bluuebunny/crossref_metadata_2025 huggingface dataset:
The Relationship Between Cardiac CT-based Left Atrial Structure and Epicardial Adipose Tissue and Postablation Atrial Fibrillation Recurrence Within 2 Years
Authors: Yuan Yuan , Zhu Yinsu , Wu Dandan , Wang Jun , Lin Shushen , Zhu Yaxin , Xu Yi , Wu Feiyun
Published: May 2024

Purpose:

The aim of this study was to explore the association of cardiac CT-based left atrium (LA) structural and functional parameters and left atrial epicardial adipose tissue (LA-EAT) parameters with postablation atrial fibrillation (AF) recurrence within 2 years.

Materials and Methods:

Contrast-enhanced cardiac CT images of 286 consecutive AF patients (median age: 65 y; 97 females) who underwent initial ablation between June 2018 and June 2020 were retrospectively analyzed. Structural and functional parameters of LA, including maximum and minimum volume and ejection fraction of LA and left atrial appendage (LAA), and LA-EAT volume, were measured. The body surface area indexed maximum and minimum volume of LA (LAVImax, LAVImin) and LAA (LAAVImax, LAAVImin), and LA-EAT volume index (LA-EATVI) were calculated. Independent predictors of AF recurrence were determined using Cox regression analysis. The clinical predictors were added to the imaging predictors to build a combined model (clinical+imaging). The predictive performance of the clinical, imaging, and combined models was assessed using the area under the receiver operating characteristics curve (AUC).

Results:

A total of 108 (37.8%) patients recurred AF within 2 years after ablation at a median follow-up of 24 months (IQR=11, 32). LA and LAA size and LA-EAT volume were significantly increased in patients with AF recurrence (P<0.05). After the multivariable regression analysis, LA-EATVI, LAAVImax, female sex, AF duration, and stroke history were independent predictors for AF recurrence. The combined model exhibited superior predictive performance compare to the clinical model (AUC=0.712 vs. 0.641, P=0.023) and the imaging model (AUC=0.712 vs. 0.663, P=0.018).

Conclusion:

Cardiac CT-based LA-EATVI and LAAVImax are independent predictors for postablation AF recurrence within 2 years and may provide a complementary value for AF recurrence risk assessment.
Pre-PCI CT-FFR Predicts Target Vessel Failure After Stent Implantation
Authors: Wang Zewen , Tang Chunxiang , Zuo Rui , Zhou Aiming , Xu Wei , Zhong Jian , Xu Zhihan , Zhang Longjiang
Published: May 2024

Objectives:

To investigate the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) before percutaneous coronary intervention (PCI) to predict target vessel failure (TVF) after stent implantation.

Methods:

This retrospective study included 429 patients (429 vessels) who underwent PCI and stent implantation after CCTA within 3 months. All patients underwent coronary stent implantation between January 2012 and December 2019. A dedicated workstation (Syngo Via, Siemens) was used to analyze and measure the CT-FFR value. The cut-off values of pre-PCI CT-FFR for predicting TVF were defined as 0.80 and the value using the log-rank maximization method, respectively. The primary outcome was TVF, defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization (TVR), which was a secondary outcome.

Results:

During a median 64.0 months follow-up, the cumulative incidence of TVF was 7.9% (34/429). The cutoff value of pre-PCI CT-FFR based on the log-rank maximization method was 0.74, which was the independent predictor for TVF [hazard ratio (HR): 2.61 (95% CI: 1.13, 6.02); P=0.024] and TVR [HR: 3.63 (95%CI: 1.25, 10.51); P=0.018]. Compared with the clinical risk factor model, pre-PCI CT-FFR significantly improved the reclassification ability for TVF [net reclassification improvement (NRI), 0.424, P<0.001; integrative discrimination index (IDI), 0.011, P=0.022]. Adding stent information to the prediction model resulted in an improvement in reclassification for the TVF (C statistics: 0.711, P=0.001; NRI: 0.494, P<0.001; IDI: 0.020, P=0.028).

Conclusions:

Pre-PCI CT-FFR ≤0.74 was an independent predictor for TVF or TVR, and integration of clinical, pre-PCI CT-FFR, and stent information models can provide a better risk stratification model in patients with stent implantation.
Scanning the aged to minimize missed injury: An Eastern Association for the Surgery of Trauma multicenter study
Authors: Ho Vanessa P. , Kishawi Sami K. , Hill Hannah , O'Brien Joseph , Ratnasekera Asanthi , Seng Sirivan Suon , Ton Trieu Hai , Butts Christopher A. , Muller Alison , Diaz Bernardo Fabian , Baltazar Gerard A. , Petrone Patrizio , Pacheco Tulio Brasileiro Silva , Morrissey Shawna , Chung Timothy , Biller Jessica , Jacobson Lewis E. , Williams Jamie M. , Nebughr Cole S. , Udekwu Pascal O. , Tann Kimberly , Piehl Charles , Veatch Jessica M. , Capasso Thomas J. , Kuncir Eric J. , Kodadek Lisa M. , Miller Samuel M. , Altan Defne , Mentzer Caleb , Damiano Nicholas , Burke Rachel , Earley Angela , Doris Stephanie , Villa Erica , Wilkinson Michael C. , Dixon Jacob K. , Wu Esther , Moncrief Melissa L. , Palmer Brandi , Herzing Karen , Egodage Tanya , Williams Jennifer , Haan James , Lightwine Kelly , Colling Kristin P. , Harry Melissa L. , Nahmias Jeffry , Tay-Lasso Erika , Cuschieri Joseph , Hinojosa Christopher J. , Claridge Jeffrey A.
Published: May 2024

BACKGROUND

Despite the high incidence of blunt trauma in older adults, there is a lack of evidence-based guidance for computed tomography (CT) imaging in this population. We aimed to identify an algorithm to guide use of a pan-scan (head/cervical spine [C-spine]/torso) or a selective scan (head/C-spine ± torso). We hypothesized that a patient's initial history and examination could be used to guide imaging.

METHODS

We prospectively studied blunt trauma patients 65 years or older at 18 Level I/II trauma centers. Patients presenting >24 hours after injury or who died upon arrival were excluded. We collected history and physical elements and final injury diagnoses. Injury diagnoses were categorized into CT body regions of head/C-spine or Torso (chest, abdomen/pelvis, and thoracolumbar spine). Using machine learning and regression modeling as well as a priori clinical algorithms based, we tested various decision rules against our data set. Our priority was to identify a simple rule, which could be applied at the bedside, maximizing sensitivity and negative predictive value (NPV) to minimize missed injuries.

RESULTS

We enrolled 5,498 patients with 3,082 injuries. Nearly half (n = 2,587 [47.1%]) had an injury within the defined CT body regions. No rule to guide a pan-scan could be identified with suitable sensitivity/NPV for clinical use. A clinical algorithm to identify patients for pan-scan, using a combination of physical examination findings and specific high-risk criteria, was identified and had a sensitivity of 0.94 and NPV of 0.86. This rule would have identified injuries in all but 90 patients (1.6%) and would theoretically spare 11.9% (655) of blunt trauma patients a torso CT.

CONCLUSION

Our findings advocate for head/C-spine CT in all geriatric patients with the addition of torso CT in the setting of positive clinical findings and high-risk criteria. Prospective validation of this rule could lead to streamlined diagnostic care of this growing trauma population.

LEVEL OF EVIDENCE

Diagnostic Tests or Criteria; Level II.
Cytoprotective 3K3A-activated protein C and plasma: A comparison of therapeutics for the endotheliopathy of trauma
Authors: Thielen Otto , Stafford Preston , Debot Margot , Kelher Marguerite , Mitra Sanchayita , Hallas William , Gallagher Lauren T. , Schaid Terry , Stocker Benjamin , Ramser Benjamin , D’Alessandro Angelo , Hansen Kirk , Silliman Christopher C. , Moore Ernest , Mosnier Laurent , Griffin John , Cohen Mitchell
Published: May 2024

BACKGROUND

Both healthy plasma and cytoprotective aPC (3K3A-aPC) have been shown to mitigate the endotheliopathy of trauma (EoT), but optimal therapeutics remain unknown. Our aim was therefore to determine optimal therapies to mitigate EoT by investigating the effectiveness of 3K3A-aPC with and without plasma-based resuscitation strategies.

METHODS

Electric cell-substrate impedance sensing (ECIS) was used to measure real-time permeability changes in endothelial cells. Cells were treated with a 2-μg/mL solution of aPC 30 minutes prior to stimulation with plasma taken from severely injured trauma patients (ISS > 15 and BD < −6) (TP). Healthy plasma, or plasma frozen within 24 hours (FP24), was added concomitantly with TP. Cells treated with thrombin and untreated cells were included in this study as control groups.

RESULTS

A dose-dependent difference was found between the 5% and 10% plasma-treated groups when human umbilical vein endothelial cells were simultaneously stimulated with TP (μd, 7.346; 95% confidence interval [CI], 4.574–10.12). There was no difference when compared with TP alone in the 5% (μd, 5.713; 95% CI, −1.751 to 13.18) or 10% group (μd, −1.633; 95% CI, −9.097 to 5.832). When 3K3A-aPC was added to plasma and TP, the 5% group showed improvement in permeability compared with TP alone (μd, 10.11; 95% CI, 2.642 to 17.57), but there was no difference in the 10% group (μd −1.394; 95% CI, −8.859 to 6.070). The combination of 3K3A-aPC, plasma, and TP at both the 5% plasma (μd, −28.52; 95% CI, −34.72 to −22.32) and 10% plasma concentrations (μd, −40.02; 95% CI, −46.22 to −33.82) had higher intercellular permeability than the 3K3A-aPC preincubation group.

CONCLUSION

Our data show that FP24, in a posttrauma environment, pretreatment with 3K3A-aPC can potentially mitigate the EoT to a greater degree than FP24 with or without 3K3A-aPC. Although further exploration is needed, this represents a potentially ideal and perhaps superior therapeutic treatment for the dysregulated thromboinflammation of injured patients.
Development and Initial Characterization of Pigs withDNAI1Mutations and Primary Ciliary Dyskinesia
Authors: Abou Alaiwa Mahmoud A. , Hilkin Brie M. , Price Margaret P. , Gansemer Nicholas D. , Rector Michael R. , Stroik Mal R. , Powers Linda S. , Whitworth Kristin M. , Samuel Melissa S. , Jain Akansha , Ostedgaard Lynda S. , Ernst Sarah E. , Philibert Winter , Boyken Linda D. , Moninger Thomas O. , Karp Phillip H. , Hornick Douglas B. , Sinn Patrick L. , Fischer Anthony J. , Pezzulo Alejandro A. , McCray Paul B. , Meyerholz David K. , Zabner Joseph , Prather Randy S. , Welsh Michael J. , Stoltz David A.
Published: May 2024

Mutations in more than 50 different genes cause primary ciliary dyskinesia (PCD) by disrupting the activity of motile cilia that facilitate mucociliary transport (MCT). Knowledge of PCD has come from studies identifying disease-causing mutations, characterizing structural cilia abnormalities, finding genotype-phenotype relationships, and studying the cell biology of cilia. Despite these important findings, we still lack effective treatments and people with PCD have significant pulmonary impairment. As with many other diseases, a better understanding of pathogenic mechanisms may lead to effective treatments. To pursue disease mechanisms, we used CRISPR-Cas9 to develop a PCD pig with a disruptedDNAI1gene. PCD pig airway cilia lacked the outer dynein arm and had impaired beating. MCT was impaired under both baseline conditions and after cholinergic stimulation in PCD pigs. Neonatal PCD pigs developed neonatal respiratory distress with evidence of atelectasis, air trapping, and airway mucus obstruction. Despite airway mucus accumulation, lung bacterial counts were similar between neonatal wild-type and PCD pigs. Sinonasal disease was present in all neonatal PCD pigs. Older PCD pigs developed worsening airway mucus obstruction, inflammation, and bacterial infection. This pig model closely mimics the disease phenotype seen in people with PCD and can be used to better understand the pathophysiology of PCD airway disease.
CRASH2p: Closed-loop Two Photon Imaging in a Freely Moving Animal
Authors: McNulty Paul , Wu Rui , Yamaguchi Akihiro , Heckscher Ellie H , Haas Andrew , Nwankpa Amajindi , Skanata Mirna Mihovilovic , Gershow Marc H
Published: May 2024

Direct measurement of neural activity in freely moving animals is essential for understanding how the brain controls and represents behaviors. Genetically encoded calcium indicators report neural activity as changes in fluorescence intensity, but brain motion confounds quantitative measurement of fluorescence. Translation, rotation, and deformation of the brain and the movements of intervening scattering or auto-fluorescent tissue all alter the amount of fluorescent light captured by a microscope. Compared to single-photon approaches, two photon microscopy is less sensitive to scattering and off-target fluorescence, but more sensitive to motion, and two photon imaging has always required anchoring the microscope to the brain. We developed a closed-loop resonant axial-scanning high-speed two photon (CRASH2p) microscope for real-time 3D motion correction in unrestrained animals, without implantation of reference markers. We complemented CRASH2p with a novel scanning strategy and a multi-stage registration pipeline. We performed volumetric ratiometrically corrected functional imaging in the CNS of freely movingDrosophilalarvae and discovered previously unknown neural correlates of behavior.
Identifying maximally informative signal-aware representations of single-cell data using the Information Bottleneck
Authors: Dubnov Serafima , Piran Zoe , Soreq Hermona , Nitzan Mor
Published: May 2024

Rapid advancements in single-cell RNA-sequencing (scRNA-seq) technologies revealed the richness of myriad attributes encompassing cell identity, such as diversity of cell types, organ-of-origin, or developmental stage. However, due to the large scale of the data, obtaining an interpretable compressed representation of cellular states remains a computational challenge. For this task we introduce bioIB, a method based on the Information Bottleneck algorithm, designed to extract an optimal compressed representation of scRNA-seq data with respect to a desired biological signal, such as cell type or disease state. BioIB generates a hierarchy of weighted gene clusters, termed metagenes, that maximize the information regarding the signal of interest. Applying bioIB to a scRNA-seq atlas of differentiating macrophages and setting either the organ-of-origin or the developmental stage as the signal of interest provided two distinct signal-specific sets of metagenes that captured the attributes of the respective signal. BioIB’s representation can also be used to expose specific cellular subpopulations, for example, when applied to a single-nucleus RNA-sequencing dataset of an Alzheimer’s Disease mouse model, it identified a subpopulation of disease-associated astrocytes. Lastly, the hierarchical structure of metagenes revealed interconnections between the corresponding biological processes and cellular populations. We demonstrate this over hematopoiesis scRNA-seq data, where the metagene hierarchy reflects the developmental hierarchy of hematopoietic cell types.

Significance

Single-cell gene expression represents an invaluable resource, encoding multiple aspects of cellular identity. However, its high complexity poses a challenge for downstream analyses. We introduce bioIB, a methodology based on the Information Bottleneck, that compresses data while maximizing the information about a biological signal-of-interest, such as disease state. bioIB generates a hierarchy of metagenes, probabilistic gene clusters, which compress the data at gradually changing resolutions, exposing signal-related processes and informative connections between gene programs and their corresponding cellular populations. Across diverse single-cell datasets, bioIB generates distinct metagene representations of the same dataset, each maximally informative relative to a different signal; uncovers signal-associated cellular populations; and produces a metagene hierarchy that reflects the developmental hierarchy of the underlying cell types.
Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition
Authors: Thomas Oshane O. , Maga A. Murat
Published: May 2024

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.
Skeletal muscle PGC-1α remodels mitochondrial phospholipidome but does not alter energy efficiency for ATP synthesis
Authors: Karasawa Takuya , Choi Ran Hee , Meza Cesar A. , Maschek J. Alan , Cox James E. , Funai Katsuhiko
Published: May 2024

Background

Exercise training is thought to improve the mitochondrial energy efficiency of skeletal muscle. Some studies suggest exercise training increases the efficiency for ATP synthesis by oxidative phosphorylation (OXPHOS), but the molecular mechanisms are unclear. We have previously shown that exercise remodels the lipid composition of mitochondrial membranes, and some of these changes could contribute to improved OXPHOS efficiency (ATP produced by O2consumed or P/O). Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) is a transcriptional co-activator that coordinately regulates exercise-induced adaptations including mitochondria. We hypothesized that increased PGC-1α activity is sufficient to remodel mitochondrial membrane lipids and promote energy efficiency.

Methods

Mice with skeletal muscle-specific overexpression of PGC-1α (MCK-PGC-1α) and their wildtype littermates were used for this study. Lipid mass spectrometry and quantitative PCR were used to assess muscle mitochondrial lipid composition and their biosynthesis pathway. The abundance of OXPHOS enzymes was determined by western blot assay. High-resolution respirometry and fluorometry analysis were used to characterize mitochondrial bioenergetics (ATP production, O2consumption, and P/O) for permeabilized fibers and isolated mitochondria.

Results

Lipidomic analyses of skeletal muscle mitochondria from wildtype and MCK-PGC-1α mice revealed that PGC-1α increases the concentrations of cone-shaped lipids such as phosphatidylethanolamine (PE), cardiolipin (CL), and lysophospholipids, while decreases the concentrations of phosphatidylcholine (PC), phosphatidylinositol (PI) and phosphatidic acid (PA). However, while PGC-1α overexpression increased the abundance of OXPHOS enzymes in skeletal muscle and the rate of O2consumption (JO2), P/O values were unaffected with PGC-1α in permeabilized fibers or isolated mitochondria.

Conclusions

Collectively, overexpression of PGC-1α promotes the biosynthesis of mitochondrial PE and CL but neither PGC-1α nor the mitochondrial membrane lipid remodeling induced in MCK-PGC-1α mice is sufficient to increase the efficiency for mitochondrial ATP synthesis. These findings suggest that exercise training may increase OXPHOS efficiency by a PGC-1α-independent mechanism, and question the hypothesis that mitochondrial lipids directly affect OXPHOS enzymes to improve efficiency for ATP synthesis.
A Rigorous Multi-Laboratory Study of Known PDAC Biomarkers Identifies Increased Sensitivity and Specificity Over CA19-9 Alone
Authors: Haab Brian , Qian Lu , Staal Ben , Jain Maneesh , Fahrmann Johannes , Worthington Christine , Prosser Denise , Velokokhatnaya Liudmila , Lopez Camden , Tang Runlong , Hurd Mark W. , Natarajan Gopalakrishnan , Kumar Sushil , Smith Lynette , Hanash Sam , Batra Surinder K. , Maitra Anirban , Lokshin Anna , Huang Ying , Brand Randall E.
Published: May 2024

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.
Analytic Challenges in Clinical Trials of Early Alzheimer’s Disease
Authors: Mallinckrodt Craig , Lipkovich Ilya , Hendrix Suzanne , Dickson Sam , Molenberghs Geert
Published: May 2024

The present investigation assessed how the heavily right-skewed data seen in recently reported results in Alzheimer’s Disease (AD) clinical trials influenced treatment contrasts when data were analyzed via the typical mixed-effects model for repeated measures (MMRM) versus robust regression (RR) and the non-parametric Hodges-Lehman estimator (HL).

Results in simulated data patterned after AD trials showed that imbalance across treatment arms in the number of patients in the extreme right tail (those with rapid disease progression) frequently occurred by chance alone. Each analysis method controlled Type I error at or below the nominal level. The RR analysis yielded smaller standard errors, and more power than MMRM and HL. In datasets with appreciable imbalance in the number of rapid progressing patients, MMRM results favored the treatment arm with fewer rapid progressors. Results from HL showed the same trend, but to a lesser degree. Robust regression yielded similar results regardless of the ratio of rapid progressors. Although more research is needed over a wider range of scenarios, it should not be assumed that MMRM is the optimal approach for trials in early Alzheimer’s Disease.
Detection of anti-nucleocapsid antibodies associated with primary SARS-CoV-2 infection in unvaccinated and vaccinated blood donors
Authors: Grebe Eduard , Stone Mars , Spencer Bryan R. , Akinseye Akintunde , Wright David , Di Germanio Clara , Bruhn Roberta , Zurita Karla G. , Contestable Paul , Green Valerie , Lanteri Marion C. , Saa Paula , Biggerstaff Brad J. , Coughlin Melissa M. , Kleinman Steve , Custer Brian , Jones Jefferson M. , Busch Michael P.
Published: May 2024

Anti-nucleocapsid (N) antibody assays can be used to estimate SARS-CoV-2 infection prevalence in regions employing anti-spike based COVID-19 vaccines. However, poor sensitivity of anti-N assays in detecting infections after vaccination (VI) has been reported. To support serological monitoring of infections, including VI, in a large blood donor cohort (N=142,599), we derived a lower cutoff for identifying previous infection using the Ortho VITROS Anti-SARS-CoV-2 Total-N Antibody assay, improving sensitivity while maintaining specificity >98%. Sensitivity was validated in samples donated after self-reported infections diagnosed by a swab-based test. Sensitivity for first infections in unvaccinated donors was 98.1% (95% CI: 98.0,98.2) and for VI was 95.6% (95.6,95.7), using the standard cutoff. Regression analysis showed sensitivity was reduced in the Delta compared to Omicron period, in older donors, asymptomatic infections, ≤30 days after infection and for VI. The standard Ortho anti-N threshold demonstrated good sensitivity, which was modestly improved with the revised cutoff.
Spatial variation in housing construction material in low- and middle-income countries: a Bayesian spatial prediction model of a key infectious diseases risk factor and social determinant of health
Authors: Colston Josh M , Fang Bin , Nong Malena K , Chernyavskiy Pavel , Annapareddy Navya , Lakshmi Venkataraman , Kosek Margaret N
Published: May 2024

Housing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions of the world where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to the major dwelling components to identify and target resources towards populations at risk. The aim of this study was to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map the predicted coverage across the world’s low- and middle-income countries (LMICs). Data relating to the materials used in dwelling construction were sourced from nationally representative household surveys conducted since 2005. Materials used for construction of flooring, walls, and roof were reclassified as improved or unimproved. Households lacking location information were georeferenced using a novel methodology, and a suite of environmental and demographic spatial covariates were extracted at those locations for use as model predictors. Integrated nested Laplace approximation (INLA) models were fitted to obtain and map predicted probabilities for each dwelling component. The dataset compiled included information from households in 283,000 clusters from 350 surveys. Low coverage of improved housing was predicted across the Sahel and southern Sahara regions of Africa, much of inland Amazonia, and areas of the Tibetan plateau. Coverage of improved roofs and walls was high in the Central Asia, East Asia and Pacific and Latin America and the Caribbean regions, while improvements in all three components, but most notably floors, was low in Sub-Saharan Africa. Human development was by far the strongest determinant of dwelling component quality, though vegetation greenness and land use were also relevant markers These findings are made available to the reader as files that can be imported into a GIS for integration into relevant analysis to derive improved estimates of preventable health burdens attributed to housing.
EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints
Authors: Kharitonova Elena V. , Sun Quan , Ockerman Frank , Chen Brian , Zhou Laura Y. , Cao Hongyuan , Mathias Rasika A. , Auer Paul L. , Ober Carole , Raffield Laura M. , Reiner Alexander P. , Cox Nancy J. , Kelada Samir , Tao Ran , Li Yun
Published: May 2024

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.
Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression
Authors: Aboharb Farid , Davoudian Pasha A , Shao Ling-Xiao , Liao Clara , Rzepka Gillian N , Wojtasiewicz Cassandra , Indajang Jonathan , Dibbs Mark , Rondeau Jocelyne , Sherwood Alexander M , Kaye Alfred P , Kwan Alex C
Published: May 2024

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for characterizing and validating psychoactive drugs with psychedelic properties.
Predicting the infecting dengue serotype from antibody titre data using machine learning
Authors: Cracknell Daniels Bethan , Buddhari Darunee , Hunsawong Taweewun , Iamsirithaworn Sopon , Farmer Aaron R , Cummings Derek A.T. , Anderson Kathryn B. , Dorigatti Ilaria
Published: May 2024

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correlates of protection. The plaque reduction neutralisation test (PRNT) is the gold standard assay for measuring serotype-specific antibodies, but this test cannot differentiate homotypic and heterotypic antibodies and characterising the infection history is challenging. To address this, we present an analysis of pre- and post-infection antibody titres measured using the PRNT, collected from a prospective cohort of Thai children. We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. The models were validated against the true infecting serotype, identified using RT-PCR. Model performance was calculated using 100 bootstrap samples of the train and out-of-sample test sets. Our analysis showed that, on average, the greatest change in titre was against the infecting serotype. However, in 53.4% (109/204) of the subjects, the highest titre change did not correspond to the infecting serotype, including in 34.3% (12/35) of dengue-naïve individuals. The highest post-infection titres of seropositive cases were more likely to match the serotype of the highest pre-infection titre than the infecting serotype, consistent with original antigenic sin. Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9-89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. Incorporating additional spatiotemporal data improved accuracy to 80.6% (95% CI 63.2-94.7%), while using only post-infection titres as predictor variables yielded an accuracy of 71.7% (95% CI 57.9-84.2%). These results show that machine learning classifiers can be used to overcome challenges in interpreting PRNT titres, making them useful tools in investigating dengue immune dynamics, infection history and identifying serotype-specific correlates of protection, which in turn can support the evaluation of clinical trial endpoints and vaccine development.
Compound Mutations in the Abl1 Kinase Cause Inhibitor Resistance by Shifting DFG Flip Mechanisms and Relative State Populations
Authors: Monteiro da Silva Gabriel , Lam Kyle , Dalgarno David C. , Rubenstein Brenda M.
Published: May 2024

The intrinsic dynamics of most proteins are central to their function. Protein tyrosine kinases such as Abl1 undergo significant conformational changes that modulate their activity in response to different stimuli. These conformational changes constitute a conserved mechanism for self-regulation that dramatically impacts kinases’ affinities for inhibitors. Few studies have attempted to extensively sample the pathways and elucidate the mechanisms that underlie kinase inactivation. In large part, this is a consequence of the steep energy barriers associated with many kinase conformational changes, which present a significant obstacle for computational studies using traditional simulation methods. Seeking to bridge this knowledge gap, we present a thorough analysis of the “DFG flip” inactivation pathway in Abl1 kinase. By leveraging the power of the Weighted Ensemble methodology, which accelerates sampling without the use of biasing forces, we have comprehensively simulated DFG flip events in Abl1 and its inhibitor-resistant variants, revealing a rugged landscape punctuated by potentially druggable intermediate states. Through our strategy, we successfully simulated dozens of uncorrelated DFG flip events distributed along two principal pathways, identified the molecular mechanisms that govern them, and measured their relative probabilities. Further, we show that the compound Glu255Lys/Val Thr315Ile Abl1 variants owe their inhibitor resistance phenotype to an increase in the free energy barrier associated with completing the DFG flip. This barrier stabilizes Abl1 variants in conformations that can lead to loss of binding for Type-II inhibitors such as Imatinib or Ponatinib. Finally, we contrast our Abl1 observations with the relative state distributions and propensity for undergoing a DFG flip of evolutionarily-related protein tyrosine kinases with diverging Type-II inhibitor binding affinities. Altogether, we expect that our work will be of significant importance for protein tyrosine kinase inhibitor discovery, while also furthering our understanding of how enzymes self-regulate through highly-conserved molecular switches.
Different state-dependence of population codes across cortex
Authors: Bandi Akhil C. , Runyan Caroline A.
Published: May 2024

During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an ‘optimal’ state and two ‘sub-optimal’ states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.
Senescent-like microglia limit remyelination through the senescence associated secretory phenotype
Authors: Gross Phillip S. , Laforet Violeta Duran , Manavi Zeeba , Zia Sameera , Lee Sung Hyun , Shults Nataliia , Selva Sean , Alvarez Enrique , Plemel Jason R. , Schafer Dorothy P. , Huang Jeffrey K.
Published: May 2024

The capacity to regenerate myelin in the central nervous system (CNS) diminishes with age. This decline is particularly evident in multiple sclerosis (MS), which has been suggested to exhibit features of accelerated biological aging. Whether cellular senescence, a hallmark of aging, contributes to remyelination impairment remains unknown. Here, we show that senescent cells (SCs) accumulate within demyelinated lesions after injury, and their elimination enhances remyelination in young mice but not in aged mice. In young mice, we observed the upregulation of senescence-associated transcripts primarily in microglia after demyelination, followed by their reduction during remyelination. However, in aged mice, senescence-associated factors persisted within lesions, correlating with inefficient remyelination. We found that SC elimination enhanced remyelination in young mice but was ineffective in aged mice. Proteomic analysis of senescence-associated secretory phenotype (SASP) revealed elevated levels of CCL11/Eotaxin-1 in lesions, which was found to inhibit efficient oligodendrocyte maturation. These results suggest therapeutic targeting of SASP components, such as CCL11, may improve remyelination in aging and MS.
RF coil design strategies for improving SNR at the ultrahigh magnetic field of 10.5 Tesla
Authors: Waks Matt , Lagore Russell L. , Auerbach Edward , Grant Andrea , Sadeghi-Tarakameh Alireza , DelaBarre Lance , Jungst Steve , Tavaf Nader , Lattanzi Riccardo , Giannakopoulos Ilias , Moeller Steen , Wu Xiaoping , Yacoub Essa , Vizioli Luca , Schmidt Simon , Metzger Gregory J. , Eryaman Yigitcan , Adriany Gregor , Uğurbil Kamil
Published: May 2024

Purpose

To develop multichannel transmit and receive arrays towards capturing the ultimate-intrinsic-SNR (uiSNR) at 10.5 Tesla (T) and to demonstrate the feasibility and potential of whole-brain, high-resolution human brain imaging at this high field strength.

Methods

A dual row 16-channel self-decoupled transmit (Tx) array was converted to a 16Tx/Rx transceiver using custom transmit/receive switches. A 64-channel receive-only (64Rx) array was built to fit into the 16Tx/Rx array. Electromagnetic modeling and experiments were employed to define safe operation limits of the resulting 16Tx/80Rx array and obtain FDA approval for human use.

Results

The 64Rx array alone captured approximately 50% of the central uiSNR at 10.5T while the identical 7T 64Rx array captured ∼76% of uiSNR at this lower field strength. The 16Tx/80Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the performance of the 64Rx array at 7T. SNR data obtained at the two field strengths with these arrays displayeddependent increases over a large central region. Whole-brain high resolution T2* and T1weighted anatomical and gradient-recalled echo EPI BOLD fMRI images were obtained at 10.5T for the first time with such an advanced array, illustrating the promise of >10T fields in studying the human brain.

Conclusion

We demonstrated the ability to approach the uiSNR at 10.5T over the human brain with a novel, high channel count array, achieving large SNR gains over 7T, currently the most commonly employed ultrahigh field platform, and demonstrate high resolution and high contrast anatomical and functional imaging at 10.5T.
Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer’s spectrum
Authors: Vanderlip Casey R. , Stark Craig E.L. ,   Alzheimer’s Disease Neuroimaging Initiative
Published: May 2024

Digital cognitive assessments, particularly those that can be done at home, present as low burden biomarkers for participants and patients alike, but their effectiveness in diagnosis of Alzheimer’s or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk for cognitive decline. We analyzed >500 ADNI participants who underwent a brief digital cognitive assessment and Aβ/tau PET scans, examining their ability to distinguish cognitive status and predict cognitive decline. Performance on the digital cognitive assessment were superior to both cortical Aβ and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. Digital assessments are effective in identifying at-risk individuals, supporting their utility as low-burden tools for early Alzheimer’s detection and monitoring.
Characterization of immunity-inducing rhizobacteria highlights diversity in plant-microbe interactions
Authors: Loranger Mackenzie Eli William , Yim Winfield , Toffoli Matthew , Groleau Marie-Christine , Nickzad Arvin , Morales-Lizcano Nadia , Berleth Thomas , Moeder Wolfgang , Déziel Eric , Yoshioka Keiko
Published: May 2024

The narrow region of soil surrounding roots (rhizosphere) contains an astonishing diversity of microorganisms. Some rhizosphere bacteria can improve plant health and immunity, via direct competition with pathogens or by establishing heightened immunity in aboveground tissues, a phenomenon known as Induced Systemic Resistance (ISR). We screened a bacterial library from agricultural soils to identify strains that, after root treatment, induce immunity inSolanum lycopersicum(tomato) against the fungal pathogenBotrytis cinerea. Here, we report the establishment of a screening method and characterization of a subset of five strains, belonging to the speciesBacillus velezensis, Paenibacillus peoriaeandPseudomonas parafulva, that induced systemic resistance in tomato. However interestingly, only two of them triggered canonical ISR in Arabidopsis, indicating plant host specificity and/or alternative modes of actions. Furthermore, some of the strains displayed direct anti-microbial activity. We also found the requirement of the lipid-binding protein DIR1 in ISR establishment, indicating a possible convergence of SAR and ISR signaling. Finally, we found thatP. parafulvaTP18m, also displayed strong effects on root development. Taken together, we have identified taxonomically diverse immunity-inducing bacteria. Our characterization revealed diverse features, highlighting the complexity of bacteria- host interaction in the rhizosphere.

Highlight

We identified taxonomically diverse rhizobacteria that induce systemic resistance in tomato plants to Botrytis after application to the root. These bacteria display diverse modes of action to improve plant health.
Assessing Plasticity in the Primary Sensory Cortex and Its Relation with Atypical Tactile Reactivity in Autism: A TMS-EEG Protocol
Authors: Kariminezhad Shohreh , Zomorrodi Reza , Zrenner Christoph , Blumberger Daniel M. , Ameis Stephanie H. , Lin Hsiang-Yuan , Lai Meng-Chuan , Rajji Tarek K. , Lunsky Yona , Sanches Marcos , Desarkar Pushpal
Published: May 2024

Background

Atypical sensory reactivity is a cardinal presentation in autism. Within the tactile domain, atypical tactile reactivity (TR) is common, it emerges early, persists into adulthood, and impedes social interaction and daily functioning. Hence, atypical TR is a key target for biological intervention to improve outcomes. Brain mechanisms informing biological interventions for atypical TR remains elusive. We previously reported hyper-plasticity in the motor cortex in autistic adults and found that repetitive transcranial magnetic stimulation (rTMS), designed to strengthen inhibitory processes in the brain, reduced hyper-plasticity. Whether the primary sensory cortex (S1) is characterized by hyper-plasticity, which may underlie atypical TR in autism is unknown.

Objectives

We aim to test whether hyper-plasticity in the S1 underlies atypical TR in autism, and investigate if a single session of rTMS can safely reduce hyper-plasticity in S1 in autistic adults.

Method

Plasticity will be assessed in the left S1 with integrated paired associative stimulation and electroencephalography (PAS-EEG) paradigm in 32 autistic adults and 32 age-, sex-, and intelligence quotient-matched controls. Autistic participants will be further randomized (double-blind, 1:1) to receive a single-session of either sham or active 20 Hz bilateral rTMS over the S1 and the plasticity will be re-assessed over the left S1 on the same day.

Conclusions

Atypical TR has been identified as one of the top clinical research priorities that can influence outcome in autistic population. The study findings can be highly valuable to further elucidate the mechanism underlying atypical TR, which in turn can help with developing a mechanism-driven intervention.