AU - James Fong AU - Hannah K. Doyle AU - Congli Wang AU - Alexandra E. Boehm AU - Sofie R. Herbeck AU - Vimal Prabhu Pandiyan AU - Brian P. Schmidt AU - Pavan Tiruveedhula AU - John E. Vanston AU - William S. Tuten AU - Ramkumar Sabesan AU - Austin Roorda AU - Ren Ng TI - Novel color via stimulation of individual photoreceptors at population scale PT - Journal Article DP - 2025 TA - Science Advances PG - eadu1052 VI - 11 IP - 16 AID - 10.1126/sciadv.adu1052 [doi] PMID - 40249825 4099 - https://www.science.org/doi/abs/10.1126/sciadv.adu1052 4100 - https://www.science.org/doi/full/10.1126/sciadv.adu1052 SO - Science Advances 2025-04-18 11(16): eadu1052 AB - We introduce a principle, Oz, for displaying color imagery: directly controlling the human eye’s photoreceptor activity via cell-by-cell light delivery. Theoretically, novel colors are possible through bypassing the constraints set by the cone spectral sensitivities and activating M cone cells exclusively. In practice, we confirm a partial expansion of colorspace toward that theoretical ideal. Attempting to activate M cones exclusively is shown to elicit a color beyond the natural human gamut, formally measured with color matching by human subjects. They describe the color as blue-green of unprecedented saturation. Further experiments show that subjects perceive Oz colors in image and video form. The prototype targets laser microdoses to thousands of spectrally classified cones under fixational eye motion. These results are proof-of-principle for programmable control over individual photoreceptors at population scale. Image display by cell-by-cell retina stimulation, enabling colors impossible to see under natural viewing.

TROPICAL SYNAPSES
Reflections on topics including clinical neurology, recent publications in neuroscience,
philosophy of biology, "neuro-doubt" about modern media hype of new neuro-scientific procedures and methods, consciousness, scuba diving, horticulture, jazz, blues, slack key guitar music, the Hawai'i health scene, and whatever else dat's da kine...
A new color to be seen
You are what you eat?
Well, maybe your microbiome is about 3% exactly what you ate, at least.
ABSTRACT
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TITLE: Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome
AUTHORS: Niccolò Carlino, Aitor Blanco-Míguez, Michal Punčochář, Paul D. Cotter, Nicola Segata1, Edoardo Pasolli and many more...
DOI: doi: 10.1016/j.cell.2024.07.039
Published online in Cell, 2024/09/03
Highlights
With curated Food Metagenomic Data (FMD), we integrated and analyzed >2,500 food metagenomes.
Over 10,000 prokaryotic and eukaryotic MAGs uncover substantial food microbial diversity.
Food microbes account for up to an average of 3% of the adult gut microbiome.
Strain-level analysis highlights potential instances of food-to-gut microbe transmission.
Summary
Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.
Risks for impaired post-stroke cognitive function
The paper used patients admitted to Massachusetts General Hospital as their cases for which to form a risk factor formula, and then validated the formula against cases in a sister Boston hospital, Brighamm and Women’s Hospital.
The results are quoted as a table below:
The results found were that older age was the biggest overall risk factor (weighted for example as 4 for > 65, 8 for > 85), followed by those weighted 2: insurance of Medicare (a proxy for age over 65), mobility problems, delirium, peripheral vascular disease, and depression. Other factors were rated only at a weight of 1: these included Medicaid(a proxy for low income), prior falls, Parkinson’s, kidney disease, weight loss, and nursing home placement from the acute hospital stay.
Indeed, it seems that the factors found more significant, such as age, gait status, depression, delirium, vascular disease, are actually all also known from other, prospective studies to be risk factors for dementia in the general population, even in those without any stroke.
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ABSTRACT
Predicting post-stroke cognitive impairment using electronic health record data
Jeffrey M. Ashburner, PhD, MPH1,2; Yuchiao Chang, PhD1,2; Bianca Porneala, MS1; Sanjula D. Singh, MD, PhD3; Nirupama Yechoor, MD, MSc3; Jonathan M. Rosand, MD, MSc3; Daniel E. Singer, MD1,2; Christopher D. Anderson, MD, MMSc4; Steven J. Atlas, MD, MPH
medRxiv preprint: https://doi.org/10.1101/2024.02.02.24302240
Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.
Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.
Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022. Setting: Primary care practices affiliated with two academic medical centers.
Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).
Exposures: Predictors of PSCI were ascertained from the electronic health record.
Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.
Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
Key Words: post-stroke cognitive impairment, risk stratification, risk prediction
On medical AI in 2023
Large language model-based AI (LLM’s) are the epitome of what can be constituted by language alone. They can easily take isolated linguistic philosophy to its absurd extreme.
Unfortunately, this means there is a disconnect between a statement that is correct in the context of its LLM and the statements we want, which appropriately address a scientific or clinical context in the world outside of the LLM. This results in AI dialog replies that currently make medical advisory AI impossible to trust.
Current LLM base AI’s are masters of what Harry G. Frankfurt called “bullshit.” Until AI can distinguish and eliminate fictional or obsolete diagnoses and treatments within its model, separating them from those which help the patient, it cannot be trusted with any unsupervised role in patient care.
A new color to be seen
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