Multidrug-resistant tuberculosis in children: A practical update on epidemiology, diagnosis, treatment and prevention.

Pediatric multidrug-resistant tuberculosis (MDR-TB) remains a significant global problem, and there are numerous barriers preventing children with MDR-TB from being identified, confirmed with microbiologic tests, and treated with a safe, practical, and effective regimen. However, several recent advances in diagnostics and treatment regimens have the promise to improve outcomes for children with MDR-TB. We introduce this review with two cases that exemplify both the challenges in management of MDR-TB in children, but also the potential to achieve a positive outcome. More than 30,000 cases of MDR-TB per year are believed to occur in children but less than 5% are confirmed microbiologically, contributing to poorer outcomes and excess mortality. Rapid molecular-based testing that provides information on rifampin susceptibility is increasingly globally available and recommended for all children suspected of TB disease--but remains limited by challenges obtaining appropriate samples and the paucibacillary nature of most pediatric TB. More complex assays allowing better characterization of drug-resistant isolates are emerging. For children diagnosed with MDR-TB, treatment regimens have traditionally been long and utilize multiple drugs associated with significant side effects, particularly injectable agents. Several new or repurposed drugs including bedaquiline, delamanid, clofazimine and linezolid now allow most treatment regimens to be shorter and all-oral. Yet data to support short, all-oral, novel regimens for young children containing pretomanid remain insufficient at present, and there is a compelling need to conduct pediatric trials of promising therapeutics and MDR-TB treatment regimens.

El papel del antígeno prostático específico (PSA) y el estadio patológico antes de la radioterapia en la predicción de los resultados de la resonancia magnética multiparamétrica (RMmp) en pacientes con recidiva del cancer de próstata tras prostatectomía radical / Role of PSA levels and pathological stadiation before radiation therapy in predicting mp-MRI results in patients with prostate cancer recurrence after radical prostatectomy

Objetivo Evaluar el valor del antígeno prostático específico (PSA) en la predicción de los resultados de la resonancia magnética multiparamétrica (RMmp) en pacientes con cáncer de próstata (CaP) de alto (puntuación de Gleason≥8, pT≥3, pN1) y bajo grado (puntuación de Gleason<8, pT<3, pN0). Materiales y métodos Ciento ochenta y ocho pacientes se sometieron a una RMmp de 1,5-T después de la prostatectomía radical y antes de la radioterapia. Los pacientes se dividieron en 2 grupos: el grupo A incluía pacientes con recidiva bioquímica (RB) y el grupo B pacientes sin RB pero con alto riesgo de recidiva local. Teniendo en cuenta la puntuación de Gleason, pT y pN como variables de agrupación independientes, se realizaron análisis ROC de los niveles de PSA en el momento del diagnóstico del CaP primario y antes de la radioterapia con el fin de identificar el punto de corte óptimo para predecir el resultado de la RMmp. Resultados En los grupos A y B, el área bajo la curva del PSA antes de la radioterapia fue superior a la del PSA en el momento del diagnóstico del CaP, en tumores de bajo y alto grado. Para los tumores de bajo grado, la mejor área bajo la curva fue de 0,646 y 0,685 en el grupo A y B, respectivamente; para los tumores de alto grado, la mejor área bajo la curva fue de 0,705 y 1 en el grupo A y B, respectivamente. Para los tumores de bajo grado, el punto de corte óptimo del PSA fue de 0,565-0,58ng/ml en el grupo A (sensibilidad y especificidad: 70,5% y 66%), y de 0,11-0,13ng/ml en el B (sensibilidad y especificidad: 62,5% y 84,6%). Para los tumores de alto grado, el punto de corte de PSA óptimo fue de 0,265-0,305ng/ml en el grupo A (sensibilidad y especificidad: 95% y 42,1%), y de 0,13-0,15ng/ml en el grupo B (sensibilidad y especificidad: 100%). Conclusión La RMmp se debe realizar como herramienta diagnóstica complementaria siempre que se detecte una RB, especialmente en el CaP de alto grado... (AU)

Objective To evaluate prostate-specific antigen (PSA) value in multiparametric magnetic resonance imagin (mp-MRI) results prediction, analyzing patients with high (Gleason Score ≥8, pT≥3, pN1) and low grade (Gleason Score <8, pT<3, pN0) prostate cancer (PCa). Materials and methods One hundred eighty-eight patients underwent 1.5-T mp-MRI after radical prostatectomy and before radiotherapy. They were divided into 2 groups: A and B, for patients with biochemical recurrence (BCR) and without BCR but with high local recurrence risk. Considering Gleason Score, pT and pN as independent grouping variables, ROC analyses of PSA levels at primary PCa diagnosis and PSA before radiotherapy were performed in order to identify the optimal cut-off to predict mp-MRI result. Results Group A and B showed higher area under the curve for PSA before radiotherapy than PSA at PCa diagnosis, in low and high grade tumors. For low grade tumors the best area under the curve was 0.646 and 0.685 in group A and B; for high grade the best area under the curve was 0.705 and 1 in group A and B, respectively. For low grade tumors the best PSA cut-off was 0.565-0.58ng/ml in group A (sensitivity, specificity: 70.5%, 66%), and 0.11-0.13ng/ml in B (sensitivity, specificity: 62.5%, 84.6%). For high grade tumors, the best PSA cut-off obtained was 0.265-0.305ng/ml in group A (sensitivity, specificity: 95%, 42.1%), and 0.13-0.15ng/ml in B (sensitivity, specificity: 100%). Conclusion Mp-MRI should be performed as added diagnostic tool always when a BCR is detected, especially in high grade PCa. In patients without BCR, mp-MRI results, although poorly related to pathological stadiation, still have a good diagnostic performance, mostly when PSA>0.1-0.15ng/ml. (AU)

Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Revealing the mechanism of action of a first-in-class covalent inhibitor of KRASG12C (ON) and other functional properties of oncogenic KRAS by 31P NMR.

Individual oncogenic KRAS mutants confer distinct differences in biochemical properties and signaling for reasons that are not well understood. KRAS activity is closely coupled to protein dynamics and is regulated through two interconverting conformations: state 1 (inactive, effector binding deficient) and state 2 (active, effector binding enabled). Here, we use 31P NMR to delineate the differences in state 1 and state 2 populations present in WT and common KRAS oncogenic mutants (G12C, G12D, G12V, G13D, and Q61L) bound to its natural substrate GTP or a commonly used nonhydrolyzable analog GppNHp (guanosine-5'-[(ß,γ)-imido] triphosphate). Our results show that GppNHp-bound proteins exhibit significant state 1 population, whereas GTP-bound KRAS is primarily (90% or more) in state 2 conformation. This observation suggests that the predominance of state 1 shown here and in other studies is related to GppNHp and is most likely nonexistent in cells. We characterize the impact of this differential conformational equilibrium of oncogenic KRAS on RAF1 kinase effector RAS-binding domain and intrinsic hydrolysis. Through a KRAS G12C drug discovery, we have identified a novel small-molecule inhibitor, BBO-8956, which is effective against both GDP- and GTP-bound KRAS G12C. We show that binding of this inhibitor significantly perturbs state 1-state 2 equilibrium and induces an inactive state 1 conformation in GTP-bound KRAS G12C. In the presence of BBO-8956, RAF1-RAS-binding domain is unable to induce a signaling competent state 2 conformation within the ternary complex, demonstrating the mechanism of action for this novel and active-conformation inhibitor.

Role of psa levels and pathological stadiation before radiation therapy in predicting mp-MRI results in patients with prostate cancer recurrence after radical prostatectomy.

OBJECTIVE: To evaluate PSA value in mp-MRI results prediction, analyzing patients with high (GS≥8, pT≥3, pN1) and low grade (GS<8, pT<3, pN0) Prostate Cancer (PCa). MATERIALS AND METHODS: One hundred eighty-eight patients underwent 1.5-Tmp-MRI after Radical Prostatectomy (RP) and before Radiotherapy (RT). They were divided into 2 groups: A and B, for patients with biochemical recurrence (BCR) and without BCR but with high local recurrence risk. Considering Gleason Score (GS), pT and pN as independent grouping variables, ROC analyses of PSA levels at primary PCa diagnosis and PSA before RT were performed in order to identify the optimal cut-off to predict mp-MRI result. RESULTS: Group A and B showed higher AUC for PSA before RT than PSA at PCa diagnosis, in low and high grade tumors. For low grade tumors the best AUC was 0.646 and 0.685 in group A and B; for high grade the best AUC was 0.705 and 1 in group A and B, respectively. For low grade tumors the best PSA cut-off was 0.565-0.58ng/mL in group A (sensitivity, specificity: 70.5%, 66%), and 0.11-0.13ng/mL in B (sensitivity, specificity: 62.5%, 84.6%). For high grade tumors, the best PSA cut-off obtained was 0.265-0.305ng/mL in group A (sensitivity, specificity: 95%, 42.1%), and 0.13-0.15ng/mL in B (sensitivity, specificity: 100%). CONCLUSION: Mp-MRI should be performed as added diagnostic tool always when a BCR is detected, especially in high grade PCa. In patients without BCR, mp-MRI results, although poorly related to pathological stadiation, still have a good diagnostic performance, mostly when PSA>0.1-0.15ng/mL.

Corrigendum: Increase in pediatric recurrent fever evaluations during the first year of the COVID-19 pandemic in North America.

[This corrects the article DOI: 10.3389/fped.2023.1240242.].

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction.

Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.

Increase in pediatric recurrent fever evaluations during the first year of the COVID-19 pandemic in North America.

The impact of the COVID-19 pandemic on new diagnoses of recurrent fevers and autoinflammatory diseases is largely unknown. The Childhood Arthritis and Rheumatology Research Alliance (CARRA) PFAPA/AID Working Group aimed to investigate the impact of the COVID-19 pandemic on the number of pediatric patients evaluated for recurrent fevers and autoinflammatory diseases in North America. The absolute number of new outpatient visits and the proportion of these visits attributed to recurrent fever diagnoses during the pre-pandemic period (1 March 2019-29 February 2020) and the first year of the COVID-19 pandemic (1 March 2020-28 February 2021) were examined. Data were collected from 27 sites in the United States and Canada. Our results showed an increase in the absolute number of new visits for recurrent fever evaluations in 21 of 27 sites during the COVID-19 pandemic compared to the pre-pandemic period. The increase was observed across different geographic regions in North America. Additionally, the proportion of new visits to these centers for recurrent fever in relation to all new patient evaluations was significantly higher during the first year of the pandemic, increasing from 7.8% before the pandemic to 10.9% during the pandemic year (p < 0.001). Our findings showed that the first year of the COVID-19 pandemic was associated with a higher number of evaluations by pediatric subspecialists for recurrent fevers. Further research is needed to understand the reasons behind these findings and to explore non-infectious triggers for recurrent fevers in children.

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules.

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. OFraMP applied to paclitaxel (ATB ID 35922).

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

PDBspheres: a method for finding 3D similarities in local regions in proteins.

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ('spheres') adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA (Local-Global Alignment) structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres-a strictly structure-based method-performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the 'refined set' of the PDBbind 2019 dataset.

Deep generative molecular design reshapes drug discovery.

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

Exploring CRD mobility during RAS/RAF engagement at the membrane.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease.

The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. We review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.