Medicine & Health

455 items

IEEE Spectrum Sep 29, 2026
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
Organ chips and computational models have won FDA and NIH policy openings, but validation costs and research culture still slow adoption
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
Communicable diseases intelligence (2018) Sep 29, 2026
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Google search data plus machine learning forecast weekly influenza counts across Australian states, with best-model correlation from -0.353 to 0.977
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
bioRxiv Sep 29, 2026
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
In four-way mood and psychosis classification across 1,520 subjects, class-conditional (Mondrian) calibration cut the between-diagnosis coverage gap from 12.3 points to 0.4 points at a cost of 0.07 labels in mean set size
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
发表出处待核验 Sep 29, 2026
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
Researchers reverse-engineer Meta Pixel configurations, finding 98.4% default-driven tracking on health sites and Core Setup covering only 34.3% while being bypassable via hashed URLs
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
bioRxiv Sep 29, 2026
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
DECIPHER estimates cell-type proportions from bulk omics via disentangled representation learning and supports prognostic stratification in lung adenocarcinoma
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
Microsystems & Nanoengineering Sep 29, 2026
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
A flexible wireless stethoscope built on a 25-element AlN PMUT array captures cardiopulmonary sounds from 10 Hz to 10 kHz and classifies five respiratory states with 98.7% accuracy
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
bioRxiv Sep 29, 2026
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
A single intra-articular injection of TGFB1-engineered iMSCs lowered synovial macrophage numbers and the MHCII/CD206 ratio in a mouse osteoarthritis model but did not reduce cartilage degeneration
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
bioRxiv Sep 29, 2026
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
Across 10,000 Mycobacterium tuberculosis complex strains, researchers reconstruct IS6110 dynamics, finding copy numbers from 1 to over 30 and 5% hotspot regions carrying half of independent insertions
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
arXiv Sep 29, 2026
SentZero is a sentence-centric vision-language pretraining framework for chest X-ray: it uses an LLM to extract phrases from radiology reports and map them to concise topic-presence sentences that expand positive-pair diversity, adds an auxiliary loss that attracts the highest-similarity image patches toward clinical sentences recurring across studies to mitigate false negatives, and applies sentence-conditioned residual modulation to visual embeddings; after pretraining on MIMIC-CXR it outperforms prior multi-task zero-shot methods on most metrics for zero-shot multi-label classification on Open-I, ChestXray14, PadChest, ChestXDet10 and CheXpert and for zero-shot grounding on ChestXDet10 and MS-CXR.
SentZero is a sentence-centric vision-language pretraining framework for chest X-ray: it uses an LLM to extract phrases from radiology reports and map them to concise topic-presence sentences that expand positive-pair diversity, adds an auxiliary loss that attracts the highest-similarity image patches toward clinical sentences recurring across studies to mitigate false negatives, and applies sentence-conditioned residual modulation to visual embeddings; after pretraining on MIMIC-CXR it outperforms prior multi-task zero-shot methods on most metrics for zero-shot multi-label classification on Open-I, ChestXray14, PadChest, ChestXDet10 and CheXpert and for zero-shot grounding on ChestXDet10 and MS-CXR.
SentZero pairs abstract-level sentence mapping with patch-level false-negative alignment to beat prior multi-task zero-shot methods on chest X-ray classification and grounding after MIMIC-CXR pretraining
SentZero is a sentence-centric vision-language pretraining framework for chest X-ray: it uses an LLM to extract phrases from radiology reports and map them to concise topic-presence sentences that expand positive-pair diversity, adds an auxiliary loss that attracts the highest-similarity image patches toward clinical sentences recurring across studies to mitigate false negatives, and applies sentence-conditioned residual modulation to visual embeddings; after pretraining on MIMIC-CXR it outperforms prior multi-task zero-shot methods on most metrics for zero-shot multi-label classification on Open-I, ChestXray14, PadChest, ChestXDet10 and CheXpert and for zero-shot grounding on ChestXDet10 and MS-CXR.
SentZero is a sentence-centric vision-language pretraining framework for chest X-ray: it uses an LLM to extract phrases from radiology reports and map them to concise topic-presence sentences that expand positive-pair diversity, adds an auxiliary loss that attracts the highest-similarity image patches toward clinical sentences recurring across studies to mitigate false negatives, and applies sentence-conditioned residual modulation to visual embeddings; after pretraining on MIMIC-CXR it outperforms prior multi-task zero-shot methods on most metrics for zero-shot multi-label classification on Open-I, ChestXray14, PadChest, ChestXDet10 and CheXpert and for zero-shot grounding on ChestXDet10 and MS-CXR.
Massachusetts Institute of Technology Sep 28, 2026
Working with MIT's CSAIL, researchers developed a machine-learning algorithm that predicts from very small datasets, used it to screen nearly 50 FDA-approved excipients and predict excipient ratios for the lipid nanoparticle (LNP) formulations used by the Moderna and Pfizer Covid-19 vaccines, and produced vaccines that after vacuum drying remained stable for two months at 37 C (about 98 F) or one year at room temperature while generating immune responses in mice equivalent to those from a vaccine carried by LNPs similar to the original Moderna formulation, and also built solid microneedle patches that produced similar immune responses.
Working with MIT's CSAIL, researchers developed a machine-learning algorithm that predicts from very small datasets, used it to screen nearly 50 FDA-approved excipients and predict excipient ratios for the lipid nanoparticle (LNP) formulations used by the Moderna and Pfizer Covid-19 vaccines, and produced vaccines that after vacuum drying remained stable for two months at 37 C (about 98 F) or one year at room temperature while generating immune responses in mice equivalent to those from a vaccine carried by LNPs similar to the original Moderna formulation, and also built solid microneedle patches that produced similar immune responses.
MIT team uses an AI algorithm to screen excipient ratios, yielding RNA vaccines that stay stable for a year at room temperature or two months at 37 C and still match a Moderna-like vaccine's immune response in mice
Working with MIT's CSAIL, researchers developed a machine-learning algorithm that predicts from very small datasets, used it to screen nearly 50 FDA-approved excipients and predict excipient ratios for the lipid nanoparticle (LNP) formulations used by the Moderna and Pfizer Covid-19 vaccines, and produced vaccines that after vacuum drying remained stable for two months at 37 C (about 98 F) or one year at room temperature while generating immune responses in mice equivalent to those from a vaccine carried by LNPs similar to the original Moderna formulation, and also built solid microneedle patches that produced similar immune responses.
Working with MIT's CSAIL, researchers developed a machine-learning algorithm that predicts from very small datasets, used it to screen nearly 50 FDA-approved excipients and predict excipient ratios for the lipid nanoparticle (LNP) formulations used by the Moderna and Pfizer Covid-19 vaccines, and produced vaccines that after vacuum drying remained stable for two months at 37 C (about 98 F) or one year at room temperature while generating immune responses in mice equivalent to those from a vaccine carried by LNPs similar to the original Moderna formulation, and also built solid microneedle patches that produced similar immune responses.

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