bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pay attention: you can fall! The Mini-BESTest scale and the turning duration of the TUG test provide valid balance measures in neurological patients: a prospective study with falls as the balance criterion
Authors: Caronni Antonio , Picardi Michela , Scarano Stefano , Malloggi Chiara , Tropea Peppino , Gilardone Giulia , Aristidou Evdoxia , Pintavalle Giuseppe , Redaelli Valentina , Antoniotti Paola , Corbo Massimo 
Published: September 2023

BackgroundBalance, i.e., the ability not to fall, is often poor in neurological patients and this impairment increases their risk of falling. The Mini-Balance Evaluation System Test (Mini-BESTest), a rating scale, the Timed Up and Go (TUG) test, and gait measures are commonly used to quantify balance. This study assesses the criterion validity of these measures as balance measures.MethodsThe probability of being a faller within nine months was used as the balance criterion. The Mini-BESTest, TUG (instrumented with inertial sensors), and walking test were administered before and after inpatient rehabilitation. Multiple and LASSO logistic regressions were used for the analysis. The diagnostic accuracy of the model was assessed with the area under the curve (AUC) of the receiver operating characteristic curve. Mobility measure validity was compared with the Akaike Information Criterion (AIC).ResultsTwo hundred and fourteen neurological patients (stroke, peripheral neuropathy, or parkinsonism) were recruited. In total, 82 patients fell at least once in the nine-month follow-up. The Mini-BESTest (AUC = 0.69; 95%CI: 0.62–0.76), the duration of the TUG turning phase (AUC = 0.69; 0.62–0.76), and other TUG measures were significant faller predictors in regression models. However, only the turning duration (AIC = 274.0) and Mini-BESTest (AIC = 276.1) substantially improved the prediction of a baseline model, which only included fall risk factors from the medical history (AIC = 281.7). The LASSO procedure selected gender, disease chronicity, urinary incontinence, the Mini-BESTest, and turning duration as optimal faller predictors.ConclusionThe TUG turning duration and the Mini-BESTest predict the chance of being a faller. Their criterion validity as balance measures in neurological patients is substantial.
What is the current status of primary care in the diagnosis and treatment of patients with vertigo and dizziness in Switzerland? A national survey
Authors: Zwergal Andreas , Mantokoudis Georgios , Heg Dierik , Kerkeni Hassen , Diener Suzie , Kalla Roger , Korda Athanasia , Candreia Claudia , Welge-Lüssen Antje , Tarnutzer Alexander A. 
Published: September 2023

BackgroundVertigo and dizziness are among the most frequent presenting symptoms in the primary care physicians' (PCPs) office. With patients facing difficulties in describing their complaints and clinical findings often being subtle and transient, the diagnostic workup of the dizzy patient remains challenging. We aimed to gain more insights into the current state of practice in order to identify the limitations and needs of the PCPs and define strategies to continuously improve their knowledge in the care of the dizzy patient.Materials and methodsBoard-certified PCPs working in Switzerland were invited to participate in an online survey. A descriptive statistical analysis was performed, and prospectively defined hypotheses were assessed using regression analyses.ResultsA vast majority of participating PCPs (n = 152) were familiar with the key questions when taking the dizzy patient's history and with performing provocation/repositioning maneuvers when posterior-canal benign paroxysmal positional vertigo (BPPV) was suspected (91%). In contrast, strong agreement that performing the alternating cover test (21%), looking for a spontaneous nystagmus with fixation removed (42%), and performing the head-impulse test (47%) were important was considerably lower, and only 19% of PCPs were familiar with lateral-canal BPPV treatment. No specific diagnosis could be reached in substantial fractions of patients with acute (35% [25; 50%], median [inter-quartile range]) and episodic/chronic (50% [40; 65.8%]) dizziness/vertigo. Referral to specialists was higher in patients with episodic/chronic dizziness than in acutely dizzy patients (50% [20.3; 75] vs. 30% [20; 50]), with younger PCPs (aged 30–40 years) demonstrating significantly increased odds of referral to specialists (odds ratio = 2.20 [1.01–4.81], p = 0.048).ConclusionThe assessment of dizzy patients takes longer than that of average patients in most primary care practices. Many dizzy patients remain undiagnosed even after a thorough examination, highlighting the challenges faced by PCPs and potentially leading to frequent referrals to specialists. To address this, it is crucial to promote state-of-the-art neuro-otological examination and treatment techniques that are currently neglected by most PCPs, such as “HINTS” and lateral-canal BPPV treatment. This can help reduce referral rates allowing more targeted treatment and referrals.
The different contributions of the eight prefrontal cortex subregions to reactive responses after unpredictable slip perturbations and vibrotactile cueing
Authors: Lee Beom-Chan , Choi Jongkwan , Ahn Jooeun , Martin Bernard J. 
Published: September 2023

IntroductionRecent advancements in functional near-infrared spectroscopy technology have offered a portable, wireless, wearable solution to measure the activity of the prefrontal cortex (PFC) in the human neuroscience field. This study is the first to validate the different contributions made by the PFC's eight subregions in healthy young adults to the reactive recovery responses following treadmill-induced unpredictable slip perturbations and vibrotactile cueing (i.e., precues).MethodsOur fall-inducing technology platform equipped with a split-belt treadmill provided unpredictable slip perturbations to healthy young adults while walking at their self-selected walking speed. A portable, wireless, wearable, and multi-channel (48 channels) functional near-infrared spectroscopy system evaluated the activity of PFC's eight subregions [i.e., right and left dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPFC), and orbitofrontal cortex (OFC)] as quantified by oxyhemoglobin and deoxyhemoglobin concentrations. A motion capture system and two force plates beneath the split-belt treadmill were used to quantify participants' kinematic and kinetic behavior. All participants completed 6 trials: 2 consecutive trials without vibrotactile cueing and with a slip perturbation (control trials); 3 trials with vibrotactile cueing [2 trials with the slip perturbation (cueing trial) and 1 trial without the slip perturbation (catch trial)], and 1 trial without vibrotactile cueing and with a slip perturbation (post-control trial). The PFC subregions' activity and kinematic behavior were assessed during the three periods (i.e., standing, walking, and recovery periods).ResultsCompared to the walkers' standing and walking periods, recovery periods showed significantly higher and lower levels of oxyhemoglobin and deoxyhemoglobin concentrations, respectively, in the right and left DLPFC, VLPFC, and FPFC, regardless of the presence of vibrotactile cueing. However, there was no significant difference in the right and left OFC between the three periods. Kinematic analyses confirmed that vibrotactile cueing significantly improved reactive recovery responses without requiring more involvement by the PFC subregions, which suggests that the sum of attentional resources is similar in cued and non-cued motor responses.DiscussionThe results could inform the design of wearable technologies that alert their users to the risks of falling and assist with the development of new gait perturbation paradigms that prompt reactive responses.
Testing for pharmacogenomic predictors of ppRNFL thinning in individuals exposed to vigabatrin
Authors: Boothman Isabelle , Clayton Lisa M. , McCormack Mark , Driscoll Alexandra McKibben , Stevelink Remi , Moloney Patrick , Krause Roland , Kunz Wolfram S. , Diehl Sarah , O’Brien Terence J. , Sills Graeme J. , de Haan Gerrit-Jan , Zara Federico , Koeleman Bobby P. , Depondt Chantal , Marson Anthony G. , Stefansson Hreinn , Stefansson Kari , Craig John , Johnson Michael R. , Striano Pasquale , Lerche Holger , Furney Simon J. , Delanty Norman ,   Consortium EpiPGX, Sisodiya Sanjay M. , Cavalleri Gianpiero L. 
Published: September 2023

BackgroundThe anti-seizure medication vigabatrin (VGB) is effective for controlling seizures, especially infantile spasms. However, use is limited by VGB-associated visual field loss (VAVFL). The mechanisms by which VGB causes VAVFL remains unknown. Average peripapillary retinal nerve fibre layer (ppRNFL) thickness correlates with the degree of visual field loss (measured by mean radial degrees). Duration of VGB exposure, maximum daily VGB dose, and male sex are associated with ppRNFL thinning. Here we test the hypothesis that common genetic variation is a predictor of ppRNFL thinning in VGB exposed individuals. Identifying pharmacogenomic predictors of ppRNFL thinning in VGB exposed individuals could potentially enable safe prescribing of VGB and broader use of a highly effective drug.MethodsOptical coherence topography (OCT) and GWAS data were processed from VGB-exposed individuals (n = 71) recruited through the EpiPGX Consortium. We conducted quantitative GWAS analyses for the following OCT measurements: (1) average ppRNFL, (2) inferior quadrant, (3) nasal quadrant, (4) superior quadrant, (5) temporal quadrant, (6) inferior nasal sector, (7) nasal inferior sector, (8) superior nasal sector, and (9) nasal superior sector. Using the summary statistics from the GWAS analyses we conducted gene-based testing using VEGAS2. We conducted nine different PRS analyses using the OCT measurements. To determine if VGB-exposed individuals were predisposed to having a thinner RNFL, we calculated their polygenic burden for retinal thickness. PRS alleles for retinal thickness were calculated using published summary statistics from a large-scale GWAS of inner retinal morphology using the OCT images of UK Biobank participants.ResultsThe GWAS analyses did not identify a significant association after correction for multiple testing. Similarly, the gene-based and PRS analyses did not reveal a significant association that survived multiple testing.ConclusionWe set out to identify common genetic predictors for VGB induced ppRNFL thinning. Results suggest that large-effect common genetic predictors are unlikely to exist for ppRNFL thinning (as a marker of VAVFL). Sample size was a limitation of this study. However, further recruitment is a challenge as VGB is rarely used today because of this adverse reaction. Rare variants may be predictors of this adverse drug reaction and were not studied here.
Genetic deletion of α7 nAChRs reduces hippocampal granule and pyramidal cell number in both sexes but impairs pattern separation in males only
Authors: Letsinger Ayland C. , Nacer Samir A. , Stevanovic Korey D. , Larson Gary J. , DeFilipp Jemma S. , Cushman Jesse D. , Yakel Jerrel L. 
Published: September 2023

IntroductionNeurogenesis within the dentate gyrus is thought to play an important role in cognitive processes such as reversal learning and pattern separation. The α7 nicotinic acetylcholine receptor (α7 nAChR) is expressed early in newly formed granule cells of the dentate gyrus, though its role in neurogenesis and related cognitive function is not fully understood.MethodsTo better characterize relevant function of α7 nAChRs, we performed unbiased stereology to quantify hippocampal granule cells, pyramidal cells, and total volume and used a touchscreen operant spatial discrimination/reversal task to test pattern separation in a global α7 nAChR knockout mouse line.ResultsThe knockout resulted in an ≈22% reduction in granule cells and a ≈ 20% reduction in pyramidal cells in both sexes, with no change in total hippocampal volume. However, the knockout impaired performance in the touchscreen task for males only. The sex-dependent difference in behavioral, but not stereological, results suggest a divergence in the structure–function relationship in males versus females. Detailed analyses revealed males were more biased by the initial reversal contingency relative to females indicating a potential source of the sex-specific interaction with the loss of α7 nAChRs.DiscussionThese findings argue that the α7 nAChR plays a critical role in hippocampal development, not just granule cell neurogenesis, and plays a sex-dependent role in cognitive function.
Examining the current health of Gulf War veterans with the veterans affairs frailty index
Authors: Chao Linda L. 
Published: September 2023

IntroductionGulf War Illness (GWI) is a chronic, multisymptom (e.g., fatigue, muscle/joint pain, memory and concentration difficulties) condition estimated to affect 25–32% of Gulf War (GW) veterans. Longitudinal studies suggest that few veterans with GWI have recovered over time and that deployed GW veterans may be at increased risks for age-related conditions.MethodsWe performed a retrospective cohort study to examine the current health status of 703 GW veterans who participated in research studies at the San Francisco VA Health Care System (SFVAHCS) between 2002 and 2018. We used the Veterans Affairs Frailty Index (VA-FI) as a proxy measure of current health and compared the VA-FIs of GW veterans to a group of randomly selected age- and sex-matched, non-GW veterans. We also examined GW veterans’ VA-FIs as a function of different GWI case definitions and in relationship to deployment-related experiences and exposures.ResultsCompared to matched, non-GW veterans, GW veterans had lower VA-FIs (0.10 ± 0.10 vs. 0.12 ± 0.11, p &lt; 0.01). However, the subset of GW veterans who met criteria for severe Chronic Multisymptom Illness (CMI) at the time of the SFVAHCS studies had the highest VA-FI (0.13 ± 0.10, p &lt; 0.001). GW veterans who had Kansas GWI exclusionary conditions had higher VA-FI (0.12 ± 0.12, p &lt; 0.05) than veterans who were Kansas GWI cases (0.08 ± 0.08) and controls (i.e., veterans with little or no symptoms, 0.04 ± 0.06) at the time of the SFVAHCS research studies. The VA-FI was positively correlated with several GW deployment-related exposures, including the frequency of wearing flea collars.DiscussionAlthough GW veterans, as a group, were less frail than non-GW veterans, the subset of GW veterans who met criteria for severe CDC CMI and/or who had Kansas GWI exclusionary conditions at the time of the SFVAHCS research studies were frailest at index date. This suggests that many ongoing studies of GWI that use the Kansas GWI criteria may not be capturing the group of GW veterans who are most at risk for adverse chronic health outcomes.
Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems
Authors: Liu Tianyu , Ye An 
Published: September 2023

BackgroundFor non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.MethodsThe multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.ResultsThe proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.ConclusionThe formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.
The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis
Authors: Yang Yujia , Tang Li , Deng Yiting , Li Xuzi , Luo Anling , Zhang Zhao , He Li , Zhu Cairong , Zhou Muke 
Published: September 2023

ObjectivesThis study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke.MethodsWe searched PubMed, Embase, and Web of Science databases to identify studies using AI for acute stroke prognosis prediction from the database inception to February 2023. Selected studies were designed cohorts and had complete data. We used the Quality Assessment of Diagnostic Accuracy Studies tool to assess the qualities and bias of included studies and used a random-effects model to summarize and analyze the data. We used the area under curve (AUC) as an indicator of the predictive accuracy of AI models.ResultsWe retrieved a total of 1,241 publications and finally included seven studies. There was a low risk of bias and no significant heterogeneity in the final seven studies. The total pooled AUC under the fixed-effects model was 0.872 with a 95% CI of (0.862–0.881). The DL subgroup showed its AUC of 0.888 (95%CI 0.872–0.904). The LR subgroup showed its AUC 0.852 (95%CI 0.835–0.869). The RF subgroup showed its AUC 0.863 (95%CI 0.845–0.882). The SVM subgroup showed its AUC 0.905 (95%CI 0.857–0.952). The Xgboost subgroup showed its AUC 0.905 (95%CI 0.805–1.000).ConclusionThe accuracy of AI models in predicting the outcomes of ischemic stroke is good from our study. It could be an assisting tool for physicians in judging the outcomes of stroke patients. With the update of AI algorithms and the use of big data, further AI predictive models will perform better.
Novel low-sodium salt formulations combined with Chinese modified DASH diet for reducing blood pressure in patients with hypertension and type 2 diabetes: a clinical trial
Authors: Zhang Ziyan , Zhou Xiaomeng , Mei Ying , Bu Xiaoqing , Tang Jie , Gong Tao , Liu Guowei , Cai Shuwen , Ren Yanni , Mu Lihong 
Published: September 2023

BackgroundIn this study, we aimed to explore the antihypertensive effect of 23 and 52% concentrations of low-sodium salt combined with the Chinese Modified Dietary Approaches to Stop Hypertension (CM-DASH) diet in patients with hypertension and type 2 diabetes.MethodsWe conducted a randomized controlled single-blind trial with a semi-open design. One hundred and thirty-two participants were randomly assigned into Group A (control group), Group B (52% low-sodium salt group), Group C (23% low-sodium salt group), and Group D (meal pack group) for 8 weeks of dietary intervention. All participants were followed weekly to collect data on blood pressure, salt use, and adverse events. Blood and 24-h urine samples were analyzed at baseline, 4 weeks, and the end of the intervention.ResultsAt the end of the intervention, the mean blood pressure decreased significantly by 10.81/5.03 mmHg, 14.32/6.32 mmHg, 14.20/6.59 mmHg, and 19.06/7.82 mmHg in Groups A–D, respectively, compared with baseline (p &lt; 0.001). Comparison between groups showed that the systolic blood pressure was lower in Groups C and D than in Groups A (−6.54 mmHg, −8.70 mmHg, p &lt; 0.05) and B (−6.60 mmHg, −8.76 mmHg, p &lt; 0.05), and the diastolic blood pressure was lower in Group D than in Group A (−5.17 mmHg, p = 0.006). The 24-h urinary Na+ and Na+/K+ values were significantly decreased in participants using low-sodium salt (p &lt; 0.001). No serious adverse events occurred during the trial.ConclusionOur preliminary results suggest that 23 and 52% concentrations of low-sodium salt combined with the CM-DASH diet can effectively reduce sodium intake and increase potassium intake in patients with hypertension and type 2 diabetes mellitus, thus achieving “salt reduction” and attaining standard, smooth, comprehensive management of patients with hypertension and type 2 diabetes.Clinical trial registrationhttp://www.chictr.org.cn/, ChiCTR2000029017.
The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis
Authors: Li Yue , Dong Bo , Yuan Puwei 
Published: September 2023

BackgroundMalignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches.ResultsThe inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set.ConclusionMachine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.