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| http://dx.doi.org/10.1371/journal.pntd.0000765 | DOI Listing |
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A POCIS-based approach for the monitoring of pharmaceuticals in wastewater treatment plants: Calibration and deployment challenges.
Environ Pollut
January 2025
Department of Analytical and Applied Chemistry, School of Engineering, IQS-Universitat Ramon Llull, Via Augusta 390, 08017 Barcelona, Spain.
Article Synopsis
- The study investigates the use of Polar Organic Chemical Integrative Samplers (POCIS) as a more effective method for monitoring pharmaceutical residues in wastewater compared to traditional grab sampling.
- POCIS allows for continuous sampling over days or weeks, providing more representative data, though challenges remain in obtaining precise quantitative results due to calibration needs.
- The research successfully identifies and calibrates sampling rates for 49 pharmaceuticals in a wastewater treatment plant near Barcelona, yielding high concentrations of specific compounds, thus establishing a methodology for better environmental monitoring of pharmaceuticals.
Resolving heterogeneity of early-onset major depressive disorder through individual differential structural covariance network analysis.
J Affect Disord
January 2025
Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China. Electronic address:
Background: Early-onset major depressive disorder (EO-MDD) is characterized by its significant heterogeneity, hindering progress in research. Traditional case-control studies, like group-level structural covariance network, struggle to capture individual heterogeneity among EO-MDD patients.
Methods: In this study, T1-weighted structural magnetic resonance imaging was obtained from 185 participants, including 103 EO-MDD patients and 82 healthy controls.
Realizing Antithermal Quenching Red Emission in Mn-Activated RbNaVF for Optical Thermometry Sensor Application.
Luminescence
January 2025
Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan, China.
Currently, the development of red Mn-activated fluoride luminescent materials attracts a lot of attention in optical thermometry sensors, solid lighting, display, and plant growth areas. Nevertheless, the thermal stability of Mn-activated fluoride luminescent materials is still a crucial issue. Herein, a new red RbNaVF:Mn luminescent material with outstanding thermal stability was successfully synthesized through the facial coprecipitation method.
View Article and Find Full Text PDFHip Region Stress Fracture in South Korean Military Recruits: Private's Fracture.
Mil Med
January 2025
Department of Orthopedic Surgery, Armed Forces Daejeon Hospital, Daejeon, 34059, Korea.
Introduction: This study aims to analyze the characteristics of hip region stress fractures (HSFs) within a South Korean military cohort and identify the associated risk factors to provide insights for treatment and prevention strategies. Additionally, we will report the epidemiologic data and clinical outcomes of treating HSF within the second largest military hospital.
Materials And Methods: Between January 2022 and December 2023, this retrospective case series revised all HSF patients' medical records to analyze demographic and epidemiologic data and clinical progress.
Integrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation.
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
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