Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app.

Objective: While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts. Methods: A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions. Results: We present the SyMptOMS-ET app's main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of µ 4.32 out of 5 [ σ 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of µ 4.57 [ σ 0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them. Conclusions: The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics.

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value. However, the initial belief might be unknown to the user, as it depends on both the technique and the system of study. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data while at the same time finding the best interpretation of the data and the structures compatible with it. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of 12 proteins with coarse physical insights and the binding of peptides with varying affinities to the extraterminal domain using chemical shift perturbation data. We find that subtle differences in data structure (e.g., locally clustered or globally distributed), starting belief, and force field preferences can have an impact on the predictions, limiting the possibility of a transferable protocol across all systems and data types. Nonetheless, we find a wide range of initial setup conditions that will lead to successful sampling and identification of native states, leading to a robust pipeline. Furthermore, disagreements about how much data is enforced and satisfied rapidly serve to identify incorrect setup conditions.

Changes in D4 and GABAA subunit α3 receptors in the thalamic reticular nucleus caused by unilateral lesion of the globus pallidus.

The thalamic reticular nucleus controls information processing in thalamocortical neurons. GABAergic neurons present in this nucleus express the α3 subunit of post­synaptic GABAA receptors, which bind GABA from globus pallidus neurons. Pallidal neurons, in turn, have dopaminergic D4 receptors in their axon terminals. The thalamic reticular nucleus connects reciprocally with the thalamus, and it receives afferents from the brain cortex, as well as from other brain structures that have an important role in the modulation of the thalamic network. Based on the above, the purpose of this study was to assess the electrophysiological and molecular effects of unilateral lesion of the globus pallidus on the electric activity of the thalamic reticular nucleus. Two­month­old male rats were used. The right globus pallidus was lesioned with quinolinic acid. Seven days after the lesion, ipsilateral turning was registered, confirming the lesion. Afterward, electrophysiological evaluation of the right thalamic reticular nucleus' electrical activity was performed. Subsequently, mRNA expression for D4 receptors and subunit α3, as well as protein content were assessed in the right reticular nucleus. Pallidum lesion caused an increase in firing frequency and decreased firing bursts of reticular neurons. In addition, dopaminergic D4 mRNA, as well as protein increased. In contrast, GABAergic GABAA subunit α3 expression was suppressed, but protein content increased. These results show that the globus pallidus regulates firing in reticular neurons through D4 receptors and subunit α3 of GABAA receptor in the reticular nucleus of the thalamus.

Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification.

Significance: Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for in vivo brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time. Aim: Our research compares linescan and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification. Approach: We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information. Results: The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at λ = 422 nm , two for HbO 2 at λ = 542 nm and λ = 576 nm , and one for water at λ = 976 nm . Conclusion: The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.

Implementing and Evaluating the Timed up and Go Test Automation Using Smartphones and Smartwatches.

Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.

Precision or Personalized Nutrition: A Bibliometric Analysis.

Food systems face the challenge of maintaining adequate nutrition for all populations. Inter-individual responses to the same diet have made precision or personalized nutrition (PN) an emerging and relevant topic. The aim of this study is to analyze the evolution of the PN field, identifying the principal actors and topics, and providing a comprehensive overview. Therefore, a bibliometric analysis of the scientific research available through the Web of Science (WOS) database was performed, revealing 2148 relevant papers up to June 2024. VOSviewer and the WOS platform were employed for the processing and analysis, and included an evaluation of diverse data such as country, author or most frequent keywords, among others. The analysis revealed a period of exponential growth from 2015 to 2023, with the USA, Spain, and England as the top contributors. The field of "Nutrition and Dietetics" is particularly significant, comprising nearly 33% of the total publications. The most highly cited institutions are the universities of Tufts, College Dublin, and Navarra. The relationship between nutrition, genetics, and omics sciences, along with dietary intervention studies, has been a defining factor in the evolution of PN. In conclusion, PN represents a promising field of research with significant potential for further advancement and growth.

AlphaFold2 knows some protein folding principles.

AlphaFold2 (AF2) has revolutionized protein structure prediction. However, a common confusion lies in equating the protein structure prediction problem with the protein folding problem. The former provides a static structure, while the latter explains the dynamic folding pathway to that structure. We challenge the current status quo and advocate that AF2 has indeed learned some protein folding principles, despite being designed for structure prediction. AF2's high-dimensional parameters encode an imperfect biophysical scoring function. Typically, AF2 uses multiple sequence alignments (MSAs) to guide the search within a narrow region of its learned surface. In our study, we operate AF2 without MSAs or initial templates, forcing it to sample its entire energy landscape - more akin to an ab initio approach. Among over 7,000 proteins, a fraction fold using sequence alone, highlighting the smoothness of AF2's learned energy surface. Additionally, by combining recycling and iterative predictions, we discover multiple AF2 intermediate structures in good agreement with known experimental data. AF2 appears to follow a "local first, global later" folding mechanism. For designed proteins with more optimized local interactions, AF2's energy landscape is too smooth to detect intermediates even when it should. Our current work sheds new light on what AF2 has learned and opens exciting possibilities to advance our understanding of protein folding and for experimental discovery of folding intermediates.

A Platform for Ultra-Fast Proton Probing of Matter in Extreme Conditions.

Recent developments in ultrashort and intense laser systems have enabled the generation of short and brilliant proton sources, which are valuable for studying plasmas under extreme conditions in high-energy-density physics. However, developing sensors for the energy selection, focusing, transport, and detection of these sources remains challenging. This work presents a novel and simple design for an isochronous magnetic selector capable of angular and energy selection of proton sources, significantly reducing temporal spread compared to the current state of the art. The isochronous selector separates the beam based on ion energy, making it a potential component in new energy spectrum sensors for ions. Analytical estimations and Monte Carlo simulations validate the proposed configuration. Due to its low temporal spread, this selector is also useful for studying extreme states of matter, such as proton stopping power in warm dense matter, where short plasma stagnation time (<100 ps) is a critical factor. The proposed selector can also be employed at higher proton energies, achieving final time spreads of a few picoseconds. This has important implications for sensing technologies in the study of coherent energy deposition in biology and medical physics.

Cost-benefit analysis of the National Immunization Program in Spain.

Broad benefits of vaccination programs are well acknowledged but difficult to measure, especially when considering all vaccines included in a National Immunization Program (NIP). The aim was to conduct a cost-benefit analysis of the entire NIP in Spain, and an expanded NIP including four potential additional programs. A cost-benefit analysis was performed in Excel to assess the economic and health benefits (€) of vaccinating a single cohort of newborns over a lifetime horizon compared to no vaccination, from a societal perspective: firstly, according to the 2020 NIP in Spain (including 2021 recommendation for herpes zoster in 65-year-olds); and secondly, with an expanded NIP (adding rotavirus and meningococcal B in infants, and pertussis booster in adults aged >65 years and herpes zoster in all adults >50 years). The main inputs were taken from published literature and Spanish databases. Results were presented as a benefit-cost ratio (economic benefit per €1 invested). A cohort of 343,126 newborns were included in the analysis. The total investment needed to vaccinate the cohort throughout their lifetime, according to the 2020 NIP and the expanded NIP, was estimated at €168.5 million and €275.5 million, respectively. Potential economic benefits were €772.2 million and €803.0 million, respectively. The societal benefit-cost ratio was €4.58 and €2.91 per €1 invested, respectively. Even with the addition of new vaccination programs, the Spanish NIP yielded positive benefit-cost ratios from the societal perspective, demonstrating that NIPs spanning the full life course are an efficient public health measure.

Role of Proinflammatory Cytokines and Genetic Factors Related to Metabolic Alterations in People Living with HIV/AIDS.

Metabolic alterations are a common problem in people living with HIV (PLHIV), as a result of a stage of chronic inflammation that affects the homeostasis of the organism. Prolonged exposure to antiretroviral therapy has been associated with developing lipodystrophies that modify lipoprotein metabolism and inflammatory markers such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which are mediators of the immune response. The study aimed to associate TNF-α and IL-6 levels with their polymorphisms and metabolic alterations in PLIHV. We hypothesized that TNF-α and IL-6 levels and their polymorphisms are associated with metabolic alterations. In total, 185 PLHIV and 51 HIV-negative people were included. Biochemical parameters were determined by colorimetric assay, cytokine levels by immunoassay, and allelic discrimination by quantitative polymerase chain reaction. A correlation was found between TNF-α levels and the variables cholesterol (r = -0.171, P = 0.020) and high-density lipoprotein (HDL) (r = -0.245, P = 0.001). There are associations between HDL levels (P = 0.011) and GG genotype of rs1800629. The results suggest a metabolic alteration related to the constant immune response, especially the production of proinflammatory cytokines such as TNF-α and IL-6. It was observed that genetic factors may influence metabolism alteration, mainly in lipids.

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices.

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O(N2) traditionally associated with comparisons of elements in a set.

Targeting IGF2 to reprogram the tumor microenvironment for enhanced viro-immunotherapy.

BACKGROUND: The FDA approval of oncolytic herpes simplex-1 virus (oHSV) therapy underscores its therapeutic promise and safety as a cancer immunotherapy. Despite this promise, the current efficacy of oHSV is significantly limited to a small subset of patients largely due to the resistance in tumor and tumor microenvironment (TME). METHODS: RNA sequencing (RNA-Seq) was used to identify molecular targets of oHSV resistance. Intracranial human and murine glioma or breast cancer brain metastasis (BCBM) tumor-bearing mouse models were employed to elucidate the mechanism underlying oHSV therapy-induced resistance. RESULTS: Transcriptome analysis identified IGF2 as one of the top-secreted proteins following oHSV treatment. Moreover, IGF2 expression was significantly upregulated in 10 out of 14 recurrent GBM patients after treatment with oHSV, rQNestin34.5v.2 (71.4%; P = .0020) (ClinicalTrials.gov, NCT03152318). Depletion of IGF2 substantially enhanced oHSV-mediated tumor cell killing in vitro and improved survival of mice bearing BCBM tumors in vivo. To mitigate the oHSV-induced IGF2 in the TME, we constructed a novel oHSV, oHSV-D11mt, secreting a modified IGF2R domain 11 (IGF2RD11mt) that acts as IGF2 decoy receptor. Selective blocking of IGF2 by IGF2RD11mt significantly increased cytotoxicity, reduced oHSV-induced neutrophils/PMN-MDSCs infiltration, and reduced secretion of immune suppressive/proangiogenic cytokines, while increased CD8 + cytotoxic T lymphocytes (CTLs) infiltration, leading to enhanced survival in GBM or BCBM tumor-bearing mice. CONCLUSIONS: This is the first study reporting that oHSV-induced secreted IGF2 exerts a critical role in resistance to oHSV therapy, which can be overcome by oHSV-D11mt as a promising therapeutic advance for enhanced viro-immunotherapy.

Structural Elucidation of Ubiquitin via Gas-Phase Ion/Ion Cross-Linking Reactions Using Sodium-Cationized Reagents Coupled with Infrared Multiphoton Dissociation.

Accurate structural determination of proteins is critical to understanding their biological functions and the impact of structural disruption on disease progression. Gas-phase cross-linking mass spectrometry (XL-MS) via ion/ion reactions between multiply charged protein cations and singly charged cross-linker anions has previously been developed to obtain low-resolution structural information on proteins. This method significantly shortens experimental time relative to conventional solution-phase XL-MS but has several technical limitations: (1) the singly deprotonated N-hydroxysulfosuccinimide (sulfo-NHS)-based cross-linker anions are restricted to attachment at neutral amine groups of basic amino acid residues and (2) analyzing terminal cross-linked fragment ions is insufficient to unambiguously localize sites of linker attachment. Herein, we demonstrate enhanced structural information for alcohol-denatured A-state ubiquitin obtained from an alternative gas-phase XL-MS approach. Briefly, singly sodiated ethylene glycol bis(sulfosuccinimidyl succinate) (sulfo-EGS) cross-linker anions enable covalent cross-linking at both ammonium and amine groups. Additionally, covalently modified internal fragment ions, along with terminal b-/y-type counterparts, improve the determination of linker attachment sites. Molecular dynamics simulations validate experimentally obtained gas-phase conformations of denatured ubiquitin. This method has identified four cross-linking sites across 8+ ubiquitin, including two new sites in the N-terminal region of the protein that were originally inaccessible in prior gas-phase XL approaches. The two N-terminal cross-linking sites suggest that the N-terminal half of ubiquitin is more compact in gas-phase conformations. By comparison, the two C-terminal linker sites indicate the signature transformation of this region of the protein from a native to a denatured conformation. Overall, the results suggest that the solution-phase secondary structures of the A-state ubiquitin are conserved in the gas phase. This method also provides sufficient sensitivity to differentiate between two gas-phase conformers of the same charge state with subtle structural variations.

Etiological diagnosis of recurrent acute pancreatitis.

Acute pancreatitis is the leading cause of inpatient care among gastrointestinal conditions. Despite early intervention, one-third of patients experience recurrent acute pancreatitis (RAP). A comprehensive diagnostic approach is warranted to identify and treat underlying factors in order to prevent recurrence. RAP is most frequent among men aged 30-40, smokers, and in those with excessive alcohol consumption. To identify the etiology is paramount to stratify patients according to their individual risk of RAP and for predicting an eventual evolution to chronic pancreatitis. Although the initial management of acute pancreatitis is widely homogeneous according to established guidelines, there are no defined protocols to investigate RAP. In the present editorial article we propose a structured algorithm with precise recommendations to investigate the etiology RAP as part of routine clinical practice. Although there are relevant knowledge gaps in this disease, we believe that our guidance would contribute for a more homogenous diagnostic approach of RAP in clinical practice.

A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era.

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

An overload of missense variants in the OTOG gene may drive a higher prevalence of familial Meniere disease in the European population.

Meniere disease is a complex inner ear disorder with significant familial aggregation. A differential prevalence of familial MD (FMD) has been reported, being 9-10% in Europeans compared to 6% in East Asians. A broad genetic heterogeneity in FMD has been described, OTOG being the most common mutated gene, with a compound heterozygous recessive inheritance. We hypothesize that an OTOG-related founder effect may explain the higher prevalence of FMD in the European population. Therefore, the present study aimed to compare the allele frequency (AF) and distribution of OTOG rare variants across different populations. For this purpose, the coding regions with high constraint (low density of rare variants) were retrieved in the OTOG coding sequence in Non-Finnish European (NFE).. Missense variants (AF < 0.01) were selected from a 100 FMD patient cohort, and their population AF was annotated using gnomAD v2.1. A linkage analysis was performed, and odds ratios were calculated to compare AF between NFE and other populations. Thirteen rare missense variants were observed in 13 FMD patients, with 2 variants (rs61978648 and rs61736002) shared by 5 individuals and another variant (rs117315845) shared by two individuals. The results confirm the observed enrichment of OTOG rare missense variants in FMD. Furthermore, eight variants were enriched in the NFE population, and six of them were in constrained regions. Structural modeling predicts five missense variants that could alter the otogelin stability. We conclude that several variants reported in FMD are in constraint regions, and they may have a founder effect and explain the burden of FMD in the European population.

Loop diuretic down-titration at discharge in patients hospitalized for acute heart failure.

AIMS: The current literature provides limited guidance on the best diuretic strategy post-hospitalization for acute heart failure (AHF). It is postulated that the efficacy and safety of the outpatient diuretic regimen may be significantly influenced by the degree of fluid overload (FO) encountered during hospitalization. We hypothesize that in patients with more pronounced FO, reducing their regular oral diuretic dosage might be associated with an elevated risk of unfavourable clinical outcomes. METHODS AND RESULTS: It was a retrospective observational study of 410 patients hospitalized for AHF in which the dose of furosemide at admission and discharge was collected. Patients were categorized across diuretic dose status into two groups: (i) the down-titration group and (ii) the stable/up-titration group. FO status was evaluated by a clinical congestion score and circulating biomarkers. The endpoint of interest was the composite of time to all-cause death and/or heart failure readmission. A multivariable Cox proportional hazard regression model was constructed to analyse the endpoints. The median age was 86 (78-92) years, 256 (62%) were women, and 80% had heart failure with preserved ejection fraction. After multivariate adjustment, the down-titration furosemide equivalent dose remained not associated with the risk of the combined endpoint in the whole sample (hazard ratio 1.34, 95% confidence interval 0.86-2.06, P = 0.184). The risk of the combination of death and/or worsening heart failure associated with the diuretic strategy at discharge was significantly influenced by FO status, including clinical congestion scores and circulating proxies of FO like BNP and cancer antigen 125. CONCLUSIONS: In patients hospitalized for AHF, furosemide down-titration does not imply an increased risk of mortality and/or heart failure readmission. However, FO status modifies the effect of down-titration on the outcome. In patients with severe congestion or residual congestion at discharge, down-titration was associated with an increased risk of mortality and/or heart failure readmission.

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.