Automated PD

In this paper we focus on the distributed cyber attack detection and physical fault diagnosis problem for a class of interconnected large-scale systems (ILSSs). In the proposed scheme, apart from node measurement, edge measurement is also used to construct distributed Kalman filter to estimate the state of each subsystem. The gain matrices of Kalman filter are determined by minimizing the covariance of estimation error in the attack-free and fault-free case, which reduces the false alarm rate of cyber attack detection and physical fault diagnosis...

BACKGROUND: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures...

Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model is proposed for PE segmentation and detection from Computed Tomography Pulmonary Angiography (CTPA) images. A Y-Net architecture is used to implement this model, which facilitates segmentation and classification jointly, improving performance and efficiency. It is leveraged with Multi Head Attention (MHA), which allows the model to focus on important regions of the image while suppressing irrelevant information, improving the accuracy of the segmentation and detection tasks...

The advancement in digital technology is transforming the world. It enables smart product-service systems that improve productivity by changing tasks, processes, and the ways we work. There are great opportunities in maintenance because many tasks require physical and cognitive work, but are still carried out manually. However, the interaction between a human and a smart system is inevitable, since not all tasks in maintenance can be fully automated. Therefore, we conducted a controlled laboratory experiment to investigate the impact on technicians' workload and performance due to the introduction of smart technology...

Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets...

BACKGROUND: The American College of Surgeons Committee on Trauma (ACS-CoT) mandated that trauma centers have mental health screening and referral protocols in place by 2023. This study compares the Injured Trauma Survivor Screen (ITSS) and the Automated Electronic Medical Record (EMR) Screen to assess their performance in predicting risk for posttraumatic stress disorder (PTSD) within the same sample of trauma patients to inform trauma centers' decision when selecting a tool to best fit their current clinical practice...

Language dysfunction is common in Parkinson's disease (PD) patients, among which, the decline of semantic fluency is usually observed. This study aims to explore the relationship between white matter (WM) alterations and semantic fluency changes in PD patients. 127 PD patients from the Parkinson's Progression Markers Initiative cohort who received diffusion tensor imaging scanning, clinical assessment and semantic fluency test (SFT) were included. Tract-based special statistics, automated fiber quantification, graph-theoretical and network-based analyses were performed to analyze the correlation between WM structural changes, brain network features and semantic fluency in PD patients...

Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing...

Hematopoietic stem and progenitor cells (HSPCs) continuously generate platelets throughout one's life. Inherited Platelet Disorders affect ≈ 3 million individuals worldwide and are characterized by defects in platelet formation or function. A critical challenge in the identification of these diseases lies in the absence of models that facilitate the study of hematopoiesis ex vivo. Here, a silk fibroin-based bioink is developed and designed for 3D bioprinting. This bioink replicates a soft and biomimetic environment, enabling the controlled differentiation of HSPCs into platelets...

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BACKGROUND: Periodontal disease (PD) is associated with an increased risk of cardiovascular disease (CVD). Pericardial adipose tissue (PAT) is known as a marker of progressive CVD. This study sought to assess the association between PD and PAT in patients with CVD. METHODS: We retrospectively investigated 135 patients admitted for CVD who underwent computed tomography coronary angiography (CTCA) and periodontal examinations. Periodontal assessment using the community periodontal index (CPI) was based on the probing pocket depth around teeth...

INTRODUCTION: The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts. AIM: This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count)...

Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the "Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms...

We addressed the limitations of subjective clinical tremor assessment by comparing routine neurological evaluation with a Tremor Occurrence Score derived from smartwatch sensor data, among 142 participants with Parkinson disease and 77 healthy controls. Our findings highlight the potential of smartwatches for automated tremor detection as a valuable addition to conventional assessments, applicable in both clinical and home settings.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP)...

BACKGROUND: Dialytic phosphate removal is a cornerstone of the management of hyperphosphatemia in peritoneal dialysis (PD) patients, but the influencing factors on peritoneal phosphate clearance (PPC) are incompletely understood. Our objective was to explore clinically relevant factors associated with PPC in patients with different PD modality and peritoneal transport status and the association of PPC with mortality. METHODS: This is a cross-sectional and prospective observational study...

Alpha-Synuclein (α-Syn) aggregation is a pathological feature of synucleinopathies, neurodegenerative disorders that include Parkinson's disease (PD). Here, we explored the efficacy of N,N,N',N'-tetraethyl-10H-phenothiazine-3,7-diamine dihydrochloride (LETC), a protein aggregation inhibitor, on α-Syn aggregation. In both cellular models and transgenic mice, α-Syn aggregation was achieved by the overexpression of full-length human α-Syn fused with a signal sequence peptide. α-Syn accumulated in transfected DH60...

PURPOSE: The aim of this investigation was to evaluate the impact of automated cardiac rehabilitation (CR) referral and nurse care coordination on patient and program outcomes. Specifically, the aim was to identify whether differences exist in physical and psychological function at CR Phase 2 enrollment and completion and CR Phase 2 participation and completion for hospitalized patients who receive in-person CR nurse visits versus phone consultation. Using a retrospective pre-/post-intervention descriptive design, a purposive sampling technique was used to select groups with matching clinical attributes...

OBJECTIVE: To assess the efficacy of pirfenidone combined with PD-L1 inhibitor for treatment of bladder cancer in a mouse model and its effect on tumor immune microenvironment modulation. METHODS: Forty C57BL/6 mouse models bearing ectopic human bladder cancer xenografts were randomized into control group, PD-L1 inhibitor group, pirfenidone group and combined treatment group ( n =10). After successful modeling, PD-L1 inhibitor treatment was administered via intraperitoneal injection at 12...

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model's comprehensive analysis of global data features...