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Background: Neural efficiency refers to the brain’s ability to function with reduced resource expenditure while maintaining high performance levels. Previous research has demonstrated that table tennis athletes have greater neural efficiency at the conscious level. However, it is unknown whether they exhibit greater neural efficiency at the unconscious level. Therefore, this study aims to investigate unconscious perceptual processing and neural efficiency in elite table tennis athletes through tasks involving the judgment of spin serves. Methods: Fifty healthy, right-handed individuals participated in this study, including 25 elite table tennis athletes and 25 control participants without professional training experience. To evaluate the unconscious perceptual characteristics of both groups, we used a combination of masked priming paradigm and event-related potential techniques. Results: The behavioral results reveal that, compared to the control group, the table tennis athletes displayed reduced reaction times (p < 0.001) and increased priming effects (p < 0.001) under unconscious conditions. The electrophysiological findings indicated that both groups elicited N1, N2, and P2 components. Notably, compared to the control group, the table tennis athletes exhibited significantly lower amplitude responses at the occipital lobe electrodes PO3, POz, PO4, O1, Oz, and O2 (p < 0.001). Conclusions: These results further support the neural efficiency hypothesis, indicating that prolonged professional training enhances athletes’ capacities for specialized unconscious cognitive processing.

[This corrects the article DOI: 10.1371/journal.pone.0243524.].

IntroductionNeuroinflammatory reactions play a significant role in the pathology and long-term consequences of traumatic brain injury (TBI) and may mediate salutogenic processes that white matter integrity. This study aimed to investigate the relationship between inflammatory markers and white matter integrity following TBI in both a rat TBI model and clinical TBI cases. MethodsIn the rat model, blood samples were collected following a controlled cortical impact (CCI) to assess a panel of inflammatory markers; MR-based diffusion tensor imaging (DTI) was employed to evaluate white matter integrity 60 days post-injury. 15 clinical TBI patients were similarly assessed for a panel of inflammatory markers and DTI post-intensive care unit discharge. Blood samples from healthy controls were used for comparison of the inflammatory markers. ResultsTime-dependent elevations in immunological markers were observed in TBI rats, with a correlation to preserved fractional anisotropy (FA) in white matter. Specifically, TBI-induced increased plasma levels of IL-1β, IL-6, G-CSF, CCL3, CCL5, and TNF-α were associated with higher white matter integrity, as measured by FA. Clinical cases had similar findings: elevated inflammatory markers (relative to controls) were associated with preservation of FA in vulnerable white matter regions. DiscussionInflammatory markers in post-TBI plasma samples are ambivalent with respect to prediction of favourable outcome versus a progression to more pervasive pathology and morbidity.

ABSTRACTSmall molecule therapy is a critical component of targeted anticancer treatment, with tyrosine kinase inhibitors (TKIs) being the first compounds to treat the clonal Chronic Myelogenous Leukaemia (CML) translocation t (9;22) (q34; q11) effectively since 2001. TKIs, such as imatinib, have improved the 10-year survival rate of CML patients to 80%. They bind the BCR::ABL1 kinase and inhibit downstream signaling pathways. However, therapy failure may be seen in 20-25% of CML patients due to intolerance or inadequacy related to BCR::ABL1 dependent or independent mechanisms. This review aimed to summarize current treatment options involving TKIs, resistance mechanisms and the prospective approaches to overcome TKI resistance. We highlight BCR::ABL1-dependent mechanisms of TKI resistance by reviewing clinically-documented BCR::ABL1 mutations and their consequences for TKI binding. In addition, we summarize BCR::ABL1 independent pathways, including the relevance of drug efflux, dysregulation of microRNA, and the involvement of alternative signaling pathways. We also discuss future approaches, such as gene-editing techniques in the context of CML, as potential therapeutic strategies.

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test–retest 3T and 7T sessions were used to extensively assess the test–retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

Objective To assess the evolution of COVID-19 severity and fatality in a unique setting that consistently applied, throughout the pandemic, rigorous and standardised criteria for defining severe COVID-19 outcomes.Methods and analysis We conducted a national cohort study on 312 109 Qatari citizens to investigate incidence of severe, critical or fatal COVID-19 classified according to the WHO criteria between 28 February 2020 and 21 April 2023. Incidence rates for severe, critical or fatal COVID-19 were estimated during the pre-omicron phase, first omicron wave, combined phases and throughout the pandemic.Results Cumulative incidence of severe, critical or fatal COVID-19 after 3.14 years of follow-up was 0.45% (95% CI 0.43% to 0.47%). Incidence rate for severe, critical or fatal COVID-19 throughout the pandemic was 1.43 (95% CI 1.35 to 1.50) per 1000 person years. In the pre-omicron phase, first omicron wave, and combined phases, it was 2.01 (95% CI 1.90 to 2.13), 3.70 (95% CI 3.25 to 4.22) and 2.18 (95% CI 2.07 to 2.30) per 1000 person years, respectively. The post-first omicron phase saw a drastic drop to 0.10 (95% CI 0.08 to 0.14) per 1000 person years, a 95.4% reduction. Among all severe, critical and fatal cases, 99.5% occurred during the primary infection. Cumulative incidence of fatal COVID-19 was 0.042% (95% CI 0.036% to 0.050%), with an incidence rate of 0.13 (95% CI 0.11 to 0.16) per 1000 person years. In the post-first omicron phase, incidence rate of fatal COVID-19 decreased by 90.0% compared with earlier stages. Both severity and fatality exhibited an exponential increase with age and a linear increase with the number of coexisting conditions.Conclusion The conclusion of the first omicron wave was a turning point in the severity of the pandemic. While vaccination and enhanced case management reduced severity gradually, the rapid accumulation of natural immunity during the first omicron wave appears to have played a critical role in driving this shift in severity.

IntroductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).MethodsThree brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.ResultsWMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.DiscussionA Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

We compared regional retention of 18F-flortaucipir between 20 patients with Lewy body disorders (LBD), 12 Alzheimer's disease patients with positive amyloid positron emission tomography (PET) scans (AD+Aβ) and 15 healthy controls with negative amyloid PET scans (HC-Aβ). In LBD subjects, we compared the relationship between 18F-flortaucipir retention and cerebrospinal fluid (CSF) tau, cognitive performance, and neuropathological tau at autopsy. The LBD cohort was stratified using an Aβ42 cut-off of 192 pg/mL to enrich for groups likely harboring tau pathology (LBD+Aβ = 11, LBD-Aβ = 9). 18F-flortaucipir retention was higher in LBD+AB than HC-Aβ in five, largely temporal-parietal regions with sparing of medial temporal regions. Higher retention was associated with higher CSF total-tau levels (p = 0.04), poorer domain-specific cognitive performance (p = 0.02-0.04), and greater severity of neuropathological tau in corresponding regions. While 18F-flortaucipir retention in LBD is intermediate between healthy controls and AD, retention relates to cognitive impairment, CSF total-tau, and neuropathological tau. Future work in larger autopsy-validated cohorts is needed to define LBD-specific tau biomarker profiles.

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

Seasonal influenza viruses may lead to severe illness and mortality in patients with comorbidities, including Diabetes Mellitus (DM). Vaccination against influenza in DM patients may reduce influenza incidence and severity. Before the emergence of the COVID-19 pandemic, influenza infections were the most prevalent respiratory infections in Qatar. Still, reports about influenza prevalence and vaccine efficacy in DM patients have not been reported. This study aimed to analyze influenza prevalence among other respiratory infections and assess influenza vaccine efficacy in DM patients in Qatar. Statistical analysis was performed on data obtained from Hamad Medical Corporation (HMC) database for patients that visited the emergency department (ED) with respiratory-like illnesses. The analysis was done for the period between January 2016 to December 2018. Among 17,525 patients who visited HMC-ED with clinical symptoms of respiratory infections, 2611(14.9%) were reported to have DM. Among DM patients, influenza was the most prevalent respiratory pathogen at 48.9%. Influenza virus A (IVA) was the most circulating type, contributing to 38.4%, followed by IVB contributing to 10.4% of total respiratory infections. Among the typed IVA-positive cases, 33.4% were H1N1, and 7.7% were H3N2. A significant decrease in influenza infections was reported in vaccinated DM patients (14.5%) when compared to non-vaccinated patients (18.9%) (p-value = 0.006). However, there was no significant relaxation in the clinical symptoms among vaccinated DM patients compared to their non-vaccinated counterparts. In conclusion, influenza was the most common etiology for respiratory viral infection among diabetic patients at the leading healthcare provider in Qatar. Although vaccination reduced the incidence rate among DM patients, it was less effective in preventing symptoms. Further studies on a larger cohort and for a more extended period are required to investigate influenza prevalence and vaccine efficacy among DM patients.

Gingival recession treatment is one of the major clinical challenges in periodontics, and various surgical techniques have been proposed to correct it. Most of these techniques are suitable for isolated recession sites and involve harvesting an autogenous tissue graft, which increases patient morbidity and might result in inferior esthetics due to incision design. This study assessed the benefit of adding platelet-rich fibrin (PRF) to the vestibular incision subperiosteal tunneling approach (VISTA) in treating multiple gingival recession compared to using VISTA alone. A total of 41 teeth with Miller Class I and II defects were randomized in a split-mouth design. Multiple clinical parameters were tested, including the change in gingival thickness over time, keratinized tissue width, and gingival phenotype (using the transparency of a periodontal probe). Patient-centered outcomes were also assessed via visual analog scale. Multiple Miller Class I and II recessions in the maxilla can be effectively treated with VISTA. However, when used in conjunction with PRF, no significant differences were detected in any parameter. VISTA has been shown to be associated with a low level of postsurgical pain for patients and can be used for patients with high esthetic demands. [ABSTRACT FROM AUTHOR]