Synthetically enhanced: unveiling synthetic data's potential in medical imaging research.

BACKGROUND: Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research. METHODS: The study employed DDPMs to create synthetic CXRs conditioned on demographic and pathological characteristics from the CheXpert dataset. These synthetic images were used to supplement training datasets for pathology classifiers, with the aim of improving their performance. The evaluation involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and various experiments, including supplementing real data with synthetic data, training with purely synthetic data, and mixing synthetic data with external datasets. Performance was assessed using the area under the receiver operating curve (AUROC). FINDINGS: Adding synthetic data to real datasets resulted in a notable increase in AUROC values (up to 0.02 in internal and external test sets with 1000% supplementation, p-value <0.01 in all instances). When classifiers were trained exclusively on synthetic data, they achieved performance levels comparable to those trained on real data with 200%-300% data supplementation. The combination of real and synthetic data from different sources demonstrated enhanced model generalizability, increasing model AUROC from 0.76 to 0.80 on the internal test set (p-value <0.01). INTERPRETATION: Synthetic data supplementation significantly improves the performance and generalizability of pathology classifiers in medical imaging. FUNDING: Dr. Gichoya is a 2022 Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program and declares support from RSNA Health Disparities grant (#EIHD2204), Lacuna Fund (#67), Gordon and Betty Moore Foundation, NIH (NIBIB) MIDRC grant under contracts 75N92020C00008 and 75N92020C00021, and NHLBI Award Number R01HL167811.

Climate, litter quality and radiation duration jointly regulate the net effect of UV radiation on litter decomposition.

Photodegradation via ultraviolet (UV) radiation is an important factor driving plant litter decomposition. Despite increasing attention to the role of UV photodegradation in litter decomposition, the specific impact of UV radiation on the plant litter decomposition stage within biogeochemical cycles remains unclear at regional and global scales. To clarify the variation rules of magnitude of UV effect on plant litter decomposition and their regulatory factors, we conducted a meta-analysis based on 54 published papers. Our results indicated that UV significantly promoted the mass loss of litter by facilitating decay of carbonaceous fractions and release of nitrogen and phosphorus. The promotion effect varied linearly or non-linearly with the time that litter exposed to UV, and with climatic factors. The UV effect on litter decomposition decreased first than increased on precipitation and temperature gradients, reaching its minimum in the area with a precipitation of 400-600 mm, and a temperature of 15-20 °C. This trend might be attributed to a potential equilibrium between the photofacilitation and photo-inhibition effects of UV under this condition. This variation in UV effect on precipitation gradient was in agreement with the fact that UV photodegradation effect was weakest in grassland ecosystems compared to that in forest and desert ecosystems. In addition, initial litter quality significantly influenced the magnitude of UV effect, but had no influence on the correlation between UV effect and climate gradient. Litter with lower initial nitrogen and lignin content shown a greater photodegradation effect, whereas those with higher hemicellulose and cellulose content had a greater photodegradation effect. Our study provides a comprehensive understanding of photodegradation effect on plant litter decomposition, indicates potentially substantial impacts of global enhancements of litter decomposition by UV, and highlights the necessity to quantify the contribution of photochemical minerallization pathway and microbial degradation pathway in litter decomposition.

Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis.

Background: The objective of this study is to understand chronic obstructive pulmonary disease (COPD) phenotypes and their progressions by quantifying heterogeneities of lung ventilation from the single photon emission computed tomography (SPECT) images and establishing associations with the quantitative computed tomography (qCT) imaging-based clusters and variables. Methods: Eight COPD patients completed a longitudinal study of three visits with intervals of about a year. CT scans of these subjects at residual volume, functional residual capacity, and total lung capacity were taken for all visits. The functional and structural qCT-based variables were derived, and the subjects were classified into the qCT-based clusters. In addition, the SPECT variables were derived to quantify the heterogeneity of lung ventilation. The correlations between the key qCT-based variables and SPECT-based variables were examined. Results: The SPECT-based coefficient of variation (CVTotal), a measure of ventilation heterogeneity, showed strong correlations (|r| ≥ 0.7) with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung on cross-sectional data. As for the two-year changes, the SPECT-based maximum tracer concentration (TCmax), a measure of hot spots, exhibited strong negative correlations with fSAD%Total, Emph%Total, average airway diameter in the left upper lobe, and airflow distribution in the middle and lower lobes. Conclusion: Small airway disease is highly associated with the heterogeneity of ventilation in COPD lungs. TCmax is a more sensitive functional biomarker for COPD progression than CVTotal. Besides fSAD%Total and Emph%Total, segmental airways narrowing and imbalanced ventilation between upper and lower lobes may contribute to the development of hot spots over time.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau.

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.

Bio-organic fertilizer facilitated phytoremediation of heavy metal(loid)s-contaminated saline soil by mediating the plant-soil-rhizomicrobiota interactions.

Bio-organic fertilizer (BOF) was effective to promote the phytoremediation efficiency of heavy metal(loid)s-contaminated saline soil (HCSS) by improving rhizosphere soil properties, especially microbiome. However, there existed unclear impacts of BOF on plant metabolome and plant-driven manipulation on rhizosphere soil microbiota in HCSS, which were pivotal contributors to stress defense of plants trapped in adverse conditions. Here, a pot experiment was conducted to explore the mechanisms of BOF in improving alfalfa (Medicago sativa)-performing phytoremediation of HCSS. BOF application significantly increased the biomass (150.87-401.58 %) to support the augments of accumulation regarding heavy metal(loid)s (87.50 %-410.54 %) and salts (38.27 %-271.04 %) in alfalfa. BOF promoted nutrients and aggregates stability but declined pH of rhizosphere soil, accompanied by the boosts of rhizomicrobiota including increased activity, reshaped community structure, enriched plant growth promoting rhizobacteria (Blastococcus, Modestobacter, Actinophytocola, Bacillus, and Streptomyces), strengthened mycorrhizal symbiosis (Leohumicola, Funneliformis, and unclassified_f_Ceratobasidiaceae), optimized co-occurrence networks, and beneficial shift of keystones. The conjoint analysis of plant metabolome and physiological indices confirmed that BOF reprogrammed the metabolic processes (synthesis, catabolism, and long-distance transport of amino acid, lipid, carbohydrate, phytohormone, stress-resistant secondary metabolites, etc) and physiological functions (energy supply, photosynthesis, plant immunity, nutrients assimilation, etc) that are associated intimately. The consortium of root metabolome, soil metabolome, and soil microbiome revealed that BOF facilitated the exudation of metabolites correlated with rhizomicrobiota (structure, biomarker, and keystone) and rhizosphere oxidative status, e.g., fatty acyls, phenols, coumarins, phenylpropanoids, highlighting the plant-driven regulation on rhizosphere soil microbes and environment. By compiling various results and omics data, it was concluded that BOF favored the adaptation and phytoremediation efficiency of alfalfa by mediating the plant-soil-rhizomicrobiota interactions. The results would deepen understanding of the mechanisms by which BOF improved phytoremediation of HCSS, and provide theoretical guidance to soil amelioration and BOF application.

Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach.

BACKGROUND: Recent studies, based on clinical data, have identified sex and age as significant factors associated with an increased risk of long COVID. These two factors align with the two post-COVID-19 clusters identified by a deep learning algorithm in computed tomography (CT) lung scans: Cluster 1 (C1), comprising predominantly females with small airway diseases, and Cluster 2 (C2), characterized by older individuals with fibrotic-like patterns. This study aims to assess the distributions of inhaled aerosols in these clusters. METHODS: 140 COVID survivors examined around 112 days post-diagnosis, along with 105 uninfected, non-smoking healthy controls, were studied. Their demographic data and CT scans at full inspiration and expiration were analyzed using a combined imaging and modeling approach. A subject-specific CT-based computational model analysis was utilized to predict airway resistance and particle deposition among C1 and C2 subjects. The cluster-specific structure and function relationships were explored. RESULTS: In C1 subjects, distinctive features included airway narrowing, a reduced homothety ratio of daughter over parent branch diameter, and increased airway resistance. Airway resistance was concentrated in the distal region, with a higher fraction of particle deposition in the proximal airways. On the other hand, C2 subjects exhibited airway dilation, an increased homothety ratio, reduced airway resistance, and a shift of resistance concentration towards the proximal region, allowing for deeper particle penetration into the lungs. CONCLUSIONS: This study revealed unique mechanistic phenotypes of airway resistance and particle deposition in the two post-COVID-19 clusters. The implications of these findings for inhaled drug delivery effectiveness and susceptibility to air pollutants were explored.

The super-healing MRL strain promotes muscle growth in muscular dystrophy through a regenerative extracellular matrix.

The Murphy Roths Large (MRL) mouse strain has "super-healing" properties that enhance recovery from injury. In mice, the DBA/2J strain intensifies many aspects of muscular dystrophy, so we evaluated the ability of the MRL strain to suppress muscular dystrophy in the Sgcg-null mouse model of limb girdle muscular dystrophy. A comparative analysis of Sgcg-null mice in the DBA/2J versus MRL strains showed greater myofiber regeneration, with reduced structural degradation of muscle in the MRL strain. Transcriptomic profiling of dystrophic muscle indicated strain-dependent expression of extracellular matrix (ECM) and TGF-ß signaling genes. To investigate the MRL ECM, cellular components were removed from dystrophic muscle sections to generate decellularized myoscaffolds. Decellularized myoscaffolds from dystrophic mice in the protective MRL strain had significantly less deposition of collagen and matrix-bound TGF-ß1 and TGF-ß3 throughout the matrix. Dystrophic myoscaffolds from the MRL background, but not the DBA/2J background, were enriched in myokines like IGF-1 and IL-6. C2C12 myoblasts seeded onto decellularized matrices from Sgcg-/- MRL and Sgcg-/- DBA/2J muscles showed the MRL background induced greater myoblast differentiation compared with dystrophic DBA/2J myoscaffolds. Thus, the MRL background imparts its effect through a highly regenerative ECM, which is active even in muscular dystrophy.

Consistent predictors of microbial community composition across spatial scales in grasslands reveal low context-dependency.

Environmental circumstances shaping soil microbial communities have been studied extensively. However, due to disparate study designs, it has been difficult to resolve whether a globally consistent set of predictors exists, or context-dependency prevails. Here, we used a network of 18 grassland sites (11 of those containing regional plant productivity gradients) to examine (i) if similar abiotic or biotic factors predict both large-scale (across sites) and regional-scale (within sites) patterns in bacterial and fungal community composition, and (ii) if microbial community composition differs consistently at two levels of regional plant productivity (low vs. high). Our results revealed that bacteria were associated with particular soil properties (such as base saturation) and both bacteria and fungi were associated with plant community composition across sites and within the majority of sites. Moreover, a discernible microbial community signal emerged, clearly distinguishing high and low-productivity soils across different grasslands independent of their location in the world. Hence, regional productivity differences may be typified by characteristic soil microbial communities across the grassland biome. These results could encourage future research aiming to predict the general effects of global changes on soil microbial community composition in grasslands and to discriminate fertile from infertile systems using generally applicable microbial indicators.

Light grazing intensity enhances ecosystem services in semi-arid grasslands through plant trait associations.

Grasslands provide multiple ecosystem services (ESs) including provisioning, regulating, supporting, and cultural services that are largely affected by livestock grazing. Linking plant functional traits (PFTs) to ecosystem processes and functions has attracted extensive ecological research to explore the responses and inter-relations of ecosystem services to environmental and management changes. However, little information is available on the links between PFTs and ESs in most ecosystems. We conducted a grazing experiment to investigate the response of PFTs at different levels, including in plant organs (leaves and stems), individual plants, and the overall community in a typical steppe region of Inner Mongolia. Additionally, we examined the effect of animal grazing at four intensities (nil, light, moderate, and heavy) and explored the dynamic interconnections between PFTs and ecosystem services in grasslands. Our analysis revealed that the highest total ecosystem service and provisioning service were achieved under light- and moderate-grazing treatments, respectively. Heavy grazing also increased provisioning service but with a large decline in regulating and total ecosystem services. These changes in ESs were closely associated with grazing-induced variations in PFTs. Compared to no grazing, light grazing increased plant size-related functional traits, such as height, leaf length, leaf area, stem length, and the ratio of stem length to diameter. In contrast, heavy grazing decreased these PFTs. Provisioning and regulating services were determined by plant above-ground community function and structural properties, while supporting service was jointly affected by the below-ground community and soil properties. Our results indicate that light grazing should be recommended for the best total ESs, although moderate grazing may lead to high short-term economic benefits. Moreover, PFTs are powerful indicators for provisioning and regulating services. These findings provide a valuable reference for developing effective management practices to achieve targeted ESs using PFTs as indicators.

The super-healing MRL strain promotes muscle growth in muscular dystrophy through a regenerative extracellular matrix.

Genetic background shifts the severity of muscular dystrophy. In mice, the DBA/2J strain confers a more severe muscular dystrophy phenotype, whereas the Murphy's Roth Large (MRL) strain has "super-healing" properties that reduce fibrosis. A comparative analysis of the Sgcg null model of Limb Girdle Muscular Dystrophy in the DBA/2J versus MRL strain showed the MRL background was associated with greater myofiber regeneration and reduced structural degradation of muscle. Transcriptomic profiling of dystrophic muscle in the DBA/2J and MRL strains indicated strain-dependent expression of the extracellular matrix (ECM) and TGF-ß signaling genes. To investigate the MRL ECM, cellular components were removed from dystrophic muscle sections to generate decellularized "myoscaffolds". Decellularized myoscaffolds from dystrophic mice in the protective MRL strain had significantly less deposition of collagen and matrix-bound TGF-ß1 and TGF-ß3 throughout the matrix, and dystrophic myoscaffolds from the MRL background were enriched in myokines. C2C12 myoblasts were seeded onto decellularized matrices from Sgcg-/- MRL and Sgcg-/- DBA/2J matrices. Acellular myoscaffolds from the dystrophic MRL background induced myoblast differentiation and growth compared to dystrophic myoscaffolds from the DBA/2J matrices. These studies establish that the MRL background also generates its effect through a highly regenerative ECM, which is active even in muscular dystrophy.

Tissue Clearing To Localize Microplastics via Three-Dimensional Imaging of Whole Organisms.

Understanding the biological impacts of plastic pollution requires an effective methodology to detect unlabeled microplastics in environmental samples. Detecting unlabeled microplastics in an organism generally requires a digestion protocol, which results in the loss of spatial information on the distribution of microplastic within the organism and could lead to the disappearance of the smaller plastics. Fluorescence microscopy allows visualization of ingested microplastics but many labeling strategies are nonspecific and label biomass, thus limiting our ability to distinguish internalized plastics. While prelabeled plastics can be used to avoid nonspecific labeling, this approach precludes the detection of environmental microplastics in organisms. Also, using prelabeled microplastics can affect the viability of the organism and impact plastic uptake. Thus, a method was developed that employs nonspecific labeling with a tissue-clearing technique. Briefly, unlabeled microplastics are stained with a fluorescent dye after ingestion by the organism. The tissue-clearing technique then removes tissue-bound dye while rendering the structurally intact organism transparent. The internalized plastics remain stained and can be visualized in the cleared tissue with fluorescence microscopy. The technique is demonstrated using polystyrene beads in living aquatic organismsTigriopus californicusandDaphnia magnaand by spiking a model vertebrate (Cephalochordata) with different microplastics.

The reciprocal changes in dominant species with complete metabolic functions explain the decoupling phenomenon of microbial taxonomic and functional composition in a grassland.

The decoupling of microbial functional and taxonomic components refers to the phenomenon that a drastic change in microbial taxonomic composition leads to no or only a gentle change in functional composition. Although many studies have identified this phenomenon, the mechanisms underlying it are still unclear. Here we demonstrate, using metagenomics data from a steppe grassland soil under different grazing and phosphorus addition treatments, that there is no "decoupling" in the variation of taxonomic and metabolic functional composition of the microbial community within functional groups at species level. In contrast, the high consistency and complementarity between the abundance and functional gene diversity of two dominant species made metabolic functions unaffected by grazing and phosphorus addition. This complementarity between the two dominant species shapes a bistability pattern that differs from functional redundancy in that only two species cannot form observable redundancy in a large microbial community. In other words, the "monopoly" of metabolic functions by the two most abundant species leads to the disappearance of functional redundancy. Our findings imply that for soil microbial communities, the impact of species identity on metabolic functions is much greater than that of species diversity, and it is more important to monitor the dynamics of key dominant microorganisms for accurately predicting the changes in the metabolic functions of the ecosystems.

NRAP reduction rescues sarcomere defects in nebulin-related nemaline myopathy.

Nemaline myopathy (NM) is a rare neuromuscular disorder associated with congenital or childhood-onset of skeletal muscle weakness and hypotonia, which results in limited motor function. NM is a genetic disorder and mutations in 12 genes are known to contribute to autosomal dominant or recessive forms of the disease. Recessive mutations in nebulin (NEB) are the most common cause of NM affecting about 50% of patients. Because of the large size of the NEB gene and lack of mutational hot spots, developing therapies that can benefit a wide group of patients is challenging. Although there are several promising therapies under investigation, there is no cure for NM. Therefore, targeting disease modifiers that can stabilize or improve skeletal muscle function may represent alternative therapeutic strategies. Our studies have identified Nrap upregulation in nebulin deficiency that contributes to structural and functional deficits in NM. We show that genetic ablation of nrap in nebulin deficiency restored sarcomeric disorganization, reduced protein aggregates and improved skeletal muscle function in zebrafish. Our findings suggest that Nrap is a disease modifier that affects skeletal muscle structure and function in NM; thus, therapeutic targeting of Nrap in nebulin-related NM and related diseases may be beneficial for patients.

Postpartum Hospital Readmissions Among Massachusetts Women Who are Deaf or Hard of Hearing.

Objectives: Deaf or hard of hearing (DHH) women are at a higher risk of adverse pregnancy and birth outcomes compared with other women. However, little is known about postpartum outcomes among DHH women. The objective was to compare the risk of postpartum hospitalizations for DHH compared with non-DHH women and the leading indications for postpartum admissions. Materials and Methods: We analyzed data from the 1998-2017 Massachusetts Pregnancy to Early Life Longitudinal Data System and identified 3,546 singleton deliveries to DHH women and 1,381,439 singleton deliveries to non-DHH women. We used Cox proportional hazard models to compare the first hospital admission and ≥2 hospital admissions between DHH and non-DHH women within 1-42, 43-90, and 91-365 days after delivery. Results: DHH women had a higher risk for any hospital admissions across all periods (hazard ratios [HR] = 1.84; 95% confidence intervals [CI] 1.46-2.34 within 1-42 days; HR = 2.76; 95%CI 1.99-3.83 within 43-90 days; and HR = 3.10; 95%CI 2.66-3.60 91-365 days) after childbirth compared with non-DHH women. They had an almost seven times higher risk for repeated hospital admissions within 43-90 days (HR = 6.84; 95%CI 1.66-28.21) and nearly four times higher the risk within 91-365 days (HR = 3.63; 95%CI 2.00-6.59) after delivery compared with non-DHH women. The leading indications for readmission among DHH women included: conditions complicating the puerperium/hemorrhage and soft tissues disorders. Conclusion: Compared with other women, DHH women had significantly higher readmissions across all postpartum periods and for repeated admissions >42 days. Leading postpartum indications were distinct from those of non-DHH women.

Partner Violence Hospitalizations Among Adults With Intellectual Disabilities.

INTRODUCTION: The objective of this study is to determine the RR of intimate partner violence‒related hospitalization among men and women with and without intellectual disabilities. METHODS: This is a retrospective cohort study using Healthcare Cost and Utilization Project, National Inpatient Sample, 2013-2019. Adults with intellectual disabilities were identified using diagnosis codes. A control group without intellectual disabilities was matched in a 1:5 ratio. A total of 1,179,282 hospitalization records were analyzed, with identified cases of 112,565 men and 83,982 women with intellectual disabilities. Analyses were conducted in 2021 and 2022. RESULTS: Men with intellectual disabilities (RR=6.00; 95% CI=4.43, 8.13; p<0.001) were at higher risk for intimate partner violence‒related hospitalizations than men without intellectual disabilities, as were women without intellectual disabilities (RR=3.36; 95% CI=2.57, 4.39; p<0.001). Women with intellectual disabilities experienced the highest risk of intimate partner violence‒related hospitalizations (RR=16.44; 95% CI=12.60, 21.45; p<0.001). Risks remained robust after adjusting for sociodemographic and hospital characteristics. CONCLUSIONS: Intellectual disability and female sex compound to elevate the risk of intimate partner violence‒related hospitalizations. This study underscores the need for improved and specialized service provision across healthcare, legal, criminal, and other systems to promote safety and healing for people with disabilities (especially for those with intellectual disabilities) who are survivors of intimate partner violence.

Treatment Patterns in US Patients Receiving First-Line and Second-Line Therapy for Metastatic Pancreatic Ductal Adenocarcinoma in the Real World.

INTRODUCTION: Metastatic pancreatic ductal adenocarcinoma (mPDAC) is a common cancer with poor survival outcomes. Although treatment options are limited, real-world treatment patterns and outcomes are not well understood, particularly beyond first-line treatment. This study described real-world treatment patterns and outcomes for mPDAC in the USA. METHODS: This retrospective analysis used electronic health record-derived de-identified data of patients with mPDAC diagnosed between January 1, 2014 and June 30, 2021. Treatments were classified into six groups: (1) standard combination chemotherapy; (2) nonstandard combination chemotherapy; (3) single-agent chemotherapy; (4) targeted therapy; (5) clinical study drugs; and (6) off-label therapies. Analyses were descriptive in nature. Treatment utilization and switching, and time on treatment and time to discontinuation, were described by first-line (1LOT) and second-line (2LOT) treatment groups. Median overall survival (mOS) from 1LOT and 2LOT was stratified by treatment group, and for 1LOT on the basis of whether patients received further treatment. RESULTS: 1LOT included 6979 patients, 3241 (46%) of whom received further 2LOT. Standard combination chemotherapy was the most common 1LOT (70%) and 2LOT (46%). Nonstandard combination chemotherapy was used more as 2LOT (35%) than 1LOT (11%). First-line time on treatment was generally higher than second-line time on treatment, and time to discontinuation was lower than time on treatment. mOS in days (months) from 1LOT was 271 (8.9), 252 (8.3), 219 (7.2), 170 (5.6), 280 (9.2), and 182 (6.0), and mOS from 2LOT was 202 (6.6), 193 (6.3), 186 (6.1), 193 (6.3), 179 (5.9), and 97 (3.2), for groups 1-6, respectively. Within group 1, mOS from 1LOT was 318 days (10.4 months) for FOLFIRINOX and 241 days (7.9 months) for gemcitabine and nab-paclitaxel. CONCLUSION: Most patients with mPDAC received 1LOT in line with clinical practice guidelines, yet mOS remains poor. This study highlights the need for novel therapies to demonstrate improved patient survival compared with therapies in current clinical practice guidelines.

Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach.

Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HDLI). However, many people who have claimed that they experienced HD exposure were not diagnosed with HDLI but still felt discomfort, possibly due to the unknown effects of HD. Therefore, this study examined HD-exposed subjects with normal-appearing lungs, as well as unexposed subjects, in clusters (subgroups) with distinct characteristics, classified by deep-learning-derived computed-tomography (CT)-based tissue pattern latent traits. Among the major clusters, cluster 0 (C0) and cluster 5 (C5) were dominated by HD-exposed and unexposed subjects, respectively. C0 was characterized by features attributable to lung inflammation or fibrosis in contrast with C5. The computational fluid and particle dynamics (CFPD) analysis suggested that the smaller airway sizes observed in the C0 subjects led to greater airway resistance and particle deposition in the airways. Accordingly, women appeared more vulnerable to HD-associated lung abnormalities than men.

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.

A computed tomography imaging-based subject-specific whole-lung deposition model.

The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model. The geometries of airways and lobes were segmented from computed tomography (CT) lung images at total lung capacity (TLC), and the regional air-volume changes were calculated by registering CT images at TLC and functional residual capacity (FRC). The geometries were used to create the structure of entire subject-specific conducting airways and acinar units. The air-volume changes were used to estimate the function of subject-specific ventilation distributions among acinar units and regulate flow rates in respiratory airway models. With the airway dimensions rescaled to a desired lung volume and the airflow field simulated by a computational fluid dynamics model, particle deposition fractions were calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of secondary flow and airway geometry in proximal airways. The proposed model was validated in silico against existing whole-lung deposition models, three-dimensional (3D) computational fluid and particle dynamics (CFPD) for an acinar unit, and 3D CFPD deep lung model comprising conducting and respiratory regions. The model was further validated in vivo against the lobar particle distribution and the coefficient of variation of particle distribution obtained from CT and single-photon emission computed tomography (SPECT) images, showing good agreement. Subject-specific airway structure increased the deposition fraction of 10.0-µm particles and 0.01-µm particles by approximately 10%. An enhancement factor increased the overall deposition fractions, especially for particle sizes between 0.1 and 1.0 µm.