A decision support system in the framework of the geographic information system (GIS) and subsurface flow model, Hydrosub, were used to identify critical areas from simulated spatial distributions of relative nitrogen export. Diagnosis and prescription Expert Systems (ES) are developed and applied to the identification of probable causes of excessive nitrogen export and selection of appropriate Best Management Practices (BMPs). The result is a spatially distributed set of recommended Best Management Practices that are feasible economically and environmentally. For the study watershed, using catch crops and rhizobium-legume (instead of using conventional commercial fertilizers) were the most recommended Best Management Practices.
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http://dx.doi.org/10.1080/10934520701244003 | DOI Listing |
BMC Urol
January 2025
Department of Urology, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215006, People's Republic of China.
Background: Bacillus Calmette-Guerin (BCG) immunotherapy is the standard adjuvant treatment for high-risk, non-muscle invasive bladder cancer (NMIBC). However, BCG immunotherapy is commonly accompanied by significant lower urinary tract symptoms (LUTS) including symptoms such as urinary urgency, frequency, dysuria and pelvic pain. These symptoms can undermine treatment adherence and clinical outcomes.
View Article and Find Full Text PDFRadiol Med
January 2025
Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
Purpose: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Material And Methods: Primary tumor and the most significant lymph node metastasis were manually segmented in baseline [F]FDG PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC and conventional semiquantitative PET parameters were collected.
Sci Rep
January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021) using electronic medical records. Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts.
View Article and Find Full Text PDFAm J Perinatol
January 2025
Center for Advanced Research Training and Innovation, Center for Birth Defects Research, University of Maryland School of Medicine, Baltimore, Maryland.
This study aimed to assess the strengths, limitations, opportunities, and threats presented by diabetes-in-pregnancy. We review the improvements in maternal and fetal mortality since the advent of insulin therapy, evaluate current health challenges, and identify opportunities for preventing increased mortality due to diabetes-in-pregnancy. Prior to 1922, women with type 1 diabetes mellitus (T1DM) of childbearing age were discouraged from becoming pregnant as the maternal and fetal/neonatal mortality rates were extremely high.
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