Hevelius Report: Visualizing Web-Based Mobility Test Data For Clinical Decision and Learning Support.

Inflammation-associated peripheral blood cells and serum lipid levels are associated with Parkinson's disease.

CA19-9, a carbohydrate antigen, is commonly used for disease monitoring in patients with pancreatic ductal adenocarcinoma (PDAC). However, 15–20% of PDAC patients do not exhibit elevated CA19-9 levels, making it suboptimal for disease monitoring in those patients. Previously, we identified serum extracellular vesicle (EV) based ALPPL2 and THBS2 as highly accurate biomarkers capable of distinguishing PDAC, including early-stage disease (Stages I and II), from non-cancerous conditions and healthy controls. Moreover, changes in serum concentrations of ALPPL2+ and THBS2+ EVs significantly correlate with treatment response. In this study, we sought to further validate the clinical performance of circulating EV ALPPL2 and THBS2 using plasma samples from patients undergoing neoadjuvant therapy. We determined the concentrations of ALPPL2+ or THBS2+ EVs in plasma samples from healthy individuals and longitudinal samples from patients with PDAC who have undergone neoadjuvant treatment. The longitudinal samples were from 15 patients who were treated with three different neoadjuvant regimens for 3-7 months with monthly blood sample collection and CT scans. We found that the concentrations of ALPPL2+ and THBS2+ EVs in plasma samples were on average 8 and 9 times higher in PDAC patients than in the healthy controls, respectively. Correlation analysis showed that the concentrations of both ALPPL2+ and THBS2+ EVs significantly correlate with changes in CA19-9 for patients with elevated levels of CA19-9. Furthermore, changes in plasma ALPPL2+ and THBS2+ EV concentrations correlated with tumor size, as measured by standard RECIST criteria, regardless of CA19-9 status. Our data indicate that plasma extracellular vesicle associated ALPPL2 and THBS2 levels are elevated in patients with PDAC and that changes correlate with patient response to neoadjuvant treatment. We are in the process of further validating these markers through a prospective trial involving patients with PDAC who do not exhibit elevated CA19-9 levels. Citation Format: KUNTAL HALDER, Erkut Borazanci, Gayle Jameson, Daniel Von Hoff, Derek Cridebring, Haiyong Han. Extracellular vesicle based ALPPL2 and THBS2 as biomarkers for disease monitoring in pancreatic cancer patients undergoing neoadjuvant therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B062.

This paper develops neural network models that can recognize Parkinson’s disease (PD) at its early stage. PD is a common neurodegenerative disorder that presents with progressive slow movement, tremor, limb rigidity, and gait alterations, including stooped posture, shuffling steps, festination, freezing of gait, and falling. Early detection of PD enables timely initiation of therapeutic management that decreases morbidity. However, correct recognition of PD, especially in early-stage disease, is challenging because the aging population, which has a high PD prevalence, also commonly exhibits progressive gait slowness due to other disorders, such as joint osteoarthritis or sarcopenia. Therefore, developing a reliable and objective method is crucial for differentiating PD gait characteristics from those of the normal elderly. The aim of this study was to develop neural network models that could use the participants’ motion data during walking to identify PD. We recruited 32 drug-naïve PD patients with variable disease severity and 16 age/sex-matched healthy controls, and we measured their motions using inertial measurement unit (IMU) sensors. The IMU data were used to develop neural network models that could identify patients with advanced-stage PD with an average accuracy of 92.72% in validation processes. The models also differentiated patients with early-stage PD from normal elderly subjects with an accuracy of 99.67%. Another independent group of participants recruited to test the developed models confirmed the successful discrimination of PD-affected from healthy elderly, as well as patients at different severity stages. Our results provide support for early diagnosis and disease severity monitoring in patients with PD.

Over the past two decades, the development of functional imaging methods has greatly promoted our understanding on the changes of neurons following neurodegenerative disorders, such as Parkinson's disease (PD). The application of a spatial covariance analysis on 18F-FDG PET imaging has led to the identification of a distinctive disease-related metabolic pattern. This pattern has proven to be useful in clinical diagnosis, disease progression monitoring as well as assessment of the neuronal changes before and after clinical treatment. It may potentially serve as an objective biomarker on disease progression monitoring, assessment, histological and functional evaluation of related diseases. PD is one of the most common neurodegenerative disorders in the elderly. It is characterized by progressive loss of dopamine neurons in the substantia nigra pars compacta. Throughout the course of disease, the most obvious symptoms are movement-related, such as resting tremor, muscle rigidity, hypokinesia and postural instability (Worth, 2013). Currently, a definite diagnosis of PD is made by clinical evaluation with at least 2 years of follow-up (Hughes et al., 2002; Bhidayasiri and Reichmann, 2013), due to the overlap of motor symptoms between early PD and atypical parkinsonism including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). However, this classic diagnostic criterion does not benefit the early diagnosis of disease. The prognostic outcome and treatment option are substantially different between PD and atypical parkinsonism. Thus it is critical to develop biomarkers for earlier and more accurate diagnosis of PD. Generally, appropriate diagnostic biomarker for PD ought to cover several key characteristics: (i) minimal invasiveness to detect the biomarker in easily accessible body tissue or fluids, (ii) excellent sensitivity to explore the patients with PD, (iii) high specificity to prevent false-positive results in PD-free individuals, and (iv) robustness against potential affecting factors. A PD-related spatial covariance pattern (PDRP) with quantifiable expression on 18F-FDG PET imaging has been gradually detected using a spatial covariance method during the last two decades and it has been demonstrated to be the right diagnostic biomarker for PD (Eidelberg et al., 1994). PDRP has proven not only to be effective in early discrimination of PD from atypical parkinsonian disorders, but also to be able to assess the disease progression and treatment response. Thus it is considered as a multifunctional biomarker. In this review, we aim to provide an overview of the development in pattern-based biomarker for PD.

Impulsivity is a behavioral trait that is elevated in many neuropsychiatric disorders. Parkinson's disease (PD) patients can exhibit a specific pattern of reward-seeking impulsive-compulsive behaviors (ICBs), as well as more subtle changes to generalized trait impulsivity. Prior studies in healthy controls (HCs) suggest that trait impulsivity is regulated by D2/3 autoreceptors in mesocorticolimbic circuits. While altered D2/3 binding is noted in ICB+ PD patients, there is limited prior assessment of the trait impulsivity-D2/3 relationship in PD, and no prior direct comparison with patterns in HCs. We examined 54 PD (36 M; 18 F) and 31 sex- and age-matched HC (21 M; 10 F) subjects using [18F]fallypride, a high-affinity D2/3 receptor ligand, to measure striatal and extrastriatal D2/3 nondisplaceable binding potential (BPND). Subcortical and cortical assessment exclusively used ROI or exploratory-voxelwise methods, respectively. All completed the Barratt Impulsiveness Scale, a measure of trait impulsivity. Subcortical ROI analyses indicated a negative relationship between trait impulsivity and D2/3 BPND in the ventral striatum and amygdala of HCs but not in PD. By contrast, voxelwise methods demonstrated a positive trait impulsivity-D2/3 BPND correlation in ventral frontal olfactocentric-paralimbic cortex of subjects with PD but not HCs. Subscale analysis also highlighted different aspects of impulsivity, with significant interactions between group and motor impulsivity in the ventral striatum, and attentional impulsivity in the amygdala and frontal paralimbic cortex. These results suggest that dopamine functioning in distinct regions of the mesocorticolimbic circuit influence aspects of impulsivity, with the relative importance of regional dopamine functions shifting in the neuropharmacological context of PD.SIGNIFICANCE STATEMENT The biological determinants of impulsivity have broad clinical relevance, from addiction to neurodegenerative disorders. Here, we address biomolecular distinctions in Parkinson's disease. This is the first study to evaluate a large cohort of Parkinson's disease patients and age-matched healthy controls with a measure of trait impulsivity and concurrent [18F]fallypride PET, a method that allows quantification of D2/3 receptors throughout the mesocorticolimbic network. We demonstrate widespread differences in the trait impulsivity-dopamine relationship, including (1) loss of subcortical relationships present in the healthy brain and (2) emergence of a new relationship in a limbic cortical area. This illustrates the loss of mechanisms of behavioral regulation present in the healthy brain while suggesting a potential compensatory response and target for future investigation.

Fallibility is inherent in human cognition and so a system that will monitor performance is indispensable. While behavioral evidence for such a system derives from the finding that subjects slow down after trials that are likely to produce errors, the neural and behavioral characterization that enables such control is incomplete. Here, we report a specific role for dopamine/basal ganglia in response conflict by accessing deficits in performance monitoring in patients with Parkinson's disease. To characterize such a deficit, we used a modification of the oculomotor countermanding task to show that slowing down of responses that generate robust response conflict, and not post-error per se, is deficient in Parkinson's disease patients. Poor performance adjustment could be either due to impaired ability to slow RT subsequent to conflicts or due to impaired response conflict recognition. If the latter hypothesis was true, then PD subjects should show evidence of impaired error detection/correction, which was found to be the case. These results make a strong case for impaired performance monitoring in Parkinson's patients.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Quantum Dots: Bringing Nanoscience and Engineering into the High School Classroom Abstract This study traces the lesson design process for a professional development initiative on nano- education. In particular, a lesson on quantum dots is traced throughout the iterative design process based on a learning performances framework combined with design-based research. Teacher feedback, pre- and post-tests covering conceptual information, and researcher field notes were used as the primary sources of data. From these data, themes were identified, and actions were taken to address each of these feedback themes to better correspond to the learning goals identified for the lesson. Introduction The face of science, engineering, and technology is rapidly changing. The biggest trends are also the smallest, as nano-scale phenomena prove to be more and more important in a wide range of applications. However, we still have yet to include these nano-scale phenomena in our secondary science curricula, leaving students unprepared to enter important careers in nanoscience, engineering, and technology. Professional development efforts are one way to combat this issue. This study focused on curriculum design for a particular professional development program geared towards science teachers in grades 7-12. This professional development program was run through the National Center for Learning and Teaching in Nanoscale Science and Engineering (NCLT) at Purdue University in summer 2007. This was the third year of the program, and another professional development institute will take place in summer 2008. To address the design of lessons for professional development and future classroom use, the researchers used an iterative design process structured around learning goals and performances, basing revisions on teacher feedback and conceptual understanding. This paper will trace the iterative lesson design process, describing teacher feedback, assessments of conceptual understanding, and actions taken to improve the lesson based on this data. Review of Literature Nano-scale phenomena are playing a greater and greater role in every aspect of contemporary scientific research. Nanoscience, engineering, and technology (NSET) have wide-ranging applications in medicine, defense, development of electronics, environmental science, and materials science, to name a few.1-3 It follows from this information that we will need many more workers in the nano-industries; one estimate suggests that the United States will need two million workers in NSET fields in the next decade alone.4 At the same time, the United States is experiencing the need for drastic reforms in science, technology, engineering, and mathematics (STEM) education. U.S. students do not measure up

Concept design of the DEMO divertor cassette-to-vacuum vessel locking system adopting a systems engineering approach

BackgroundCardiovascular disease (CVD) is the leading cause of death among women in the United States. A considerable number of young women already have risk factors for CVD. Awareness of CVD and its risk factors is critical to preventing CVD, yet younger women are less aware of CVD prevalence, its risk factors, and preventative behaviors compared to older women.ObjectiveThe purpose of this study is to assess CVD awareness among adolescent and young adult women and develop a lifestyle-based cardiovascular risk assessment tool for the promotion of CVD awareness among this population.MethodsThis study used a 3-phase iterative design process with young women and health care practitioners from primary care and reproductive care clinics in Atlanta, Georgia. In phase 1, we administered a modified version of the American Heart Association Women’s Health Survey to young women, aged 15-24 years (n=67), to assess their general CVD awareness. In phase 2, we interviewed young women, aged 13-21 years (n=10), and their health care practitioners (n=10), to solicit suggestions for adapting the Healthy Heart Score, an existing adult cardiovascular risk assessment tool, for use with this age group. We also aimed to learn more about the barriers and challenges to health behavior change within this population and the clinical practices that serve them. In phase 3, we used the findings from the first 2 phases to create a prototype of a new online cardiovascular risk assessment tool designed specifically for young women. We then used an iterative user-centered design process to collect feedback from approximately 105 young women, aged 13-21 years, as we adapted the tool.ResultsOnly 10.5% (7/67) of the young women surveyed correctly identified CVD as the leading cause of death among women in the United States. Few respondents reported having discussed their personal risk (4/67, 6%) or family history of CVD (8/67, 11.9%) with a health care provider. During the interviews, young women reported better CVD awareness and knowledge after completing the adult risk assessment tool and suggested making the tool more teen-friendly by incorporating relevant foods and activity options. Health care practitioners emphasized shortening the assessment for easier use within practice and discussed other barriers adolescents may face in adopting heart-healthy behaviors. The result of the iterative design process was a youth-friendly prototype of a cardiovascular risk assessment tool.ConclusionsAdolescent and young adult women demonstrate low awareness of CVD. This study illustrates the potential value of a cardiovascular risk assessment tool adapted for use with young women and showcases the importance of user-centered design when creating digital health interventions.

Objectives: Remote assessment of gait in patients’ homes has become a valuable tool for monitoring the progression of Parkinson’s disease (PD). However, these measurements are often not as accurate or reliable as clinical evaluations because it is challenging to objectively distinguish the unique gait characteristics of PD. We explore the inference of patients’ stage of PD from their gait using machine learning analyses of data gathered from their smartphone sensors. Specifically, we investigate supervised machine learning (ML) models to classify the severity of the motor part of the UPDRS (MDS-UPDRS 2.10-2.13). Our goals are to facilitate remote monitoring of PD patients and to answer the following questions: (1) What is the patient PD stage based on their gait? (2) Which features are best for understanding and classifying PD gait severities? (3) Which ML classifier types best discriminate PD patients from healthy controls (HC)? and (4) Which ML classifier types can discriminate the severity of PD gait anomalies? Methodology: Our work uses smartphone sensor data gathered from 9520 patients in the mPower study, of whom 3101 participants uploaded gait recordings and 344 subjects and 471 controls uploaded at least 3 walking activities. We selected 152 PD patients who performed at least 3 recordings before and 3 recordings after taking medications and 304 HC who performed at least 3 walking recordings. From the accelerometer and gyroscope sensor data, we extracted statistical, time, wavelet and frequency domain features, and other lifestyle features were derived directly from participants’ survey data. We conducted supervised classification experiments using 10-fold cross-validation and measured the model precision, accuracy, and area under the curve (AUC). Results: The best classification model, best feature, highest classification accuracy, and AUC were (1) random forest and entropy rate, 93% and 0.97, respectively, for walking balance (MDS-UPDRS-2.12); (2) bagged trees and MinMaxDiff, 95% and 0.92, respectively, for shaking/tremor (MDS-UPDRS-2.10); (3) bagged trees and entropy rate, 98% and 0.98, respectively, for freeze of gait; and (4) random forest and MinMaxDiff, 95% and 0.99, respectively, for distinguishing PD patients from HC. Conclusion: Machine learning classification was challenging due to the use of data that were subjectively labeled based on patients’ answers to the MDS-UPDRS survey questions. However, with use of a significantly larger number of subjects than in prior work and clinically validated gait features, we were able to demonstrate that automatic patient classification based on smartphone sensor data can be used to objectively infer the severity of PD and the extent of specific gait anomalies.

Mild cognitive impairment in Parkinson's disease is associated with progression to dementia (Parkinson's disease dementia) in a majority of patients. Determining structural imaging biomarkers associated with prodromal Parkinson's disease dementia may allow for the earlier identification of those at risk, and allow for targeted disease modifying therapies. One hundred and five non-demented subjects with newly diagnosed idiopathic Parkinson's disease and 37 healthy matched controls had serial 3 T structural magnetic resonance imaging scans with clinical and neuropsychological assessments at baseline, which were repeated after 18 months. The Movement Disorder Society Task Force criteria were used to classify the Parkinson's disease subjects into Parkinson's disease with mild cognitive impairment (n = 39) and Parkinson's disease with no cognitive impairment (n = 66). Freesurfer image processing software was used to measure cortical thickness and subcortical volumes at baseline and follow-up. We compared regional percentage change of cortical thinning and subcortical atrophy over 18 months. At baseline, cases with Parkinson's disease with mild cognitive impairment demonstrated widespread cortical thinning relative to controls and atrophy of the nucleus accumbens compared to both controls and subjects with Parkinson's disease with no cognitive impairment. Regional cortical thickness at baseline was correlated with global cognition in the combined Parkinson's disease cohort. Over 18 months, patients with Parkinson's disease with mild cognitive impairment demonstrated more severe cortical thinning in frontal and temporo-parietal cortices, including hippocampal atrophy, relative to those with Parkinson's disease and no cognitive impairment and healthy controls, whereas subjects with Parkinson's disease and no cognitive impairment showed more severe frontal cortical thinning compared to healthy controls. At baseline, Parkinson's disease with no cognitive impairment converters showed bilateral temporal cortex thinning relative to the Parkinson's disease with no cognitive impairment stable subjects. Although loss of both cortical and subcortical volume occurs in non-demented Parkinson's disease, our longitudinal analyses revealed that Parkinson's disease with mild cognitive impairment shows more extensive atrophy and greater percentage of cortical thinning compared to Parkinson's disease with no cognitive impairment. In particular, an extension of cortical thinning in the temporo-parietal regions in addition to frontal atrophy could be a biomarker in therapeutic studies of mild cognitive impairment in Parkinson's disease for progression towards dementia.

PurposeTo demonstrate the value in conducting a usability study and following an iterative design process to create a more user‐centered and sustainable digital library.Design/methodology/approachAfter identifying three key user groups, a series of usability tests and focus groups were conducted to assess how users interact with the site's interface. An iterative design process followed involving the development and testing of prototypes by representative users and stakeholders.FindingsUsers' interaction with a digital library is task‐oriented and context dependent. Serving the needs of multiple audiences is an iterative process and requires an ongoing dialog with users.Research limitations/implicationsLike most usability studies, the results are not generalizable.Practical implicationsIt offers an example of how an informal usability study and iterative design process can be conducted to create a more user‐centered digital library.Originality/valueThis paper provides new insights into the information needs and behaviors of users of cultural heritage digital libraries and builds on the literature on usability and iterative design.

Typical engineering design processes involve an initial design, followed by iterations of analyses, interpreting and evaluating analyses results, and proposing new design or modifying existing design. The final design often results from intuitions obtained during this iterative process. Most computer-aided engineering (CAE) software focuses on the computer-aided analysis by advancing capabilities in pre-processing, computation, and post-processing. However, little attention has been paid to the support of the aforementioned iterative design process, which is the common practice in engineering design. Authors believe the next-generation CAE software should evolve into CAID (ComputerAided Iterative Design) software to help engineers go through the iterative design process and develop better engineering designs. In this paper, key software requirements for the iterative design process are discussed in this work. Furthermore, prototype CAID software developed using C, Qt, and VTK is demonstrated. The analyzing capability of the developed CAID software is based on an essential software framework for meshfree methods (ESFM). The proposed CAID concept and prototype software shall provide guidelines for future CAE software development.

Dual-action ADHD drug provides both early- and late-onset symptom relief

Background: Recently, many studies have shown that low vitamin D (VD) levels may be related to an increased risk of Parkinson’s disease (PD), but the underlying mechanisms remain unclear.Objective: To explore the relationship between PD and VD levels, as well as to analyze the effects of VD on spontaneous brain activity and explore the possible mechanism of its involvement in PD risk.Methods: In a cross-sectional study, we quantified the difference in VD levels between 330 PD patients and 209 healthy controls (HC) to explore the correlation between VD and PD risk. We also acquired resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data from 46 PD patients and 21 HC. The PD patients were divided into three groups according to 25(OH)D levels: PD patients with VD deficiency (PD + VDD), PD patients with VD insufficiency (PD + VDI), and PD patients with normal VD (PD + NVD). The effect of VD status on spontaneous neuronal activity in the whole brain was analyzed by measuring the fraction amplitude of low-frequency fluctuation (fALFF).Results: Compared with HC, the PD patients had lower serum 25(OH)D levels (23.60 ± 7.27 vs. 25.60 ± 5.78, P < 0.001). The 25(OH)D level may have a potential dose-dependent effect on the risk of PD (Ptrend = 0.007). A high risk of PD was associated with VD deficiency [25(OH)D < 20 ng/mL, OR = 2.319], and the lowest quartile of 25(OH)D concentration was associated with a high risk of PD (OR = 1.941). In the rs-fMRI study, PD + VDD patients had wider brain regions with altered fALFF than other PD groups when compared with the corresponding HC groups. Both PD + VDD and PD + VDI showed higher fALFF in the cuneus, left precuneus, calcarine cortex and right lingual, as well as lower fALFF in the left middle temporal gyrus. PD + VDD patients also showed higher fALFF in the left superior, middle and inferior frontal gyri, as well as the left precentral gyrus than HC. Among PD patients, there was only a statistically significant difference in fALFF between the PD + VDD and PD + NVD groups. Compared with the PD + NVD group, PD + VDD patients exhibited higher fALFF in the left precentral and left postcentral gyrus, as well as the left inferior parietal lobule.Conclusion: These results demonstrate that PD patients had lower serum VD levels than HC, and VD may have a potential dose-dependent effect on PD risk. Lower serum VD levels can affect the spontaneous neuronal activity of default-mode network (DMN) and visual pathway neurons in PD patients, providing a possible mechanism for its effect on PD risk.