled , Mullan Aidan , Camerucci Emanuele , Stang Cole , Piat Capucine , Bower James H. , Savica Rodolfo
Published: May 2024

Introduction

Impulse control disorders (ICDs) are defined as excessive and repetitive behaviors that may affect Parkinson’s disease (PD) patients exposed to dopamine agonists. Current data on ICDs in patients with early-onset Parkinson’s disease (EOPD) is lacking. In this study we aim to assess the frequency of use of dopamine agonists, the prevalence of ICDs, and to explore potential factors associated with their development in patients with EOPD.

Methods

We used the Mayo Clinic Data Explorer system to investigate a population-based cohort of EOPD patients between 1990 and 2022 at Mayo Clinic, Rochester, MN. We used ICD coding for parkinsonism; then, we reviewed all the clinical records and included only those patients with a clinical diagnosis of PD with symptoms onset at or before the age of 50, and who developed ICDs after using therapeutic doses of dopamine agonists.

Results

A total of 831 (513 males and 318 females) patients with EOPD were included with a median age at symptom onset of 42 years of age (CI: 37–46). Dopamine agonists were used in 49.7% of all patients; of these, only 14.5% developed symptoms of one or more ICDs. Hypersexuality was the most commonly observed ICD (38.3%), and the only one having a statistically significant male predominance (p = 0.011).

Conclusion

ICDs are common in EOPD, particularly when associated with the use of dopamine agonists.
Changes in cortical grey matter volume with Cognitive Orientation to daily Occupational Performance intervention in children with developmental coordination disorder
Authors: Malik Myrah Anum , Weber Alexander Mark , Lang Donna , Vanderwal Tamara , Zwicker Jill G.
Published: May 2024

Introduction

Cognitive Orientation to daily Occupational Performance (CO-OP) is a cognitive-based, task-specific intervention recommended for children with developmental coordination disorder (DCD). We recently showed structural and functional brain changes after CO-OP, including increased cerebellar grey matter. This study aimed to determine whether CO-OP intervention induced changes in cortical grey matter volume in children with DCD, and if these changes were associated with improvements in motor performance and movement quality.

Methods

This study is part of a randomized waitlist-control trial (ClinicalTrials.gov ID: NCT02597751). Children with DCD (N = 78) were randomized to either a treatment or waitlist group and underwent three MRIs over 6 months. The treatment group received intervention (once weekly for 10 weeks) between the first and second scan; the waitlist group received intervention between the second and third scan. Cortical grey matter volume was measured using voxel-based morphometry (VBM). Behavioral outcome measures included the Performance Quality Rating Scale (PQRS) and Bruininks-Oseretsky Test of Motor Proficiency-2 (BOT-2). Of the 78 children, 58 were excluded (mostly due to insufficient data quality), leaving a final N = 20 for analyses. Due to the small sample size, we combined both groups to examine treatment effects. Cortical grey matter volume differences were assessed using a repeated measures ANOVA, controlling for total intracranial volume. Regression analyses examined the relationship of grey matter volume changes to BOT-2 (motor performance) and PQRS (movement quality).

Results

After CO-OP, children had significantly decreased grey matter in the right superior frontal gyrus and middle/posterior cingulate gyri. We found no significant associations of grey matter volume changes with PQRS or BOT-2 scores.

Conclusion

Decreased cortical grey matter volume generally reflects greater brain maturity. Decreases in grey matter volume after CO-OP intervention were in regions associated with self-regulation and motor control, consistent with our other studies. Decreased grey matter volume may be due to focal increases in synaptic pruning, perhaps as a result of strengthening networks in the brain via the repeated learning and actions in therapy. Findings from this study add to the growing body of literature demonstrating positive neuroplastic changes in the brain after CO-OP intervention.
Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs
Authors: Liu Tianyu , Wu Yu , Ye An , Cao Lei , Cao Yongnian
Published: May 2024

Background

Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.

Methods

In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.

Results

The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.

Conclusion

The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
DTI-ALPS index decreased in patients with Type 2 Diabetes Mellitus
Authors: Yu Shuncheng , Jiang Hongjun , Yu Langxuan , Liu Tieli , Yang Chun , Cao Jiajun , Song Qingwei , Miao Yanwei , Wang Weiwei
Published: May 2024

Backgrounds

Type 2 Diabetes Mellitus (T2DM) has become a significant global public health issue, characterized by a rising prevalence and associated deficits across multiple organ systems. Our study aims to utilize the DTI-ALPS technique to assess the change of ALPS index in T2DM patients, and to explore whether such changes are correlated with cognition level and diffusion parameters.

Methods

The study involved 41 patients with T2DM (mean age, 60.49 ± 8.88 years) and 27 healthy controls (mean age, 58.00 ± 7.63 years). All subjects underwent MRI examination, cognitive assessment, and laboratory tests. Tract-based spatial statistics (TBSS) was used to evaluate white matter changes. GLM was performed to check the DTI-ALPS index difference between T2DM and HC groups. Spearman correlation analysis and partial correlation analysis were used to analyze the correlation between the DTI-ALPS index and diffusion properties & cognitive scores.

Results

The results show that the ALPS index was lower in T2DM patients. MoCA score was significantly correlated with the ALPS index. Patients with T2DM had a significant increase in both mean diffusivity (MD) and radial diffusivity (RD) and decrease in fractional anisotropy (FA) compared to the HC group.

Conclusion

The results suggest that the ALPS index is decreased in T2DM patients and associates with cognitive level.
Hidden regulators: the emerging roles of lncRNAs in brain development and disease
Authors: Alammari Farah , Al-Hujaily Ensaf M. , Alshareeda Alaa , Albarakati Nada , Al-Sowayan Batla S.
Published: May 2024

Long non-coding RNAs (lncRNAs) have emerged as critical players in brain development and disease. These non-coding transcripts, which once considered as “transcriptional junk,” are now known for their regulatory roles in gene expression. In brain development, lncRNAs participate in many processes, including neurogenesis, neuronal differentiation, and synaptogenesis. They employ their effect through a wide variety of transcriptional and post-transcriptional regulatory mechanisms through interactions with chromatin modifiers, transcription factors, and other regulatory molecules. Dysregulation of lncRNAs has been associated with certain brain diseases, including Alzheimer’s disease, Parkinson’s disease, cancer, and neurodevelopmental disorders. Altered expression and function of specific lncRNAs have been implicated with disrupted neuronal connectivity, impaired synaptic plasticity, and aberrant gene expression pattern, highlighting the functional importance of this subclass of brain-enriched RNAs. Moreover, lncRNAs have been identified as potential biomarkers and therapeutic targets for neurological diseases. Here, we give a comprehensive review of the existing knowledge of lncRNAs. Our aim is to provide a better understanding of the diversity of lncRNA structure and functions in brain development and disease. This holds promise for unravelling the complexity of neurodevelopmental and neurodegenerative disorders, paving the way for the development of novel biomarkers and therapeutic targets for improved diagnosis and treatment.
Bridging the gap: fostering interactive stimming between non-speaking autistic children and their parents
Authors: Chen Rachel S. Y.
Published: May 2024

Non-speaking autistic individuals grapple with the profound impact of the ‘double empathy problem’ in their daily interactions with speaking others. This study rethinks the communication challenges faced by non-speaking autistic individuals, challenging traditional approaches that predominantly focus on speech and complex communication devices (AAC). By spotlighting the natural phenomenon of “interactive stimming,” a powerful mode of communication among autistic individuals, this study advocates for a shift from a conventional emphasis on speech towards the foundational role of the body in autistic communication. Central to this exploration is the introduction of the Magical Musical Mat (MMM), an innovative interactive environment mapping interpersonal touch to musical sounds. Through a robust mixed-methods approach integrating video-based fieldwork and design-based research, this paper engages three non-speaking autistic children and their mothers in a 5-day empirical intervention. Results reveal significant transformations in parent–child interactions as both parents and children are acquainted with touch in a new environment. Children assert their autonomy, exploring at their own pace, and discovering sensory features of the environment. Notably, the introduction of sound prompts heightened awareness of the stims, leading to diversified and expressive stim movements. Foregrounding interpersonal touch eventually guides parents into their children’s sensory activities where parents attune to the stims of their children by joining in and facilitating their expressiveness, co-creating extended, evolving patterns of repetitive cycles. The collaborative stim cycles can be likened to free improvisation, where dynamical coherence between individuals occurs through a blend of stability and active flexibility. By shifting the focus from speech to co-created sensory experiences, this paper sheds light on the value of transformative multisensory environments, envisioning a world where varied modes of communication are valued and embraced.
Adolescent binge ethanol impacts H3K9me3-occupancy at synaptic genes and the regulation of oligodendrocyte development
Authors: Brocato Emily R. , Easter Rachel , Morgan Alanna , Kakani Meenakshi , Lee Grace , Wolstenholme Jennifer T.
Published: May 2024

Introduction

Binge drinking in adolescence can disrupt myelination and cause brain structural changes that persist into adulthood. Alcohol consumption at a younger age increases the susceptibility of these changes. Animal models to understand ethanol’s actions on myelin and white matter show that adolescent binge ethanol can alter the developmental trajectory of oligodendrocytes, myelin structure, and myelin fiber density. Oligodendrocyte differentiation is epigenetically regulated by H3K9 trimethylation (H3K9me3). Prior studies have shown that adolescent binge ethanol dysregulates H3K9 methylation and decreases H3K9-related gene expression in the PFC.

Methods

Here, we assessed ethanol-induced changes to H3K9me3 occupancy at genomic loci in the developing adolescent PFC. We further assessed ethanol-induced changes at the transcription level with qPCR time course approaches in oligodendrocyte-enriched cells to assess changes in oligodendrocyte progenitor and oligodendrocytes specifically.

Results

Adolescent binge ethanol altered H3K9me3 regulation of synaptic-related genes and genes specific for glutamate and potassium channels in a sex-specific manner. In PFC tissue, we found an early change in gene expression in transcription factors associated with oligodendrocyte differentiation that may lead to the later significant decrease in myelin-related gene expression. This effect appeared stronger in males.

Conclusion

Further exploration in oligodendrocyte cell enrichment time course and dose response studies could suggest lasting dysregulation of oligodendrocyte maturation at the transcriptional level. Overall, these studies suggest that binge ethanol may impede oligodendrocyte differentiation required for ongoing myelin development in the PFC by altering H3K9me3 occupancy at synaptic-related genes. We identify potential genes that may be contributing to adolescent binge ethanol-related myelin loss.
Deciphering glycosylation-driven prognostic insights and therapeutic prospects in glioblastoma through a comprehensive regulatory model
Authors: Jin Xingyi , Chen Zhuo , Zhao Hang
Published: May 2024

The oncogenesis and development of glioblastoma multiforme have been linked to glycosylation modifications, which are common post-translational protein modifications. Abnormal glycosyltransferase development leads to irregular glycosylation patterns, which hold clinical significance for GB prognosis. By utilizing both single-cell and bulk data, we developed a scoring system to assess glycosylation levels in GB. Moreover, a glycosylation-based signature was created to predict GB outcomes and therapy responsiveness. The study led to the development of an glyco-model incorporating nine key genes. This risk assessment tool effectively stratified GB patients into two distinct groups. Extensive validation through ROC analysis, RMST, and Kaplan-Meier (KM) survival analysis emphasized the model’s robust predictive capabilities. Additionally, a nomogram was constructed to predict survival rates at specific time intervals. The research revealed substantial disparities in immune cell infiltration between low-risk and high-risk groups, characterized by differences in immune cell abundance and elevated immune scores. Notably, the glyco-model predicted diverse responses to immune checkpoint inhibitors and drug therapies, with high-risk groups exhibiting a preference for immune checkpoint inhibitors and demonstrated superior responses to drug treatments. Furthermore, the study identified two potential drug targets and utilized Connectivity Map analysis to pinpoint promising therapeutic agents. Clofarabine and YM155 were identified as potent candidates for the treatment of high-risk GB. Our well-crafted glyco-model effectively discriminates patients by calculating the risk score, accurately predicting GB outcomes, and significantly enhancing prognostic assessment while identifying novel immunotherapeutic and chemotherapeutic strategies for GB treatment.
Evaluation of diagnostic accuracy of urine neutrophil gelatinase-associated lipocalin in patients with symptoms of urinary tract infections: a meta-analysis
Authors: Zhang Yin , Chen Chen , Mitsnefes Mark , Huang Bin , Devarajan Prasad
Published: May 2024

Introduction

Early and accurate diagnosis of urinary tract infection (UTI) can prevent serious sequelae including chronic kidney disease. Multiple individual studies have identified urine neutrophil gelatinase-associated lipocalin (uNGAL) as a promising biomarker for early diagnosis of UTI. We sought to understand the distribution and diagnostic accuracy of uNGAL values in patients presenting with UTI symptoms.

Methods

Our systematic literature reviews in PubMed, Embase, and Cochrane Reviews up to March 2024, identified 25 studies reporting mean/median, standard deviation/quartiles, and detection limits of uNGAL in symptomatic patients with and without culture-confirmed UTI. Seventeen studies were in children. Meta-analyses were performed using the quantile estimation (QE) method estimating the distributions of uNGAL, which were then compared between the UTI and non-UTI groups for identifying the best cut-off points maximizing the Youden index. Sensitivity analyses were performed on all 25 studies including adult patients.

Results

We found that uNGAL levels were significantly higher in samples with confirmed UTI compared to those without. In pediatric studies, median and 95% confidence interval (CI) of uNGAL values were 22.41 (95% CI of 9.94, 50.54) ng/mL in non-UTI group vs. 118.85 (95% CI of 43.07, 327.97) ng/mL in UTI group. We estimated the cut-off point of 48.43 ng/mL with highest sensitivity (96%) and specificity (97%) in children. Sensitivity analysis including both pediatric and adult studies yielded similar results.

Discussion

The level of uNGAL in symptomatic patients with confirmed UTI is much higher than that reported in patients without UTI. It may be used as a diagnostic tool to identify UTI early among symptomatic patients. The range of uNGAL concentrations and cut-off points reported in subjects with UTI is much lower than that reported in patients with acute intrinsic kidney injury.

Systematic Review Registration

https://www.crd.york.ac.uk/, PROSPERO (CRD42023370451).