Evidence-based definition of hypoprolactinemia in European men aged 40-86 years: the European male ageing study.

Empirical evidence for a low normal or reference interval for serum prolactin (PRL) is lacking for men, while the implications of very low PRL levels for human health have never been studied. A clinical state of "PRL deficiency" has not been defined except in relation to lactation. Using data from the European Male Ageing Study (EMAS), we analyzed the distribution of PRL in 3,369 community-dwelling European men, aged 40-80 years at phase-1 and free from acute illnesses. In total, 2,948 and 2,644 PRL samples were collected during phase-1 and phase-2 (3 to 5.7 years later). All samples were analysed in the same centre with the same assay. After excluding individuals with known pituitary diseases, PRL ≥ 35 ng/ml, and PRL-altering drugs including antipsychotic agents, selective serotonin reuptake inhibitors, or dopamine agonists, 5,086 data points (2,845 in phase-1 and 2,241 in phase-2) were available for analysis. The results showed that PRL declined minimally with age (slope = -0.02) and did not correlate with BMI. The positively skewed PRL distribution was log-transformed to a symmetrical distribution (skewness reduced from 13.3 to 0.015). Using two-sigma empirical rule (2[]SD about the mean), a threshold at 2.5% of the lower end of the distribution was shown to correspond to a PRL value of 2.98ng/ml. With reference to individuals with PRL levels of 5-34.9 ng/ml (event rate = 6.3%), the adjusted risk of developing type 2 diabetes increased progressively in those with PRL levels of 3-4.9 ng/ml: event rate = 9.3%, OR (95% CI) 1.59 (0.93-2.71), and more so with PRL levels of 0.3-2.9 ng/ml: event rate = 22.7%, OR 5.45 (1.78-16.62). There was also an increasing trend in prediabetes and diabetes based on fasting blood glucose levels was observed with lower categories of PRL. However, PRL levels were not associated with cancer, cardiovascular diseases, depressive symptoms or mortality. Our findings suggest that a PRL level below 3 ng/ml (64 mlU/l) significantly identifies European men with a clinically-important outcome (of type 2 diabetes), offering a lower reference-value for research and clinical practice.

A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H).

Histological assessment of autoimmune hepatitis (AIH) is challenging. As one of the possible results of these challenges, nonclassical features such as bile-duct injury stays understudied in AIH. We aim to develop a deep learning tool (artificial intelligence for autoimmune hepatitis [AI(H)]) that analyzes the liver biopsies and provides reproducible, quantifiable, and interpretable results directly from routine pathology slides. A total of 123 pre-treatment liver biopsies, whole-slide images with confirmed AIH diagnosis from the archives of the Institute of Pathology at University Hospital Basel, were used to train several convolutional neural network models in the Aiforia artificial intelligence (AI) platform. The performance of AI models was evaluated on independent test set slides against pathologist's manual annotations. The AI models were 99.4%, 88.0%, 83.9%, 81.7%, and 79.2% accurate (ratios of correct predictions) for tissue detection, liver microanatomy, necroinflammation features, bile duct damage detection, and portal inflammation detection, respectively, on hematoxylin and eosin-stained slides. Additionally, the immune cells model could detect and classify different immune cells (lymphocyte, plasma cell, macrophage, eosinophil, and neutrophil) with 72.4% accuracy. On Sirius red-stained slides, the test accuracies were 99.4%, 94.0%, and 87.6% for tissue detection, liver microanatomy, and fibrosis detection, respectively. Additionally, AI(H) showed bile duct injury in 81 AIH cases (68.6%). The AI models were found to be accurate and efficient in predicting various morphological components of AIH biopsies. The computational analysis of biopsy slides provides detailed spatial and density data of immune cells in AIH landscape, which is difficult by manual counting. AI(H) can aid in improving the reproducibility of AIH biopsy assessment and bring new descriptive and quantitative aspects to AIH histology.

Computational design of non-porous pH-responsive antibody nanoparticles.

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

"An app is just available at all times"-the process and outcomes of converting the Georgia Tuberculosis Reference Guide into a mobile application.

BACKGROUND: The physical, paper-based Georgia TB Reference Guide has served as the clinical reference handbook on tuberculosis (TB) diagnostic and treatment guidelines for the state of Georgia in the United States. Supported by the Georgia Department of Public Health, the production of the 112-page palm-sized booklet was previously led by a team of Georgia-based TB experts at Emory University and printed every three-five years with updates to clinical management guidelines and TB consult contact information. However, the costs associated with editorial printing combined with delays in updating a static printed booklet with revised guidance hampered the utility of the tool. Considering the barriers with paper-based production and based on the beneficial use of apps to support the dissemination of clinical management guidance in other settings, the booklet was converted into a mobile application. This paper describes the process of developing a mobile app version of the Georgia TB Reference Guide in an easy-to-update and readily available format. METHODS: We employed a user-centered design approach to develop the app, including a series of qualitative interviews and quantitative surveys. Participants included a mix of state officials and local TB experts. First, initial foundational interviews were conducted to conceptualize current utilization practices of both the paper and PDF versions of the tool. Second, the findings from the initial interviews were organized thematically and informed the design of the app, which was then beta tested by a round of previously unsampled TB experts as well as a re-sample from the initial interviews. Third, the designs were coded into developmental phases and beta tested among users of the current Georgia TB Reference Guide. Fourth, the app was published and downloaded by a pre-selected group of local users who provided answers to a follow-up survey after using the app for one month. Fifth, user growth, self-reported demographics, and app usage between February and July 2022 were recorded through automatic data metrics built into the app. RESULTS: The paper copy Georgia TB Reference Guide usage themes included commonly referenced content, navigation paths, and desired features and content. The themes were converted into features and designs such as prioritizing commonly reviewed topics and guide customization with bookmarks and notes. Iterations of the designs were driven by feedback from TB experts and included home page featured content, improving content readability, and improving the search feature. The follow-up survey revealed a 90% preference for the app over the paper version of the guide. In the six months following the app's release, the app was downloaded by 281 individuals in the United States. The majority of downloads were in Georgia and the app also expanded organically to 19 other states. CONCLUSION: The experience of converting the Georgia TB Reference Guide offers specific and effective steps to converting a medical reference guide into a mobile application tool that is readily available, easy to use, and easy to update. The organic dissemination of the app beyond the state of Georgia's borders within the first six months of app launch underscores desire among TB healthcare professionals for high-quality digital reference content outside the state. This experience offers clear outlines for replication in other contexts and demonstrates the utility of similar mobile medical reference tools.

Ruminal microbiome changes across lactation in primiparous Holstein cows with varying methane intensity: Heritability assessment.

Methane is a potent greenhouse gas produced during the ruminal fermentation and is associated with a loss of feed energy. Therefore, efforts to reduce methane emissions have been ongoing in the last decades. Methane production is highly influenced by factors such as the ruminal microbiome and host genetics. Previous studies have proposed to use the ruminal microbiome to reduce long-term methane emissions, as ruminal microbiome composition is a moderately heritable trait and genetic improvement accumulates over time. Lactation stage is another important factor that might influence methane production but potential associations with the ruminal microbiome have not been evaluated previously. This study sought to examine the changes in ruminal microbiome over the lactation period of primiparous Holstein cows differing in methane intensity and estimate the heritability of the abundance of relevant microorganisms. Ruminal content samples from 349 primiparous Holstein cows with 14 - 378 d in milk were collected from May 2018 to June 2019. Methane intensity (MI) of each cow was calculated as methane concentration/milk yield. Up to 64 taxonomic features (TF) from 20 phyla had a significant differential abundance between cows with low and high MI early in lactation, 16 TF during mid lactation, and none late in lactation. Taxonomical features within the Firmicutes, Proteobacteria, Melainabacteria, Cyanobacteria, Bacteroidetes and Actinobacteria phyla were associated to low MI, whereas eukaryotic TF and those within the Euryarchaeota, Verrucomicrobia, Kiritimatiellaeota, Lentisphaerae phyla were associated to high MI. Out of the 60 TF that were found to be differentially abundant between early and late lactation in cows with low MI, 56 TF were also significant when cows with low and high MI were compared in the first third of the lactation. In general, microbes associated with low MI were more abundant early in lactation (e.g., Acidaminococcus, Aeromonas and Weimeria genera) and showed low to moderate heritabilities (0.03 to 0.33). These results suggest some potential to modulate the rumen microbiome composition through selective breeding for lower MI. Differences in the ruminal microbiome of cows with extreme MI levels likely result from variations in the ruminal physiology of these cows and were more noticeable early in lactation probably due to important interactions between the host phenotype and environmental factors associated to that period. Our results suggest that the ruminal microbiome evaluated early in lactation may be more precise for MI difference, and hence, this should be considered to optimize sampling periods to establish a reference population in genomic selection scenarios.

Protein nanoparticle vaccines induce potent neutralizing antibody responses against MERS-CoV.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic betacoronavirus that causes severe and often lethal respiratory illness in humans. The MERS-CoV spike (S) protein is the viral fusogen and the target of neutralizing antibodies, and has therefore been the focus of vaccine design efforts. Currently there are no licensed vaccines against MERS-CoV and only a few candidates have advanced to Phase I clinical trials. Here we developed MERS-CoV vaccines utilizing a computationally designed protein nanoparticle platform that has generated safe and immunogenic vaccines against various enveloped viruses, including a licensed vaccine for SARS-CoV-2. Two-component protein nanoparticles displaying MERS-CoV S-derived antigens induced robust neutralizing antibody responses and protected mice against challenge with mouse-adapted MERS-CoV. Electron microscopy polyclonal epitope mapping and serum competition assays revealed the specificities of the dominant antibody responses elicited by immunogens displaying the prefusion-stabilized S-2P trimer, receptor binding domain (RBD), or N-terminal domain (NTD). An RBD nanoparticle vaccine elicited antibodies targeting multiple non-overlapping epitopes in the RBD, whereas anti-NTD antibodies elicited by the S-2P- and NTD-based immunogens converged on a single antigenic site. Our findings demonstrate the potential of two-component nanoparticle vaccine candidates for MERS-CoV and suggest that this platform technology could be broadly applicable to betacoronavirus vaccine development.

Effect of Makeup Use on Depressive Symptoms: An Open, Randomized and Controlled Trial.

INTRODUCTION: Depression is one of the most disabling diseases globally, with a high disease burden that generates high direct and indirect costs. The incidence of depression is twofold higher in adult women than in men. Biological and psychosocial factors constitute the pathophysiological bases of the condition and due to the complexity of the condition, current understanding is that the "treatment strategy must be multimodal". The objective of this study was to measure the effect of introducing the frequent use of makeup on improving depressive symptoms in adult women of medium-low purchasing power METHODS: Participants with the targeted profile who did not frequently use makeup were selected and randomised to receive (test group) or not (control group) stimuli and makeup products intended for encouraging the frequent use of makeup. The Zung Depression Self-Assessment Scale was used to assess depressive symptoms, with additional assessments on self-image perception using the mirror test and salivary cortisol level. RESULTS: The results demonstrated a sustained reduction in depressive symptoms (8.3 percentage points reduction in the Average Zung Index; P < 0.05), with a significant improvement in self-image perception (25% increase in the average score obtained in the mirror test; P < 0.05) and a specific influence on salivary cortisol levels (55% reduction in salivary cortisol concentration; P < 0.05) after the first makeup application. CONCLUSION: The results show that encouraging the frequent use of makeup, a practice that can be achieved by most people and which is simple and inexpensive to implement, can contribute to effective and sustainable improvement in the well-being and mental health of a significant portion of the population.

Practice Patterns of Pancreatic Surgery Within the Military.

INTRODUCTION: Pancreatic surgery is technically challenging, with mortality rates at high-volume centers ranging from 0% to 5%. An inverse relationship between surgeon volume and perioperative mortality has been reported suggesting that patients benefit from experienced surgeons at high-volume centers. There is little published on the volume of pancreatic surgeries performed in military treatment facilities (MTF) and there is no centralization policy regarding pancreatic surgery. This study evaluates pancreatic procedures at MTFs. We hypothesize that a small group of MTFs perform most pancreatic procedures, including more complex pancreatic surgeries. METHODS: This is a retrospective review of de-identified data from MHS Mart (M2) from 2014 to 2020. The database contains patient data from all Defense Health Agency treatment facilities. Variables collected include number and types of pancreatic procedures performed and patient demographics. The primary endpoint was the number and type of surgery for each MTF. RESULTS: Twenty-six MTFs performed pancreatic surgeries from 2014 to 2020. There was a significant decrease in the number of cases from 2014 to 2020. Nine hospitals performed one surgery over eight years. The most common surgery was a distal pancreatectomy, followed by a pancreaticoduodenectomy. There was a decrease in the number of pancreaticoduodenectomies and distal pancreatectomies performed over this period. CONCLUSIONS: Pancreatic surgery is being performed at few MTFs with a downward trajectory over time. Further studies would be needed to assess the impact on patient care regarding postoperative complications, barriers to timely patient care, and impact on readiness of military surgeons.

The oral microbiome as a proxy for feed intake in dairy cattle.

Genetic material from rumen microorganisms can be found within the oral cavity, and hence there is potential in using the oral microbiome as a proxy of the ruminal microbiome. Feed intake (FI) influences the composition of rumen microbiota and might directly influence the oral microbiome in dairy cattle. Ruminal content samples (RS) from 29 cows were collected at the beginning of the study and also 42 d later (RS0 and RS42, respectively). Additionally, 18 oral samples were collected through buccal swabbing at d 42 (OS42) from randomly selected cows. Samples were used to characterize and compare the taxonomy and functionality of the oral microbiome using Nanopore sequencing and to evaluate the feasibility of using the oral microbiome to estimate FI. Up to 186 taxonomical features were found differentially abundant (DA) between RS and OS42. Similar results were observed when comparing OS42 to RS collected at different days. Microorganisms associated with the liquid fraction of the rumen were less abundant in OS42 as these were probably swallowed after regurgitation. Up to 1,102 KEGGs were found to be DA between RS and OS42 and these results differed when comparing time of collection, but differentially abundant KEGGs were mainly associated to metabolism in both situations. Models based on the oral microbiome and rumen microbiome differed in their selection of microbial groups and biological pathways to predict FI. In the rumen, fiber-associated microorganisms are considered suitable indicators of feed intake. On the other hand, biofilm formers like Gammaproteobacteria or Bacteroidia classes are deemed appropriate proxies for predicting feed intake from oral samples. Models from RS exhibited some predictive ability to estimate FI, but OS significantly outperformed them. The best lineal model to estimate FI was obtained with the relative abundance of taxonomical feature at genera level, achieving an average R2 equal to 0.88 within the training data, and a root mean square error equal to 3.46 ± 0.83 (standard deviation; SD) kg of DM/ as well as a Pearson correlation coefficient between observed and estimated FI of 0.48 ± 0.30 in the test data. The results from this study suggest that oral microbiome has potential to predict FI in dairy cattle, and it encourages validating this potential in other populations.

Feasibility of Multiparametric Perfusion Assessment in Diabetic Foot Ulcer Using Intravoxel Incoherent Motion and Blood Oxygenation-Level Dependent MRI.

BACKGROUND: Patients with type-2 diabetes (T2DM) are at increased risk of developing diabetic foot ulcers (DFU) and experiencing impaired wound healing related to underlying microvascular disease. PURPOSE: To evaluate the sensitivity of intra-voxel incoherent motion (IVIM) and blood oxygen level dependent (BOLD) MRI to microvascular changes in patients with DFUs. STUDY TYPE: Case-control. POPULATION: 20 volunteers who were age and body mass index matched, including T2DM patients with DFUs (N = 10, mean age = 57.5 years), T2DM patients with controlled glycemia and without DFUs (DC, N = 5, mean age = 57.4 years) and healthy controls (HC, N = 5, mean age = 52.8 years). FIELD STRENGTH/SEQUENCE: 3T/multi-b-value IVIM and dynamic BOLD. ASSESSMENT: Resting IVIM parameters were obtained using a multi-b-value diffusion-weighted imaging sequence and two IVIM models were fit to obtain diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) and microvascular volume fraction (MVF) parameters. Microvascular reactivity was evaluated by inducing an ischemic state in the foot with a blood pressure cuff during dynamic BOLD imaging. Perfusion indices were assessed in two regions of the foot: the medial plantar (MP) and lateral plantar (LP) regions. STATISTICAL TESTS: Effect sizes of group mean differences were assessed using Hedge's g adjusted for small sample sizes. RESULTS: DFU participants exhibited elevated D*, f, and MVF values in both regions (g ≥ 1.10) and increased D (g = 1.07) in the MP region compared to DC participants. DC participants showed reduced f and MVF compared to HC participants in the MP region (g ≥ 1.06). Finally, the DFU group showed reduced tolerance for ischemia in the LP region (g = -1.51) and blunted reperfusion response in both regions (g < -2.32) compared to the DC group during the cuff-occlusion challenge. DATA CONCLUSION: The combined use of IVIM and BOLD MRI shows promise in differentiating perfusion abnormalities in the feet of diabetic patients and suggests hyperperfusion in DFU patients. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

Measures of population immunity can predict the dominant clade of influenza A (H3N2) and reveal age-associated differences in susceptibility and specificity.

For antigenically variable pathogens such as influenza, strain fitness is partly determined by the relative availability of hosts susceptible to infection with that strain compared to others. Antibodies to the hemagglutinin (HA) and neuraminidase (NA) confer substantial protection against influenza infection. We asked if a cross-sectional antibody-derived estimate of population susceptibility to different clades of influenza A (H3N2) could predict the success of clades in the following season. We collected sera from 483 healthy individuals aged 1 to 90 years in the summer of 2017 and analyzed neutralizing responses to the HA and NA of representative strains. The clade to which neutralizing antibody titers were lowest, indicating greater population susceptibility, dominated the next season. Titers to different HA and NA clades varied dramatically between individuals but showed significant associations with age, suggesting dependence on correlated past exposures. Despite this correlation, inter-individual variability in antibody titers to H3N2 strains increased gradually with age. This study indicates how representative measures of population immunity might improve evolutionary forecasts and inform selective pressures on influenza.

Infection after intramedullary nailing of femoral and tibial diaphyseal fractures.

PURPOSE: Management of fracture-related infection (FRI) after intramedullary fixation (IF) is a challenge. The aim of the present study is to describe a series of 26 patients with FRI after IF and to evaluate factors possibly related to the outcome. METHODS: Baseline variables were obtained at the time of IF: age, sex, body mass index, affected bone, open fracture, substance abuse, use of an external fixator, type of nail, reaming, soft-tissue reconstruction and surveillance culture result. After diagnosis of the infection, information was obtained about the time interval between IF and diagnosis and classification according to both the Willeneger and Roth and Makridis systems. Treatment modalities were grouped and analysed according to: use of antimicrobials, surgical debridement, nail removal or retention and spacer use. Cultures of bone or deep soft tissues were performed. Patients were followed up for 12 months, and outcomes (remission, relapse, death and loss of follow-up) were evaluated, as well as fracture consolidation. RESULTS: Remission was observed in 42.3% of patients. There was no significant association between any baseline variable and outcome. There was a significant association between Makridis stage 2 classification and recurrence or death. Treatment strategy was not significantly associated with outcome, and 65.4% of cases had positive culture results, with Enterobacter cloacae as the predominant agent. Consolidation was observed in 81.8% of patients and was not significantly related to the outcome. CONCLUSION: There was a significant association between Makridis classification and the outcome. Consolidation rate was not associated with the outcome regarding the treatment of the infection.

Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications.

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.

Local structural flexibility drives oligomorphism in computationally designed protein assemblies.

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. For example, clathrin coats adopt a wide variety of architectures to adapt to vesicular cargos of various sizes. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, most existing methods focus on designing static structures with high accuracy. Here we characterize the structures of three distinct computationally designed protein assemblies that each form multiple unanticipated architectures, and identify flexibility in specific regions of the subunits of each assembly as the source of structural diversity. Cryo-EM single-particle reconstructions and native mass spectrometry showed that only two distinct architectures were observed in two of the three cases, while we obtained six cryo-EM reconstructions that likely represent a subset of the architectures present in solution in the third case. Structural modeling and molecular dynamics simulations indicated that the surprising observation of a defined range of architectures, instead of non-specific aggregation, can be explained by constrained flexibility within the building blocks. Our results suggest that deliberate use of structural flexibility as a design principle will allow exploration of previously inaccessible structural and functional space in designed protein assemblies.

Precisely patterned nanofibres made from extendable protein multiplexes.

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

Multivalent antigen display on nanoparticle immunogens increases B cell clonotype diversity and neutralization breadth to pneumoviruses.

Nanoparticles for multivalent display and delivery of vaccine antigens have emerged as a promising avenue for enhancing B cell responses to protein subunit vaccines. Here, we evaluated B cell responses in rhesus macaques immunized with prefusion-stabilized respiratory syncytial virus (RSV) F glycoprotein trimer compared with nanoparticles displaying 10 or 20 copies of the same antigen. We show that multivalent display skews antibody specificities and drives epitope-focusing of responding B cells. Antibody cloning and repertoire sequencing revealed that focusing was driven by the expansion of clonally distinct B cells through recruitment of diverse precursors. We identified two antibody lineages that developed either ultrapotent neutralization or pneumovirus cross-neutralization from precursor B cells with low initial affinity for the RSV-F immunogen. This suggests that increased avidity by multivalent display facilitates the activation and recruitment of these cells. Diversification of the B cell response by multivalent nanoparticle immunogens has broad implications for vaccine design.

De novo design of highly selective miniprotein inhibitors of integrins αvß6 and αvß8.

The RGD (Arg-Gly-Asp)-binding integrins αvß6 and αvß8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between homologous αvß6 and αvß8 and other RGD integrins, stabilize specific conformational states, and have high thermal stability could have considerable therapeutic utility. Existing small molecule and antibody inhibitors do not have all these properties, and hence new approaches are needed. Here we describe a generalized method for computationally designing RGD-containing miniproteins selective for a single RGD integrin heterodimer and conformational state. We design hyperstable, selective αvß6 and αvß8 inhibitors that bind with picomolar affinity. CryoEM structures of the designed inhibitor-integrin complexes are very close to the computational design models, and show that the inhibitors stabilize specific conformational states of the αvß6 and the αvß8 integrins. In a lung fibrosis mouse model, the αvß6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method.

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter's signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model's generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our results demonstrate an average reduction of over 90% in the prediction error and 50% in the prediction time achieved by our proposal when compared to the MUSIC algorithm. These results establish DTs as competitive alternatives for DOA estimation in signal reception systems.

Germline-encoded specificities and the predictability of the B cell response.

Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles' propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination.