Tootfinder

RDMA: Cost Effective Agent-Driven Rare Disease Discovery within Electronic Health Record Systems
John Wu, Adam Cross, Jimeng Sun
https://arxiv.org/abs/2507.15867

RDMA: Cost Effective Agent-Driven Rare Disease Discovery within Electronic Health Record Systems
Rare diseases affect 1 in 10 Americans, yet standard ICD coding systems fail to capture these conditions in electronic health records (EHR), leaving crucial information buried in clinical notes. Current approaches struggle with medical abbreviations, miss implicit disease mentions, raise privacy concerns with cloud processing, and lack clinical reasoning abilities. We present Rare Disease Mining Agents (RDMA), a framework that mirrors how medical experts identify rare disease patterns in EHR. R…

Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
Fred Mutisya (Qhala, Kenya Medical Association), Shikoh Gitau (Qhala), Christine Syovata (Kenya Medical Association), Diana Oigara (Kenya Medical Association), Ibrahim Matende (Kenya Medical Association), Muna Aden (Kenya Medical Association), Munira Ali (Kenya Medical Association), Ryan Nyotu (Kenya Medical Association), Diana Marion (Kenya Medical Asso…

Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
Introduction: Existing medical LLM benchmarks largely reflect examination syllabi and disease profiles from high income settings, raising questions about their validity for African deployment where malaria, HIV, TB, sickle cell disease and other neglected tropical diseases (NTDs) dominate burden and national guidelines drive care. Methodology: We systematically reviewed 31 quantitative LLM evaluation papers (Jan 2019 May 2025) identifying 19 English medical QA benchmarks. Alama Health QA was de…

Every newborn baby in England will have their DNA mapped to assess their risk of hundreds of diseases, under NHS plans for the next 10 years.
The scheme, first reported by the Daily Telegraph, is part of a government drive towards predicting and preventing illness, which will also see £650m invested in DNA research for all patients by 2030.
Health Secretary Wes Streeting said gene technology would enable the health service to "leapfrog disease, so we're in front of it ra…

NHS plans to DNA test all babies in England to assess disease risk
Scheme is part of the government's 10-year plan for the NHS in England aimed at easing pressure on services.

CF-Seg: Counterfactuals meet Segmentation
Raghav Mehta, Fabio De Sousa Ribeiro, Tian Xia, Melanie Roschewitz, Ainkaran Santhirasekaram, Dominic C. Marshall, Ben Glocker
https://arxiv.org/abs/2506.16213

CF-Seg: Counterfactuals meet Segmentation
Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly more challenging in the presence of disease. Disease patterns can alter the appearance of surrounding healthy tissues, introduce ambiguous boundaries, or even obscure critical anatomical structures. As such, segmentation models trained on real-world datasets may struggle to provide good anatomical segmentation, leadin…

Healthy Babies Born in Britain After Scientists Used DNA From Three People to Avoid Genetic Disease - Slashdot
https://science.slashdot.org/story/25/07/20/0328203/healthy-babies-born-in-britain-after-scientists-used-dna-from-three-people-to-avoid-genetic-disease

I’ve mentioned this elsewhere, but going on record here:
This thing where AI consumes AI-generated content to “make the AI better”?
It’s Mad Cow Disease for computers.

#RIP Ozzy Osbourne :(
https://www.thejournal.ie/ozzy-osbourne-dies-at-the-age-of-76-6770657-Jul2025/

Black Sabbath frontman Ozzy Osbourne dies at the age of 76
He was diagnosed with Parkinson’s disease in 2019.

This seems pretty big news.
https://erc.europa.eu/news-events/news/gut-microbiota-metabolite-linked-atherosclerosis-could-revolutionise-diagnosis-and

A gut microbiota metabolite linked to atherosclerosis could revolutionise diagnosis and treatment
A new study led by the Spanish National Centre for Cardiovascular Research (CNIC) and funded by the European Reasearch Council (ERC) has identified imidazole propionate (ImP), a metabolite produced by gut bacteria, as a driver of atherosclerosis— as a driver of atherosclerosis, the disease behind most heart attacks and strokes. Published in Nature, the findings point to blood levels of ImP as a potential early diagnostic marker, and pave the way for novel, personalised treatment strategies.

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Mohammad Asifur Rahim, Muhammad Nazmul Arefin, Md. Mizanur Rahman, Md Ali Hossain, Ahmed Moustafa
https://arxiv.org/abs/2507.15915

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Context: Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions, making accurate early diagnosis challenging. Artificial intelligence (AI), especially Deep Learning (DL), has a strong tool for medical image analysis; however, pre-trained models like CNNs and XAI techniques for mpox detection is underexplored. Objective: This study aims to evaluate the effectiveness of pre-trained CNN models (VGG16, VGG19, InceptionV3, MobileNetV2) for the early…

SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting
Shuhao Mei, Yongchao Long, Shan Cao, Xiaobo Han, Shijia Geng, Jinbo Sun, Yuxi Zhou, Shenda Hong
https://arxiv.org/abs/2507.16145

SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting
Chronic Obstructive Pulmonary Disease (COPD), a major chronic respiratory disease with persistent airflow limitation, is a leading global cause of disability and mortality. Respiratory spirogram time series, routinely collected during pulmonary function tests (PFTs), play a critical role in the early detection of repsiratory diseases and in monitoring lung function over time. However, most current AI models for COPD diagnosis are limited to outputting classification results without providing a …

Testing Homogeneity in a heteroscedastic contaminated normal mixture
Xiaoqing Niu, Pengfei Li, Yuejiao Fu
https://arxiv.org/abs/2507.15630 https://

Testing Homogeneity in a heteroscedastic contaminated normal mixture
Large-scale simultaneous hypothesis testing appears in many areas such as microarray studies, genome-wide association studies, brain imaging, disease mapping and astronomical surveys. A well-known inference method is to control the false discovery rate. One popular approach is to model the $z$-scores derived from the individual $t$-tests and then use this model to control the false discovery rate. We propose a new class of contaminated normal mixtures for modelling $z$-scores. We further design…

GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction
Shuaitong Zhang, Yuchen Sun, Yong Ao, Xuehuan Zhang, Ruoshui Yang, Jiantao Xu, Zuwu Ai, Haike Zhang, Xiang Yang, Yao Xu, Kunwei Li, Duanduan Chen
https://arxiv.org/abs/2507.12500 …

GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction
Longitudinal medical images are essential for monitoring disease progression by capturing spatiotemporal changes associated with dynamic biological processes. While current methods have made progress in modeling spatiotemporal patterns, they face three key limitations: (1) lack of generalizable framework applicable to diverse disease progression prediction tasks; (2) frequent overlook of the ordinal nature inherent in disease staging; (3) susceptibility to representation collapse due to structu…

'‘Probiotic Can Slow Disease on Coral Reefs: They Have Microbiomes That Benefit, Like Humans"
#Oceans #Environment #CoralReef

Ingredient in Yogurt Can Slow Disease in Reefs as Corals Have Microbiomes That Benefits From Probiotics
A new Smithsonian study demonstrated how a probiotic could save coral reefs at-risk with SCTLD disease in Florida and the Caribbean.

Breaking the picomolar barrier in lateral flow assays using Bright-Dtech___ 614 -- Europium nanoparticles for enhanced sensitivity
Juliette Lajoux (UPV), Yulieth D Banguera-Ordo\~nez (UPV), Amadeo Sena-Torralba (IPHC), Lo\"ic J Charbonni\`ere (IPHC), Mohamadou Sy (UPV), Joan Goetz (UPV), {\'A}ngel Maquieira (UPV), Sergi Morais (UPV)
https://…

Breaking the picomolar barrier in lateral flow assays using Bright-Dtech___ 614 -- Europium nanoparticles for enhanced sensitivity
Lateral flow immunoassays (LFIA) are among the most widely used rapid diagnostic tests for point-of-care screening of disease biomarkers. However, their limited sensitivity hinders their use in complex clinical applications that require accurate biomarker quantification for precise medicine. To address this limitation, we evaluated Bright-Dtech___-614 Europium nanoparticles to enhance LFIA assay sensitivity. These nanoparticles exhibited a luminescence quantum yield of 70 % and a 90 % conjugati…

Exploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception
Liu He, Yuanchao Li, Rui Feng, XinRan Han, Yin-Long Liu, Yuwei Yang, Zude Zhu, Jiahong Yuan
https://arxiv.org/abs/2507.12356

Exploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception
Gender bias has been widely observed in speech perception tasks, influenced by the fundamental voicing differences between genders. This study reveals a gender bias in the perception of Alzheimer's Disease (AD) speech. In a perception experiment involving 16 Chinese listeners evaluating both Chinese and Greek speech, we identified that male speech was more frequently identified as AD, with this bias being particularly pronounced in Chinese speech. Acoustic analysis showed that shimmer values in…

Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
Soyeon Choi, Kangwook Lee, Oliver Sng, Joshua M. Ackerman
https://arxiv.org/abs/2506.13783

Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to th…

new book about medicine history, "droplets" theory etc
"Science writer Carl Zimmer’s latest book is a brilliant history of medicine that takes us from Louis Pasteur’s germ theory of the 19th century to present day. Along the way, it offers an anthropological study of medical culture — a culture capable of ignoring science when it wants to. ...
"If COVID-19 spread in droplets, then it was worthwhile to keep people two metres apart, to put up plexiglas barriers around checkout stands, and make supermarket aisles one-way. Sanitizing countertops could break the chain of infection.
"But if COVID-19 was airborne, all those measures were pointless."
Bit of an exaggeration in that part of the article. The 2m distance does put you outside the densest clouds of exhaled breath, and sanitising countertops helps against other diseases. But yeah. A lot of effort wrongly expended due to the prevailing myth.
#CovidIsAirborne #books #history

Det här är en studie där man mätt omega-6 nivåer i blodet och sen tittat på korrelationer. Men det här är även vad interventionsstudier och tidigare bättre observationsstudier också har visat.
Seed oils: Omega-6 fatty acids may lower diabetes, heart disease risk https:…

Seed oils: Omega-6 fatty acids may lower diabetes, heart disease risk
Following a diet higher in the omega-6 fatty acid linoleic acid may help support cardiometabolic health, and lower a person's risk for type 2 diabetes and heart disease, a new study indicates.

PORTLAND, Ore. — Health officials in Hood River County confirmed Monday that two local deaths have been linked to a rare brain disease within the last eight months.
During that period, the county said, there have been one confirmed and two probable cases of
Creutzfeldt-Jakob Disease, or CJD, a prion disorder somewhat similar to the more commonly known "mad cow disease."
So far, two of those cases have resulted in death

🇺🇦 Auf radioeins läuft...
Ben Lee:
🎵 Catch My Disease
#NowPlaying #BenLee
https://benleenwr.bandcamp.com/track/catch-my-disease
https://open.spotify.com/track/18qiraxE3JznGhJyx5J28Y

#MinorThreat Bassist Gives Update Amid Stage 4 #Kidney Transplant Battle
https://www.ya…

Legendary Punk Bassist Gives Update Amid Stage 4 Kidney Disease Battle
After falling out of step, you can help him back on his feet.

In some UK woodlands, every young tree has died. What’s going wrong? https://www.theguardian.com/environment/2025/jul/10/britain-ancient-woodlands-failing-regenerate-forests-climate-drought-heat-dis…

Classification of Histopathology Slides with Persistence Homology Convolutions
Shrunal Pothagoni, Benjamin Schweinhart
https://arxiv.org/abs/2507.14378 htt…

Classification of Histopathology Slides with Persistence Homology Convolutions
Convolutional neural networks (CNNs) are a standard tool for computer vision tasks such as image classification. However, typical model architectures may result in the loss of topological information. In specific domains such as histopathology, topology is an important descriptor that can be used to distinguish between disease-indicating tissue by analyzing the shape characteristics of cells. Current literature suggests that reintroducing topological information using persistent homology can im…

On the Testing of complete causal mediation and its applications
Yichin Tsai, Wan-Tzu Chang, Jia Jyun Sie, Cathy SJ Fann, Iebin Lian
https://arxiv.org/abs/2507.14246

On the Testing of complete causal mediation and its applications
The Complete Mediation Test (CMT) serves as a specialized approach of mediation analysis to assess whether an independent variable A, influences an outcome variable Y exclusively through a mediator M, without any direct effect. An application of CMT lies in Mendelian Randomization (MR) studies, where it can be used to investigate non-pleiotropy, that is, to test whether genetic variants impact a disease outcome solely through their effect on a target exposure variable. Traditionally, CMT has re…

Wer viel #Pflanzen auf dem Teller hat, senkt sein Risiko für Typ-2-Diabetes, Herz-Kreislauf-Erkrankungen, #Übergewicht und das metabolische Syndrom.
Laut Auswertung von 32 #Langzeitstudien

Plant-Based Diets and Their Role in Preventive Medicine: A Systematic Review of Evidence-Based Insights for Reducing Disease Risk
Plant-based diets have gained increasing attention for their potential role in preventive medicine, particularly in reducing the risk of chronic diseases such as type 2 diabetes, cardiovascular disease, obesity, and metabolic syndrome. This systematic review synthesizes evidence from 32 longitudinal studies to evaluate the impact of plant-based diets on disease prevention and health outcomes. The review identifies consistent patterns, including improved metabolic health, weight management, card…

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm
https://arxiv.org/abs/2506.12323

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset. To address this, diffusion models (DMs) have been considered as a promising avenue for synthetic image generation and augmentation. However, they frequently produce medically inaccurate images, deteriorating the model performance. Expert domain knowledge is critical for synthesizing images that correctly encode clin…

"Restoration, protection aim to save Belize’s coral reef from extreme heat and disease"
#Belize #Environment #CoralReef

Restoration, protection aim to save Belize’s coral reef from extreme heat and disease
PLACENCIA, Belize — After Hurricane Iris decimated the coral reef at Laughing Bird Caye National Park in 2001, many wrote off the UNESCO World Heritage site as a coral graveyard. But a small group from the coastal village of Placencia, Belize, about 18 kilometers (11 miles) from the island, saw hope. In 2006, the park […]

Simulating nationwide coupled disease and fear spread in an agent-based model
Joy Kitson, Prescott C. Alexander, Joseph Tuccillo, Abhinav Bhatele, Sara Y. Del Valle, Timothy C. Germann
https://arxiv.org/abs/2506.07842

Simulating nationwide coupled disease and fear spread in an agent-based model
Human behavior and disease spread evolve together over the course of any disease outbreak. We present a dynamic model of that relationship based on the coupled spread of a disease and fear of that disease, and implement this model in a simulator, EpiCast, that simulates disease spread between individuals in a realistic synthetic population. In our model, fear of the disease spreads through both in-person contact and broadcast media, driving people to adopt behaviors that reduce disease spread. …

Visiting my dad at the old folks home. Dementia is truly a terrible disease. So hard to see a person disappear bit by bit until nothing is left.

Improving Outbreak Forecasts Through Model Augmentation
Graham C. Gibson, Spencer J. Fox, Emily Javan, Susan E. Ptak, Oluwasegun M. Ibrahim, Michael Lachmann, Lauren Ancel Meyers
https://arxiv.org/abs/2506.16410

Improving Outbreak Forecasts Through Model Augmentation
Accurate forecasts of disease outbreaks are critical for effective public health responses, management of healthcare surge capacity, and communication of public risk. There are a growing number of powerful forecasting methods that fall into two broad categories -- empirical models that extrapolate from historical data, and mechanistic models based on fixed epidemiological assumptions. However, these methods often underperform precisely when reliable predictions are most urgently needed -- durin…

Nearly 800,000 mpox vaccine doses
the U.S. government had promised to donate to African countries experiencing an outbreak of the rash-causing disease
🆘 cannot be shipped because they’re expiring in less than six months,
according to the Africa Centres for Disease Control and Prevention.
“For a vaccine to be shipped to a country, we need a minimum of six months before expiration to ensure that the vaccine can arrive in good condition
and also allow the country t…

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG
Sungwoo Ahn, Evie A. Malaia, Leonid L Rubchinsky
https://arxiv.org/abs/2507.08728

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG
Objective While Alzheimer's disease (AD) and frontotemporal dementia (FTD) show some common memory deficits, these two disorders show partially overlapping complex spatiotemporal patterns of neural dynamics. The objective of this study is to characterize these patterns to better understand the general principles of neurodynamics in these conditions. Methods A comprehensive array of methods to study brain rhythms and functional brain networks are used in the study, from spectral power measures…

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang, Violeta J. Rodriguez, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.15047

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot …

Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
Mbithe Nzomo, Deshendran Moodley
https://arxiv.org/abs/2506.13920

Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specific healthcare settings and patient populations to achieve practical clinical use. Additionally, risk prediction systems must handle uncertainty from incomplete data and non-deterministic health outcomes while remaining explainable. These challenges can be alleviated by the integration of knowledge graphs (KGs) and Bay…

Inhibiting Alzheimer's Disease by Targeting Aggregation of Beta-Amyloid
Ananya Joshi, George Khoury, Christodoulas Floudas
https://arxiv.org/abs/2506.14052

Inhibiting Alzheimer's Disease by Targeting Aggregation of Beta-Amyloid
Alzheimer's disease is characterized by dangerous amyloid plaques formed by deposits of the protein Beta-Amyloid aggregates in the brain. The specific amino acid sequence that is responsible for the aggregates of Beta-Amyloid is lys-leu-val-phe-phe (KLVFF). KLVFF aggregation inhibitors, which we design in this paper, prevent KLVFF from binding with itself to form oligomers or fibrils (and eventually plaques) that cause neuronal death. Our binder-blocker peptides are designed such that, on one s…

“Sassy Outwater-Wright: Accessibility Champion and More, Dies at 42” by @…:
https://www.lflegal.com/2025/07/sassy-outwater/
The “and m…

Sassy Outwater-Wright: Accessibility Champion and More, Dies at 42
Sassy Outwater-Wright died on July 12, 2025 of cancer. She was the self-described Tumor Killer Girl in her fierce, honest, and often heart wrenching writing about the disease that ultimately took her life. As she said during a 2022 public radio interview “My superhero name is Tumor Killer Girl.

Low-intensity brain stimulation may restore neuron health in Alzheimer's disease https://spie.org/news/low-intensity-brain-stimulation-may-restore-neuron-health-in-alzheimers-disease

Disruption of parkinsonian brain oscillations
C\'edric Join, Jakub Or{\l}owski, Antoine Chaillet, Madeleine Lowery, Hugues Mounier, Michel Fliess
https://arxiv.org/abs/2506.15384

Disruption of parkinsonian brain oscillations
Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson's disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13--30 Hz) activity as a feedback signal, offers the potential to adapt DB…

Coolest part of this: it somehow escaped Dutch Elm Disease and they are gathering its seeds to propagate what *may* be naturally resistant genetics. https://flipboard.com/@cbcnews/top-stories-01r3k2ttz/-/a-CoiPb3nXRaK1lwOlyr7ZwA:a%3…

One of Toronto's oldest elm trees is on its last legs. Researchers are racing to re-grow copies
One of the largest old elm trees in Toronto — possibly in all of southern Ontario — is on its last legs. But as the city finalizes plans to take down …

ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
Wenlong Hou, Gangqian Yang, Ye Du, Yeung Lau, Lihao Liu, Junjun He, Ling Long, Shujun Wang
https://arxiv.org/abs/2506.11150

ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Early and precise diagnosis of AD is crucial for timely intervention and treatment planning to alleviate the progressive neurodegeneration. However, most existing methods rely on single-modality data, which contrasts with the multifaceted approach used by medical experts. While some deep learning approaches process multi-modal data, they are limited to specific tasks with a small set of input modalities and ca…

Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI
Hagar Shmuely (School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel), Michal Rivlin (School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel), Or Perlman (School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel)

Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI
Traditional approaches for molecular imaging of Parkinson's disease (PD) in vivo require radioactive isotopes, lengthy scan times, or deliver only low spatial resolution. Recent advances in saturation transfer-based PD magnetic resonance imaging (MRI) have provided biochemical insights, although the image contrast is semi-quantitative and nonspecific. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of gluta…

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Terry Yi Zhong, Cristian Tejedor-Garcia, Martha Larson, Bastiaan R. Bloem
https://arxiv.org/abs/2507.03594

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Parkinson's Disease (PD) affects over 10 million people globally, with speech impairments often preceding motor symptoms by years, making speech a valuable modality for early, non-invasive detection. While recent deep-learning models achieve high accuracy, they typically lack the explainability required for clinical use. To address this, we propose RECA-PD, a novel, robust, and explainable cross-attention architecture that combines interpretable speech features with self-supervised representati…

RFK Jr.’s chronic disease agency would eliminate or reduce funding for some prevention programs | STAT
https://www.statnews.com/2025/06/07/rfk-jr-administration-for-healthy-america-chronic-disease-budget-documents/?utm_campaign=rss

A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming
Waseem Akram, Muhayy Ud Din, Lyes Saad Soud, Irfan Hussain
https://arxiv.org/abs/2507.11974

A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming
Generative Artificial Intelligence (GAI) has rapidly emerged as a transformative force in aquaculture, enabling intelligent synthesis of multimodal data, including text, images, audio, and simulation outputs for smarter, more adaptive decision-making. As the aquaculture industry shifts toward data-driven, automation and digital integration operations under the Aquaculture 4.0 paradigm, GAI models offer novel opportunities across environmental monitoring, robotics, disease diagnostics, infrastru…

Asset control and power in America is centered among old people with lots of assets.
And they associate protecting the stock market with protecting America.
The “number go up” rule is not a story of greed or economics,
it’s a story of how we make decisions as a society.
Take the pandemic.
Some of you might remember that right before Covid hit in force, I was freaking out. I pay attention to China, and it wasn’t hard to imagine that a contagious disease would…

The Number Go Up Rule: Why America Refuses to Fix Anything
In the Booming Twenties, all decision-making is about protecting the value of financial assets held by older people. Therefore, the number must go up. And nothing else matters.

U.S. sees most single-year measles cases since it declared disease eliminated (Helen Branswell/STAT)
https://www.statnews.com/2025/07/09/measles-america-breaks-33-year-record-anti-vaccine-sentiment-key-factor-behind-outbreaks/
http://www.memeorandum.com/250709/p117#a250709p117

No part of this message may reproduce, store itself in a retrieval system,
or transmit disease, in any form, without the permissiveness of the author.
-- Chris Shaw

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Comte Valentin, Gemma Piella, Mario Ceresa, Miguel A. Gonzalez Ballester
https://arxiv.org/abs/2507.05783

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Cardiac diseases are among the leading causes of morbidity and mortality worldwide, which requires accurate and timely diagnostic strategies. In this study, we introduce an innovative approach that combines deep learning image registration with physics-informed regularization to predict the biomechanical properties of moving cardiac tissues and extract features for disease classification. We utilize the energy strain formulation of Neo-Hookean material to model cardiac tissue deformations, opti…

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Yifei Tang, Hongjie Jiang, Changhong Jing, Hieu Pham, Shuqiang Wang
https://arxiv.org/abs/2506.11671

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Functional brain network analysis has become an indispensable tool for brain disease analysis. It is profoundly impacted by deep learning methods, which can characterize complex connections between ROIs. However, the research on foundation models of brain network is limited and constrained to a single dimension, which restricts their extensive application in neuroscience. In this study, we propose a fine-tuned brain network model for brain disease diagnosis. It expands brain region representati…

Modelling the interaction between ethnicity and infectious disease transmission dynamics
Vincent X. Lomas, Tim Chambers, Michael J. Plank
https://arxiv.org/abs/2507.10925

Modelling the interaction between ethnicity and infectious disease transmission dynamics
During the COVID-19 pandemic, Aotearoa followed an elimination strategy followed by a mitigation strategy, which saw high success and kept health impact low. However, there were inequities in health outcomes, notably that Māori and Pacific Peoples had lower vaccine coverage and experienced higher age-standardised rates of hospitalisation and death. Models provide predictions of disease spread and the burden of disease, which can effectively inform policy, but are often not good at including in…

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Eun Hye Lee, Taeho Jo
https://arxiv.org/abs/2506.00673

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Alzheimer's disease (AD) dementia is the most common form of dementia. With the emergence of disease-modifying therapies, predicting disease risk before symptom onset has become critical. We introduce DuAL-Net, a hybrid deep learning framework for AD dementia prediction using whole genome sequencing (WGS) data. DuAL-Net integrates two components: local probability modeling, which segments the genome into non-overlapping windows, and global annotation-based modeling, which annotates SNPs and reo…

You may be a redneck if you got a venereal disease from your grandma.
- some damn Jeff Foxworthy thing

Improving Drug Identification in Overdose Death Surveillance using Large Language Models
Arthur J. Funnell, Panayiotis Petousis, Fabrice Harel-Canada, Ruby Romero, Alex A. T. Bui, Adam Koncsol, Hritika Chaturvedi, Chelsea Shover, David Goodman-Meza
https://arxiv.org/abs/2507.12679

Improving Drug Identification in Overdose Death Surveillance using Large Language Models
The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical overdose data are often buried in free-text coroner reports, leading to delays and information loss when coded into ICD (International Classification of Disease)-10 classifications. Natural language processing (NLP) models may automate and enhance overdose surveillance, but prior applications have been limited. A dataset of 35,433 death records fr…

I’ve been researching this story for years and Elon just… tweeted it out.

Mechanism of Alzheimer's disease treatment by sound and light stimulation https://www.sciencedirect.com/science/article/pii/S2211383525001704 "The combination of sound and light stimulation can serve as new approaches for treating neurodegenerative diseases.…

Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Keran Wang, Dong Whi Yoo, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.14196

Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregivers
Alzheimer's Disease and Related Dementias (AD/ADRD) are progressive neurodegenerative conditions that impair memory, thought processes, and functioning. Family caregivers of individuals with AD/ADRD face significant mental health challenges due to long-term caregiving responsibilities. Yet, current support systems often overlook the evolving nature of their mental wellbeing needs. Our study examines caregivers' mental wellbeing concerns, focusing on the practices they adopt to manage the burden…

A Compartmental Model for Epidemiology with Human Behavior and Stochastic Effects
Christian Parkinson, Weinan Wang
https://arxiv.org/abs/2507.01046 https:/…

A Compartmental Model for Epidemiology with Human Behavior and Stochastic Effects
We propose a compartmental model for epidemiology wherein the population is split into groups with either comply or refuse to comply with protocols designed to slow the spread of a disease. Parallel to the disease spread, we assume that noncompliance with protocols spreads as a social contagion. We begin by deriving the reproductive ratio for a deterministic version of the model, and use this to fully characterize the local stability of disease free equilibrium points. We then append the determ…

Critic as Censor: How the Humanities Sacrificed Art at the Altar of Theory
My beloved friend, mentor, and Columbia University Professor Howard Stein, was fond of saying, “The Enemy of the Arts is the Humanities.” That insight, and advice, has stuck with me over the past 35 years. Now, that phrase is not the glib provocation it may seem. It is a precise diagnosis of an institutional disease, a declaration of war against a century of academic drift that has created a…

Critic as Censor: How the Humanities Sacrificed Art at the Altar of Theory | David Boles, Blogs
My beloved friend, mentor, and Columbia University Professor Howard Stein, was fond of saying, “The Enemy of the Arts is the Humanities.” That insight, and advice, has stuck with me over the past 3…

Fighting poultry disease with mRNA: Researchers pioneer nanoparticle approach
https://phys.org/news/2025-06-poultry-disease-mrna-nanoparticle-approach.html

An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases
Saahil Chadha, David Weiss, Anastasia Janas, Divya Ramakrishnan, Thomas Hager, Klara Osenberg, Klara Willms, Joshua Zhu, Veronica Chiang, Spyridon Bakas, Nazanin Maleki, Durga V. Sritharan, Sven Schoenherr, Malte Westerhoff, Matthew Zawalich, Melissa Davis, Ajay Malhotra, Khaled Bousabarah, Cornelius Deuschl, MingDe Lin, Sanjay Aneja, Mariam S. Aboian

An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases
Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation…

InSight: AI Mobile Screening Tool for Multiple Eye Disease Detection using Multimodal Fusion
Ananya Raghu, Anisha Raghu, Alice S. Tang, Yannis M. Paulus, Tyson N. Kim, Tomiko T. Oskotsky
https://arxiv.org/abs/2507.12669

InSight: AI Mobile Screening Tool for Multiple Eye Disease Detection using Multimodal Fusion
Background/Objectives: Age-related macular degeneration, glaucoma, diabetic retinopathy (DR), diabetic macular edema, and pathological myopia affect hundreds of millions of people worldwide. Early screening for these diseases is essential, yet access to medical care remains limited in low- and middle-income countries as well as in resource-limited settings. We develop InSight, an AI-based app that combines patient metadata with fundus images for accurate diagnosis of five common eye diseases to…

Phospho-Tau 217, Plasma

USEFUL FOR
Evaluation of individuals, aged 50 years and older, presenting with cognitive impairment who are being assessed for Alzheimer disease and other causes of cognitive decline
 
This test is not intended as a screening test for Alzheimer disease in asymptomatic individuals.
https:/…

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Ming Wang, Zhaoyang Duan, Dong Xue, Fangzhou Liu, Zhongheng Zhang
https://arxiv.org/abs/2507.08050 https://arxiv.org/pdf/2507.08050 https://arxiv.org/html/2507.08050
arXiv:2507.08050v1 Announce Type: new
Abstract: The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions.
toXiv_bot_toot

Recent trends in socio-epidemic modelling: behaviours and their determinants
Daniele Proverbio, Riccardo Tessarin, Giulia Giordano
https://arxiv.org/abs/2506.13837

Recent trends in socio-epidemic modelling: behaviours and their determinants
The spreading dynamics of infectious diseases is influenced by individual behaviours, which are in turn affected by the level of awareness about the epidemic. Modelling the co-evolution of disease transmission and behavioural changes within a population enables better understanding, prediction and control of epidemics. Here, our primary goal is to provide an overview of the most popular modelling approaches, ranging from compartmental mean-field to agent-based models, with a particular focus on…

Inhaled tuberculosis vaccine effective against world's deadliest infectious disease - ABC News
https://www.abc.net.au/news/2025-07-16/tuberculosis-vaccination-via-lungs-may-be-more-effective/105535198

Measles Cases Hit Record High, 25 Years After U.S. Eliminated the Disease (New York Times)
https://www.nytimes.com/2025/07/09/well/us-measles-record-outbreaks.html
http://www.memeorandum.com/250709/p72#a250709p72

Robust Incomplete-Modality Alignment for Ophthalmic Disease Grading and Diagnosis via Labeled Optimal Transport
Qinkai Yu, Jianyang Xie, Yitian Zhao, Cheng Chen, Lijun Zhang, Liming Chen, Jun Cheng, Lu Liu, Yalin Zheng, Yanda Meng
https://arxiv.org/abs/2507.04999

Robust Incomplete-Modality Alignment for Ophthalmic Disease Grading and Diagnosis via Labeled Optimal Transport
Multimodal ophthalmic imaging-based diagnosis integrates color fundus image with optical coherence tomography (OCT) to provide a comprehensive view of ocular pathologies. However, the uneven global distribution of healthcare resources often results in real-world clinical scenarios encountering incomplete multimodal data, which significantly compromises diagnostic accuracy. Existing commonly used pipelines, such as modality imputation and distillation methods, face notable limitations: 1)Imputat…

A DNA Methylation Classification Model Predicts Organ and Disease Site
Keng-Jung Lee, Dharanya Sampath, Konstantinos Mavrommatis
https://arxiv.org/abs/2506.00146

A DNA Methylation Classification Model Predicts Organ and Disease Site
Cell-free DNA (cfDNA) analysis is a powerful, minimally invasive tool for monitoring disease progression, treatment response, and early detection. A major challenge, however, is accurately determining the tissue of origin, especially in complex or heterogeneous disease contexts. To address this, we developed a machine learning framework that leverages tissue-specific DNA methylation signatures to classify both tissue and disease origin from cfDNA data. Our model integrates methylation datasets …

Infectious Disease Transmission In A Modified SEIRS model
Kasturi Banerjee, Subhankar Ray, Jayalakshmi Shamanna
https://arxiv.org/abs/2507.08385 https://…

Infectious Disease Transmission In A Modified SEIRS model
Compartmental models like the Susceptible-Infected-Recovered (SIR)\cite{Kermack1927} and its extensions such as the Susceptible-Exposed-Infected-Recovered (SEIRS)\cite{Ottar2020,Ignazio2021,Grimm2021,Paoluzzi2021} are commonly used to model the spread of infectious diseases. We propose here, a modified SEIRS, namely, an SEIRSD model which comprises of (i) a reverse transmission from exposed to susceptible compartment to account for the probabilistic character of disease transmission seen in nat…

Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Pinar Bisgin, Tom Strube, Niklas Tschorn, Michael Pantf\"order, Maximilian Fecke, Ingrid Ljungvall, Jens H\"aggstr\"om, Gerhard Wess, Christoph Schummer, Sven Meister, Falk M. Howar
https://arxiv.org/abs/2507.05950…

Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Noisy labels pose significant challenges for AI model training in veterinary medicine. This study examines expert assessment ambiguity in canine auscultation data, highlights the negative impact of label noise on classification performance, and introduces methods for label noise reduction. To evaluate whether label noise can be minimized by incorporating multiple expert opinions, a dataset of 140 heart sound recordings (HSR) was annotated regarding the intensity of holosystolic heart murmurs ca…

Overcoming Standardization: Revealing Hidden Age Patterns of Suicide with Spatiotemporal Models
J. Mart\'in-Pozuelo (Department of Statistics and Operations Research. University of Valencia), A. L\'opez-Qu\'ilez (Department of Statistics and Operations Research. University of Valencia), X. Barber (Joint Research Unit UMH-FISABIO), M. Marco (Department of Social Psychology, University of Valencia)

Overcoming Standardization: Revealing Hidden Age Patterns of Suicide with Spatiotemporal Models
Indirect standardization is widely used in disease mapping to control for confounding, but relies on restrictive assumptions that may bias estimates if violated. Using data on suicide-related emergency calls, this study highlights such limitations and proposes age-structured hierarchical Bayesian models as an alternative. These models incorporate space-time, space-age, and time-age interactions, allowing for more accurate estimation without strong assumptions. The results show improved model fi…

U.S. health officials have changed their advice to international travelers about measles,
saying that Americans should be vaccinated against the virus
-- no matter where they’re going.
U.S. residents are recommended to get measles-mumps-rubella shots, anyway.
But the Centers for Disease Control and Prevention previously emphasized the importance of vaccination for travelers going to countries with outbreaks.

All international travelers should get measles vaccinations, CDC says
U.S. health officials have changed their advice to international travelers about measles, saying Americans should be vaccinated against the disease no matter where they travel in the world. U.S. residents are recommended to get measles-mumps-rubella shots, anyway. But the Centers for Disease Control and Prevention previously emphasized the importance of vaccination for travelers going to countries with outbreaks. Last week, the CDC updated its guidance to call for vaccinations for travelers goi…

An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Weike Zhao, Chaoyi Wu, Yanjie Fan, Xiaoman Zhang, Pengcheng Qiu, Yuze Sun, Xiao Zhou, Yanfeng Wang, Ya Zhang, Yongguo Yu, Kun Sun, Weidi Xie
https://arxiv.org/abs/2506.20430

An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Rare diseases collectively affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains a pervasive challenge. This is largely due to their clinical heterogeneity, low individual prevalence, and the limited familiarity most clinicians have with rare conditions. Here, we introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM), capable of processing heterogeneous clinical inputs. The system generates ranked diagnostic…

A wireless cortical surface implant for diagnosing and alleviating Parkinson's disease symptoms in freely moving animals https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/adhm.202570101

Two unvaccinated babies die of whooping cough in KY as disease rise is predicted to continue (Alternet.org)
https://www.alternet.org/whooping-cough/
http://www.memeorandum.com/250607/p38#a250607p38

The United States has reached its highest annual measles case tally in 33 years,
hitting at least 1,277 confirmed cases across 38 states and the District of Columbia.

The milestone marks a public health reversal in defeating a highly contagious, vaccine-preventable disease
as the anti-vaccine movement gains strength.

U.S. measles cases reach 33-year high as outbreaks spread
Johns Hopkins University data reflects the public health reversal in defeating the vaccine-preventable disease since measles was officially eliminated from the U.S. in 2000.

Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM)
Yang Zhou, Chrystie Wan Ning Quek, Jun Zhou, Yan Wang, Yang Bai, Yuhe Ke, Jie Yao, Laura Gutierrez, Zhen Ling Teo, Darren Shu Jeng Ting, Brian T. Soetikno, Christopher S. Nielsen, Tobias Elze, Zengxiang Li, Linh Le Dinh, Lionel Tim-Ee Cheng, Tran Nguyen Tuan Anh, Chee Leong Cheng, Tien Yin Wong, Nan Liu, Iain Beehuat Tan, Tony Kiat Hon Lim, Rick Siow Mong Goh, Yong Liu, Daniel Shu Wei Ting

Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM)
Current artificial intelligence models for medical imaging are predominantly single modality and single disease. Attempts to create multimodal and multi-disease models have resulted in inconsistent clinical accuracy. Furthermore, training these models typically requires large, labour-intensive, well-labelled datasets. We developed MerMED-FM, a state-of-the-art multimodal, multi-specialty foundation model trained using self-supervised learning and a memory module. MerMED-FM was trained on 3.3 mi…

Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Samhita Pal, Dhrubajyoti Ghosh, Shu Yang
https://arxiv.org/abs/2507.00173

Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebrospinal fluid (CSF) biomarkers, such as alpha-synuclein seed amplification assays (alphaSyn-SAA), offer diagnostic potential, their clinical utility is limited by invasiveness and incomplete specificity. Plasma biomarkers provide a minimally invasive alternative, but their mechanistic role in PD remains unclear. A major…

Palantir's Collection of Disease Data at C.D.C. Stirs Privacy Concerns (Apoorva Mandavilli/New York Times)
https://www.nytimes.com/2025/06/06/health/cdc-data-privacy-palantir.html?unlocked_article_code=1.M08.jzJB.cljQA69ueENV&smid=url-share
http://www.memeorandum.com/250606/p108#a250606p108

Studies done with this stuff " have lead to major breakthroughs, including links between cigarettes and cardiovascular disease and alcohol consumption and breast cancer. The research also uncovered the dangers of trans fats, which the U.S. has now largely restricted."
Trump cuts threaten ‘irreplaceable’ Harvard stockpile of human feces, urine - masslive.com
https://www.masslive.com/news/2025/06/trump-cuts-threaten-irreplaceable-harvard-stockpile-of-human-feces-urine.html

Unleashing Vision Foundation Models for Coronary Artery Segmentation: Parallel ViT-CNN Encoding and Variational Fusion
Caixia Dong, Duwei Dai, Xinyi Han, Fan Liu, Xu Yang, Zongfang Li, Songhua Xu
https://arxiv.org/abs/2507.12938

Unleashing Vision Foundation Models for Coronary Artery Segmentation: Parallel ViT-CNN Encoding and Variational Fusion
Accurate coronary artery segmentation is critical for computeraided diagnosis of coronary artery disease (CAD), yet it remains challenging due to the small size, complex morphology, and low contrast with surrounding tissues. To address these challenges, we propose a novel segmentation framework that leverages the power of vision foundation models (VFMs) through a parallel encoding architecture. Specifically, a vision transformer (ViT) encoder within the VFM captures global structural features, …

Three of the new advisers appointed by Health Secretary Robert F. Kennedy Jr. to guide the government on immunization policy
took part in lawsuits casting doubt on the safety or efficacy of vaccines,
public records show.
In dismissing all 17 members of an influential Centers for Disease Control and Prevention advisory panel on Monday,
Mr. Kennedy cited what he said was a history of conflicts of interest that he claimed made those experts a “rubber stamp” on approving …

Kennedy’s New Vaccine Advisers Helped Lawyers Raise Doubts About Their Safety
Three of the health secretary’s picks to replace fired members of an influential panel that sets U.S. vaccine policies have filed statements in court flagging concerns about vaccines.

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Christian Tinauer, Maximilian Sackl, Stefan Ropele, Christian Langkammer
https://arxiv.org/abs/2506.03890

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, raising concerns about interpretability. Previous studies suggest biases in model decisions, necessitating further analysis. This study uses Layer-wise Relevance Propagation (LRP) and spectral clustering to explore classifier decision strategies across preprocessing and training configurations using R2* maps. We trained a 3D convolutional neural net…

The proportion of the U.S. population who are Latino or Asian American is expected to rise to 33% by 2050,
and will number over 135 million people.
Understanding rates of Age Related Macular Degeneration, #AMD, in these groups is imperative,
so that clinicians can have better insight into who is most at risk for developing this disease and health policy-makers can use this information to he…

Racial Differences in Age-Related Macular Degeneration Rates in the United States: A Longitudinal Analysis of a Managed Care Network

Measles cases surge to record high since disease was declared eliminated in the US
https://www.cnn.com/2025/07/05/health/measles-cases-us-record-since-elimination?cid=ios_app

This https://arxiv.org/abs/2504.19203 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction
Knee osteoarthritis (KOA) is a common joint disease that causes pain and mobility issues. While MRI-based deep learning models have demonstrated superior performance in predicting total knee replacement (TKR) and disease progression, their generalizability remains challenging, particularly when applied to imaging data from different sources. In this study, we have shown that replacing batch normalization with instance normalization, using data augmentation, and applying contrastive loss improve…

The Centers for Disease Control and Prevention (CDC) has
ended its emergency response
to the H5N1 avian flu.
https://thehill.com/policy/healthcare/5389863-cdc-ends-bird-flu-emergency/

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Yifan Gao, Shaohao Rui, Haoyang Su, Jinyi Xiang, Lianming Wu, Xiaosong Wang
https://arxiv.org/abs/2507.11886

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Accurate segmentation of myocardial lesions from multi-sequence cardiac magnetic resonance imaging is essential for cardiac disease diagnosis and treatment planning. However, achieving optimal feature correspondence is challenging due to intensity variations across modalities and spatial misalignment caused by inconsistent slice acquisition protocols. We propose CAA-Seg, a composite alignment-aware framework that addresses these challenges through a two-stage approach. First, we introduce a sel…

Advancing Lung Disease Diagnosis in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen
https://arxiv.org/abs/2507.00993

Advancing Lung Disease Diagnosis in 3D CT Scans
To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove non-lung regions, which helps the model focus on lesion-related areas and reduces computational cost. We adopt ResNeSt50 as a strong feature extractor, and use a weighted cross-entropy loss to mitigate class imbalance, especially for the underrepresented squamous cell carcinoma category. Our model achieves a Macr…

California has already reported more measles cases this year than in all of 2024,
a worrisome development that comes as the nation is suffering its largest outbreak of the super-infectious disease in decades.
The extent of the national outbreak has rocketed measles from a back-of-mind issue
— one rarely, if ever, encountered by a whole generation of Americans
— to a pressing public health concern.
“Measles is completely preventable,”
Dr. Elizabeth Hudso…

Measles cases are on the rise in California
California has already reported more measles cases this year than in all of 2024, a worrisome development that comes as the nation is suffering its largest outbreak of the super-infectious disease in decades.

A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs
Niccol\`o McConnell, Pardeep Vasudev, Daisuke Yamada, Daryl Cheng, Mehran Azimbagirad, John McCabe, Shahab Aslani, Ahmed H. Shahin, Yukun Zhou, The SUMMIT Consortium, Andre Altmann, Yipeng Hu, Paul Taylor, Sam M. Janes, Daniel C. Alexander, Joseph Jacob

A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs
Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility …

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi, Patricia Brassard, Hakim Khellaf, Vincent Quoc-Huy Trinh, Jennifer Zhang, Lina Chen, Corwyn Rowsell, Sonal Varma, Kostas Plataniotis, Mahdi S. Hosseini
https://ar…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to mul…

Lakshmi Panagiotakopoulos,
a pediatric infectious disease expert, was a co-leader of a working group on COVID-19 vaccines who advised experts on the CDC's Advisory Committee on Immunization Practices (ACIP).
She informed her ACIP colleagues of her resignation in an email on Tuesday.
"My career in public health and vaccinology started with a deep-seated desire to help the most vulnerable members of our population,
and that is not something I am able to continue…

Top CDC COVID vaccine expert resigns after RFK Jr. unilaterally restricts access
The expert said she could no longer help vulnerable people in her CDC role.

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Ajay Mittal, Raghav Mehta, Omar Todd, Philipp Seeb\"ock, Georg Langs, Ben Glocker
https://arxiv.org/abs/2506.22226

Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challe…

Measles is on the rise in Kentucky.
Health officials in the state confirmed that the disease has spread to Fayette County,
marking the seventh case of the highly contagious illness in Kentucky since the beginning of the year.
Previous cases had been reported in Woodfood County.
There are five active measles cases in the Bluegrass State, four of which are connected to the current outbreak,
while another unrelated measles case was reported in Todd County last week,…

New State Reports Measles Outbreak as RFK Jr. Guts Vaccine Access
Measles have reached a record peak, but Robert F. Kennedy Jr. doesn’t want to vaccinate people.

RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis
Songxiao Yang, Haolin Wang, Yao Fu, Ye Tian, Tamotsu Kamishima, Masayuki Ikebe, Yafei Ou, Masatoshi Okutomi
https://arxiv.org/abs/2507.05193

RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis
Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease monitoring. In clinical settings, conventional radiography (CR) is widely used for the screening and evaluation of RA due to its low cost and accessibility. The wrist is a critical region for the diagnosis of RA. However, CAD research in this area remains limited, primarily due to the challenges in acquiring high-quality instance-level annotations. (i) The w…

Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Javier Salazar Cavazos, Scott Peltier
https://arxiv.org/abs/2506.02060

Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D…

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
Joris Wuts, Jakub Ceranka, Nicolas Michoux, Fr\'ed\'eric Lecouvet, Jef Vandemeulebroucke
https://arxiv.org/abs/2506.19590

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obtaining reliable segmentations requires large, well-annotated datasets capturing lesion variability. Generating such datasets requires substantial time and expertise, and is prone to error. While self-supervised learning (SSL) can leverage large unlabeled datasets, learned generi…

Classification based deep learning models for lung cancer and disease using medical images
Ahmad Chaddad, Jihao Peng, Yihang Wu
https://arxiv.org/abs/2507.01279

Classification based deep learning models for lung cancer and disease using medical images
The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate …