The Lancet Rheumatology, Journal Year: 2024, Volume and Issue: unknown
Published: July 1, 2024
Language: Английский
Nano Energy, Journal Year: 2023, Volume and Issue: 116, P. 108783 - 108783
Published: Aug. 9, 2023
Language: Английский
Citations
45
Nature Electronics, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 23, 2024
Language: Английский
Citations
33
Nature Sustainability, Journal Year: 2024, Volume and Issue: 7(5), P. 602 - 615
Published: April 22, 2024
Language: Английский
Citations
31
Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)
Published: Jan. 3, 2024
Wearable humidity sensors are attracting strong attention as they allow for real-time and continuous monitoring of important physiological information by enabling activity tracking well air quality assessment. Amongst 2Dimensional (2D) materials, graphene oxide (GO) is very attractive sensing due to its tuneable surface chemistry, high area, processability in water, easy integration onto flexible substrates. However, hysteresis, low sensitivity, cross-sensitivity issues limit the use GO practical applications, where preferred. Herein, a wearable wireless impedance-based sensor made with pyrene-functionalized hexagonal boron nitride (h-BN) nanosheets demonstrated. The device shows enhanced sensitivity towards relative (RH) (>10
Language: Английский
Citations
27
Clinical Pharmacology & Therapeutics, Journal Year: 2023, Volume and Issue: 115(4), P. 720 - 726
Published: Dec. 18, 2023
The increasing breadth and depth of resolution in biological clinical data, including -omics real-world requires advanced analytical techniques like artificial intelligence (AI) machine learning (ML) to fully appreciate the impact multi-dimensional population variability intrinsic extrinsic factors on disease progression treatment outcomes. Integration data analytics Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient inclusive drug development utilization - an application we refer as model-informed precision medicine. AI/ML enables characterization molecular sources heterogeneity trajectory, advancing end point qualification biomarker discovery, informing patient enrichment proof-of-concept studies well trial designs evidence generation incorporating digital twins virtual control arms. Explainable ML methods are valuable elucidating predictors efficacy safety pharmacological treatments, thereby response monitoring risk mitigation strategies. In oncology, emerging opportunities exist next models via ML-assisted joint longitudinal modeling high-dimensional such circulating tumor DNA radiomics profiles survival Finally, mining leveraging algorithms understanding exclusion criteria outcomes, rational design appropriately trials through data-driven broadening eligibility criteria. Herein, provide overview aforementioned contexts use based examples across multiple therapeutic areas neurology, rare diseases, autoimmune oncology immuno-oncology.
Language: Английский
Citations
28
Neurotherapeutics, Journal Year: 2023, Volume and Issue: 20(4), P. 1066 - 1080
Published: May 30, 2023
We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) discussed ways which these methodologies may be employed to enhance progress clinical trials research, with particular attention applications the design, conduct, interpretation of for neurologic diseases. ML help accelerate pace subject recruitment, provide realistic simulation medical interventions, remote trial administration via novel digital biomarkers therapeutics. Lastly, we a brief overview technical, administrative, regulatory challenges that must addressed as achieves greater integration into workflows.
Language: Английский
Citations
25
ACS Nano, Journal Year: 2024, Volume and Issue: 18(34), P. 22734 - 22751
Published: Aug. 15, 2024
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role real-time sensing data analysis to provide clinical-grade information personalized healthcare. To this end, supervised unsupervised algorithms emerged as powerful tools, allowing the detection of complex patterns relationships large, high-dimensional sets. In Review, we aim delineate latest advancements sensors, focusing on key developments algorithmic techniques, applications, challenges intrinsic evolving landscape. Additionally, highlight potential machine-learning approaches enhance accuracy, reliability, interpretability sensor discuss opportunities limitations emerging field. Ultimately, our work aims a roadmap future research endeavors exciting rapidly area.
Language: Английский
Citations
13
Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1123 - 1123
Published: Feb. 8, 2024
Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications those differences outside laboratory have been elusive. In this work, we measured vertical, mediolateral, anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across typical range velocities. Fifteen TD fifteen DMD from 3 16 years age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at slow walk running speeds (SC-L1 SC-L5), 6-min test (6MWT), 100 fast walk/jog/run (100MRW), free (FW). For clinical anchoring purposes, participants completed Northstar Ambulatory Assessment (NSAA). We extracted temporospatial features (CFs) applied multiple machine learning (ML) approaches differentiate between CFs raw data. Extracted showed reduced step length greater mediolateral component total power (TP) consistent shorter strides Trendelenberg-like commonly observed DMD. ML data varied effectiveness differentiating controls different speeds, an accuracy up 100%. demonstrate that by consumer-grade smartphone, can capture DMD-associated characteristics toddlers teens.
Language: Английский
Citations
9
Journal of Parkinson s Disease, Journal Year: 2024, Volume and Issue: 14(s2), P. S287 - S296
Published: Feb. 16, 2024
Motor deficits typical of Parkinson’s disease (PD), such as gait and balance disturbances, tremor, reduced arm swing finger movement, voice breathing changes, are believed to manifest several years prior clinical diagnosis. Here we describe the evidence for presence progression motor in this pre-diagnostic phase order provide suggestions design future observational studies an effective, quantitatively oriented investigation. On one hand, these must detect large (potentially, population-based) cohorts possible with high sensitivity specificity. other they accurately possible, support testing effect pharmacological non-pharmacological interventions. Digital technologies artificial intelligence can substantially accelerate process.
Language: Английский
Citations
6
Deleted Journal, Journal Year: 2024, Volume and Issue: 2(4)
Published: May 11, 2024
Abstract Wearable devices have opened up exciting possibilities for monitoring and managing home health, particularly in the realm of neurological psychiatric diseases. These capture signals related to physiological behavioral changes, including heart rate, sleep patterns, motor functions. Their emergence has resulted significant advancements management such conditions. Traditional clinical diagnosis assessment methods heavily rely on patient reports evaluations conducted by healthcare professionals, often leading a detachment patients from their environment creating additional burdens both providers. The increasing popularity wearable offers potential solution these challenges. This review focuses utility diagnosing Through research findings practical examples, we highlight role conditions as autism spectrum disorder, depression, epilepsy, stroke prognosis, Parkinson's disease, dementia, other Additionally, discusses benefits limitations applications, while highlighting challenges they face. Finally, it provides prospects enhancing value
Language: Английский
Citations
5