bluuebunny/crossref_metadata_2025_split huggingface dataset:
A deep learning model for brain age prediction using minimally preprocessed T1w images as input
Authors: Dartora Caroline , Marseglia Anna , Mårtensson Gustav , Rukh Gull , Dang Junhua , Muehlboeck J-Sebastian , Wahlund Lars-Olof , Moreno Rodrigo , Barroso José , Ferreira Daniel , Schiöth Helgi B. , Westman Eric ,   for the Alzheimer’s Disease Neuroimaging Initiative,   the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing,   the Japanese Alzheimer’s Disease Neuroimaging Initiative,   the AddNeuroMed Consortium
Published: January 2024

IntroductionIn the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction.MethodsWe used a multicohort dataset of cognitively healthy individuals (age range = 32.0–95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2–4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset’s images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4).Results:The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1–4, respectively. The model’s performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63–2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction.DiscussionWe provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.
Assessing the contribution of mild high-altitude exposure to obstructive sleep apnea-hypopnea syndrome comorbidities
Authors: Hao Lijuan , Peng Kangkang , Bian Qi , Guo Suting , Duan Chengmin , Feng Lei , Chen Zhenguo , Renzeng Caiang , Pang Huaixia , Ma Zhen 
Published: January 2024

BackgroundObstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder. The lower atmospheric pressure and decreased oxygen levels of high-altitude areas can exacerbate the severity of OSAHS, but research into OSAHS in high-altitude areas remains limited. This study, from June 2015 to January 2020, involved 4,667 patients with suspected OSAHS and 38 healthy volunteers. The non-OSAHS group (AHI &lt;5/h) had 395 patients, while the larger OSAHS group (AHI ≥5/h) comprised 4,272 patients. The significant size difference between the groups emphasized the study’s focus on OSAHS, using the non-OSAHS mainly for comparison.MethodsSleep technicians monitored the OSAHS patient group overnight by polysomnography (PSG), the apnea-hypopnea index (AHI), the mean oxygen saturation (MSpO2), lowest oxygen saturation (LSpO2), the oxygen desaturation index (ODI) and the total sleep time with oxygen saturation less than 90% (TST-SpO2 &lt;90%). Healthy volunteers self-monitored sleep patterns at home, using the CONTEC RS01 respiration sleep monitor with a wristwatch sleep apnea screen meter. The RSO1 wristwatch-style device has already been studied for consistency and sensitivity with the Alice-6 standard multi-lead sleep monitor and can be used for OSAHS screening in this region.ResultsLSpO2 recordings from healthy volunteers (86.36 ± 3.57%) and non-OSAHS (AHI &lt;5/h) cohort (78.59 ± 11.99%) were much lower than previously reported normal values. Regression analysis identified no correlations between AHI levels and MSpO2 or TST-SpO2 &lt;90%, weak correlations between AHI levels and LSpO2 or MSpO2, and a strongly significant correlation between AHI levels and the ODI (r = 0.76, p &lt; 0.05). The data also indicated that the appropriate clinical thresholds for OSAHS patients living at mild high altitude are classified as mild, moderate, or severe based on LSpO2 saturation criteria of 0.85–0.90, 0.65–0.84, or &lt;0.65, respectively.ConclusionThe study findings suggest that individuals with an AHI score below 5 in OSAHS, who reside in high-altitude areas, also require closer monitoring due to the elevated risk of nocturnal hypoxia. Furthermore, the significant correlation between ODI values and the severity of OSAHS emphasizes the importance of considering treatment options. Additionally, the assessment of hypoxemia severity thresholds in OSAHS patients living in high-altitude regions provides valuable insights for refining diagnostic guidelines.
Effect of TMAO on the incidence and prognosis of cerebral infarction: a systematic review and meta-analysis
Authors: Wang Lin , Nan Yinan , Zhu Wenhao , Wang Shaoqing 
Published: January 2024

ObjectiveThis study aimed to evaluate the effect of trimethylamine oxide (TMAO) on the incidence and prognosis of cerebral infarction.MethodsWe searched PubMed, Embase, and Cochrane databases for all clinical studies on the association of TMAO with cerebral infarction incidence and prognosis from inception to April 2023. A systematic review and meta-analysis were conducted using the meta-analysis of observational studies in epidemiology (MOOSE) declaration list. The Newcastle–Ottawa Scale (NOS) was used to assess the quality of the study. This study protocol was registered on the PROSPERO database with the ID: CRD42023459661. The extracted data included the OR value of the effect of TMAO on the incidence and prognosis of cerebral infarction, the HR value between TMAO and underlying diseases, the RR value, 95% confidence intervals, and the AUC value of TMAO in the prediction model of cerebral infarction.ResultsFifteen studies including 40,061 patients were included. All the patients were from China or Germany. The TMAO level was significantly correlated with the Modified Rankin Score (mRS) 3 months after the onset of cerebral infarction (OR, 1.581; 95% CI, 1.259–1.987; p &lt; 0.01). The TMAO level was significantly correlated with the rate of first-time incidence and recurrence of cerebral infarction (OR, 1.208; 95% CI, 1.085–1.344; p &lt; 0.01 and HR, 1.167; 95% CI, 1.076–1.265; p &lt; 0.01, respectively). The TMAO level was also highly correlated with disease severity at onset (National Institutes of Health Stroke Scale, NIHSS &gt;5) (OR, 5.194; 95% CI, 1.206–22.363; p &lt; 0.05), but had no significant correlation with mortality after cerebral infarction (p &gt; 0.05). Correlation analysis of TMAO with underlying diseases in the population indicated that TMAO had a significant correlation with histories of hypertension, diabetes mellitus, coronary artery disease, and cerebral infarction (p &lt; 0.05), but not with hyperlipidemia (p &gt; 0.05). Six risk prediction models of TMAO for cerebral infarction reported in four studies were systematically evaluated; five of them had good predictive value (AUC ≥ 0.7).ConclusionTMAO is an independent risk factor affecting the onset, prognosis, and severity of cerebral infarction.
Inhibitory control deficits in patients with mesial temporal lobe epilepsy: an event-related potential analysis based on Go/NoGo task
Authors: Shao Chenjing , Li Desheng , Zhang Xu , Xiang Feng , Zhang Xi , Wang Xiangqing 
Published: January 2024

ObjectiveNeuropsychiatric comorbidities are common among patients with mesial temporal lobe epilepsy (MTLE). One of these comorbidities, impulsivity, can significantly impact the quality of life and prognosis. However, there have been few studies of impulsivity in these patients, and the existing findings are inconsistent. The present study investigates impulsivity in MTLE patients from the perspective of inhibitory control and its underlying processes using event-related potentials (ERPs) initiated using a Go/NoGo task.MethodsA total of 25 MTLE patients and 25 age-, gender-, and education-matched healthy controls (HCs) completed an unequal visual Go/NoGo task. Different waveforms as well as behavioral measures were analyzed between Go and NoGo conditions (N2d and P3d). Impulsivity was also assessed using self -rating scales, and clinical variables that may be related to ERPs were explored.ResultsCompared with HCs, MTLE patients exhibited significantly longer reaction time (RT) (p = 0.002) and lower P3d especially at the frontal electrode sites (p = 0.001). In the MTLE group, the seizure frequency (p = 0.045) and seizure types (p &lt; 0.001) were correlated with the P3d amplitude. A self-rated impulsivity assessment revealed that MTLE patients had higher non-planning (p = 0.017) and total scores (p = 0.019) on the BIS-11 as well as higher DI (p = 0.010) and lower FI (p = 0.007) on the DII.ConclusionThe findings demonstrate that the presence of inhibitory control deficits in patients with MTLE are characterized by deficits in the late stage of inhibition control, namely the motor inhibition stage. This study improves our understanding of impulsivity in MTLE patients and suggests that ERPs may constitute a sensitive means of detecting this trait.
Research hotspots and frontiers of neuromodulation techniques in disorders of consciousness: a bibliometric analysis
Authors: Guo Bilian , Han Qiong , Ni Jun , Yan Zhipeng 
Published: January 2024

BackgroundThe characteristics of disorders of consciousness (DOC) are changes in arousal and/or awareness caused by severe brain injuries. To date, the management of DOC patients remains a complex and challenging task, and neuromodulation techniques offer a promising solution. However, a bibliometric analysis focusing on neuromodulation techniques in DOC is currently absent. The aim of this study is to provide a bibliometric visualization analysis to investigate the research hotspots and frontiers in the field of neuromodulation techniques in DOC from 2012 to 2022.MethodsThe publications were collected and retrieved from the Web of Science (WoS) from 2012 to 2022. CiteSpace and Microsoft Excel were utilized perform the first global bibliographic analysis of the literature related to neuromodulation techniques for DOC.ResultsThe analysis included a total of 338 publications. From 2012 to 2022, a consistent yet irregular increase in the number of articles published on neuromodulation techniques in DOC was observed. Frontiers in Neurology published the highest number of papers (n = 16). Neurosciences represented the main research hotspot category (n = 170). The most prolific country, institution, and author were the USA (n = 105), the University of Liege (n = 41), and Laureys Steven (n = 38), respectively. An analysis of keywords revealed that UWS/VS, MCS, and TMS constituted the primary research trends and focal points within this domain.ConclusionThis bibliometric study sheds light on the current progress and emerging trends of neuromodulation techniques in DOC from 2012 to 2022. The focal topics in this domain encompass the precise diagnosis of consciousness levels in patients suffering from DOC and the pursuit of efficacious neuromodulation-based evaluation and treatment protocols for such patients.
The role of the brainstem in sleep disturbances and chronic pain of Gulf War and Iraq/Afghanistan veterans
Authors: Zhang Yu , Moore Matthew , Jennings Jennifer S. , Clark J. David , Bayley Peter J. , Ashford J. Wesson , Furst Ansgar J. 
Published: January 2024

IntroductionGulf War Illness is a type of chronic multisymptom illness, that affects about 30% of veterans deployed to the 1990–91 Persian Gulf War. Veterans deployed to Iraq/Afghanistan after 2000 are reported to have a similar prevalence of chronic multisymptom illness. More than 30 years after the Persian Gulf War, Gulf War Illness still has an unexplained symptom complex, unknown etiology and lacks definitive diagnostic criteria and effective treatments. Our recent studies have found that substantially smaller brainstem volumes and lower fiber integrity are associated with increased sleep difficulty and pain intensity in 1990–91 Persian Gulf War veterans. This study was conducted to investigate whether veterans deployed to Iraq/Afghanistan present similar brainstem damage, and whether such brainstem structural differences are associated with major symptoms as in Gulf War Illness.MethodsHere, we used structural magnetic resonance imaging and diffusion tensor imaging to measure the volumes of subcortices, brainstem subregions and white matter integrity of brainstem fiber tracts in 188 veterans including 98 Persian Gulf War veterans and 90 Iraq/Afghanistan veterans.ResultsWe found that compared to healthy controls, veterans of both campaigns presented with substantially smaller volumes in brainstem subregions, accompanied by greater periaqueductal gray matter volumes. We also found that all veterans had reduced integrity in the brainstem-spinal cord tracts and the brainstem-subcortical tracts. In veterans deployed during the 1990–91 Persian Gulf War, we found that brainstem structural deficits significantly correlated with increased sleep difficulties and pain intensities, but in veterans deployed to Iraq/Afghanistan, no such effect was observed.DiscussionThese structural differences in the brainstem neurons and tracts may reflect autonomic dysregulation corresponding to the symptom constellation, which is characteristic of Gulf War Illness. Understanding these neuroimaging and neuropathological relationships in Gulf War and Iraq/Afghanistan veterans may improve clinical management and treatment strategies for modern war related chronic multisymptom illness.
Nutritional status of hospitalized elderly patients in Ethiopia: a cross-sectional study of an important yet neglected problem in clinical practice
Authors: Tesfaye Behailu Terefe , Yizengaw Mengist Awoke , Birhanu Tesema Etefa , Bosho Dula Dessalegn 
Published: January 2024

BackgroundMalnutrition is a common geriatric syndrome affecting approximately half of the older population with a more pronounced occurrence rate in those hospitalized. It affects the physiology, and results in poor humanistic and clinical outcomes. In Africa, particularly in Ethiopia, albeit multiple studies are available on malnutrition in non-hospitalized older population, similar studies in inpatient settings are scarce. Therefore, this study was conducted with the intention to quantify the prevalence of malnutrition in older patients on inpatient admission and determine its associated factors.MethodsA total of 157 older inpatients aged 60 years and above were included in the present study. The data collection format was developed after an in-depth review of relevant literatures. The full Mini-Nutritional Assessment (MNA) tool was employed to assess the nutritional status on admission. Data completeness was checked thoroughly. Descriptive statistics and logistic regression analysis were conducted using STATA 15.0. The area under the receiver operating characteristic curve (ROC), Hosmer–Lemeshow test, and classification table were computed to evaluate the final model goodness-of-fit.ResultsOf the total study subjects, 81% were malnourished (MNA score &lt;17) and 17% were at risk for malnutrition (MNA score of 17.5–23.5). However, upon review of the patients’ medical charts, malnutrition diagnosis was recorded in only two patients. Rural residence (AOR = 2.823, 95%CI: 1.088, 7.324), self-reported financial dependence for expenses (AOR = 4.733, 95%CI: 1.011, 22.162), and partial dependence in functional autonomy on admission (AOR = 3.689, 95%CI: 1.190, 11.433) significantly increased the risk of malnutrition. The area under the ROC curve (0.754) and the Hosmer–Lemeshow test (p = 0.7564) indicated that the final model reasonably fits the data. The model`s sensitivity is 96.85%.ConclusionIn the present study, an alarmingly high prevalence of malnutrition was identified older inpatients. The problem went undiagnosed in a similar percentage of patients. Several available literatures indicate the presence of an association between nutritional status and patient outcomes, thus strict nutritional screening at inpatient admission and intervention are recommended with special emphasis for those from rural areas, with financial dependence, and with functional impairment on admission.
Prospective cohort study of broccoli consumption frequency and all-cause and cause-specific mortality risks
Authors: Liu Xiangliang , Chang Yu , Li Yuguang , Zhang Xinwei , Li Fangqi , Song Jia , Shi Hanping , Chen Xiao , Cui Jiuwei 
Published: January 2024

BackgroundBroccoli is rich in vitamins, minerals, and antioxidants with broad health benefits, but its intake frequency and dose–response relationship with mortality risk remain unclear.MethodsUsing data from the U.S. National Health and Nutrition Examination Survey 2003–2006, 12,486 adults were included. Broccoli intake frequency was evaluated by a food frequency questionnaire, and all-cause and cause-specific mortality risks were followed up. The relationship between broccoli intake and mortality risk was analyzed using Cox models.ResultsCompared with never consumption of broccoli, different frequencies of broccoli consumption were associated with significantly decreased risks of all-cause mortality (p for trend &lt;0.001). Consuming broccoli 1–2 times per week was associated with a 32–43% lower mortality risk. More frequent broccoli consumption was negatively correlated with cardiovascular and cancer mortality risks (p &lt; 0.05). Consuming broccoli 1–2 times per week for males and ≥ 3 times per week for females could significantly reduce all-cause mortality risk.ConclusionModerate and frequent consumption of broccoli may reduce the risks of all-cause and cause-specific mortality. Optimal intake frequencies may differ by gender.
The association between plant-based diet indices and metabolic syndrome: a systematic review and dose–response meta-analysis
Authors: Nikparast Ali , Etesami Elahe , Rahmani Jamal , Rafiei Nazgoli , Ghanavati Matin 
Published: January 2024

Aim/introductionThe prevalence of metabolic syndrome (MetS) and its components have markedly increased worldwide. Among lifestyle factors introduced to lower the risk of MetS, healthy dietary patterns have gained considerable attention. This study aimed to assess the association between adherence to plant-based diet indices including O-PDI (overall plant-based diet index), H-PDI (healthy plant-based diet index), U-PDI (unhealthy plant-based diet index), and risk of MetS development.MethodsTo find related observational studies which assessed the association between Plant-based Diet indices and risk of MetS development, PubMed/Medline, Scopus, and Web of Science databases were searched from January 2016 to November 2023. A random effects model was used to estimate pooled odds ratios (OR) and 95% confidence intervals (95% CI). To assess the heterogeneity of included studies, the I2 index was used.ResultsNine studies including 34,953 participants from the initial 288 studies were recognized to include in this meta-analysis study. According to pooled analysis, there was a significant relationship between the adherence to H-PDI and the lower risk of MetS (ES: 0.81; 95% CI: 0.67, 0.97; I2 = 77.2%, p &lt; 0.001), while greater adherence to U-PDI was associated with 27% increases in the risk of MetS (ES: 1.27; 95% CI: 1.05, 1.54; I2 = 76.8%, p &lt; 0.001). According to our analysis of the association between adherence to PDIs and the risk of MetS components, greater adherence to O-PDI and H-PDI was significantly associated with a higher risk of elevated FBS and obesity, respectively. As well, greater adherence to U-PDI was significantly associated with a higher risk of obesity, hypertriglyceridemia, low HDL-C, and elevated FBS.ConclusionOur results highlighted the importance of food choices in the context of a plant-based dietary pattern, indicating that adherence to unhealthy plant-based dietary patterns rich in less healthful carbohydrates may induce the risk of MetS development.Systematic review registrationPROSPERO CRD42023428981.
Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images
Authors: Zhou You , Lalande Alain , Chevalier Cédric , Baude Jérémy , Aubignac Léone , Boudet Julien , Bessieres Igor 
Published: January 2024

IntroductionLinear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation for abdominal tumors and organs at risk (OARs) required for the treatment planning is becoming possible. Currently, this segmentation is performed manually by radiation oncologists. This process is very time consuming and subject to inter and intra operator variabilities. In this work, deep learning based automatic segmentation solutions were investigated for abdominal OARs on 0.35 T MR-images.MethodsOne hundred and twenty one sets of abdominal MR images and their corresponding ground truth segmentations were collected and used for this work. The OARs of interest included the liver, the kidneys, the spinal cord, the stomach and the duodenum. Several UNet based models have been trained in 2D (the Classical UNet, the ResAttention UNet, the EfficientNet UNet, and the nnUNet). The best model was then trained with a 3D strategy in order to investigate possible improvements. Geometrical metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD) and analysis of the calculated volumes (thanks to Bland-Altman plot) were performed to evaluate the results.ResultsThe nnUNet trained in 3D mode achieved the best performance, with DSC scores for the liver, the kidneys, the spinal cord, the stomach, and the duodenum of 0.96 ± 0.01, 0.91 ± 0.02, 0.91 ± 0.01, 0.83 ± 0.10, and 0.69 ± 0.15, respectively. The matching IoU scores were 0.92 ± 0.01, 0.84 ± 0.04, 0.84 ± 0.02, 0.54 ± 0.16 and 0.72 ± 0.13. The corresponding HD scores were 13.0 ± 6.0 mm, 16.0 ± 6.6 mm, 3.3 ± 0.7 mm, 35.0 ± 33.0 mm, and 42.0 ± 24.0 mm. The analysis of the calculated volumes followed the same behavior.DiscussionAlthough the segmentation results for the duodenum were not optimal, these findings imply a potential clinical application of the 3D nnUNet model for the segmentation of abdominal OARs for images from 0.35 T MR-Linac.