Factor Eight Inhibitor Bypass Activity use in cardiac surgery: A propensity matched analysis of safety outcomes.

BACKGROUND: Bleeding during cardiac surgery may be refractory to standard interventions. Off-label use of Factor Eight Inhibitor Bypass Activity (FEIBA) has been described to treat such bleeding. However, reports of safety, particularly thromboembolic outcomes, show mixed results and reported cohorts have been small. METHODS: Adult patients undergoing cardiac surgery on cardiopulmonary bypass between July 1, 2018 and June 30, 2023 at Stanford Hospital were reviewed (n=3335). Patients who received FEIBA to treat post-cardiopulmonary bypass bleeding were matched with those who did not by propensity scores in a 1:1 ratio using nearest neighbor matching (n= 352 per group). The primary outcome was a composite outcome of thromboembolic complications including any one of deep vein thrombosis (DVT), pulmonary embolism (PE), unplanned coronary artery intervention, ischemic stroke, and acute limb ischemia, in the postoperative period. Secondary outcomes included renal failure, reoperation, postoperative transfusion, ICU length of stay (LOS), and 30-day mortality. RESULTS: 704 encounters were included in our propensity matched analysis. The mean dose of FEIBA administered was 7.3 ±5.5 units/kg. In propensity matched multivariate logistic regression models there was no statistically significant difference in odds ratios for thromboembolic outcomes, ICU LOS, or mortality. Patients who received >750 units of FEIBA had an increased odds ratio for acute renal failure (OR 4.14; 95% CI 1.61 to 10.36, p <0.001). In multivariate linear regression, patients receiving FEIBA were transfused more plasma and cryoprecipitate postoperatively. However, only the dose range of 501-750 units was associated with an increase in transfusion of RBCs (ß 2.73; 95% CI 0.68 to 4.78; p=0.009), and platelets (ß 1.74; 95% CI 0.85 to 2.63; p <0.001). CONCLUSIONS: Low dose FEIBA administration during cardiac surgery does not increase risk of thromboembolic events, ICU LOS, or mortality in a propensity matched cohort. Higher doses were associated with increased acute renal failure and postoperative transfusion. Further studies are required to establish the efficacy of activated factor concentrates to treat refractory bleeding during cardiac surgery.

Toxicokinetics and bioavailability of indoxacarb enantiomers and their new metabolites in rats.

Indoxacarb is a chiral insecticide that consists of two enantiomers, S-(+)-indoxacarb and R-(-)-indoxacarb, of which only S-(+)-indoxacarb has insecticidal activity. Previous enantioselective toxicology studies of indoxacarb focused mostly on simple environmental model organisms. The lack of a toxicology evaluation of indoxacarb conducted in a mammalian system could mean that the extent of the potential health risk posed by the insecticide to humans is not adequately known. In this study, we reported on a new pair of enantiomers, S-IN-RM294 and R-IN-RM294, derived from the metabolic breakdown of S-(+)-indoxacarb and R-(-)-indoxacarb, respectively, in rats. The toxicokinetics of S-(+)-indoxacarb, R-(-)-indoxacarb, S-IN-RM294, and R-IN-RM294 in rats were evaluated to provide a more comprehensive risk assessment of these molecules. The bioavailability and excretion rates of both S-(+)-indoxacarb and R-(-)-indoxacarb were relatively low, which may be due to their faster metabolism and accumulation in the tissues. In addition, there were significant differences in the metabolism and distribution between the two indoxacarb enantiomers and their metabolites in vivo. S-(+)-Indoxacarb was found to be more easily metabolized in the blood compared with R-(-)-indoxacarb, as shown by the differences in pharmacokinetic parameters between oral and intravenous administration. Analysis of their tissue distribution showed that S-(+)-indoxacarb was less likely to accumulate in most tissues. The results obtained for the two metabolites were consistent with those of the two parent compounds. S-IN-RM294 was more readily cleared from the blood and less likely to accumulate in the tissues compared with R-IN-RM294. Therefore, whether from the perspective of insecticidal activity or from the perspective of mammalian and environmental friendliness, the application of optically pure S-(+)-indoxacarb in agriculture may be a more efficient and safer strategy.

Some evidence supporting the use of optically pure R-(-)-diniconazole: Toxicokinetics and configuration conversion on chiral diniconazole.

Diniconazole is a chiral pesticide that exists in two enantiomers, R-(-)-diniconazole and S-(+)-diniconazole, with the R-enantiomer being much more active than the S-enantiomer. Previous enantioselective toxicology studies of diniconazole focused mostly on simple environmental model organisms. In this study, we evaluated the toxicokinetics of the two diniconazole enantiomers in rats and mice to provide a more comprehensive risk assessment. The two enantiomers displayed clear differences in their stereoselective contents in vivo. The t1/2 of R-(-)-diniconazole was 7.06 ± 3.35 h, whereas that of S-(+)-diniconazole was 9.14 ± 4.60 h, indicating that R-(-)-diniconazole was eliminated faster in vivo. The excretion rates of R-(-)-diniconazole and S-(+)-diniconazole were 4.08 ± 0.50 % and 2.68 ± 0.58 %, respectively, indicating more excretion of R-(-)-diniconazole. S-(+)-diniconazole had a higher bioavailability than R-(-)-diniconazole (52.19 % vs. 42.44 %). S-(+)-Diniconazole was also found in relatively high abundance in tissues such as the stomach, large intestine, small intestine, cecum, liver, kidney, brain, and testes, with the abundance being 1.71-2.48-fold that of R-(-)-diniconazole. The selective degradation of both enantiomers in the tissues and their mutual conversion in vivo were not observed, and this could indicate that configuration conversion did not contribute to the differences in the content of enantiomers in the tissues. Instead, such differences were mainly caused by the differences in affinity of each enantiomer for the tissues. Furthermore, investigation of the interconversion between optically pure R-(-)-diniconazole and S-(+)-diniconazole monomers in soil revealed no interconversion. All of the above results indicated no interconversion between R-(-)-diniconazole and S-(+)-diniconazole in vivo and in the soil, and that S-(+)-diniconazole tends to have a greater potential to accumulate in vivo. Thus, if only R-(-)-diniconazole is used as a pesticide, the negative impact on mammals and the environment will be reduced, suggesting that in agriculture, the application of optically pure R-(-)-diniconazole may be a better strategy.

Recovery from antibody-mediated biliary ductopenia and multiorgan inflammation after COVID-19 vaccination.

The coronavirus disease 2019 (COVID-19) pandemic has caused significant morbidity and mortality. Spike messenger RNA (mRNA)-based vaccines against severe acute respiratory syndrome coronavirus 2 may contribute to immune-mediated injuries. Here we present a case of a previously healthy 47-year-old man, who developed progressive jaundice 2 weeks after receiving his 3rd COVID-19 vaccination (1st mRNA-based vaccine). Apart from elevated serum total bilirubin levels (peaked at >70 mg/dL), deteriorating renal (blood urea nitrogen: peak, 108.5 mg/dL; creatinine: peak, 6 mg/dL) and exocrine pancreas (amylase: peak, 1717 U/L; lipase: peak, 5784 U/L) profiles were also seen. Vanishing bile duct syndrome characterized by ductopenia and cholangiocyte vacuolation, positive C4d deposition, and high titer of anti-angiotensin II type 1 receptor antibody consistently explain the overall antibody-mediated pathogenesis resembling antibody-mediated "rejection" in the solid organ transplant setting. Corticosteroids and plasmapheresis were administered, leading to gradual resolution of the symptoms, and the jaundice completely resolved 2 months later. In conclusion, we reported a case of antibody-mediated multiorgan injury after an mRNA COVID-19 vaccine, characterized by severe cholangiopathy. The patient recovered with corticosteroids and plasmapheresis, and long-term follow-up is necessary.

Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning". Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning. METHODS: Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis. RESULTS: Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster. CONCLUSION: Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases. IMPACT: Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

Transcriptome Analysis and Identification of Cadmium-Induced Oxidative Stress Response Genes in Different Meretrix meretrix Developmental Stages.

Cadmium (Cd) is one of the major pollutants in the aquatic environment, and it can easily accumulate in aquatic animals and result in toxic effects by changing the metabolism of the body, causing a serious impact on the immune system, reproductive system, and the development of offspring. The clam Meretrix meretrix is one of the commercially important species that is cultivated in large-scale aquaculture in China. To elucidate the underlying molecular mechanisms of Cd2+ in the developmental processes, fertilized eggs and larvae of M. meretrix at different developmental stages were exposed to Cd2+ (27.2 mg L-1 in natural seawater) or just natural seawater without Cd2+ (control), and high-throughput transcriptome sequencing and immunohistochemistry techniques were used to analyze the toxic effects of Cd on larvae at different early developmental stages. The results revealed 31,914 genes were differentially expressed in the different stages of M. meretrix development upon treatment with Cd2+. Ten of these genes were differentially expressed in all stages of development examined, but they comprised only six unigenes (CCO, Ndh, HPX, A2M, STF, and pro-C3), all of which were related to the oxidative stress response. Under Cd exposure, the expression levels of CCO and Ndh were significantly upregulated in D-shaped and pediveliger larvae, while pro-C3 expression was significantly upregulated in the fertilized egg, D-shaped larva, and pediveliger. Moreover, HPX, A2M, and STF expression levels in the fertilized egg and pediveliger larvae were also significantly upregulated. In contrast, CCO, Ndh, HPX, A2M, STF, and pro-C3 expression levels in the postlarva were all downregulated under Cd exposure. Besides the genes with changes in expression identified by the transcriptome, the expression of two other oxidative stress-related genes (MT and Nfr2) was also found to change significantly in the different developmental stages of M. meretrix upon Cd exposure, confirming their roles in combating oxidative stress. Overall, the findings of this study indicated that Cd would interfere with cellular respiration, ion transport, and immune response through inducing oxidative stress, and changes in the expression of oxidative stress-related genes might be an important step for M. meretrix to deal with the adverse effects of Cd at different stages of its development.

Determination of Optimal Eosinophil Thresholds for Diagnosis of Eosinophilic Gastritis and Duodenitis: A Pooled Analysis of 4 Prospective Studies.

INTRODUCTION: Consensus is lacking regarding the number of eosinophils (eos) required for the diagnosis of eosinophilic gastritis (EoG) and eosinophilic duodenitis (EoD). In addition, thresholds that require multiple high-power fields (HPFs) may not be practical for clinical use, resulting in delayed or missed diagnoses. This pooled analysis of 4 prospective studies assessed thresholds for multiple and single HPFs used to diagnose EoG and EoD. METHODS: Studies included the phase 2 ENIGMA1, the phase 3 ENIGMA2, an EoG/EoD prevalence study and a healthy volunteer study. Eos were quantified in the epithelium and lamina propria for controls and symptomatic participants. Symptomatic participants were further divided by histologic diagnosis of EoG/EoD. Peak eos counts were assessed, and the area under the receiver operating characteristic curve was analyzed to identify eos cutoffs for detection of EoG/EoD using the Youden index and sensitivity and specificity equality approaches. RESULTS: Based on the highest specificity analysis in 740 patients, the optimal eos threshold was determined to be 20 eos/HPF in 5 gastric HPFs for EoG (71% sensitivity and 94% specificity) and 33 eos/HPF in 3 duodenal HPFs for EoD (49% sensitivity and 100% specificity). For single-field analysis, the optimal eos thresholds were 33 eos/HPF (EoG) and 37 eos/HPF (EoD), both corresponding to 93% sensitivity and 93% specificity. DISCUSSION: Highly specific single gastric and duodenal HPF thresholds may have more clinical applicability than thresholds requiring multiple HPFs and could better facilitate development of practical histopathologic guidelines to aid pathologists and clinicians in the detection and diagnosis of EoG and/or EoD.

Identification of Novel Alkaloids from Portulaca oleracea L. and Characterization of Their Pharmacokinetics and GLP-1 Secretion-Promoting Activity in STC-1 Cells.

Six new alkaloids (compounds 1-6) were isolated from Portulaca oleracea L. The compounds were triple pair (1 and 2, 3 and 4, and 5 and 6) enantiomers, with 1, 3, and 5 in the R-configuration and 2, 4, and 6 in the S-configuration, and all could bind to SUR1 according to molecular docking analysis. Treatment of STC-1 cells with each compound led to an influx of intracellular Ca2+, eventually leading to the secretion of glucagon-like peptide-1 (GLP-1), with compound 3 giving the highest secretion, resulting in 24.3 ± 7.03% more GLP-1 than nateglinide-treated cells, suggesting that these alkaloids may be able to reduce blood glucose based on their ability to stimulate the release of GLP-1. Furthermore, compound 3 also exhibited slightly faster absorption than nateglinide, as shown by pharmacokinetic analysis conducted in rats. Therefore, the results showed that some purslane alkaloids (such as compound 3) had good pharmacological activity in vivo and may have preventive and therapeutic effects on diabetes.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

An Unexpected Finding of Poorly Differentiated Thyroid Carcinoma in a Toxic Thyroid Nodule.

Poorly differentiated thyroid carcinoma (PDTC) is a rare entity of thyroid cancer with an intermediate clinical behavior between differentiated and anaplastic thyroid cancer. Here we present a patient who was referred to the endocrinology clinic for evaluation of hyperthyroidism and multinodular goiter. Due to presence of right toxic thyroid nodules and compressive symptoms, the patient underwent right lobectomy and isthmectomy, where surgical pathology revealed PDTC in the right thyroid lobe. Based on this unusual case of malignancy within a toxic nodule, we propose further evaluation of hot nodules with concerning features such as growth rate. Furthermore, exploration of relative sodium iodine symporter (NIS) expression in PDTC may help us better understand how iodine uptake changes as PDTC develops, which may impact our approach to assessing and treating PDTC in the future.

Quantitative estimate of cognitive resilience and its medical and genetic associations.

BACKGROUND: We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined. METHODS: This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR. RESULTS: CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms. CONCLUSIONS: Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Assessment of toxic metal pollution in Yueqing Bay and the extent of metal-induced oxidative stress in Tegillarca granosa raised in this water.

Yueqing Bay is an important economic shellfish culture zone in Zhejiang Province, China. However, increased pollution in the water caused by toxic metals has led to the bioaccumulation of toxic metals in cockles such as Tegillarca granosa, and the consequence of toxic metal-associated toxicity in these animals. This study aimed to assess the concentration of toxic metals in the water and sediment in four different sites (Baisha, Qingjiang, Nanyue, and Wengyang) within Yueqing Bay and to evaluate the extent of metal bioaccumulation in T. granosa raised in the aquaculture farms located within the four sites, as well as the changes in biomarkers in T. granosa in response to the metals. The assessment was carried out at two different times of the year, January and July. The water and sediment samples taken from the aquaculture farms in Baisha (S1), Qingjiang (S2) and Nanyue (S3) were found to have a comprehensive toxic metal pollution index (Pc) <1, indicating that these farms were not polluted. However, the water and sediment samples taken from the aquaculture farm in Wengyang (S4) had a Pc between 1 and 2, indicating mild toxic metal pollution. The edible risk assessments (HQ) of T. granosa in all four farms were <1, and therefore, these cockles could be considered safe for human consumption. The toxic metal enrichment in T. granosa exhibited a strong correlation with the toxic metal content in the sediment. In all four farms, CAT and SOD activity levels in the visceral mass of T. granosa were higher than those found in the foot, and a significantly higher level of CAT activity was detected in July compared with January. Similarly, MDA and H2O2 contents in the visceral mass were also higher in July than in January. Tegillarca granosa individuals taken from S4 and S3 farms exhibited significantly higher levels of metallothionein (MT) mRNA and MDA compared with individuals from S1 and S2 farms. Furthermore, the levels of MDA and MT mRNA showed significant positive correlations with Cd, Cr, Hg, and Cu. Elevation of lipid peroxidation in these cockles coincided with increasing levels of endogenous antioxidants. The visceral mass of T. granosa and its MDA level could be used as a tissue indicator and a biochemical marker, respectively, for detecting toxic metal pollution. MT mRNA might also be used as a molecular marker of toxic metal pollution. The integrated biomarker response version 2 (IBRv2) values of the four aquaculture farms in Yueqing Bay showed the order S4 > S3 > S2 > S1, indicating that S4 had the most serious metal-induced stress. Furthermore, the IBRv2 values correlated with the Nemerow composite index (Pc) for all the cockles examined. Thus, as far as the contamination of aquaculture farms in Yueqing Bay by toxic metals is concerned, the aquaculture farm in Wengyang (S4) was mildly contaminated by toxic metals. However, the contamination was relatively low, presenting a low risk for the local population of T. granosa.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

Cross-species comparative analysis of single presynapses.

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Safety and efficacy of lirentelimab in patients with refractory indolent systemic mastocytosis: a first-in-human clinical trial.

BACKGROUND: Indolent systemic mastocytosis (ISM) is characterized by excessive mast cell (MC) accumulation and MC-driven signs and symptoms. Currently used therapies are not approved and have limited efficacy. Lirentelimab (AK002) is a monoclonal antibody against sialic acid-binding immunoglobulin-like lectin (Siglec)-8 that inhibits MC activation. OBJECTIVES: To determine the safety, tolerability and efficacy of lirentelimab in reducing the symptoms of ISM. METHODS: At a specialty centre for mastocytosis in Germany, we conducted a phase I first-in-human single-ascending and multidose clinical trial of lirentelimab in patients with ISM. Eligible adults had World Health Organization-confirmed ISM and an unsatisfactory response to available treatment. In part A, patients received a single dose of lirentelimab 0.0003, 0.001, 0.003, 0.01 or 0.03 mg kg-1; in part B, patients received one lirentelimab dose of 0.3 mg kg-1 or 1.0 mg kg-1; and in part C, patients received either 1.0 mg kg-1 lirentelimab every 4 weeks for 6 months or ascending doses of lirentelimab (one dose of 1 mg kg-1 followed by five doses of 3-10 mg kg-1 every 4 weeks). The primary endpoint was safety/tolerability. Secondary endpoints included changes from baseline in Mastocytosis Symptom Questionnaire (MSQ), Mastocytosis Activity Score (MAS) and Mastocytosis Quality of Life Questionnaire (MC-QoL) scores at 2 weeks after the final dose. RESULTS: In 25 patients with ISM (13 in parts A + B and 12 in part C; median age 51 years, 76% female, median 4.6 years from diagnosis), the most common treatment-related adverse events (AEs) were feeling hot (76%) and experiencing a headache (48%). No serious AEs occurred. Median MSQ and MAS symptom severity scores in part C improved (vs. baseline) across all symptoms [MSQ: skin (38-56%), gastrointestinal (49-60%), neurological (47-59%), musculoskeletal (26-27%); MAS: skin (53-59%), gastrointestinal (72-85%), neurological (20-57%), musculoskeletal (25%)]. Median MC-QoL scores improved across all domains: symptoms (39%), social life/functioning (42%), emotions (57%) and skin (44%). CONCLUSIONS: Lirentelimab was generally well tolerated and improved symptoms and quality of life in patients with ISM. The therapeutic potential of lirentelimab should be considered for ISM.

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine.

Many aspects of health and disease are modeled using the abstraction of a "pathway"-a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up-/down-regulation links between them-rewiring the molecular hardware. However, protein pathways and transcriptional networks exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences in behavioral science). If true, this would enable a new class of biomedical interventions that target aspects of the dynamic physiological "software" implemented by pathways and gene-regulatory networks. Here, we briefly review clinical and laboratory data that show how high-level cognitive inputs and mechanistic pathway modulation interact to determine outcomes in vivo. Further, we propose an expanded view of pathways from the perspective of basal cognition and argue that a broader understanding of pathways and how they process contextual information across scales will catalyze progress in many areas of physiology and neurobiology. We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today's pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.