Accelerated Chemical Shift Encoded Cardiac MRI with Use of Resolution Enhancement Network.

BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE. METHODS: FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm2, 2.5 × 1.5 mm2, and 3.8 × 1.9mm2 resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm2 and 3.8 × 1.9 mm2 to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance. RESULTS: FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm2 (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm2 (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90). CONCLUSION: We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

Dorsal root ganglion inflammation by oxaliplatin toxicity: DPEP1 as possible target for peripheral neuropathy prevention.

BACKGROUND: Peripheral neuropathy (PN) constitutes a dose-limiting side effect of oxaliplatin chemotherapy that often compromises the efficacy of antineoplastic treatments. Sensory neurons damage in dorsal root ganglia (DRG) are the cellular substrate of PN complex molecular origin. Dehydropeptidase-1 (DPEP1) inhibitors have shown to avoid platin-induced nephrotoxicity without compromising its anticancer efficiency. The objective of this study was to describe DPEP1 expression in rat DRG in health and in early stages of oxaliplatin toxicity. To this end, we produced and characterized anti-DPEP1 polyclonal antibodies and used them to define the expression, and cellular and subcellular localization of DPEP1 by immunohistochemical confocal microscopy studies in healthy controls and short term (six days) oxaliplatin treated rats. RESULTS: DPEP1 is expressed mostly in neurons and in glia, and to a lesser extent in endothelial cells. Rats undergoing oxaliplatin treatment developed allodynia. TNF-𝛼 expression in DRG revealed a pattern of focal and at different intensity levels of neural cell inflammatory damage, accompanied by slight variations in DPEP1 expression in endothelial cells and in nuclei of neurons. CONCLUSIONS: DPEP1 is expressed in neurons, glia and endothelial cells of DRG. Oxaliplatin caused allodynia in rats and increased TNF-α expression in DRG neurons. The expression of DPEP1 in neurons and other cells of DRG suggest this protein as a novel strategic molecular target in the prevention of oxaliplatin-induced acute neurotoxicity.

Changes in AmotL2 Expression in Cells of the Human Enteral Nervous System in Oxaliplatin-Induced Enteric Neuropathy.

Gastrointestinal (GI) toxicity is a common side effect in patients undergoing oxaliplatin (OxPt)-based chemotherapy for colorectal cancer (CRC). Frequently, this complication persists in the long term and could affect the efficacy of the treatment and the patient's life quality. This long-term GI toxicity is thought to be related to OxPt-induced enteral neuropathy. AmotL2 is a member of the Angiomotin family of proteins, which play a role in cell survival, neurite outgrowth, synaptic maturation, oxidative stress protection, and inflammation. In order to assess the role of AmotL2 in OxPt-induced enteral neuropathy, we studied the expression of AmotL2 in cells of the enteric nervous system (ENS) of untreated and OxPt-treated CRC patients and its relationship with inflammation, using immunofluorescence confocal microscopy. Our results in human samples show that the total number of neurons and glial cells decreased in OxPt-treated patients, and TNF-α and AmotL2 expression was increased and colocalized in both neurons and glia. AmotL2 differential expression between OxPt-treated and untreated CRC patients shows the involvement of this scaffold protein in the inflammatory component and toxicity by OxPt in the ENS.

Maximizing efficiency in sedimentary ancient DNA analysis: a novel extract pooling approach.

In the last few decades, the field of ancient DNA has taken a new direction towards using sedimentary ancient DNA (sedaDNA) for studying human and mammalian population dynamics as well as past ecosystems. However, the screening of numerous sediment samples from archaeological sites remains a time-consuming and costly endeavor, particularly when targeting hominin DNA. Here, we present a novel high-throughput method that facilitates the fast and efficient analysis of sediment samples by applying a pooled testing approach. This method combines multiple extracts, enabling early parallelization of laboratory procedures and effective aDNA screening. Pooled samples with detectable aDNA signals undergo detailed analysis, while empty pools are discarded. We have successfully applied our method to multiple sediment samples from Middle and Upper Paleolithic sites in Europe, Asia, and Africa. Notably, our results reveal that an aDNA signal remains discernible even when pooled with four negative samples. We also demonstrate that the DNA yield of double-stranded libraries increases significantly when reducing the extract input, potentially mitigating the effects of inhibition. By embracing this innovative approach, researchers can analyze large numbers of sediment samples for aDNA preservation, achieving significant cost reductions of up to 70% and reducing hands-on laboratory time to one-fifth.

The Method of Everything vs. Experimenter Bias of Loophole-Free Bell Experiments.

Experimenter bias compromises the integrity and advancement of science, especially when awarded as such. For example, the 2022 Nobel Prize in Physics awarded for the loophole-free experiments that tested physicist John S. Bell's inequality theorem. These experiments employed the logic of conducting local experiments to obtain local evidence that contradicted local realistic theories of nature, thereby validating quantum mechanics as a fundamental non-local theory. However, there was one loophole that was wittingly not tested by the Nobel laureates. The notable exception was Bell's "super-deterministic" loophole, which was validated (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) non-locally, thus compromising the subsequent Nobel Prize. More importantly, the discovery of two mutually exclusive and jointly exhaustive non-local hidden variables revealed why local scientific methods obtain false-positive and false-negative results. With knowledge of this fundamental omission, the inclusion of the non-local hidden variables in the local methods used in science can then advance it to be a complete study of nature.

Store-and-forward teledermatology in a Spanish health area significantly increases access to dermatology expertise.

INTRODUCTION: Teledermatology is the practice of dermatology through communication technologies. The aim of this study is to analyze its implementation in a Spanish health area during its first two years. METHODS: Cross-sectional descriptive study. It included interconsultations between dermatologists and family physicians in the Salamanca Health Area (Spain) after the implementation of the non-face-to-face modality over a period of two consecutive years. A total of 25,424 consultations were performed (20,912 face-to-face and 4,512 non-face-to-face); 1000 were selected by random sampling, half of each modality. MAIN MEASURES: referral rate, response time and resolution time, type of pathology, diagnostic concordance, and quality of consultation. RESULTS: The annual referral rate was 42.9/1000 inhabitants (35.3 face-to-face and 7.6 non-face- to-face). The rate of face-to-face referrals was higher in urban areas (37.1) and the rate of non- face-to-face referrals in rural areas (10.4). The response time for non-face-to-face consultations was 2.4 ± 12.7 days and 56 ± 34.8 days for face-to-face consultations (p < 0.001). The resolution rate for non-face-to-face consultations was 44%. Diagnostic concordance, assessed by the kappa index, was 0.527 for face-to-face consultations and 0.564 for non-face-to-face consultations. Greater compliance with the quality criteria in the non-attendance consultations. CONCLUSIONS: Teledermatology appears to be an efficient tool in the resolution of dermatological problems, with a rapid, effective, and higher quality response for attention to skin pathologies. REGISTRY: ClinicalTrials.gov Identifier: NCT05625295. Registered on 21 November 2022 ( https://clinicaltrials.gov/ct2/show/ NCT05625295).

An innovative 12-lead resting electrocardiogram dataset in professional football.

This paper aims to provide a comprehensive and innovative 12-lead electrocardiogram (ECG) dataset tailored to understand the unique needs of professional football players. Other ECG datasets are available but collected from common people, normally with diseases confirmed, while it is well known that ECG characteristics change in athletes and elite players as a result of their intense long-term physical training. This initiative is part of a broader research project employing machine learning (ML) to analyse ECG data in this athlete population and explore them according to the International criteria for ECG interpretation in athletes. The dataset is generated through the establishment of a prospective observational cohort consisting of 54 male football players from La Liga, representing a UEFA Pro-level team. Named the Pro-Football 12-lead Resting Electrocardiogram Database (PF12RED), it comprises 163 10-s ECG recordings, offering a detailed examination of the at-rest heart activity of professional football athletes. Data collection spans five phases over multiple seasons, including the 2018-2019 postseason, the 2019-20 preseason, the 2020-21 preseason, and the 2021-22 preseason. Athletes undergo medical evaluations that include a 10-s resting 12-lead ECG performed with General Electric's USB-CAM 14 module (https://co.services.gehealthcare.com/gehcstorefront/p/900995-002), with data saved using General Electric's CardioSoft V6.73 12SL V21 ECG Software. (https://www.gehealthcare.es/products/cardiosoft-v7) The data collection adheres to ethical principles, with clearance granted by the Autonomous Community of Andalusia Ethics Committee (Spain) under protocol number 1573-N-19 in December 2019. Participants provide informed consent, and data sharing is permitted following anonymization. The study aligns with the Declaration of Helsinki and adheres to the recommendations of the International Committee of Medical Journal Editors (ICMJE). The generated dataset serves as a valuable resource for research in sports cardiology and cardiac health. Its potential for reuse encompasses:1.International Comparison: Enabling cross-regional comparisons of cardiac characteristics among elite football players, enriching international studies.2.ML Model Development: Facilitating the development and refinement of machine learning models for arrhythmia detection, serving as a benchmark dataset.3.Validation of Diagnostic Methods: Allowing the validation of automatic diagnostic methods, contributing to enhanced accuracy in detecting cardiac conditions.4.Research in Sports Cardiology: Supporting future investigations into specific cardiac adaptations in elite athletes and their relation to cardiovascular health.5.Reference for Athlete Protection Policies: Influencing athlete protection policies by providing data on cardiac health and suggesting guidelines for medical assessments.6.Health Professionals Training: Serving as a training resource for health professionals interested in interpreting ECGs in sports contexts.7.Tool and Application Development: Facilitating the development of tools and applications related to the visualization, simulation and analysis of ECG signals in athletes.

Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.

Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5840 patients (mean age, 55 years ± 19 [SD]; 3527 male patients) referred for clinical cardiac MRI from 2003 to 2021 at nine centers; images were acquired using 1.5- and 3-T scanners from three vendors. Data from three centers were used for training and testing (4:1 ratio). The remaining data were used for external testing. Cines with downsampled frame rates were restored using linear, bicubic, and model-based interpolation. The root mean square error between interpolated and original cine images was modeled using ordinary least squares regression. In a prospective study of 49 participants referred for clinical cardiac MRI (mean age, 56 years ± 13; 25 male participants) and 12 healthy participants (mean age, 51 years ± 16; eight male participants), the model was applied to cines acquired at 25 frames per second (fps), thereby doubling the frame rate, and these interpolated cines were compared with actual 50-fps cines. The preference of two readers based on perceived temporal smoothness and image quality was evaluated using a noninferiority margin of 10%. Results The model generated artifact-free interpolated images. Ordinary least squares regression analysis accounting for vendor and field strength showed lower error (P < .001) with model-based interpolation compared with linear and bicubic interpolation in internal and external test sets. The highest proportion of reader choices was "no preference" (84 of 122) between actual and interpolated 50-fps cines. The 90% CI for the difference between reader proportions favoring collected (15 of 122) and interpolated (23 of 122) high-frame-rate cines was -0.01 to 0.14, indicating noninferiority. Conclusion A transformer-based deep learning model increased cardiac cine frame rates while preserving both spatial resolution and scan time, resulting in images with quality comparable to that of images obtained at actual high frame rates. Keywords: Functional MRI, Heart, Cardiac, Deep Learning, High Frame Rate Supplemental material is available for this article. © RSNA, 2024.

Evaluation of intrinsic and extrinsic factors affecting pregnancy rate in dual-purpose cows under tropical conditions.

Embryo transfer (ET) in bovines was created with the purpose of increasing the pregnancy rate (PR) of animals with high genetic value; however, multiple factors have been found to affect the success of this reproductive biotechnology. These factors are frequently grouped in intrinsic and extrinsic factors. Thus, the objective of the present experiments was to assess the effect of intrinsic and extrinsic factors on the pregnancy rate under tropical conditions. To do this a total of 648 embryo transfer (ET) procedures were performed between January and December 2021. The intrinsic factors were size and location of the corpus luteum, body condition, genetic group, age and parity; while extrinsic factors were location of the farm, environmental comfort, season in which the ET was carried out, prevailing weather conditions, and the preservation, quality, and the development stage of embryos at the time of ET. A χi2 was used for analysis of main effects, and logistic regression analysis to calculate the probability of pregnancy and the association between intrinsic or extrinsic factors; additionally, a multivariate analysis of data clusters was used to find a linkage between the effects. While recipient female age had a negative effect (Odds ratio = 0.345-0.871) on PR (p < 0.05), being higher in younger cows, the rest of the intrinsic factors did not affect the PR. The significant (p < 0.05) extrinsic factors were THI category, season of year and type of embryo preservation, showing that the highest PR (p < 0.05) was obtained in the comfort THI category, during the winter season and using fresh embryos for transfer. The clustering analysis did not show any linkage between PR and intrinsic factors, while a linkage (p < 0.05) was found with season of the year and embryo preservation as extrinsic factors. It is concluded that age of the recipient cow and environmental conditions at the time of the embryo transfer are key factors to be considered for a successful pregnancy rate from in-vitro ET programs using dual-purpose cows under tropical conditions.

Landscape features influencing gene flow and connectivity of an endangered passerine.

Dispersal of individuals and gene flow are crucial aspects to maintain genetic diversity and viability of populations, especially in the case of threatened species. Landscape composition and structure may facilitate or limit individual movement within and among populations. We used a landscape genetics approach to assess the connectivity patterns of the threatened Dupont's lark (Chersophilus duponti subsp. duponti), considering their genetic patterns and the landscape features associated with its gene flow in Spain. We analysed the genetic relatedness based on 11 species-specific polymorphic microsatellites on 416 Dupont's lark individuals sampled across peninsular Spain between 2017 and 2019, covering most of the European distribution of the species. To assess the relationship between the landscape composition and the species gene flow, we estimated genetic distance at the individual level (Dps). Next, we built a set of environmental surfaces from two time periods (years 1990 and 2018), based on factors such as land use and topography, influencing individuals' movement. We then obtained resistance surfaces from an optimization process on landscape variables. Landscape genetics analyses were done for single and composite surface models for each year separately. Our findings from both time points show that scatter or mosaic-structured vegetation composed by low agricultural and tree cover and high presence of sclerophyllous shrubs favoured Dupont's lark dispersal, while dense and continuous tree cover, as well as areas of intensive agriculture, were limiting factors. Our results suggest the importance of steppe habitat patches for the species' establishment and dispersal. In addition, our results provide key information to develop conservation measures, including conserving and restoring steppe habitats as scattered and/or mosaic-structured vegetation that could warrant the connectivity and persistence of Dupont's lark populations.

Classification of skin blemishes with cell phone images using deep learning techniques.

Skin blemishes can be caused by multiple events or diseases and, in some cases, it is difficult to distinguish where they come from. Therefore, there may be cases with a dangerous origin that go unnoticed or the opposite case (which can lead to overcrowding of health services). To avoid this, the use of artificial intelligence-based classifiers using images taken with mobile devices is proposed; this would help in the initial screening process and provide some information to the patient prior to their final diagnosis. To this end, this work proposes an optimization mechanism based on two phases in which a global search for the best classifiers (from among more than 150 combinations) is carried out, and, in the second phase, the best candidates are subjected to a phase of evaluation of the robustness of the system by applying the cross-validation technique. The results obtained reach 99.95% accuracy for the best case and 99.75% AUC. Comparing the developed classifier with previous works, an improvement in terms of classification rate is appreciated, as well as in the reduction of the classifier complexity, which allows our classifier to be integrated in a specific purpose system with few computational resources.

A lightweight xAI approach to cervical cancer classification.

Cervical cancer is caused in the vast majority of cases by the human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine is available, the incidence of cervical cancer is up to ten times higher in areas without adequate healthcare resources. In recent years, liquid cytology has been used to overcome these shortcomings and perform mass screening. In addition, classifiers based on convolutional neural networks can be developed to help pathologists diagnose the disease. However, these systems always require the final verification of a pathologist to make a final diagnosis. For this reason, explainable AI techniques are required to highlight the most significant data to the healthcare professional, as it can be used to determine the confidence in the results and the areas of the image used for classification (allowing the professional to point out the areas he/she thinks are most important and cross-check them against those detected by the system in order to create incremental learning systems). In this work, a 4-phase optimization process is used to obtain a custom deep-learning classifier for distinguishing between 4 severity classes of cervical cancer with liquid-cytology images. The final classifier obtains an accuracy over 97% for 4 classes and 100% for 2 classes with execution times under 1 s (including the final report generation). Compared to previous works, the proposed classifier obtains better accuracy results with a lower computational cost.

Clinical Simulation in Palliative Care for Undergraduate Nursing Students: A Randomized Clinical Trial and Complementary Qualitative Study.

BACKGROUND: a lack of adequate training in palliative care leads to a greater emotional burden on nurses. PURPOSE: to assess the effect of a simulation using standardized patients on self-efficacy in palliative care, ability to cope with death, and emotional intelligence among nursing students. METHODS: a randomized clinical trial and qualitative study. A total of 264 nursing students in a palliative care module completed the Bugen, trait meta-mood, and self-efficacy in palliative care scales after active participation in the simulation (n = 51), watching the simulation (n = 113), and the control group (n = 100). An ANOVA with a multi-comparative analysis and McNemar's tests for paired samples were calculated. Active participants were interviewed, and a thematic analysis was conducted. RESULTS: there was an improvement after the assessment in all three groups assessed for coping with death (p < 0.01), emotional intelligence (p < 0.01), and self-efficacy (p < 0.01). In addition, the active group improved more than the observer group and the control group in coping with death, attention, and repair. The students in the interviews identified sadness and an emotional lack of control. CONCLUSIONS: the simulation improved nursing students' self-efficacy in palliative care. This effect was partially stronger in the active group.

Reliability of a point of care testing blood gas analyzer for measurement of lactate levels in cerebrospinal fluid.

Analysis of cerebrospinal fluid (CSF), including lactate, is key for diagnosis of acute meningitis. Since blood gas analyzers (BGA) enable rapid and safe blood-lactate measurements, we evaluated the reliability of RAPIDPoint 500 BGA to provide a fast and accurate measure of CSF lactate. In this study, CSF lactate levels were measured by a reference assay and on RAPIDPoint 500 BGA. Comparability was evaluated through difference analysis, using Bland Altman test, and linear regression analysis, using the Passing Bablok test. Agreement rate according to CSF lactate (≥3.5 and <3.5 mmol/L) was calculated using kappa (κ) statistic. Population study included 98 CSF samples. Concerning difference analysis, according to Bland-Altman test, bias was 0.13 mmol/L (CI 95%: -0.26 to 0.52 mmol/L. In regression analysis, according to Passing-Bablok equation a systematic difference between both assays was found. In concordance analysis, the interrate realibility was very high (κ: 0.964). According to our resuls, although a systematic difference was detected when lactate levels were measured on RAPIDPoint 500 BGA, the results from Bland-Altman test and the high agreement rate support that this POCT analyzer could be useful for a early and safe detection of patients with high probability of increased CSF lactate level.

Usefulness of extended inflammatory parameters related to neutrophil activation reported by Sysmex XN-1000 hematology analyzer for predicting complicated acute appendicitis. Comparison with canonical inflammatory laboratory tests.

AIM: Accurate diagnosis of complicated appendicitis is of importance to ensure that patients receive early and effective treatment, minimizing the risk of postoperative complications to promote successful recovery. Biochemical markers are a promising tool to identify complicated appendicitis. We aimed to evaluate the potential role of novel parameters related with neutrophil activation, known as "Extended Inflammation Parameters" (EIP), included in blood cell count reported by Sysmex XN-Series analyzers, compared to other canonical biomarkers in identifying complicated appendicitis. METHOD: Prospective observational study including patients with confirmed diagnosis of acute appendicitis. C-reactive protein (CRP), procalcitonin, cell blood count, including white blood cell (WBC), absolute neutrophil (ANC) and immature granulocyte (IG) count and EIP (neutrophil reactivity [NEUT-RI] and granularity intensity [NEUT-GI]) were analyzed before surgery. Their accuracy to diagnose complicated appendicitis was tested in an ROC curve analysis. RESULTS: Our population study included 119 patients, and appendicitis was complicated in 58 (48.7%). NLR, CRP and procalcitonin levels, ANC and IG count and NEUT-RI and NEUT-GI were higher in patients with complicated appendicitis. Regarding accuracy for complicated appendicitis, CRP was the biomarker with the highest performance (ROC AUC: 0.829), with an optimal cutoff of 73.1 mg/L (sensitivity: 63.8%, specificity: 88.5%). NEUT-RI and NEUT-GI achieved both significant but poor accuracy, with ROC AUC of 0.606 and 0.637, respectively. CONCLUSIONS: Novel laboratory tests reported by Sysmex XN-Series analyzers have poor accuracy for identifying complicated appendicitis. In this study, CRP was the biomarker with the highest performance and may be useful as predictor of the severity of acute appendicitis.

Microglial TLR4 Mediates White Matter Injury in a Combined Model of Diesel Exhaust Exposure and Cerebral Hypoperfusion.

BACKGROUND: Air pollution particulate matter exposure and chronic cerebral hypoperfusion (CCH) contribute to white matter toxicity through shared mechanisms of neuroinflammation, oxidative stress, and myelin breakdown. Prior studies showed that exposure of mice to joint particulate matter and CCH caused supra-additive injury to corpus callosum white matter. This study examines the role of TLR4 (toll-like receptor 4) signaling in mediating neurotoxicity and myelin damage observed in joint particulate matter and CCH exposures. METHODS: Experiments utilized a novel murine model of inducible monocyte/microglia-specific TLR4 knockout (i-mTLR4-ko). Bilateral carotid artery stenosis (BCAS) was induced surgically to model CCH. TLR4-intact (control) and i-mTLR4-ko mice were exposed to 8 weeks of either aerosolized diesel exhaust particulate (DEP) or filtered air (FA) in 8 experimental groups: (1) control/FA (n=10), (2) control/DEP (n=10), (3) control/FA+BCAS (n=9), (4) control/DEP+BCAS (n=10), (5) i-mTLR4-ko/FA (n=9), (6) i-mTLR4-ko/DEP (n=8), (7) i-mTLR4-ko/FA+BCAS (n=8), and (8) i-mTLR4-ko/DEP+BCAS (n=10). Corpus callosum levels of 4-hydroxynonenal, 8-Oxo-2'-deoxyguanosine, Iba-1 (ionized calcium-binding adapter molecule 1), and dMBP (degraded myelin basic protein) were assayed via immunofluorescence to measure oxidative stress, neuroinflammation, and myelin breakdown, respectively. RESULTS: Compared with control/FA mice, control/DEP+BCAS mice exhibited increased dMBP (41%; P<0.01), Iba-1 (51%; P<0.0001), 4-hydroxynonenal (100%; P<0.0001), and 8-Oxo-2'-deoxyguanosine (65%; P<0.05). I-mTLR4 knockout attenuated responses to DEP/BCAS for all markers. CONCLUSIONS: i-mTLR4-ko markedly reduced neuroinflammation and oxidative stress and attenuated white matter degradation following DEP and CCH exposures. This suggests a potential role for targeting TLR4 signaling in individuals with vascular cognitive impairment, particularly those exposed to substantial ambient air pollution.

Efficacy of photobiomodulation therapy combined with mobile health education in patients with head and neck cancer suffering from chronic xerostomia after radiotherapy: protocol for a three-arm, randomised, placebo-controlled, double-blinded study.

INTRODUCTION: The role of photobiomodulation (PBM) therapy for oral tissue damage induced by cancer treatment is currently unclear, and there is low-quality to moderate-quality evidence supporting the use of this approach for treating xerostomia and/or hyposalivation. Consequently, patients with head and neck cancer increasingly turn to basic oral hygiene to alleviate salivary gland dysfunction, and their adherence can be improved by mobile health (mHealth) education. The primary objective of this study will be to analyse the effects of different doses of PBM therapy (7.5 J/cm2 vs 3 J/cm2) plus mHealth education on quality of life (QoL), oral health, salivary secretion and salivary gland ultrasound assessment at postintervention and at the 6-month follow-up in patients with head and neck cancer after radiotherapy compared with those in control group. METHODS AND ANALYSIS: A prospective, three-arm, randomised, placebo-controlled, double-blinded study will be conducted among patients with head and neck cancer suffering from chronic xerostomia. A total of 20 patients per arm will be included and randomly assigned to receive 7.5 J/cm2 of PBM, 3 J/cm2 of PBM or placebo therapy. PBM therapy will be applied during 24 sessions at 22 points extra and intraorally two times per week for 3 months, combined with a mobile application (https://www.laxer.es). The assessments will be recorded at the beginning of the study, at postintervention and at the 6-month follow-up. The primary outcomes will be QoL, oral health, salivary secretion and salivary gland ultrasound. The pain pressure threshold, functional performance, mood and sleep quality will be secondary indicators. ETHICS AND DISSEMINATION: This study received ethics approval from the Andalusian Biomedical Research Ethics Portal (2402-N-21 CEIM/CEI Provincial de Granada) according to the Declaration of Helsinki for Biomedical Research. The results of this study will be presented at national and international conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT05106608.

Temporal trends of land-use favourability for the strongly declining little bustard: assessing the role of protected areas.

The little bustard (Tetrax tetrax) is a steppe bird strongly and negatively influenced by agricultural intensification in Europe. Here, we use the little bustard as a model species to examine how favourability (relative occurrence likelihood of a species based on environmental characteristics, such as habitat availability) varies regionally with degree of protection in north-western Spain. The Natura2000 network is one of the main biodiversity conservation tools of the European Union, aiming to protect areas hosting species of conservation concern from unfavourable land-use changes. The network covers many landscapes across the continent, including farmland. Additionally, we examine the relationship between trends in land-use favourability and little bustard population trends over a decade in the Nature Reserve of Lagunas de Villafáfila, a protected area also in the Natura2000 network where active and intense management focused on steppe bird conservation is carried out. Favourability was much greater in Villafáfila than in both protected areas with lower degree of protection and in non-protected areas. Land-use favourability increased slightly between 2011 and 2020 both in and out of protected areas, whereas little bustard populations declined sharply in that period, even in Villafáfila. Spatial variations in little bustard abundance within Villafáfila depended on social attraction (increasing with the number of neighbouring males) but not significantly on small-scale variations in land-use favourability. These results suggest that land-use management in Natura2000 areas needs to be more conservation-focused, favouring natural and seminatural habitats and traditional farming practices to improve land-use favourability for little bustards and other steppe birds. Additional factors, such as field-level agricultural management or social interaction variables that may cause an Allee effect, should be incorporated in little bustard favourability models to improve their use in conservation planning.

Present and Future Innovations in AI and Cardiac MRI.

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.