Nevin Manimala

J Chem Inf Model. 2024 Mar 12. doi: 10.1021/acs.jcim.3c02054. Online ahead of print.

ABSTRACT

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

PMID:38470995 | DOI:10.1021/acs.jcim.3c02054

Haemophilia. 2024 Mar 12. doi: 10.1111/hae.14988. Online ahead of print.

ABSTRACT

INTRODUCTION: Activity wristbands have been shown to be effective in relation to self-monitoring activity levels and increasing exercise adherence. However, previous reports have been based on short-term follow-ups in people with haemophilia (PWH).

AIM: (1) To evaluate compliance with physical activity (PA) recommendations in PWH during a 1-year follow-up period using activity wristbands to record daily steps and intensity; (2) To determine the effect of PA self-monitoring on clinical outcomes.

METHODS: A prospective observational study was conducted in 27 adults with severe haemophilia undergoing prophylactic treatment. The Fitbit Charge HR was used to track daily PA for an entire year. The participants were encouraged to try to reach a goal of 10,000 steps/day and to track their progress. The pre- and post-evaluation included quality of life (A36 Hemophilia-QoL Questionnaire), joint health (Haemophilia Joint Health Score), functionality (Timed Up and Go test), and muscle strength.

RESULTS: A total of 323.63 (95%CI: 194-364) valid days (i.e., > 2000 steps) were recorded. The annual average number of steps per day taken by participants was 10,379. Sixteen (59%) PWH reached 10,000 steps/day at baseline and 17 (63%) at 1 year follow-up, with no significant differences (x² = .33; p = .56). A statistically significant improvement was observed in daily moderate activity time (p = .012) and in the ‘physical health’ quality of life subscale (mean difference: 2.15 points; 95%CI: .64-3.65; p = .007).

CONCLUSION: Our results suggest that patients with severe haemophilia who self-managed their PA can improve their long-term quality of life in the domain of physical health and also the daily time spent in moderate-intensity PA.

PMID:38470981 | DOI:10.1111/hae.14988

Clin Orthop Relat Res. 2024 Mar 12. doi: 10.1097/CORR.0000000000003018. Online ahead of print.

ABSTRACT

BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty.

QUESTION/PURPOSE: Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty?

METHODS: Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model’s ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 – (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error.

RESULTS: Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models.

CONCLUSION: Machine learning did not outperform traditional regression models.

CLINICAL RELEVANCE: Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

PMID:38470976 | DOI:10.1097/CORR.0000000000003018

J Patient Saf. 2024 Mar 13. doi: 10.1097/PTS.0000000000001214. Online ahead of print.

ABSTRACT

OBJECTIVES: Accidental falls are among the leading hospitals’ adverse events, with incidence ranging from 2 to 20 events per 1.000 days/patients. The objective of this study is to assess the relationship between in-hospital falls and the score of 3 DEPendence and Clinical-Social Fragility indexes.

METHODS: A monocentric case-control study was conducted by retrieving data of in-hospital patients from the electronic health records.

RESULTS: Significant differences between the mean scores at the hospital admission and discharge were found. The BRASS scale mean (SD) values at the admission and at the discharge were also significantly higher in cases of in-hospital falls: at the admission 10.2 (±7.7) in cases versus 7.0 (±8.0) in controls (P = 0.003); at the discharge 10.0 (±6.4) versus 6.7 (±7.5) (P = 0.001). Barthel index mean (SD) scores also presented statistically significant differences: at the admission 60.3 (±40.6) in cases versus 76.0 (±34.8) in controls (P = 0.003); at discharge 51.3 (±34.9) versus 73.3 (±35.2) (P = 0.000).Odds ratios were as follows: for Barthel index 2.37 (95% CI, 1.28-4.39; P = 0.003); for Index of Caring Complexity 1.45 (95% CI, 0.72-2.91, P = 0. 255); for BRASS index 1.95 (95% CI, 1.03-3.70, P = 0.026). With BRASS index, the area under the curve was 0.667 (95% CI, 0.595-0.740), thus indicating a moderate predictive power of the scale.

CONCLUSIONS: The use of only Conley scale-despite its sensitivity and specificity-is not enough to fully address this need because of the multiple and heterogeneous factors that predispose to in-hospital falls. Therefore, the combination of multiple tools should be recommended.

PMID:38470963 | DOI:10.1097/PTS.0000000000001214

J Patient Saf. 2024 Mar 13. doi: 10.1097/PTS.0000000000001220. Online ahead of print.

ABSTRACT

OBJECTIVE: This article aims to assess the reproducibility of Manufacturer and User Facility Device Experience (MAUDE) data-driven studies by analyzing the data queries used in their research processes.

METHODS: Studies using MAUDE data were sourced from PubMed by searching for “MAUDE” or “Manufacturer and User Facility Device Experience” in titles or abstracts. We manually chose articles with executable queries. The reproducibility of each query was assessed by replicating it in the MAUDE Application Programming Interface. The reproducibility of a query is determined by a reproducibility coefficient that ranges from 0.95 to 1.05. This coefficient is calculated by comparing the number of medical device reports (MDRs) returned by the reproduced queries to the number of reported MDRs in the original studies. We also computed the reproducibility ratio, which is the fraction of reproducible queries in subgroups divided by the query complexity, the device category, and the presence of a data processing flow.

RESULTS: As of August 8, 2022, we identified 523 articles from which 336 contained queries, and 60 of these were executable. Among these, 14 queries were reproducible. Queries using a single field like product code, product class, or brand name showed higher reproducibility (50%, 33.3%, 31.3%) compared with other fields (8.3%, P = 0.037). Single-category device queries exhibited a higher reproducibility ratio than multicategory ones, but without statistical significance (27.1% versus 8.3%, P = 0.321). Studies including a data processing flow had a higher reproducibility ratio than those without, although this difference was not statistically significant (42.9% versus 17.4%, P = 0.107).

CONCLUSIONS: Our findings indicate that the reproducibility of queries in MAUDE data-driven studies is limited. Enhancing this requires the development of more effective MAUDE data query strategies and improved application programming interfaces.

PMID:38470959 | DOI:10.1097/PTS.0000000000001220

Proc Natl Acad Sci U S A. 2024 Mar 19;121(12):e2319496121. doi: 10.1073/pnas.2319496121. Epub 2024 Mar 12.

ABSTRACT

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give-directly or indirectly-some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the “between-group heritability” sometimes invoked in behavior genetics, the statistic [Formula: see text] used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

PMID:38470926 | DOI:10.1073/pnas.2319496121

Proc Natl Acad Sci U S A. 2024 Mar 19;121(12):e2314995121. doi: 10.1073/pnas.2314995121. Epub 2024 Mar 12.

ABSTRACT

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

PMID:38470918 | DOI:10.1073/pnas.2314995121

PLoS Comput Biol. 2024 Mar 12;20(3):e1011901. doi: 10.1371/journal.pcbi.1011901. eCollection 2024 Mar.

ABSTRACT

A novel class of protein misfolding characterized by either the formation of non-native noncovalent lasso entanglements in the misfolded structure or loss of native entanglements has been predicted to exist and found circumstantial support through biochemical assays and limited-proteolysis mass spectrometry data. Here, we examine whether it is possible to design small molecule compounds that can bind to specific folding intermediates and thereby avoid these misfolded states in computer simulations under idealized conditions (perfect drug-binding specificity, zero promiscuity, and a smooth energy landscape). Studying two proteins, type III chloramphenicol acetyltransferase (CAT-III) and D-alanyl-D-alanine ligase B (DDLB), that were previously suggested to form soluble misfolded states through a mechanism involving a failure-to-form of native entanglements, we explore two different drug design strategies using coarse-grained structure-based models. The first strategy, in which the native entanglement is stabilized by drug binding, failed to decrease misfolding because it formed an alternative entanglement at a nearby region. The second strategy, in which a small molecule was designed to bind to a non-native tertiary structure and thereby destabilize the native entanglement, succeeded in decreasing misfolding and increasing the native state population. This strategy worked because destabilizing the entanglement loop provided more time for the threading segment to position itself correctly to be wrapped by the loop to form the native entanglement. Further, we computationally identified several FDA-approved drugs with the potential to bind these intermediate states and rescue misfolding in these proteins. This study suggests it is possible for small molecule drugs to prevent protein misfolding of this type.

PMID:38470915 | DOI:10.1371/journal.pcbi.1011901

PLoS One. 2024 Mar 12;19(3):e0297386. doi: 10.1371/journal.pone.0297386. eCollection 2024.

ABSTRACT

BACKGROUND: Prevention and management of childhood overweight involves the entire family. We aimed to investigate purchase patterns in households with at least one member with overweight in childhood by describing expenditure on different food groups.

METHODS: This Danish register-based cohort study included households where at least one member donated receipts concerning consumers purchases in 2019-2021 and at least one member had their Body mass index (BMI) measured in childhood within ten years prior to first purchase. A probability index model was used to evaluate differences in proportion expenditure spent on specific food groups.

RESULTS: We identified 737 households that included a member who had a BMI measurement in childhood, 220 with overweight and 517 with underweight or normal weight (reference households). Adjusting for education, income, family type, and urbanization, households with a member who had a BMI classified as overweight in childhood had statistically significant higher probability of spending a larger proportion of expenditure on ready meals 56.29% (95% CI: 51.70;60.78) and sugary drinks 55.98% (95% CI: 51.63;60.23). Conversely, they had a statistically significant lower probability of spending a larger proportion expenditure on vegetables 38.44% (95% CI: 34.09;42.99), compared to the reference households.

CONCLUSION: Households with a member with BMI classified as overweight in childhood spent more on unhealthy foods and less on vegetables, compared to the reference households. This study highlights the need for household/family-oriented nutrition education and intervention.

PMID:38470907 | DOI:10.1371/journal.pone.0297386

PLOS Glob Public Health. 2024 Mar 12;4(3):e0002888. doi: 10.1371/journal.pgph.0002888. eCollection 2024.

ABSTRACT

Despite widespread adoption of community health (CH) systems, there are evidence gaps to support global best practice in remote settings where access to health care is limited and community health workers (CHWs) may be the only available providers. The nongovernmental health organization Pivot partnered with the Ministry of Public Health (MoPH) to pilot a new enhanced community health (ECH) model in rural Madagascar, where one CHW provided care at a stationary CH site while additional CHWs provided care via proactive household visits. The program included professionalization of the CHW workforce (i.e., targeted recruitment, extended training, financial compensation) and twice monthly supervision of CHWs. For the first eighteen months of implementation (October 2019-March 2021), we compared utilization and proxy measures of quality of care in the intervention commune (local administrative unit) and five comparison communes with strengthened community health programs under a different model. This allowed for a quasi-experimental study design of the impact of ECH on health outcomes using routinely collected programmatic data. Despite the substantial support provided to other CHWs, the results show statistically significant improvements in nearly every indicator. Sick child visits increased by more than 269.0% in the intervention following ECH implementation. Average per capita monthly under-five visits were 0.25 in the intervention commune and 0.19 in the comparison communes (p<0.01). In the intervention commune, 40.3% of visits were completed at the household via proactive care. CHWs completed all steps of the iCCM protocol in 85.4% of observed visits in the intervention commune (vs 57.7% in the comparison communes, p-value<0.01). This evaluation demonstrates that ECH can improve care access and the quality of service delivery in a rural health district. Further research is needed to assess the generalizability of results and the feasibility of national scale-up as the MoPH continues to define the national community health program.

PMID:38470906 | DOI:10.1371/journal.pgph.0002888