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Abstract We tested the feasibility and preliminary efficacy of an online diet and physical activity program for women with early-stage breast cancer who had completed surgery, chemotherapy, and radiation therapy (ongoing endocrine therapy allowed). Participants with low fruit and vegetable (F/V) consumption and/or low moderate-to-vigorous physical activity (MVPA) levels were randomized to one of two doses - low (one Zoom group session) or high (12 Zoom group sessions) - of an online lifestyle program with the goal of improving F/V intake and MVPA. All participants received eHealth communications (text messages, study website access), a Fitbit, and a WiFi-enabled scale. Primary objectives evaluated feasibility. Secondary objectives compared the 6-month change in F/V intake and MVPA between the two dose groups. Seventy-four women (mean age = 58.4 years; 87% non-Hispanic White; mean time since diagnosis = 4.6 years) were accrued. Among women in the low dose group, 94% attended the single session; among women in the high dose group, 84% attended at least 8 of the 12 sessions. Retention at 6 months was 93%. High relative to low dose participants consumed 1.5 more servings/day of F/V at 6 months (P = 0.007) but MVPA levels did not differ between groups. We successfully implemented an online lifestyle program for early-stage breast cancer survivors. The high dose intervention demonstrated preliminary efficacy in improving F/V consumption in early-stage breast cancer survivors. Future trials can test the intervention in a larger and more diverse population of breast cancer survivors.

Objective. The journey to a diagnosis of spondyloarthritis (SpA) can be difficult for women, who often experience delays in receiving the correct diagnosis as their symptoms are frequently misinterpreted due to other conditions like osteoarthritis, fibromyalgia, or other psychosomatic disorders. The purpose of this article is to examine the challenges in the diagnosis of SpA in women and the possible role of musculoskeletal ultrasound in early diagnosis and in avoiding misdiagnosis. Methods. We have performed a narrative review of the currently available literature on the subject. Results. The complexity of diagnosing SpA in women is compounded by the misconception that the disease predominantly affects men. To facilitate early diagnosis and prevent misdiagnosis, it is crucial not to overlook gender differences in the clinical presentation of SpA. Since women have more peripheral and enthesitic involvement, performing an ultrasound of entheses, tendons, and joints in women with musculoskeletal symptoms that could refer to SpA may help both in the early and differential diagnosis. Conclusions. There is a need to increase awareness among physicians of the existence of a different clinical presentation of SpA between men and women. The use of musculoskeletal ultrasound, which allows the detection of even subclinical inflammation and structural damage since early disease at the level of joints, tendons, and entheses can help make an early diagnosis and avoid misdiagnosis. Early diagnosis and timely treatment of SpA are crucial to reducing irreversible damage.

Longitudinal multimethod data across three time points were examined to explore the associations between previously institutionalized toddlers' (N = 71; 59% female) socioemotional skills (Time Point 1: 18 months to 3-years-old), executive functioning (i.e., attention, working memory, inhibitory control) in the preschool years (Time Point 2: 2-4-years-old), and adjustment in kindergarten (5-6-years-old). Children were from multiple regions (35% Eastern European, 31% Southeast Asian, 25% African, and 9% Latin American), and 90% of adoptive parents were White from the Midwestern United States. Socioemotional competency at Time Point 1 was associated with fewer attention problems and greater inhibitory control at Time Point 2, which were subsequently associated with more observed social competence, greater observed classroom competence, and less teacher-reported teacher-child conflict in kindergarten. Indirect effects from socioemotional competencies in toddlerhood to kindergarten adjustment via executive functioning during the preschool period emerged.

Abstract Background Silver-111 is a promising β −-emitting radioisotope with ideal characteristics for targeted radionuclide therapy and associated single photon emission tomography imaging. Its decay properties closely resemble the clinically established lutetium-177, making it an attractive candidate for therapeutic applications. In addition, the clinical value of silver-111 is further enhanced by the existence of the positron-emitting counterpart silver-103, thus imparting a truly theranostic potential to this element. A so-fitting matching pair could potentially overcome the current limitations associated with the forced use of chemically different isotopes as imaging surrogates of lutetium-177, leading to more accurate and efficient diagnosis and treatment. However, the use of silver-111-based radiopharmaceuticals in vivo has faced obstacles due to the challenges related to its production and radiochemical separation from the target material. To address these issues, this study aims to implement a chromatographic separation methodology for the purification of reactor-produced silver-111. The ultimate goal is to achieve a ready-to-use formulation for the direct radiolabeling of tumour-seeking biomolecules. Results A two-step sequence chromatographic process was validated for cold Ag-Pd separation and then translated to the radioactive counterpart. Silver-111 was produced via the 110Pd(n,γ)111Pd nuclear reaction on a natural palladium target and the subsequent β −-decay of palladium-111. Silver-111 was chemically separated from the metallic target via the implemented chromatographic process by using commercially available LN and TK200 resins. The effectiveness of the separations was assessed by inductively coupled plasma optical emission spectroscopy and γ-spectrometry, respectively, and the Ag+ retrieval was afforded in pure water. Recovery of silver-111 was > 90% with a radionuclidic purity > 99% and a separation factor of around 4.21·10−4. Conclusions The developed separation method was suitable to obtain silver-111 with high molar activity in a ready-to-use water-based formulation that can be directly employed for the labeling of radiotracers. By successfully establishing a robust and efficient production and purification method for silver-111, this research paves the way for its wider application in targeted radionuclide therapy and precision imaging.

Abstract Background Discrimination between patients affected by inflammatory bowel diseases and healthy controls on the basis of endoscopic imaging is an challenging problem for machine learning models. Such task is used here as the testbed for a novel deep learning classification pipeline, powered by a set of solutions enhancing characterising elements such as reproducibility, interpretability, reduced computational workload, bias-free modeling and careful image preprocessing. Results First, an automatic preprocessing procedure is devised, aimed to remove artifacts from clinical data, feeding then the resulting images to an aggregated per-patient model to mimic the clinicians decision process. The predictions are based on multiple snapshots obtained through resampling, reducing the risk of misleading outcomes by removing the low confidence predictions. Each patient’s outcome is explained by returning the images the prediction is based upon, supporting clinicians in verifying diagnoses without the need for evaluating the full set of endoscopic images. As a major theoretical contribution, quantization is employed to reduce the complexity and the computational cost of the model, allowing its deployment on small power devices with an almost negligible 3% performance degradation. Such quantization procedure holds relevance not only in the context of per-patient models but also for assessing its feasibility in providing real-time support to clinicians even in low-resources environments. The pipeline is demonstrated on a private dataset of endoscopic images of 758 IBD patients and 601 healthy controls, achieving Matthews Correlation Coefficient 0.9 as top performance on test set. Conclusion We highlighted how a comprehensive pre-processing pipeline plays a crucial role in identifying and removing artifacts from data, solving one of the principal challenges encountered when working with clinical data. Furthermore, we constructively showed how it is possible to emulate clinicians decision process and how it offers significant advantages, particularly in terms of explainability and trust within the healthcare context. Last but not least, we proved that quantization can be a useful tool to reduce the time and resources consumption with an acceptable degradation of the model performs. The quantization study proposed in this work points up the potential development of real-time quantized algorithms as valuable tools to support clinicians during endoscopy procedures.

This narrative review aims to update the current evidence and offer insight into the new non-invasive ultrasound techniques used to early identify degenerative vascular changes in subjects with periodontitis and to investigate if these methodologies could be useful to identify subclinical cardiovascular disease (CVD) dysfunction in periodontitis patients and to monitor changes in CVD risk after periodontal treatment.

INTRODUÇÃO: A incontinência urinária (IU) se refere à queixa de qualquer perda involuntária de urina. De modo geral, sua prevalência é de 26,2% em mulheres e tende a aumentar com o avanço da idade. O CrossFit® é uma modalidade de exercício físico que tem se popularizado nos últimos anos no Brasil, tendo contribuído na diminuição do sedentarismo. OBJETIVO: Este estudo teve como objetivo de levantar informações sobre o grau de conhecimento de praticantes de CrossFit® sobre à IU, quantificar sua prevalência e determinar o impacto sobre a qualidade de vida das praticantes jovens, na microrregião de Jaú/SP. MÉTODOS: A amostra foi composta por 53 mulheres com média de idade de 33,4±8,5 anos. Os dados foram obtidos mediante a aplicação de dois questionários fechados, cujo tratamento estatístico descritivo assumiu uma abordagem quantitativa. RESULTADOS: Foi evidenciado que 33,9% das mulheres apresentavam IU, sendo que 61% relataram episódios moderados de perda urinária, demonstrando algum impacto na qualidade de vida das inqueridas. CONCLUSÃO: Dessa forma, pode-se observar que a IU está presente até mesmo na população feminina, jovem e fisicamente ativa.

Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real-world variations, e.g. vehicles of different shapes, positions, colors, and so forth. Supervised learning needs annotated datasets, and several automotive datasets are available. However, these datasets are tremendous in volume, and labeling accuracy and quality can vary across different datasets and within dataset frames. Accurate and appropriate ground truth is especially important for automotive, as “incomplete” or “incorrect” learning can negatively impact vehicle safety when these neural networks are deployed. This work investigates the ground truth quality of widely adopted automotive datasets, including a detailed analysis of KITTI MoSeg. According to the identified and classified errors in the annotations of different automotive datasets, this article provides three different criteria collections for producing improved annotations. These criteria are enforceable and applicable to a wide variety of datasets. The three annotations sets are created to (i) remove dubious cases; (ii) annotate to the best of human visual system; and (iii) remove clear erroneous BBs. KITTI MoSeg has been reannotated three times according to the specified criteria, and three state-of-the-art deep neural network object detectors are used to evaluate them. The results clearly show that network performance is affected by ground truth variations, and removing clear errors is beneficial for predicting real-world objects only for some networks. The relabeled datasets still present some cases with “arbitrary”/“controversial” annotations, and therefore, this work concludes with some guidelines related to dataset annotation, metadata/sublabels, and specific automotive use cases.

PurposeWe examined the one-year test re-test reliability and validity criterion of survey-assessed sleep duration collected from two separate questions.MethodsThe Activity Validation Sub Study included 751 participants of the Cancer Prevention Study-3 study to further investigate rest/activity cycles. Sleep duration was collected using three methods: survey, Daysimeter device, and sleep diary. Survey-assessed sleep duration was collected using 2 different questions, each with different response options (categorical and continuous). Selected participants (n = 170) were asked to wear a Daysimeter device for seven consecutive days for two non-consecutive quarters. Participants were excluded from the current study due to incomplete/implausible survey or device data or reported working night shift. We calculated reliability of pre- and post-survey sleep duration for both survey question using Spearman correlation. We used the method of triads to estimate the validity coefficient (VC) between the three sleep duration measurements in the present study and the "true" latent sleep duration measure, and bootstrapping methods to calculate the 95% confidence intervals (95%CI).ResultsOf 119 participants included in the study (52.10% male), test-retest correlation showed strong and moderate correlations for sleep duration collected continuously and categorically, respectively. The VC for survey-assessed continuous sleep duration was 0.82 (95%CI 0.71, 0.90) for weekday and 0.68 (95%CI 0.46, 0.83) for weekend. Performance of the VC was slightly weaker for survey-assessed categorical sleep duration (weekday VC = 0.57 95%CI 0.42, 0.71; weekend VC = 0.47 95%CI 0.29, 0.62).ConclusionThe two survey-assessed sleep duration questions used in the AVSS and CPS-3 cohorts are valid approximations of sleep duration.

Adversity during infancy can affect neurobehavioral development and perturb the maturation of physiological systems. Dysregulated immune and inflammatory responses contribute to many of the later effects on health. Whether normalization can occur following a transition to more nurturing, benevolent conditions is unclear. To assess the potential for recovery, blood samples were obtained from 45 adolescents adopted by supportive families after impoverished infancies in institutional settings (post-institutionalized, PI). Their immune profiles were compared to 39 age-matched controls raised by their biological parents (non-adopted, NA). Leukocytes were immunophenotyped, and this analysis focuses on natural killer (NK) cell populations in circulation. Cytomegalovirus (CMV) seropositivity was evaluated to determine if early infection contributed to the impact of an atypical rearing. Associations with tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ), two cytokines released by activated NK cells, were examined. Compared to the NA controls, PI adolescents had a lower percent of CD56bright NK cells in circulation, higher TNF-α levels, and were more likely to be infected with CMV. PI adolescents who were latent carriers of CMV expressed NKG2C and CD57 surface markers on more NK cells, including CD56dim lineages. The NK cell repertoire revealed lingering immune effects of early rearing while still maintaining an overall integrity and resilience.

Steel wire ropes offer manufacturer-independent safety performance, while the durability performance can be different. To assess the damage status of a wire rope during its service, fail-safe methods are used, so it is necessary to know the progression of the damage to apply them. This paper proposes an analytical method for estimating the fatigue life of steel wire ropes. Using a dimensionless analysis, the characteristic damage curve of each rope category has been suggested, setting the basis of a predictive maintenance procedure extendable to all areas of rope application where bending fatigue is the first discard criterion for rope replacement. Rotary bending fatigue tests are performed on different rope construction specimens, and the evolution of broken wires is monitored. It has been demonstrated that the trend of breakage level is exponential with time, regardless of the type of rope and the stress level imposed.

Abstract Background The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn’s disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N). Methods In this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn’s Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N). Results The model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD. Conclusion Our DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.