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Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review

Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review

While NIPT has emerged as a powerful tool for detecting common chromosomal abnormalities such as Down syndrome, its accessibility and potential for broader application through internet-based platforms remain relatively unexplored. This review focuses on understanding the feasibility, benefits, and challenges of using internet-based technologies to deliver NIPT services effectively.

Mega Obukohwo Sr Oyovwi, Ejiro Peggy Ohwin, Rume Arientare Rotu, Temitope Gideon Olowe

JMIR Bioinform Biotech 2024;5:e58439

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Nursing records, which contain many records of patient care, were expected to be effective in detecting adverse drug reactions such as PN, but the detection rate was less than half that of physician and pharmacist records. The detection rate for discharge summaries, radiology reports, and radioisotope reports was very low (less than half), suggesting that these types of documents are less useful for the target diseases and target drugs in this study.

Tomohiro Nishiyama, Ayane Yamaguchi, Peitao Han, Lis Weiji Kanashiro Pereira, Yuka Otsuki, Gabriel Herman Bernardim Andrade, Noriko Kudo, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki, Masahiro Takada, Masakazu Toi

JMIR Med Inform 2024;12:e58977

Central Hemodynamic and Thermoregulatory Responses to Food Intake as Potential Biomarkers for Eating Detection: Systematic Review

Central Hemodynamic and Thermoregulatory Responses to Food Intake as Potential Biomarkers for Eating Detection: Systematic Review

In addition, detecting eating episodes during concurrent activities, such as walking, remains a substantial hurdle that necessitates further algorithmic improvements [7]. By the same token, most of the existing systems rely on a single sensor with limited provisions for sensor failure or suboptimal performance.

Lucy Chikwetu, Parker Vakili, Andrew Takais, Rabih Younes

Interact J Med Res 2024;13:e52167

Active Tuberculosis Screening via a Mobile Health App in Myanmar: Incremental Cost-Effectiveness Evaluation

Active Tuberculosis Screening via a Mobile Health App in Myanmar: Incremental Cost-Effectiveness Evaluation

A previous analysis of national TB prevalence survey data showed that a propensity TB risk score derived from an individual’s social and pathological characteristics resulted in improved predictive accuracy for detecting active TB cases compared to TBSS screening [7]. A simple mobile app was previously developed to calculate the propensity TB risk score based on a provided scale [7]. The app used a cutoff score ≥0.0053 to identify presumptive TB, that is, people with ≥0.5% probability of having TB.

Kyaw Ko Ko Htet, Aye Nyein Phyu, Nyi Nyi Zayar, Virasakdi Chongsuvivatwong

JMIR Form Res 2023;7:e51998

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

These results suggest that the analytical techniques used produced results that align with other validated methods of detecting substance use. However, the combination of wastewater and analytical methods used here has the potential to produce much more rapid and automated results than traditional methods. The current techniques used also provide insights into treatments for OUD. Although methadone is a well-known treatment for opioid misuse, its use is limited by accessibility [39-41].

Marie A Severson, Sathaporn Onanong, Alexandra Dolezal, Shannon L Bartelt-Hunt, Daniel D Snow, Lisa M McFadden

JMIR Form Res 2023;7:e45353