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.

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.

MRI Assessment of Myocardial Deformation for Risk Stratification of Major Arrhythmic Events in Patients With Non-Ischemic Cardiomyopathy Eligible for Primary Prevention Implantable Cardioverter Defibrillators.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) intervention is an established prophylactic measure. Identifying high-benefit patients poses challenges. PURPOSE: To assess the prognostic value of cardiac magnetic resonance imaging (MRI) parameters including myocardial deformation for risk stratification of ICD intervention in non-ischemic cardiomyopathy (NICM) while accounting for competing mortality risk. STUDY TYPE: Retrospective and prospective. POPULATION: One hundred and fifty-nine NICM patients eligible for primary ICD (117 male, 54 ± 13 years) and 49 control subjects (38 male, 53 ± 5 years). FIELD STRENGTH/SEQUENCE: Balanced steady state free precession (bSSFP) and three-dimensional phase-sensitive inversion-recovery late gadolinium enhancement (LGE) sequences at 1.5 T or 3 T. ASSESSMENT: Patients underwent MRI before ICD implantation and were followed up. Functional parameters, left ventricular global radial, circumferential and longitudinal strain, right ventricular free wall longitudinal strain (RV FWLS) and left atrial strain were measured (Circle, cvi42). LGE presence was assessed visually. The primary endpoint was appropriate ICD intervention. Models were developed to determine outcome, with and without accounting for competing risk (non-sudden cardiac death), and compared to a baseline model including LGE and clinical features. STATISTICAL TESTS: Wilcoxon non-parametric test, Cox's proportional hazards regression, Fine-Gray competing risk model, and cumulative incidence functions. Harrell's c statistic was used for model selection. A P value <0.05 was considered statistically significant. RESULTS: Follow-up duration was 1176 ± 960 days (median: 896). Twenty-six patients (16%) met the primary endpoint. RV FWLS demonstrated a significant difference between patients with and without events (-12.5% ± 5 vs. -16.4% ± 5.5). Univariable analyses showed LGE and RV FWLS were significantly associated with outcome (LGE: hazard ratio [HR] = 3.69, 95% CI = 1.28-10.62; RV FWLS: HR = 2.04, 95% CI = 1.30-3.22). RV FWLS significantly improved the prognostic value of baseline model and remained significant in multivariable analysis, accounting for competing risk (HR = 1.73, 95% CI = 1.12-2.66). DATA CONCLUSIONS: In NICM, RV FWLS may provide additional predictive value for predicting appropriate ICD intervention. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 5.

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.

The evolution and changing ecology of the African hominid oral microbiome.

The oral microbiome plays key roles in human biology, health, and disease, but little is known about the global diversity, variation, or evolution of this microbial community. To better understand the evolution and changing ecology of the human oral microbiome, we analyzed 124 dental biofilm metagenomes from humans, including Neanderthals and Late Pleistocene to present-day modern humans, chimpanzees, and gorillas, as well as New World howler monkeys for comparison. We find that a core microbiome of primarily biofilm structural taxa has been maintained throughout African hominid evolution, and these microbial groups are also shared with howler monkeys, suggesting that they have been important oral members since before the catarrhine-platyrrhine split ca. 40 Mya. However, community structure and individual microbial phylogenies do not closely reflect host relationships, and the dental biofilms of Homo and chimpanzees are distinguished by major taxonomic and functional differences. Reconstructing oral metagenomes from up to 100 thousand years ago, we show that the microbial profiles of both Neanderthals and modern humans are highly similar, sharing functional adaptations in nutrient metabolism. These include an apparent Homo-specific acquisition of salivary amylase-binding capability by oral streptococci, suggesting microbial coadaptation with host diet. We additionally find evidence of shared genetic diversity in the oral bacteria of Neanderthal and Upper Paleolithic modern humans that is not observed in later modern human populations. Differences in the oral microbiomes of African hominids provide insights into human evolution, the ancestral state of the human microbiome, and a temporal framework for understanding microbial health and disease.

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.

Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network.

Background Cardiac cine can benefit from deep learning-based image reconstruction to reduce scan time and/or increase spatial and temporal resolution. Purpose To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS). Materials and Methods The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network, trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 to September 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cine images collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS. The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected. Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-rank tests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis. Results The deep learning model was trained on 1616 patients (mean age ± SD, 56 years ± 16; 920 men) and evaluated in 181 individuals, 126 patients (mean age, 57 years ± 16; 77 men) and 55 healthy subjects (mean age, 27 years ± 10; 15 men). In breath-hold ECG-gated segmented cine and free-breathing real-time cine, the deep learning model and GRAPPA showed similar diagnostic quality scores (2.9 vs 2.9, P = .41, deep learning vs GRAPPA) and artifact score (4.4 vs 4.3, P = .55, deep learning vs GRAPPA). Deep learning acquired more sections per breath-hold than GRAPPA (3.1 vs one section, P < .001). In free-breathing real-time cine, the deep learning showed a similar diagnostic quality score (2.9 vs 2.9, P = .21, deep learning vs GRAPPA) and lower artifact score (3.9 vs 4.3, P < .001, deep learning vs GRAPPA). For both sequences, the deep learning model showed excellent agreement for LV parameters, with near-zero mean differences and narrow limits of agreement compared with GRAPPA. Conclusion Deep learning-accelerated cardiac cine showed similarly accurate quantification of cardiac function, volume, and strain to a standardized parallel imaging method. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis.

BACKGROUND: Myocardial feature tracking (FT) provides a comprehensive analysis of myocardial deformation from cine balanced steady-state free-precession images (bSSFP). However, FT remains time-consuming, precluding its clinical adoption. PURPOSE: To compare left-ventricular global radial strain (GRS) and global circumferential strain (GCS) values measured using automated DeepStrain analysis of short-axis cine images to those calculated using manual commercially available FT analysis. STUDY TYPE: Retrospective, single-center. POPULATION: A total of 30 healthy subjects and 120 patients with cardiac disease for DeepStrain development. For evaluation, 47 healthy subjects (36 male, 53 ± 5 years) and 533 patients who had undergone a clinical cardiac MRI (373 male, 59 ± 14 years). FIELD STRENGTH/SEQUENCE: bSSFP sequence at 1.5 T (Phillips) and 3 T (Siemens). ASSESSMENT: Automated DeepStrain measurements of GRS and GCS were compared to commercially available FT (Circle, cvi42) measures obtained by readers with 1 year and 3 years of experience. Comparisons were performed overall and stratified by scanner manufacturer. STATISTICAL TESTS: Paired t-test, linear regression slope, Pearson correlation coefficient (r). RESULTS: Overall, FT and DeepStrain measurements of GCS were not significantly different (P = 0.207), but measures of GRS were significantly different. Measurements of GRS from Philips (slope = 1.06 [1.03 1.08], r = 0.85) and Siemens (slope = 1.04 [0.99 1.09], r = 0.83) data showed a very strong correlation and agreement between techniques. Measurements of GCS from Philips (slope = 0.98 [0.98 1.01], r = 0.91) and Siemens (slope = 1.0 [0.96 1.03], r = 0.88) data similarly showed a very strong correlation. The average analysis time per subject was 4.1 ± 1.2 minutes for FT and 34.7 ± 3.3 seconds for DeepStrain, representing a 7-fold reduction in analysis time. DATA CONCLUSION: This study demonstrated high correlation of myocardial GCS and GRS measurements between freely available fully automated DeepStrain and commercially available manual FT software, with substantial time-saving in the analysis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. METHODS: We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm2 and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). RESULTS: In the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. CONCLUSION: We developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique.

Effect of a Multimodal Supervised Therapeutic Exercise Program on Quality of Life, Pain, and Lumbopelvic Impairments in Women With Endometriosis Unresponsive to Conventional Therapy: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the effectiveness of "Physio-EndEA", a multimodal nine-week supervised exercise intervention, on quality of life, pain, and lumbopelvic impairments in women with endometriosis unresponsive to conventional therapy. DESIGN: Parallel-group randomized controlled trial. Outcomes were measured at baseline, post-intervention, and at 1 year. SETTING: Two Public University Hospitals. PARTICIPANTS: This trial included 31 women with endometriosis (N=31) randomly allocated to "Physio-EndEA" group (n=16) or control group (n=15). Four participants dropped out of the study for causes unrelated to the intervention. INTERVENTIONS: The "Physio-EndEA" program consisted of a 1-week lumbopelvic stabilization learning phase followed by an 8-week phase of stretching, aerobic, and resistance exercises focused on the lumbopelvic area. It was sequentially instructed and supervised by a trained physiotherapist (with volume and intensity progression) and adapted daily to the potential of each participant. Control group received the usual treatment stipulated by their gynecologist. MAIN OUTCOME MEASURES: The primary outcome was quality of life. Secondary outcomes were pain intensity, pressure pain thresholds, pain-related catastrophic thoughts, abdominal and back strength, lumbopelvic stability, and muscle architecture. RESULTS: Adherence rate was 90.6% and mean (±standard deviation) satisfaction was 9.44±0.73 out of 10. No remarkable health problems were reported during the trial. In comparison with controls, the quality of life was improved post-intervention and at 1 year in the Physio-EndEA group with large effect sizes (d>0.80). This group also evidenced: a reduced intensity of dyspareunia, catastrophic thoughts; an increase in pelvic, lumbar, and distal pressure pain thresholds; increases in abdominal and back strength and lumbopelvic stability; and increased thickness of transversus abdominis (right side) and width of lumbar multifidus (left side). CONCLUSION: A 9-week program of multimodal supervised therapeutic exercise is a feasible and effective intervention to improve QoL in women with endometriosis. This program also offers benefits in terms of pain/sensitization and lumbopelvic impairments.

A Robust Ensemble of Convolutional Neural Networks for the Detection of Monkeypox Disease from Skin Images.

Monkeypox is a smallpox-like disease that was declared a global health emergency in July 2022. Because of this resemblance, it is not easy to distinguish a monkeypox rash from other similar diseases; however, due to the novelty of this disease, there are no widely used databases for this purpose with which to develop image-based classification algorithms. Therefore, three significant contributions are proposed in this work: first, the development of a publicly available dataset of monkeypox images; second, the development of a classification system based on convolutional neural networks in order to automatically distinguish monkeypox marks from those produced by other diseases; and, finally, the use of explainable AI tools for ensemble networks. For point 1, free images of monkeypox cases and other diseases have been searched in government databases and processed until we are left with only a section of the skin of the patients in each case. For point 2, various pre-trained models were used as classifiers and, in the second instance, combinations of these were used to form ensembles. And, for point 3, this is the first documented time that an explainable AI technique (like GradCAM) is applied to the results of ensemble networks. Among all the tests, the accuracy reaches 93% in the case of single pre-trained networks, and up to 98% using an ensemble of three networks (ResNet50, EfficientNetB0, and MobileNetV2). Comparing these results with previous work, a substantial improvement in classification accuracy is observed.

Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies.

The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed.

An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR. METHODS: A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal). RESULTS: The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [- 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [- 1.3, 15.3], P < 0.001) and LV ejection fraction (- 5.0% [- 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame. CONCLUSIONS: Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR.

The genetic history of Ice Age Europe.

Modern humans arrived in Europe ~45,000 years ago, but little is known about their genetic composition before the start of farming ~8,500 years ago. Here we analyse genome-wide data from 51 Eurasians from ~45,000-7,000 years ago. Over this time, the proportion of Neanderthal DNA decreased from 3-6% to around 2%, consistent with natural selection against Neanderthal variants in modern humans. Whereas there is no evidence of the earliest modern humans in Europe contributing to the genetic composition of present-day Europeans, all individuals between ~37,000 and ~14,000 years ago descended from a single founder population which forms part of the ancestry of present-day Europeans. An ~35,000-year-old individual from northwest Europe represents an early branch of this founder population which was then displaced across a broad region, before reappearing in southwest Europe at the height of the last Ice Age ~19,000 years ago. During the major warming period after ~14,000 years ago, a genetic component related to present-day Near Easterners became widespread in Europe. These results document how population turnover and migration have been recurring themes of European prehistory.

Colorectal cancer pain upon diagnosis and after treatment: a cross-sectional comparison with healthy matched controls.

BACKGROUND: The current study sought to explore whether cancer pain (CP) already exists in patients at colorectal cancer (CRC) diagnosis before treatment compared with patients with colorectal cancer (CRC) after treatment and a healthy matched control group. The study also sought to examine whether factors related to physical health status could enhance pain processes. METHODS: An observational cross-sectional study was conducted following the STROBE checklist. Twenty-nine newly diagnosed and forty post-treatment patients with CRC and 40 healthy age/sex-matched controls were included for comparison. Pain, local muscle function, and body composition outcomes were assessed by a physiotherapist with > 3 years of experience. ANCOVA and Kruskal-Wallis tests were performed, with Bonferroni and Dunn-Bonferroni post hoc analyses and Cohen's d and Hedge's effect size, as appropriate. RESULTS: The analysis detected lower values of pressure pain threshold (PPT) points, the PPT index, and abdominal strength and higher values of self-reported abdominal pain in newly diagnosed patients, with even more marked results observed in the post-treatment patients, where lower lean mass and skeletal muscle index values were also found than those in the healthy matched controls (p < 0.05). In the post-treatment and healthy matched control groups, positive associations were observed between the PPT lumbar dominant side points and abdominal isometric strength and lean mass, and negative associations were observed between the lumbar dominant side points and body fat (p < 0.05). CONCLUSION: Upon diagnosis, patients with CRC already show signs of hyperalgesia and central sensitization and deteriorated physical conditions and body composition, and this state could be aggravated by subsequent treatments.

Widespread Pain Hypersensitivity and Lumbopelvic Impairments in Women Diagnosed with Endometriosis.

OBJECTIVE: To explore hypersensitivity to pain and musculoskeletal impairments in the lumbopelvic area in women with and without endometriosis. METHODS: This cross-sectional study included 66 women (41 women with endometriosis and 25 healthy women). Pain and related catastrophizing thoughts were assessed through a numeric rating scale, pressure pain thresholds (PPTs), the slump test, and the Pain Catastrophizing Scale. Lumbopelvic muscles were evaluated through ultrasound imaging, flexor/extensor resistance tests, and the lumbopelvic stability test. RESULTS: Women with endometriosis showed increased self-reported intensity of current pelvic pain (CuPP), reduced local PPTs (42.8-64.7% in the affected area, P-value <.001) and higher prevalence of lumbar nerve root impingement/irritation pain and catastrophizing thoughts (P-value ≤.002). Moreover, affected women showed decreased thickness of transversus abdominis, reduced resistance of flexor and extensor trunk muscles and lower lumbopelvic stability (P-values <.030). Endometriosis stage and severity of CuPP were related to worse results in these parameters. CONCLUSIONS: The presence of pain sensitization signs and lumbopelvic impairments, more pronounced in patients with stage IV endometriosis and moderate/severe CuPP, warrants the development of rehabilitation interventions targeting pain and lumbopelvic impairments in women with endometriosis.

Neurotoxicity prevention with a multimodal program (ATENTO) prior to cancer treatment versus throughout cancer treatment in women newly diagnosed for breast cancer: Protocol for a randomized clinical trial.

A current challenge in breast cancer (BC) patients is how to reduce the side effects of cancer and cancer treatments and prevent a decrease in quality of life (QoL). Neurotoxic side effects, especially from chemotherapy, are present in up to 75% of women with BC, which implies a large impact on QoL. There is a special interest in the preventive possibilities of therapeutic exercise (TE) for these neurological sequelae, and the benefits of TE could be improved when it is combined with vagal activation techniques (VATs). This superiority randomized controlled trial aims to examine the feasibility and efficacy of an 8-week multimodal intervention (ATENTO) based on moderate-vigorous intensity and individualized TE (aerobic and strength exercises) and VAT (myofascial and breathing exercises), on neurotoxicity prevention in women with BC before starting adjuvant chemotherapy (ATENTO-B) versus throughout adjuvant chemotherapy (ATENTO-T). A sample of 56 women newly diagnosed with BC, as calculated with a power of 85%, will be randomly allocated into these two groups. This study could provide an impetus for the introduction of early multimodal intervention methods to prevent neurotoxicity and consequently avoid the QoL deterioration that BC patients presently suffer throughout their treatments.

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach.

In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from real LIGO detectors. Here, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. We used Morlet wavelets to convert strain time series to time-frequency images. Moreover, we only worked with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand, in order to reproduce more realistic experimental conditions. After hyperparameter adjustments, we found that resampling through repeated k-fold cross-validation smooths the stochasticity of mini-batch stochastic gradient descent present in accuracy perturbations by a factor of 3.6. CNNs are quite precise to detect noise, 0.952 for H1 data and 0.932 for L1 data; but, not sensitive enough to recall GW signals, 0.858 for H1 data and 0.768 for L1 data-although recall values are dependent on expected SNR. Our predictions are transparently understood by exploring tthe distribution of probabilistic scores outputted by the softmax layer, and they are strengthened by a receiving operating characteristic analysis and a paired-sample t-test to compare with a random classifier.

Multi-Probe Measurement System Based on Single-Cut Transformation for Fast Testing of Linear Arrays.

This paper introduces a near-field measurement system concept for the fast testing of linear arrays suited for mass production scenarios where a high number of nominally identical antennas needs to be measured. The proposed system can compute the radiation pattern, directivity and gain on the array plane, as well as the array complex feeding coefficients in a matter of seconds. The concept is based on a multi-probe antenna array arranged in a line which measures the near field of the antenna under test in its array plane. This linear measurement is postprocessed with state-of-the-art single-cut transformation techniques. To compensate the lack of full 3D information, a previous complete characterization of a "Gold Antenna" is performed. This antenna is nominally identical to the many ones that will be measured with the proposed system. Therefore, the data extracted from this full characterization can be used to complement the postprocessing steps of the single-cut measurements. An X-band 16-probe demonstrator of the proposed system is implemented and introduced in this paper, explaining all the details of its architecture and operation steps. Finally, some measurement results are given to compare the developed demonstrator with traditional anechoic measurements, and show the potential capabilities of the proposed concept to perform fast and reliable measurements.

AnkFall-Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks.

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system's feasibility.