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@article{Qi2025,

title = {Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait},

author = {Wenhao Qi and Shiying Shen and Chaoqun dong and Mengjiao Zhao and Shuaiqi Zang and Xiaohong Zhu and Jiaqi Li and Bin Wang and Yankai Shi and Yongze Dong and Huajuan Shen and Junling Kang and Xiaodong Lu and Guowei Jiang and Jingsong Du and Eryi Shu and Qingbo Zhou and Jinghua Wang and Shihua Cao},

url = {https://www.jmir.org/2025/1/e71560/

https://www.jmir.org/2025/1/e71560/PDF},

doi = {10.2196/71560},

year  = {2025},

date = {2025-05-20},

journal = {Journal of Medical Internet Research},

volume = {27},

abstract = {Background: With the rapid development of digital biomarkers in Parkinson disease (PD) research, it has become increasingly important to explore the current research trends and key areas of focus.



Objective: This study aimed to comprehensively evaluate the current status, hot spots, and future trends of global PD biomarker research, and provide a systematic review of deep learning models for freezing of gait (FOG) digital biomarkers.



Methods: This study used bibliometric analysis based on the Web of Science Core Collection database to conduct a comprehensive analysis of the multidimensional landscape of Parkinson digital biomarkers. After identifying research hot spots, the study also followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for a scoping review of deep learning models for FOG from 5 databases: Web of Science, PubMed, IEEE Xplore, Embase, and Google Scholar.



Results: A total of 750 studies were included in the bibliometric analysis, and 40 studies were included in the scoping review. The analysis revealed a growing number of related publications, with 3700 researchers contributing. Neurology had the highest average annual participation rate (12.46/19, 66%). The United States contributed the most research (192/1171, 16.4%), with 210 participating institutions, which was the highest among all countries. In the study of deep learning models for FOG, the average accuracy of the models was 0.92, sensitivity was 0.88, specificity was 0.90, and area under the curve was 0.91. In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures.



Conclusions: Research on digital biomarkers for PD is currently at a stable stage of development, with widespread global interest from countries, institutions, and researchers. However, challenges remain, including insufficient interdisciplinary and interinstitutional collaboration, as well as a lack of corporate funding for related projects. Current research trends primarily focus on motor-related studies, particularly FOG monitoring. However, deep learning models for FOG still lack external validation and standardized performance reporting. Future research will likely progress toward deeper applications of artificial intelligence, enhanced interinstitutional collaboration, comprehensive analysis of different data types, and the exploration of digital biomarkers for a broader range of Parkinson symptoms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Purnasasmita2025,

title = {Data Landscape as the representation of nighttime urban dynamics},

author = {Ruth Kartika Purnasasmita and Yandi Andri Yatmo and Paramita Atmodiwirjo},

doi = {/10.62476/ndi.91.227},

year  = {2025},

date = {2025-04-02},

journal = {New Design Ideas},

volume = {9},

issue = {1},

pages = {227-247},

publisher = {Jomard Publishing},

school = {Universitas Indonesia},

abstract = {This paper explores the data landscape as the representation of the network of entities that construct the nighttime urban environment. The utilization of data landscape is understood as the collective reading of nighttime place dynamics which could unfold the complex narratives of urban context. It reveals two main elements involved in the emergence of the nighttime environment: what constructs urban nighttime and how it is captured. The mapping of data from social media was conducted using Instagram posts that contain the hashtag #pasarmalam, which indicates the representation of the night market as an element of the urban nighttime environment in the context of Indonesian cities.

Analysis was conducted by identifying the network of relations that emerged from the hashtags. The findings from the study indicate the emergence of data landscape as the network of entities related to elements of events, food, entertainment and experience that construct nighttime urban places. It also reveals how nighttime is represented through various methods, tools and techniques for capturing nighttime. The study suggests the critical role of data landscape in understanding the construction of nighttime urban environment as the basis of data-driven urban placemaking that can capture the dynamics and complexities of nighttime urban elements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khan2025,

title = {Frailty after Covid: tracing emergent shifts through heterogenous network mapping},

author = {Salman Khan and Tiago Moreira},

url = {https://link.springer.com/article/10.1057/s41285-025-00216-x

https://link.springer.com/content/pdf/10.1057/s41285-025-00216-x.pdf},

doi = {/10.1057/s41285-025-00216-x},

year  = {2025},

date = {2025-03-28},

journal = {Social Theory & Health},

volume = {23},

issue = {1},

edition = {Paul Higgs; Ruth Graham},

abstract = {Taking as a point of departure the role that the category of frailty increasingly plays in the classification, sorting and management of ageing populations in contemporary societies, this paper examines how the onset of Covid-19—as a disease posing the most risk to older adults—affected scientific knowledge production on frailty. Drawing on a theoretically driven network mapping of scientific literature on frailty before and after the pandemic, the paper traces emergent shifts in the evolution of two key discourses of frailty, namely that of the accumulation of deficits and the phenotype, respectively. Our analysis identifies an increased enrolment of frailty as a clinical, prognostic category post-Covid, underpinned by the deficit accumulation model and its key instrument, the frailty index. In parallel, we observe the continuation of laboratory and experimental research on frailty, as aligned with the phenotype approach. We note that in comparison to before Covid, this shift seems to be taking place across a more diversified scientific terrain, with the field of geriatrics playing a central, mediating role between distinct-yet-relational articulations of frailty—those tied to the clinic on one end, and the lab on the other.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2025,

title = {From virtual to reality: innovative practices of digital twins in tumor therapy},

author = {Shiying Shen and Wenhao and Xin Liu and Jianwen Zeng and Sixie Li and Xiaohong Zhu and Chaoqun Dong and Bin Wang and Yankai Shi and Jiani Yao and Bingsheng Wang and Louxia Jing and Shihua Cao and Guanmian Liang},

url = {https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-025-06371-z},

doi = {/10.1186/s12967-025-06371-z},

year  = {2025},

date = {2025-03-19},

urldate = {2025-03-19},

journal = {Journal of Translational Medicine},

volume = {23},

issue = {348},

abstract = {Background As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.

Objective This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.

Methods Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.

Results Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved.

However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment

decision-making, prognosis prediction, and surgical planning.

Conclusion Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}