Bioterrorism

Machine learning for predicting climate change impacts on Pseudopithomyces chartarum spore counts: a risk indicator of facial eczema.

AIMS: To determine the importance of 11 climate variables on pasture spore count of Pseudopithomyces chartarum, a risk indicator of facial eczema (FE), and to forecast spore counts in New Zealand until 2100, using longitudinal P. chartarum pasture spore count data. METHODS: Between 2010 and 2017, spore counts (n = 6,975) were collected from 862 paddocks spread over 102 farms in the North Island of...

Zoonoticus: A machine learning model for genomic prediction of zoonotic bacterial strains using virulence, resistance, and mobile genetic elements.

Zoonotic diseases continue to rise globally, yet no existing genomic tool integrates virulence, antimicrobial resistance (AMR), and mobile genetic elements to predict zoonotic potential. Here, we present Zoonoticus, a machine learning-based model that classifies bacterial strains as zoonotic or non-zoonotic using whole-genome data. The model was developed using a curated reference database of 37,2...

Medical students' and faculty members' perceptions and experiences of AI integration in health care practice and in medical curricula: A meta-ethnographic review.

With the increasing adoption of artificial intelligence (AI), health care systems and medical education are undergoing significant changes. This revie...

Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.

BACKGROUND: Dengue fever is a major global health concern, with Brazil experiencing recurrent and severe outbreaks due to its favorable climate factor...

Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

OBJECTIVES: To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...

coPERCIST: AI-assisted PET-CT response assessment.

PURPOSE: The PET Response Criteria in Solid Tumours (PERCIST) 1.0 provides a standardized framework for evaluating treatment response using [18F]fluor...

Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features.

BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography ...

Machine learning model for predicting a high comprehensive complication index following rectal cancer surgery.

Postoperative complications following rectal cancer surgery can significantly affect patient's health and prognosis. It has been reported that the com...

Natural Language Response Formats for Assessing Depression and Worry With Large Language Models: A Sequential Evaluation With Model Pre-Registration.

Large language models can transform individuals' mental health descriptions into scores that correlate with rating scales approaching theoretical uppe...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

Machine Learning Insights Into Social Determinants Driving Child Abuse in Pediatric Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Social determinants of health (SDOH) are key drivers of health inequities, shaping disparities in patient outcomes that mus...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...

Identifying meaningful drug response biomarkers from public pharmacogenomic datasets with biologically informed interpretable neural networks.

With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developin...

Large Language Models Use in Dry Eye Disease: Perplexity AI versus ChatGPT4.

PURPOSE: To compare the utility of two large language models (LLM) in dry eye disease (DED) clinics and research. METHODS: Trained ocular surface expe...

Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in pa...

Deep Learning-Enhanced Hand-Driven Spatial Encoding Microfluidics for Multiplexed Molecular Testing at Home.

The frequent global outbreaks of viral infectious diseases have significantly heightened the urgent demand for molecular testing at home. However, the...

pyDOSEIA: A Python Package for Radiological Impact Assessment during Long-term or Accidental Atmospheric Releases.

pyDOSEIA is a Python package designed for meteorological data processing and radiological impact assessment in diverse scenarios, including nuclear an...

Dual Structure Reinforces Interfacial Polarized MXene/PVDF-TrFE Piezoelectric Nanocomposite for Pressure Monitoring.

The emerging interfacial polarization strategy exhibits applicative potential in piezoelectric enhancement. However, there is an ongoing effort to add...

Multiomic integration reveals subtype-specific predictors of neoadjuvant treatment response in breast cancer.

Neoadjuvant therapy has been widely used in breast cancer, but treatment response varies among individuals. We conducted multiomic profiling on tumor ...