Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy.

Background and purpose: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes. Materials and methods: A retrospective study of 47 MR-guided lung SBRT treatments for 39 patients was conducted. Radiomic features were extracted using Pyradiomics, and stability was evaluated temporally and spatially. Delta radiomics were correlated with radiation dose delivery and assessed for associations with tumor control and survival with Cox regressions. Results: Among 107 features, 49 demonstrated temporal stability, and 57 showed spatial stability. Fifteen stable and non-collinear features were analyzed. Median Skewness and surface to volume ratio decreased with radiation dose fraction delivery, while coarseness and 90th percentile values increased. Skewness had the largest relative median absolute changes (22 %-45 %) per fraction from baseline and was associated with locoregional failure (p = 0.012) by analysis of covariance. Skewness, Elongation, and Flatness were significantly associated with local recurrence-free survival, while tumor diameter and volume were not. Conclusions: Our study establishes the feasibility and stability of delta radiomics analysis for MR-guided lung SBRT. Findings suggest that MR delta radiomics can capture short-term radiographic manifestations of the intra-tumoral radiation effect.

End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas.

BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. METHODS: We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from two institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained three DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with 1) clinical features, 2) DL-MRI features, and 3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model. RESULTS: Of the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, p<0.0001). CONCLUSIONS: DL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.

Modelling the impacts of climate change on agrochemical fate and transport by water on a catchment scale.

The export of agrochemicals and their transformation products (TPs) following their application in the agricultural fields poses a threat to water quality. Future changes in climatic conditions (e.g. extreme weather events such as heavy rainfall or extended dry periods) could alter the degradation and mobility of agrochemicals. In this research, we use an integrated modelling framework to understand the impact of extreme climate events on the fate and transport of the agrochemical S-Metolachlor and two of its TPs (M-OXA, Metolachlor Oxanilic Acid and M-ESA, Metolachlor Ethyl Sulfonic Acid). This is done by coupling climate model outputs to the Zin-AgriTra agrochemical reactive transport model in four simulation scenarios. 1) Reference (2015-2018), 2) Very dry (2038-2041), 3) Very wet (2054-2057) and 4) High temperature (2096-2099) conditions of a selected RCP8.5 based regional climate scenario. The modelling framework is tested on an agricultural catchment, Wulka, in Burgenland, Austria. The model results indicate that 13-14 % of applied S-Metolachlor is retained in the soil, and around 85 % is degraded into TPs in the different scenarios. In very dry and high-temperature scenarios, degradation is higher, and hence, there is less S-Metolachlor in the soil. However, a large share of formed M-OXA and M-ESA are retained in the soil, which is transported via overland and groundwater flow, leading to a build-up effect in M-OXA and M-ESA river concentrations over the years. Though a small share of S-Metolachlor and TPs are transported to rivers, their river export is affected by the intensity and amount of rainfall. The very wet and high-temperature scenarios show higher S-Metolachlor and TP concentrations at the catchment outlet due to higher river discharge. The reference scenario shows higher river peak concentrations associated with higher overland flow caused by measured hourly rainfall compared to disaggregated daily precipitation data in the other scenarios.

Alzheimer's disease protective allele of Clusterin modulates neuronal excitability through lipid-droplet-mediated neuron-glia communication.

Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified a plethora of risk loci. However, the disease variants/genes and the underlying mechanisms remain largely unknown. For a strong AD-associated locus near Clusterin (CLU), we tied an AD protective allele to a role of neuronal CLU in promoting neuron excitability through lipid-mediated neuron-glia communication. We identified a putative causal SNP of CLU that impacts neuron-specific chromatin accessibility to transcription-factor(s), with the AD protective allele upregulating neuronal CLU and promoting neuron excitability. Transcriptomic analysis and functional studies in induced pluripotent stem cell (iPSC)-derived neurons co-cultured with mouse astrocytes show that neuronal CLU facilitates neuron-to-glia lipid transfer and astrocytic lipid droplet formation coupled with reactive oxygen species (ROS) accumulation. These changes cause astrocytes to uptake less glutamate thereby altering neuron excitability. Our study provides insights into how CLU confers resilience to AD through neuron-glia interactions.

Tau is required for glial lipid droplet formation and resistance to neuronal oxidative stress.

The accumulation of reactive oxygen species (ROS) is a common feature of tauopathies, defined by Tau accumulations in neurons and glia. High ROS in neurons causes lipid production and the export of toxic peroxidated lipids (LPOs). Glia uptake these LPOs and incorporate them into lipid droplets (LDs) for storage and catabolism. We found that overexpressing Tau in glia disrupts LDs in flies and rat neuron-astrocyte co-cultures, sensitizing the glia to toxic, neuronal LPOs. Using a new fly tau loss-of-function allele and RNA-mediated interference, we found that endogenous Tau is required for glial LD formation and protection against neuronal LPOs. Similarly, endogenous Tau is required in rat astrocytes and human oligodendrocyte-like cells for LD formation and the breakdown of LPOs. Behaviorally, flies lacking glial Tau have decreased lifespans and motor defects that are rescuable by administering the antioxidant N-acetylcysteine amide. Overall, this work provides insights into the important role that Tau has in glia to mitigate ROS in the brain.

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images. Using 2321 chest radiographs in 897 patients (median age, 76 years [range, 18-96 years]; 625 male, 272 female), CIEDs were categorized into four manufacturers, 27 models, and one "other" category. Five smartphones were used to acquire 11 072 images. Performance was reported using the Dice coefficient on the validation set for segmentation or balanced accuracy on the test set for manufacturer and model classification, respectively. Results The segmentation tool achieved a mean Dice coefficient of 0.936 (IQR: 0.890-0.958). The model had an accuracy of 94.36% (95% CI: 90.93%, 96.84%; 251 of 266) for CIED manufacturer classification and 84.21% (95% CI: 79.31%, 88.30%; 224 of 266) for CIED model classification. Conclusion The proposed deep learning model, trained on both traditional DICOM and smartphone images, showed high accuracy for segmentation and classification of CIEDs on chest radiographs. Keywords: Conventional Radiography, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Júdice de Mattos Farina and Celi in this issue.

Loss-of-function in RBBP5 results in a syndromic neurodevelopmental disorder associated with microcephaly.

PURPOSE: Epigenetic dysregulation has been associated with many inherited disorders. RBBP5 (HGNC:9888) encodes a core member of the protein complex that methylates histone 3 lysine-4 and has not been implicated in human disease. METHODS: We identify 5 unrelated individuals with de novo heterozygous variants in RBBP5. Three nonsense/frameshift and 2 missense variants were identified in probands with neurodevelopmental symptoms, including global developmental delay, intellectual disability, microcephaly, and short stature. Here, we investigate the pathogenicity of the variants through protein structural analysis and transgenic Drosophila models. RESULTS: Both missense p.(T232I) and p.(E296D) variants affect evolutionarily conserved amino acids located at the interface between RBBP5 and the nucleosome. In Drosophila, overexpression analysis identifies partial loss-of-function mechanisms when the variants are expressed using the fly Rbbp5 or human RBBP5 cDNA. Loss of Rbbp5 leads to a reduction in brain size. The human reference or variant transgenes fail to rescue this loss and expression of either missense variant in an Rbbp5 null background results in a less severe microcephaly phenotype than the human reference, indicating both missense variants are partial loss-of-function alleles. CONCLUSION: Haploinsufficiency of RBBP5 observed through de novo null and hypomorphic loss-of-function variants is associated with a syndromic neurodevelopmental disorder.

Pushing the limits: expanding the temperature tolerance of a coral photosymbiont through differing selection regimes.

Coral thermal bleaching resilience can be improved by enhancing photosymbiont thermal tolerance via experimental evolution. While successful for some strains, selection under stable temperatures was ineffective at increasing the thermal threshold of an already thermo-tolerant photosymbiont (Durusdinium trenchii). Corals from environments with fluctuating temperatures tend to have comparatively high heat tolerance. Therefore, we investigated whether exposure to temperature oscillations can raise the upper thermal limit of D. trenchii. We exposed a D. trenchii strain to stable and fluctuating temperature profiles, which varied in oscillation frequency. After 2.1 yr (54-73 generations), we characterised the adaptive responses under the various experimental evolution treatments by constructing thermal performance curves of growth from 21 to 31°C for the heat-evolved and wild-type lineages. Additionally, the accumulation of extracellular reactive oxygen species, photophysiology, photosynthesis and respiration rates were assessed under increasing temperatures. Of the fluctuating temperature profiles investigated, selection under the most frequent oscillations (diurnal) induced the greatest widening of D. trenchii's thermal niche. Continuous selection under elevated temperatures induced the only increase in thermal optimum and a degree of generalism. Our findings demonstrate how differing levels of thermal homogeneity during selection drive unique adaptive responses to heat in a coral photosymbiont.

Nesting Biology of Megachile (Chrysosarus) jenseni (Hymenoptera: Megachilidae) in Two Contrasting Pampean Agroecosystems: A Potential Pollinator for Alfalfa?

Several crops depend on both managed and wild bees to produce fruits and/or seeds, and the efficiency of numerous wild bees is higher than that of some managed species. Therefore, knowing and understanding the required resources for wild bees could enabled the establishment of management practices to increase their populations. Here, we provide information about the nesting biology of Megachile (Chrysosarus) jenseni, a Faboideae-specialist bee species. Based on observations from two populations occurring in contrasting agroecosystems, this bivoltine species showed common behavioral features shared with other species of subgenus Chrysosarus, such as the use of petal pieces and mud as nesting materials and the utilization of pre-existing cavities. Both studied populations showed a bivoltine life cycle with a rapid early-summer generation and a second generation, with most individuals overwintering. Main causes of mortality were unknown diseases (or other factors), causing the death of preimaginal stages. Moreover, this species was attacked by a cleptoparasite megachilid (Coelioxys remissa), a parasitic eulophid wasp (Melittobia sp.), and a bee fly (Anthrax oedipus). Finally, we discussed the potential use of this leaf-cutter bee species for alfalfa pollination.

Homopolymer switches mediate adaptive mutability in mismatch repair-deficient colorectal cancer.

Mismatch repair (MMR)-deficient cancer evolves through the stepwise erosion of coding homopolymers in target genes. Curiously, the MMR genes MutS homolog 6 (MSH6) and MutS homolog 3 (MSH3) also contain coding homopolymers, and these are frequent mutational targets in MMR-deficient cancers. The impact of incremental MMR mutations on MMR-deficient cancer evolution is unknown. Here we show that microsatellite instability modulates DNA repair by toggling hypermutable mononucleotide homopolymer runs in MSH6 and MSH3 through stochastic frameshift switching. Spontaneous mutation and reversion modulate subclonal mutation rate, mutation bias and HLA and neoantigen diversity. Patient-derived organoids corroborate these observations and show that MMR homopolymer sequences drift back into reading frame in the absence of immune selection, suggesting a fitness cost of elevated mutation rates. Combined experimental and simulation studies demonstrate that subclonal immune selection favors incremental MMR mutations. Overall, our data demonstrate that MMR-deficient colorectal cancers fuel intratumor heterogeneity by adapting subclonal mutation rate and diversity to immune selection.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders.

BACKGROUND: Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. METHODS: AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts. RESULTS: AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network. CONCLUSIONS: AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024.

Alzheimer's disease risk allele of PICALM causes detrimental lipid droplets in microglia.

Despite genome-wide association studies of late-onset Alzheimer's disease (LOAD) having identified many genetic risk loci1-6, the underlying disease mechanisms remain largely unknown. Determining causal disease variants and their LOAD-relevant cellular phenotypes has been a challenge. Leveraging our approach for identifying functional GWAS risk variants showing allele-specific open chromatin (ASoC)7, we systematically identified putative causal LOAD risk variants in human induced pluripotent stem cells (iPSC)-derived neurons, astrocytes, and microglia (MG) and linked PICALM risk allele to a previously unappreciated MG-specific role of PICALM in lipid droplet (LD) accumulation. ASoC mapping uncovered functional risk variants for 26 LOAD risk loci, mostly MG-specific. At the MG-specific PICALM locus, the LOAD risk allele of rs10792832 reduced transcription factor (PU.1) binding and PICALM expression, impairing the uptake of amyloid beta (Aß) and myelin debris. Interestingly, MG with PICALM risk allele showed transcriptional enrichment of pathways for cholesterol synthesis and LD formation. Genetic and pharmacological perturbations of MG further established a causal link between the reduced PICALM expression, LD accumulation, and phagocytosis deficits. Our work elucidates the selective LOAD vulnerability in microglia for the PICALM locus through detrimental LD accumulation, providing a neurobiological basis that can be exploited for developing novel clinical interventions.

Associations Between Stroke Type, Ischemic Stroke Subtypes, and Post-Stroke Cognitive Trajectories.

Background: It is unclear how post-stroke cognitive trajectories differ by stroke type and ischemic stroke subtype. We studied associations between stroke types (ischemic, hemorrhagic), ischemic stroke subtypes (cardioembolic, large artery atherosclerotic, lacunar/small vessel, cryptogenic/other determined etiology), and post-stroke cognitive decline. Methods: This pooled cohort analysis from four US cohort studies (1971-2019) identified 1,143 dementia-free individuals with acute stroke during follow-up: 1,061 (92.8%) ischemic, 82 (7.2%) hemorrhagic, 49.9% female, 30.8% Black. Median age at stroke was 74.1 (IQR, 68.6, 79.3) years. Outcomes were change in global cognition (primary) and changes in executive function and memory (secondary). Outcomes were standardized as T-scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1-SD difference in cognition. Median follow-up for the primary outcome was 6.0 (IQR, 3.2, 9.2) years. Linear mixed-effects models estimated changes in cognition after stroke. Results: On average, the initial post-stroke global cognition score was 50.78 points (95% CI, 49.52, 52.03) in ischemic stroke survivors and did not differ in hemorrhagic stroke survivors (difference, -0.17 points [95% CI, -1.64, 1.30]; P=0.82) after adjusting for demographics and pre-stroke cognition. On average, ischemic stroke survivors showed declines in global cognition, executive function, and memory. Post-stroke declines in global cognition, executive function, and memory did not differ between hemorrhagic and ischemic stroke survivors. 955 ischemic strokes had subtypes: 200 (20.9%) cardioembolic, 77 (8.1%) large artery atherosclerotic, 207 (21.7%) lacunar/small vessel, 471 (49.3%) cryptogenic/other determined etiology. On average, small vessel stroke survivors showed declines in global cognition and memory, but not executive function. Initial post-stroke cognitive scores and cognitive declines did not differ between small vessel survivors and survivors of other ischemic stroke subtypes. Post-stroke vascular risk factor levels did not attenuate associations. Conclusion: Stroke survivors had cognitive decline in multiple domains. Declines did not differ by stroke type or ischemic stroke subtype.

The use of experimentally evolved coral photosymbionts for reef restoration.

The heat tolerance of corals is largely determined by their microbial photosymbionts (Symbiodiniaceae, colloquially known as zooxanthellae). Therefore, manipulating symbiont communities may enhance the ability of corals to survive summer heatwaves. Although heat-tolerant and -sensitive symbiont species occur in nature, even corals that harbour naturally tolerant symbionts have been observed to bleach during summer heatwaves. Experimental evolution (i.e., laboratory selection) of Symbiodiniaceae cultures under elevated temperatures has been successfully used to enhance their upper thermal tolerance, both in vitro and, in some instances, following their reintroduction into corals. In this review, we present the state of this intervention and its potential role within coral reef restoration, and discuss the next critical steps required to bridge the gap to implementation.

Orchidinae-205: A new genome-wide custom bait set for studying the evolution, systematics, and trade of terrestrial orchids.

Terrestrial orchids are a group of genetically understudied, yet culturally and economically important plants. The Orchidinae tribe contains many species that produce edible tubers that are used for the production of traditional delicacies collectively called 'salep'. Overexploitation of wild orchids in the Eastern Mediterranean and Western Asia threatens to drive many of these species to extinction, but cost-effective tools for monitoring their trade are currently lacking. Here we present a custom bait kit for target enrichment and sequencing of 205 novel genetic markers that are tailored to phylogenomic applications in Orchidinae s.l. A subset of 31 markers capture genes putatively involved in the production of glucomannan, a water-soluble polysaccharide that gives salep its distinctive properties. We tested the kit on 73 taxa native to the area, demonstrating universally high locus recovery irrespective of species identity, that exceeds the total sequence length obtained with alternative kits currently available. Phylogenetic inference with concatenation and coalescent approaches was robust and showed high levels of support for most clades, including some which were previously unresolved. Resolution for hybridizing and recently radiated lineages remains difficult, but could be further improved by analysing multiple haplotypes and the non-exonic sequences captured by our kit, with the promise to shed new light on the evolution of enigmatic taxa with a complex speciation history. Offering a step-up from traditional barcoding and universal markers, the genome-wide custom loci targeted by Orchidinae-205 are a valuable new resource to study the evolution, systematics and trade of terrestrial orchids.

Forecasting the Burden of Cardiovascular Disease and Stroke in the United States Through 2050-Prevalence of Risk Factors and Disease: A Presidential Advisory From the American Heart Association.

BACKGROUND: Cardiovascular disease and stroke are common and costly, and their prevalence is rising. Forecasts on the prevalence of risk factors and clinical events are crucial. METHODS: Using the 2015 to March 2020 National Health and Nutrition Examination Survey and 2015 to 2019 Medical Expenditure Panel Survey, we estimated trends in prevalence for cardiovascular risk factors based on adverse levels of Life's Essential 8 and clinical cardiovascular disease and stroke. We projected through 2050, overall and by age and race and ethnicity, accounting for changes in disease prevalence and demographics. RESULTS: We estimate that among adults, prevalence of hypertension will increase from 51.2% in 2020 to 61.0% in 2050. Diabetes (16.3% to 26.8%) and obesity (43.1% to 60.6%) will increase, whereas hypercholesterolemia will decline (45.8% to 24.0%). The prevalences of poor diet, inadequate physical activity, and smoking are estimated to improve over time, whereas inadequate sleep will worsen. Prevalences of coronary disease (7.8% to 9.2%), heart failure (2.7% to 3.8%), stroke (3.9% to 6.4%), atrial fibrillation (1.7% to 2.4%), and total cardiovascular disease (11.3% to 15.0%) will rise. Clinical CVD will affect 45 million adults, and CVD including hypertension will affect more than 184 million adults by 2050 (>61%). Similar trends are projected in children. Most adverse trends are projected to be worse among people identifying as American Indian/Alaska Native or multiracial, Black, or Hispanic. CONCLUSIONS: The prevalence of many cardiovascular risk factors and most established diseases will increase over the next 30 years. Clinical and public health interventions are needed to effectively manage, stem, and even reverse these adverse trends.

Dominant missense variants in SREBF2 are associated with complex dermatological, neurological, and skeletal abnormalities.

PURPOSE: We identified 2 individuals with de novo variants in SREBF2 that disrupt a conserved site 1 protease (S1P) cleavage motif required for processing SREBP2 into its mature transcription factor. These individuals exhibit complex phenotypic manifestations that partially overlap with sterol regulatory element binding proteins (SREBP) pathway-related disease phenotypes, but SREBF2-related disease has not been previously reported. Thus, we set out to assess the effects of SREBF2 variants on SREBP pathway activation. METHODS: We undertook ultrastructure and gene expression analyses using fibroblasts from an affected individual and utilized a fly model of lipid droplet (LD) formation to investigate the consequences of SREBF2 variants on SREBP pathway function. RESULTS: We observed reduced LD formation, endoplasmic reticulum expansion, accumulation of aberrant lysosomes, and deficits in SREBP2 target gene expression in fibroblasts from an affected individual, indicating that the SREBF2 variant inhibits SREBP pathway activation. Using our fly model, we discovered that SREBF2 variants fail to induce LD production and act in a dominant-negative manner, which can be rescued by overexpression of S1P. CONCLUSION: Taken together, these data reveal a mechanism by which SREBF2 pathogenic variants that disrupt the S1P cleavage motif cause disease via dominant-negative antagonism of S1P, limiting the cleavage of S1P targets, including SREBP1 and SREBP2.

Acute Cholecystitis Due to Lactococcus lactis and Single-Center Experience With Infections Due to Lactococcus spp.

Background: Lactococcus species are used to ferment milk to yogurt, cheese, and other products. The gram-positive coccus causes diseases in amphibia and fish and is a rare human pathogen. Patients and Methods: A 51-year-old male underwent laparoscopic cholecystectomy for acute and chronic calculous cholecystitis. Lactococcus lactis was isolated from pus from his gallbladder empyema. Results: Our institutional database was searched for other cases of Lactococcus spp. infections and four patients (2 males, 2 females; aged 51, 64, 78, and 80 years) were identified during a four-year period. The three other patients had positive blood cultures associated with pneumonia, toxic megacolon, and severe gastroenteritis. All isolates were monocultures with Lactococcus lactis (2), Lactococcus garvieae (1) and Lactococcus raffinolactis (1). Two patients died related to their sepsis. We report the second case of cholecystitis involving Lactococcus. Conclusions: Lactococcus is a very rare pathogen mainly causing blood stream infections but needs to be considered to cause serious surgical infections in humans.