Sensory integration of food and population density during the diapause exit decision involves insulin-like signaling in Caenorhabditis elegans.

Decisions made over long time scales, such as life cycle decisions, require coordinated interplay between sensory perception and sustained gene expression. The Caenorhabditis elegans dauer (or diapause) exit developmental decision requires sensory integration of population density and food availability to induce an all-or-nothing organismal-wide response, but the mechanism by which this occurs remains unknown. Here, we demonstrate how the Amphid Single Cilium J (ASJ) chemosensory neurons, known to be critical for dauer exit, perform sensory integration at both the levels of gene expression and calcium activity. In response to favorable conditions, dauers rapidly produce and secrete the dauer exit-promoting insulin-like peptide INS-6. Expression of ins-6 in the ASJ neurons integrates population density and food level and can reflect decision commitment since dauers committed to exiting have higher ins-6 expression levels than those of noncommitted dauers. Calcium imaging in dauers reveals that the ASJ neurons are activated by food, and this activity is suppressed by pheromone, indicating that sensory integration also occurs at the level of calcium transients. We find that ins-6 expression in the ASJ neurons depends on neuronal activity in the ASJs, cGMP signaling, and the pheromone components ascr#8 and ascr#2. We propose a model in which decision commitment to exit the dauer state involves an autoregulatory feedback loop in the ASJ neurons that promotes high INS-6 production and secretion. These results collectively demonstrate how insulin-like peptide signaling helps animals compute long-term decisions by bridging sensory perception to decision execution.

Peripheral blood interferon responses to toll-like receptor 1/2 signaling associate with longer survival in men with metastatic prostate cancer treated with Sipleucel-T.

Mounting evidence links systemic innate immunity with cancer immune surveillance. In advanced metastatic castration resistant prostate cancer (mCRPC), Black patients have been found to have increased inflammatory markers and longer survival after sipuleucel-T (sip-T) therapy, an FDA-approved, autologous cell therapy. We hypothesized these differences may be explained by previously reported ancestral differences in pattern recognition receptor (PRR) signaling, which broadly governs innate inflammation to control adaptive immune cell activation, chemotaxis, and functionality. We discovered that PBMC interferon (IFN)-ß responses to TLR1/2, a sensor of bacterial and gut microbiome constituents, associated with significantly longer survival after sip-T therapy in two separate cohorts of men with mCRPC (discovery cohort: n=106, HR=0.12, p=0.019; validation cohort: n=28, HR<0.01, p=0.047). Higher IFN-ß induction after TLR1/2 stimulation was associated with lower hazard ratios compared to biomarkers of vaccine potency and other prognostic factors in mCRPC. TLR1/2 dependent cytokine induction was stronger in Black individuals (1.2-fold higher for IFN-ß; p=0.04) but was associated with survival independently of race or numbers of vaccine-induced tumor antigen-specific T cells. IFN-ß responses to TLR1/2 signaling correlated with increased numbers of IFN-ß producing T cells after broad, tumor antigen independent stimulation. Thus, peripheral innate immunity differs by race, may predict survival after sip-T, and associates with peripheral T cell functionality in men with mCRPC.

Anesthetic Risk with Large Mediastinal Masses: A Management Framework Based on a Systematic Review.

BACKGROUND: Anesthesia administered to a patient with a large mediastinal mass engenders concern it may precipitate catastrophic acute hemodynamic or respiratory decompensation. A review of the available evidence is needed to define the degree of risk, the mechanisms, and preventative or reactive interventions to mitigate the risk. METHODS: A systematic review of the PubMed database was conducted of studies involving adults with large mediastinal masses undergoing a procedure or anesthesia; all types of publications were included that provided data regarding risks, mechanisms, or techniques to address potential decompensation. This literature involves primarily case reports and small retrospective series; no quality assessment was deemed appropriate. Evidence was synthesized according to the consensus judgment of the writing panel. RESULTS: Categories of low-, moderate-, high-, and very-high-risk emerged from review of the 72 included studies, based on the degree of symptoms, mass/chest ratio and degree of airway and/or vascular compression. This streamlines the preparation needed - minimal for low-risk and more extensive for higher-risk. Assessment of the impact of physiologic derangement stemming from the anatomic compression in individual patients provides a framework for anesthetic management, and back-up plans should decompensation occur. CONCLUSIONS: Despite limitations in the evidence inherent to a topic involving an uncommon but serious event, a framework was developed to streamline preparation for and management of patients with a large mediastinal mass requiring anesthesia in a rational manner.

Acute systemic macrophage depletion in osteoarthritic mice alleviates pain-related behaviors and does not affect joint damage.

Background: Osteoarthritis (OA) is a painful degenerative joint disease and a leading source of years lived with disability globally due to inadequate treatment options. Neuroimmune interactions reportedly contribute to OA pain pathogenesis. Notably, in rodents, macrophages in the DRG are associated with onset of persistent OA pain. Our objective was to determine the effects of acute systemic macrophage depletion on pain-related behaviors and joint damage using surgical mouse models in both sexes. Methods: We depleted CSF1R+ macrophages by treating male macrophage Fas-induced apoptosis (MaFIA) transgenic mice 8- or 16-weeks post destabilization of the medial meniscus (DMM) with AP20187 or vehicle control (10 mg/kg i.p., 1x/day for 5 days), or treating female MaFIA mice 12 weeks post partial meniscectomy (PMX) with AP20187 or vehicle control. We measured pain-related behaviors 1-3 days before and after depletion, and, 3-4 days after the last injection we examined joint histopathology and performed flow cytometry of the dorsal root ganglia (DRGs). In a separate cohort of male 8-week DMM mice or age-matched naïve vehicle controls, we conducted DRG bulk RNA-sequencing analyses after the 5-day vehicle or AP20187 treatment. Results: Eight- and 16-weeks post DMM in male mice, AP20187-induced macrophage depletion resulted in attenuated mechanical allodynia and knee hyperalgesia. Female mice showed alleviation of mechanical allodynia, knee hyperalgesia, and weight bearing deficits after macrophage depletion at 12 weeks post PMX. Macrophage depletion did not affect the degree of cartilage degeneration, osteophyte width, or synovitis in either sex. Flow cytometry of the DRG revealed that macrophages and neutrophils were reduced after AP20187 treatment. In addition, in the DRG, only MHCII+ M1-like macrophages were significantly decreased, while CD163+MHCII- M2-like macrophages were not affected in both sexes. DRG bulk RNA-seq revealed that Cxcl10 and Il1b were upregulated with DMM surgery compared to naïve mice, and downregulated in DMM after acute macrophage depletion. Conclusions: Acute systemic macrophage depletion reduced the levels of pro-inflammatory macrophages in the DRG and alleviated pain-related behaviors in established surgically induced OA in mice of both sexes, without affecting joint damage. Overall, these studies provide insight into immune cell regulation in the DRG during OA.

Greater exposure to PM2.5 and PM10 was associated with lower corneal nerve measures: the Maastricht study - a cross-sectional study.

BACKGROUND: Epidemiological and toxicological studies indicate that increased exposure to air pollutants can lead to neurodegenerative diseases. To further confirm this relationship, we evaluated the association between exposure to ambient air pollutants and corneal nerve measures as a surrogate for neurodegeneration, using corneal confocal microscopy. METHODS: We used population-based observational cross-sectional data from The Maastricht Study including N = 3635 participants (mean age 59.3 years, 51.6% were women, and 19.9% had type 2 diabetes) living in the Maastricht area. Using the Geoscience and hEalth Cohort COnsortium (GECCO) data we linked the yearly average exposure levels of ambient air pollutants at home address-level [particulate matter with diameters of ≤ 2.5 µm (PM2.5), and ≤ 10.0 µm (PM10), nitrogen dioxide (NO2), and elemental carbon (EC)]. We used linear regression analysis to study the associations between Z-score for ambient air pollutants concentrations (PM2.5, PM10, NO2, and EC) and Z-score for individual corneal nerve measures (corneal nerve bifurcation density, corneal nerve density, corneal nerve length, and fractal dimension). RESULTS: After adjustment for potential confounders (age, sex, level of education, glucose metabolism status, corneal confocal microscopy lag time, inclusion year of participants, smoking status, and alcohol consumption), higher Z-scores for PM2.5 and PM10 were associated with lower Z-scores for corneal nerve bifurcation density, nerve density, nerve length, and nerve fractal dimension [stß (95% CI): PM2.5 -0.10 (-0.14; -0.05), -0.04 (-0.09; 0.01), -0.11 (-0.16; -0.06), -0.20 (-0.24; -0.15); and PM10 -0.08 (-0.13; -0.03), -0.04 (-0.09; 0.01), -0.08 (-0.13; -0.04), -0.17 (-0.21; -0.12)], respectively. No associations were found between NO2 and EC and corneal nerve measures. CONCLUSIONS: Our population-based study demonstrated that exposure to higher levels of PM2.5 and PM10 are associated with higher levels of corneal neurodegeneration, estimated from lower corneal nerve measures. Our results suggest that air pollution may be a determinant for neurodegeneration assessed in the cornea and may impact the ocular surface health as well.

Single-cell RNA sequencing of pediatric Hodgkin lymphoma to study the inhibition of T cell subtypes.

Pediatric classic Hodgkin lymphoma (cHL) patients have a high survival rate but suffer from severe long-term side effects induced by chemo- and radiotherapy. cHL tumors are characterized by the low fraction (0.1%-10%) of malignant Hodgkin and Reed-Sternberg (HRS) cells in the tumor. The HRS cells depend on the surrounding immune cells for survival and growth. This dependence is leveraged by current treatments that target the PD-1/PD-L1 axis in cHL tumors. The development of more targeted therapies that are specific for the tumor and are therefore less toxic for healthy tissue compared with conventional chemotherapy could improve the quality of life of pediatric cHL survivors. Here, we applied single-cell RNA sequencing (scRNA-seq) on isolated HRS cells and the immune cells from the same cHL tumors. Besides TNFRSF8 (CD30), we identified other genes of cell surface proteins that are consistently overexpressed in HRS cells, such as NRXN3 and LRP8, which can potentially be used as alternative targets for antibody-drug conjugates or CAR T cells. Finally, we identified potential interactions by which HRS cells inhibit T cells, among which are the galectin-1/CD69 and HLA-II/LAG3 interactions. RNAscope was used to validate the enrichment of CD69 and LAG3 expression on T cells near HRS cells and indicated large variability of the interaction strength with the corresponding ligands between patients and between tumor tissue regions. In conclusion, this study identifies new potential therapeutic targets for cHL and highlights the importance of studying heterogeneity when identifying therapy targets, specifically those that target tumor-immune cell interactions.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling.

Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality. Methods: In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE. Findings: Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions. Interpretation: Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them. Funding: This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

Blood-based protein biomarkers during the acute ischemic stroke treatment window: a systematic review.

Background: Rapid and accurate acute ischemic stroke (AIS) diagnosis is needed to expedite emergent thrombolytic and mechanical thrombectomy treatment. Changes in blood-based protein biomarkers during the first 24 h of AIS, the time window for treatment, could complement imaging techniques and facilitate rapid diagnosis and treatment. Methods: We performed a systematic review according to PRISMA guidelines. MEDLINE, EMBASE, Cochrane Library, and Web of Science databases were searched for eligible studies comparing levels of blood-based protein biomarkers in AIS patients with levels in healthy controls and stroke mimics. Protein biomarkers from the following pathophysiological categories were included: neurovascular inflammation (MMP-9, TNF-alpha), endothelial integrity (VCAM-1, ICAM-1), cell migration (E-Selectin, P-Selectin, L-Selectin), markers of glial and neuronal origin (GFAP, S100, S100B, NSE), and cardiac dysfunction (BNP, NT-proBNP). The literature search was limited to English-language publications before November 7th, 2023. Results: A total of 61 studies from 20 different countries were identified, which included in total, 4,644 AIS patients, 2,242 stroke mimics, and 2,777 controls. Studies investigating TNF-alpha, MMP-9, VCAM-1, ICAM-1, E-Selectin, L-Selectin, GFAP, NSE, and S100B showed pronounced methodological heterogeneity, making between-study comparisons difficult. However, in 80% of NT-proBNP and BNP studies, and all P-selectin studies, higher biomarker levels were observed in AIS patients compared to healthy controls and/or patients with stroke mimics. Conclusion: None of the biomarkers included showed sufficient evidence for additional diagnostic benefit for AIS. Comprehensive standardized global multicenter studies are needed to (1) permit comparability, (2) enable valid statements about protein-based biomarkers, and (3) reflect real-world scenarios.

Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.

BACKGROUND: Beyond exposure to cigarette smoking and aging, the factors that influence lung function decline to incident chronic obstructive pulmonary disease (COPD) remain unclear. Advancements have been made in categorizing COPD into emphysema and airway predominant disease subtypes; however, predicting which healthy individuals will progress to COPD is difficult because they can exhibit profoundly different disease trajectories despite similar initial risk factors. This study aimed to identify clinical, genetic, and radiological features that are directly linked-and subsequently predict-abnormal lung function. METHODS AND FINDINGS: We employed graph modeling on 2,643 COPDGene participants (aged 45 to 80 years, 51.25% female, 35.1% African Americans; enrollment 11/2007-4/2011) with smoking history but normal spirometry at study enrollment to identify variables that are directly linked to future lung function abnormalities. We developed logistic regression and random forest predictive models for distinguishing individuals who maintain lung function from those who decline. Of the 131 variables analyzed, 6 were identified as informative to future lung function abnormalities, namely forced expiratory flow in the middle range (FEF25-75%), average lung wall thickness in a 10 mm radius (Pi10), severe emphysema, age, sex, and height. We investigated whether these features predict individuals leaving GOLD 0 status (normal spirometry according to Global Initiative for Obstructive Lung Disease (GOLD) criteria). Linear models, trained with these features, were quite predictive (area under receiver operator characteristic curve or AUROC = 0.75). Random forest predictors performed similarly to logistic regression (AUROC = 0.7), indicating that no significant nonlinear effects were present. The results were externally validated on 150 participants from Specialized Center for Clinically Oriented Research (SCCOR) cohort (aged 45 to 80 years, 52.7% female, 4.7% African Americans; enrollment: 7/2007-12/2012) (AUROC = 0.89). The main limitation of longitudinal studies with 5- and 10-year follow-up is the introduction of mortality bias that disproportionately affects the more severe cases. However, our study focused on spirometrically normal individuals, who have a lower mortality rate. Another limitation is the use of strict criteria to define spirometrically normal individuals, which was unavoidable when studying factors associated with changes in normalized forced expiratory volume in 1 s (FEV1%predicted) or the ratio of FEV1/FVC (forced vital capacity). CONCLUSIONS: This study took an agnostic approach to identify which baseline measurements differentiate and predict the early stages of lung function decline in individuals with previous smoking history. Our analysis suggests that emphysema affects obstruction onset, while airway predominant pathology may play a more important role in future FEV1 (%predicted) decline without obstruction, and FEF25-75% may affect both.

Inhibition of SARS-CoV-2 infection by Porphyromonas gingivalis and the oral microbiome.

The COVID-19 pandemic persists despite the availability of vaccines, and it is, therefore, crucial to develop new therapeutic and preventive approaches. In this study, we investigated the potential role of oral microbiome in SARS-CoV-2 infection. Using an in vitro SARS-CoV-2 pseudovirus infection assay, we found a potent inhibitory effect exerted by Porphyromonas gingivalis on SARS-CoV-2 infection mediated by known P. gingivalis compounds such as phosphoglycerol dihydroceramide (PGDHC) and gingipains as well as by unknown bacterial factors. We found that the gingipain-mediated inhibition of infection is likely due to cytotoxicity, whereas PGDHC inhibited virus infection by an unknown mechanism. Unidentified factors present in P. gingivalis supernatant inhibited SARS-CoV-2 likely via the fusion step of the virus life cycle. We addressed the role of other oral bacteria and found certain periodontal pathogens capable of inhibiting SARS-CoV-2 pseudovirus infection by inducing cytotoxicity on target cells. In the human oral cavity, we observed that the modulatory activity of oral microbial communities varied among individuals, in that some saliva-based cultures were capable of inhibiting while others were enhancing infection. These findings contribute to our understanding of the complex relationship between the oral microbiome and viral infections, offering potential avenues for innovative therapeutic strategies in combating COVID-19. IMPORTANCE: The oral microbiome is important in health and disease, and in this study, we addressed the potential role of the oral microbiome in COVID-19 infection. Our in vitro studies suggest that certain bacteria of the oral microbiome such as P. gingivalis produce compounds that could potentially inhibit SARS-CoV-2 infection. These findings elucidating the interactions between the oral microbiome and SARS-CoV-2 infection will be important in our understanding of COVID-19 pathogenesis and the development of innovative therapeutic and preventive strategies against COVID-19 infection.

Facet-controlled growth and soft-chemical exfoliation of two-dimensional titanium dioxide nanosheets.

TiO2 remains one of the most popular materials used in catalysts, photovoltaics, coatings, and electronics due to its abundance, chemical stability, and excellent catalytic properties. The tailoring of the TiO2 structure into two-dimensional nanosheets prompted the successful isolation of graphene and MXenes. In this review, facet-controlled TiO2 and monolayer titanate are outlined, covering their synthesis route and formation mechanism. The reactive facet of TiO2 is usually controlled by a capping agent. In contrast, the monolayer titanate is achieved by ion-exchange and delamination of layered titanates. Each route leads to 2D structures with unique physical and chemical properties, which expands its utilisation into several niche applications. We elaborate the detailed outlook for the future use and research studies of facet-controlled TiO2 and monolayer titanates. Advantages and disadvantages of both structures are provided, along with suggested applications for each type of 2D TiO2 nanosheets.

Dual Phosphodiesterase 3 and 4 Inhibitor Ensifentrine Reduces Exacerbation Rate and Risk in Patients With Moderate-to-Severe Chronic Obstructive Pulmonary Disease.

INTRODUCTION: Exacerbations in chronic obstructive pulmonary disease (COPD) can be life-threatening and lead to irreversible declines in lung function and quality of life. Medications that reduce exacerbation burden are an unmet need, as exacerbations put patients at risk for more exacerbations and decrease quality of life. Ensifentrine is a novel, first-in-class, selective, dual inhibitor of phosphodiesterase 3/4 with demonstrated nonsteroidal anti-inflammatory activity and bronchodilatory effects. RESEARCH QUESTION: Does ensifentrine reduce the rate and/or risk of COPD exacerbations? STUDY DESIGN AND METHODS: A prespecified, pooled analysis of the phase 3 clinical trials ENHANCE-1 (NCT04535986) and ENHANCE-2 (NCT04542057) was conducted to assess the effect of ensifentrine on exacerbation rate and risk (time to first exacerbation). The trials included symptomatic patients aged 40-80 years with moderate-to-severe COPD who received 3 mg twice-daily ensifentrine over 24 weeks or placebo. Subgroup analyses and frequent exacerbator transition risk were conducted post-hoc. RESULTS: In total, 975 ensifentrine-treated and 574 placebo-treated patients were included in the pooled analysis, including 62% on concomitant LAMA or LABA therapy and 18% on concomitant inhaled corticosteroid therapy. Ensifentrine was associated with significant reductions in the rate (rate ratio, 0.59; 95% CI, 0.43-0.80; P < 0.001) and risk (hazard ratio, 0.59; 95% CI, 0.44-0.81; P < 0.001) of moderate/severe exacerbations compared with placebo. Reductions in the rate and risk of exacerbations were generally consistent across patient subgroups, including age, sex, race, background maintenance medication use, chronic bronchitis, eosinophil count, COPD severity, and exacerbation history. Ensifentrine was associated with a numerical delay in transitioning from an infrequent exacerbator (GOLD B) to a frequent exacerbator (GOLD E) compared with placebo. CONCLUSION: Ensifentrine reduced the rate of exacerbations and increased the time to first exacerbation among patients with COPD across a broad range of clinically relevant subgroups.

Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans.

INTRODUCTION: Cone-beam computed tomography (CBCT) is widely used to detect jaw lesions, although CBCT interpretation is time-consuming and challenging. Artificial intelligence for CBCT segmentation may improve lesion detection accuracy. However, consistent automated lesion detection remains difficult, especially with limited training data. This study aimed to assess the applicability of pretrained transformer-based architectures for semantic segmentation of CBCT volumes when applied to periapical lesion detection. METHODS: CBCT volumes (n = 138) were collected and annotated by expert clinicians using 5 labels - "lesion," "restorative material," "bone," "tooth structure," and "background." U-Net (convolutional neural network-based) and Swin-UNETR (transformer-based) models, pretrained (Swin-UNETR-PRETRAIN), and from scratch (Swin-UNETR-SCRATCH), were trained with subsets of the annotated CBCTs. These models were then evaluated for semantic segmentation performance using the Sørensen-Dice coefficient (DICE), lesion detection performance using sensitivity and specificity, and training sample size requirements by comparing models trained with 20, 40, 60, or 103 samples. RESULTS: Trained with 103 samples, Swin-UNETR-PRETRAIN achieved a DICE of 0.8512 for "lesion," 0.8282 for "restorative materials," 0.9178 for "bone," 0.9029 for "tooth structure," and 0.9901 for "background." "Lesion" DICE was statistically similar between Swin-UNETR-PRETRAIN trained with 103 and 60 images (P > .05), with the latter achieving 1.00 sensitivity and 0.94 specificity in lesion detection. With small training sets, Swin-UNETR-PRETRAIN outperformed Swin-UNETR-SCRATCH in DICE over all labels (P < .001 [n = 20], P < .001 [n = 40]), and U-Net in lesion detection specificity (P = .006 [n = 20], P = .031 [n = 40]). CONCLUSIONS: Transformer-based Swin-UNETR architectures allowed for excellent semantic segmentation and periapical lesion detection. Pretrained, it may provide an alternative with smaller training datasets compared to classic U-Net architectures.

The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.

BACKGROUND: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data. Although ML has become well developed, studies mainly focus on engineering but lack medical outcomes. OBJECTIVE: This study aims for a scoping review of the evidence on how the use of ML on longitudinal EHRs can support the early detection and prevention of disease. The medical insights and clinical benefits that have been generated were investigated by reviewing applications in a variety of diseases. METHODS: This study was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A literature search was performed in 2022 in collaboration with a medical information specialist in the following databases: PubMed, Embase, Web of Science Core Collection (Clarivate Analytics), and IEEE Xplore Digital Library and computer science bibliography. Studies were eligible when longitudinal EHRs were used that aimed for the early detection of disease via ML in a prevention context. Studies with a technical focus or using imaging or hospital admission data were beyond the scope of this review. Study screening and selection and data extraction were performed independently by 2 researchers. RESULTS: In total, 20 studies were included, mainly published between 2018 and 2022. They showed that a variety of diseases could be detected or predicted, particularly diabetes; kidney diseases; diseases of the circulatory system; and mental, behavioral, and neurodevelopmental disorders. Demographics, symptoms, procedures, laboratory test results, diagnoses, medications, and BMI were frequently used EHR data in basic recurrent neural network or long short-term memory techniques. By developing and comparing ML and DL models, medical insights such as a high diagnostic performance, an earlier detection, the most important predictors, and additional health indicators were obtained. A clinical benefit that has been evaluated positively was preliminary screening. If these models are applied in practice, patients might also benefit from personalized health care and prevention, with practical benefits such as workload reduction and policy insights. CONCLUSIONS: Longitudinal EHRs proved to be helpful for support in health care. Current ML models on EHRs can support the detection of diseases in terms of accuracy and offer preliminary screening benefits. Regarding the prevention of diseases, ML and specifically DL models can accurately predict or detect diseases earlier than current clinical diagnoses. Adding personally responsible factors allows targeted prevention interventions. While ML models based on textual EHRs are still in the developmental stage, they have high potential to support clinicians and the health care system and improve patient outcomes.

Host-microbe interactions rewire metabolism in a C. elegans model of leucine breakdown deficiency.

In humans, defects in leucine catabolism cause a variety of inborn errors in metabolism. Here, we use Caenorhabditis elegans to investigate the impact of mutations in mccc-1, an enzyme that functions in leucine breakdown. Through untargeted metabolomic and transcriptomic analyses we find extensive metabolic rewiring that helps to detoxify leucine breakdown intermediates via conversion into previously undescribed metabolites and to synthesize mevalonate, an essential metabolite. We also find that the leucine breakdown product 3,3-hydroxymethylbutyrate (HMB), commonly used as a human muscle-building supplement, is toxic to C. elegans and that bacteria modulate this toxicity. Unbiased genetic screens revealed interactions between the host and microbe, where components of bacterial pyrimidine biosynthesis mitigate HMB toxicity. Finally, upregulated ketone body metabolism genes in mccc-1 mutants provide an alternative route for biosynthesis of the mevalonate precursor 3-hydroxy-3-methylglutaryl-CoA. Our work demonstrates that a complex host-bacteria interplay rewires metabolism to allow host survival when leucine catabolism is perturbed.

A Fully Decentralized Randomized Controlled Study of As-Needed Albuterol-Budesonide Fixed-Dose Inhaler in Mild Asthma: The BATURA Study Design.

Purpose: Decentralized clinical trials, where trial-related activities occur at locations other than traditional clinical sites(eg participant homes, local healthcare facilities), have the potential to improve trial access for people for whom time and/or distance constraints may impede participation. Albuterol-budesonide 180/160 µg pressurized metered-dose inhaler (pMDI) is FDA approved for the as-needed treatment or prevention of bronchoconstriction and to reduce the risk of exacerbations in patients with asthma 18 years or older. BATURA (NCT05505734) is a fully decentralized study, investigating as-needed albuterol-budesonide in participants with mild asthma. Methods: BATURA is a fully decentralized, phase 3b, randomized, double-blind, event-driven exacerbation study conducted in the United States. Participants aged ≥12 years using as-needed short-acting ß2-agonist (SABA), alone or with low-dose inhaled corticosteroid or leukotriene receptor antagonist maintenance, are randomized 1:1 to as-needed albuterol-budesonide 180/160 µg or albuterol 180 µg pMDI for up to 52 weeks (minimum 12 weeks). Participants continue their current maintenance therapy, if applicable. Participants must have used SABA for ≥2 days in the 2 weeks pre-enrollment and have an Asthma Impairment Risk Questionnaire score ≥2 at screening and randomization. All trial-related visits, including screening and consent, are conducted virtually, with study medication shipped directly to each participant's residence. The primary objective is to evaluate the efficacy of as-needed albuterol-budesonide versus albuterol on severe asthma exacerbation risk, measured by time-to-first severe asthma exacerbation (primary endpoint). Secondary endpoints include annualized rate of severe asthma exacerbation and total systemic corticosteroid exposure. Study medication use is captured via a Hailie sensor attached to the study medication pMDI. The intended sample size is 2500 participants. Conclusion: BATURA evaluates as-needed albuterol-budesonide in participants with mild asthma. The decentralized study model enables the trial to move out of research sites into participant homes, reducing participant burden and improving access.

Health disparities among cancer patients who received molecular testing for biomarker-directed therapy.

Health disparities present a barrier to successful oncology treatment. The potential for precision oncology to reduce health disparities has not previously been analyzed. We performed a retrospective analysis of 12,627 patients from six major cancer centers whose tumors underwent genomic testing at Caris Life Sciences between 2010-2020. Kaplan-Meier and Cox regression were used to describe and analyze overall survival (OS). Molecular and demographic features of the cohort were analyzed by Chi-square and analysis of variance (ANOVA) tests. Black patients composed 25% of the cohort and White patients 63%. Among this molecularly-tested cohort, there were minimal outcome differences based on race, geographic location, or poverty level. When analyzing the interaction of age, race, and sex, racial-based disparities were noted primarily for young non-White women in the study cohort, but were more pronounced for men and women of all ages in the broader patient population within the SEER database. Mutations in five genes-APC, EGFR, STK11, TP53, and KRAS-were found to affect OS among our cohort and their prevalence varied by race in specific tumor types. Real-world outcomes data in mutation-defined cohorts also provided additional context to previously reported therapeutic response trends. Our study shows that patients who undergo molecular testing display reduced racial health disparities compared to the general population, while persistent racial disparities are influenced by age and sex. Genomic-driven racial disparities should be examined at a tumor lineage-specific level. Increased access to molecular testing for all eligible patients may play a role in improving health equity.