Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

Cortical activation by a salient sound modulates visual temporal order judgments: An electrophysiological study of multisensory attentional processes

Previous event-related potential (ERP) studies show that a salient lateral sound activates the visual cortex more strongly contralateral to the sound, observed as an auditory-evoked contralateral occipital positivity (ACOP). Studies showed that this activation enhances the early cortical processing of co-localized visual stimuli presented after, reflected by better detection rates, better discrimination, and sharper perceived contrast. We replicated the ACOP, using earphones, and tested whether auditory cuing can influence temporal order judgments (TOJ) for two visual stimuli (horizontal arrangement) as well as if the ACOP would predict the amplitude of this influence. A lateral salient sound was followed, after 150 or 630 ms, by the visual presentation of a pair of disks, one in left and one in right hemifield, with variable SOA. The TOJ task was to indicate which disk appeared first or which disk appeared second (controlling for response bias). We observed an ACOP at posterior electrode sites and confirmed our hypothesis that the lateral sound influenced TOJ by accelerating the perception of the disk presented on the cued side, even though the sound was irrelevant to the task. Furthermore, the ACOP amplitude was correlated to this visual perceptual change, indicating that a larger change in brain activity was associated with a faster processing of co-localized visual stimuli.     

Considerations for systematic reviews of quantitative surveys: Learnings from a systematic review of the Patients’ Attitudes Towards Deprescribing questionnaire

This commentary looks at the process of conducting a systematic review of surveys and validated questionnaires. Surveys and other questionnaire style tools are often used in the field of social and administrative pharmacy, to capture beliefs, attitudes and experiences of patients and healthcare professionals (including pharmacists). Currently, there is little guidance available on how to conduct a systematic review of these types of studies. Considerations related to the process of a systematic review are highlighted, including identification of articles, data extraction, assessing quality of articles and synthesis and analysis of data.     

The social vulnerability metric (SVM) as a new tool for public health

Objective

To derive and validate a new ecological measure of the social determinants of health (SDoH), calculable at the zip code or county level.

Data Sources and Study Setting

The most recent releases of secondary, publicly available data were collected from national U.S. health agencies as well as state and city public health departments.

Study Design

The Social Vulnerability Metric (SVM) was constructed from U.S. zip-code level measures (2018) from survey data using multidimensional Item Response Theory and validated using outcomes including all-cause mortality (2016), COVID-19 vaccination (2021), and emergency department visits for asthma (2018). The SVM was also compared with the existing Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) to determine convergent validity and differential predictive validity.

Data Collection/Extraction Methods

The data were collected directly from published files available to the public online from national U.S. health agencies as well as state and city public health departments.

Principal Findings

The correlation between SVM scores and national age-adjusted county all-cause mortality was r = 0.68. This correlation demonstrated the SVM's robust validity and outperformed the SVI with an almost four-fold increase in explained variance (46% vs. 12%). The SVM was also highly correlated (r ≥ 0.60) to zip-code level health outcomes for the state of California and city of Chicago.

Conclusions

The SVM offers a measurement tool improving upon the performance of existing SDoH composite measures and has broad applicability to public health that may help in directing future policies and interventions. The SVM provides a single measure of SDoH that better quantifies associations with health outcomes.     

艾滋病合并肺结核中的CT影像学特征和诊断价值分析

目的探讨艾滋病合并肺结核患者行胸部CT检查的影像学特征及其在疾病诊断上的价值。方法选取本院在2018年3月至2019年4月诊治的40例艾滋病合并肺结核患者作为观察组,同时选择同期入院诊治40例单纯肺结核患者作为对照组。给予两组胸部CT检查,分析影像学特征,并探讨其诊断价值。结果与对照组比较,观察组淋巴结或胸膜受累所占比较明显更高,差异有统计学意义(P<0.05);与综合检查诊断结果比较,胸部CT检查继发性肺结核、血行播散型肺结核诊断准确率分别为96.55%、91.67%,漏诊2例(5.00%);艾滋病伴肺结核患者接受胸部CT检查时,影像学特征主要表现为双肺肺叶、肺段可见明显渗出性病变,肺内纤维索条影显示明显,同时还呈现明显空洞影、斑片状影,伴有明显腋窝、纵隔淋巴结肿大及心包、胸腔积液等情况。结论胸部CT检查方式用于艾滋病伴肺结核患者诊断,能够获得明显影像学特征,有助于诊断准确率提高。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Empowerment in people with Parkinson’s disease: A scoping review and qualitative interview study

ObjectiveTo synthesize empowerment definitions in Parkinson’s disease (PD) literature and understand people with PD’s perspective on empowerment in the context of an existing empowerment conceptual model.MethodsThis mixed-methods study included a scoping review of PD empowerment literature and interviews with adults with PD. Five databases were searched for articles that defined empowerment concepts. We analyzed 1:1 semi-structured interviews on empowerment with people with PD. All data were analyzed using hybrid thematic analysis.ResultsEight of 242 records were included in this review. Empowerment is defined as an intrapersonal (e.g., personal control over oneself or healthcare) or interpersonal construct (e.g., person-centered care). Thirty-seven participants completed the interview. Participants perceived empowerment as a multifaceted concept that interacts with determinants and moderators from different ecological levels.ConclusionEmpowerment is a noteworthy multilevel and relational construct that can interplay with important health-related factors. The developed working conceptual model of empowerment can inform future studies to explore empowerment concepts in more depth and develop PD empowerment-based interventions.Practice implicationsThe empowerment definitions, indicators, determinants, and moderators identified in this study can help researchers, clinicians, and policymakers critically conceptualize empowerment and develop interventions to support people with PD.     

Exploring attributions of causality for child undernutrition: Qualitative analysis in Lusaka,Zambia

Child undernutrition is responsible for 45% of all under-five deaths in low- and middle-income countries (LMICs) and numerous morbidities. Although progress has been made, high levels of child undernutrition persist in Zambia. Existing studies have explored primary caretakers' (PCs) explanatory models of child undernutrition in LMICs, without comparison with those of health care providers (HCPs). This paper examines and compares the perceived causes of child undernutrition among PCs and HCPs in Zambia. We conducted a qualitative study, using semistructured one-to-one and group interviews, with 38 PCs and 10 HCPs to explore their perceptions of child undernutrition and its perceived causes in Lusaka district, Zambia. Interview data were analysed with thematic analysis. Our findings indicate that PCs and HCPs in Lusaka district have divergent explanatory models of child undernutrition and perceive parental agency differently. In divergently framing how they conceptualise undernutrition and who is able to prevent it, these models underpin different attributions of causality and different opportunities for intervention. PCs highlighted factors such as child food preferences, child health, and household finances. Contrarily, HCPs stressed factors such as ‘improper feeding’, only highlighting factors such as wider economic conditions when these impacted specifically on health care services. One factor, identified by both groups, was ‘inadequate mothering’. To accelerate the reduction of child undernutrition, interventions must address divergences between PCs and HCPs' explanatory models. Additionally, attention needs to be paid to how wider socio-economic and cultural contexts not only impact childhood undernutrition but shape attributions of causality.