Differential diagnosis of bone marrow failure syndromes guided by machine learning.

The choice to postpone treatment while awaiting genetic testing can result in significant delay in definitive therapies in patients with severe pancytopenia. Conversely, the misdiagnosis of inherited bone marrow failure (BMF) can expose patients to ineffectual and expensive therapies, toxic transplant conditioning regimens, and inappropriate use of an affected family member as a stem cell donor. To predict the likelihood of patients having acquired or inherited BMF, we developed a 2-step data-driven machine-learning model using 25 clinical and laboratory variables typically recorded at the initial clinical encounter. For model development, patients were labeled as having acquired or inherited BMF depending on their genomic data. Data sets were unbiasedly clustered, and an ensemble model was trained with cases from the largest cluster of a training cohort (n = 359) and validated with an independent cohort (n = 127). Cluster A, the largest group, was mostly immune or inherited aplastic anemia, whereas cluster B comprised underrepresented BMF phenotypes and was not included in the next step of data modeling because of a small sample size. The ensemble cluster A-specific model was accurate (89%) to predict BMF etiology, correctly predicting inherited and likely immune BMF in 79% and 92% of cases, respectively. Our model represents a practical guide for BMF diagnosis and highlights the importance of clinical and laboratory variables in the initial evaluation, particularly telomere length. Our tool can be potentially used by general hematologists and health care providers not specialized in BMF, and in under-resourced centers, to prioritize patients for genetic testing or for expeditious treatment.

Establishing a Health Information Technology for the Vaccination of National Institutes of Health Staff.

Introduction: Healthcare organizations faced unique operational challenges during the COVID-19 pandemic. Assuring the safety of both patients and healthcare workers in hospitals has been the primary focus during the COVID-19 pandemic. Methods: The NIH Vaccine Program (VP) with the Vaccine Management System (VMS) was created based on the commitment of NIH leadership, program leadership, the development team, and the program team; defining Key Performance Indicators (KPIs) of the VP and the VMS; and the NIH Clinical Center's (NIH CC) interdisciplinary approach to deploying the VMS. Results: This article discusses the NIH business requirements of the VP and VMS, the target KPIs of the VP and the VMS, and the NIH CC interdisciplinary approach to deploying an organizational VMS for vaccinating the NIH workforce. The use of the DCRI Spiral-Agile Software Development Life Cycle enabled the development of a system with stakeholder involvement that could quickly adapt to changing requirements meeting the defined KPIs for the program and system. The assessment of the defined KPIs through a survey and comments from the survey support that the VP and VMS were successful. Conclusion: A comprehensive program to maintain a healthy workforce includes asymptomatic COVID testing, symptomatic COVID testing, contact tracing, vaccinations, and policy-driven education. The need to develop systems during the pandemic resulted in changes to build software quickly with the input of many more users and stakeholders then typical in a decreased amount of time.

Experience with voluntary severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing of asymptomatic staff at the National Institutes of Health for one year.

Voluntary asymptomatic severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing was provided by the NIH Clinical Center over 1 year. Among 105,927 tests, 0.2% were positive. Among eligible staff, 79% participated with variable frequency and 61% of positive individuals had symptoms at the time of testing. Saliva specimen collection was chosen as an option less frequently than midturbinate collection.

Partitioning of Aß Peptide Fragments into Blood-Brain Barrier Mimetic Bilayer.

We used all-atom replica-exchange umbrella sampling molecular dynamics simulations to investigate the partitioning of the charged tetrapeptide KLVF and its neutral apolar counterpart VVIA into the blood-brain barrier (BBB)-mimetic bilayer. Our findings allowed us to reconstruct the partitioning mechanism for these two Aß peptide fragments. Despite dissimilar sequences, their permeation shares significant common features. Computations of free energies and permeabilities show that partitioning of both peptides is highly unfavorable, ruling out passive transport. The peptides experience multiple rotational transitions within the bilayer and typically cause considerable lipid disorder and bilayer thinning. Near the bilayer midplane, they lose almost entirely their solvation shell and the interactions with the lipid headgroups. The peptides cause complex reorganization within the proximal bilayer region. Upon insertion, they induce striking cholesterol influx reversed by its depletion and the influx of DMPC when the peptides reach the midplane. The differences in partitioning mechanisms are due to the much higher polarity of KLVF peptide, the permeation of which is more unfavorable and which exclusively assumes vertical orientations within the bilayer. In contrast, VVIA positions itself flat between the leaflets, causing minor disorder and even thickening of the BBB-mimetic bilayer. Due to the high density of the cholesterol-rich BBB bilayer, the unfavorable work associated with the peptide insertion provides a significant, but not dominant, contribution to the partition free energy, which is still governed by dehydration and loss of peptide-headgroup interactions. Comparison with experiments indicates that KLVF and VVIA permeation is similar to that of proline tetrapeptide, mannitol, or cimetidine, all of which exhibit no passive transport.

Unveiling the silent threat among us: leveraging health information technology in the search for asymptomatic COVID 19 healthcare workers.

Assuring the safety of both patients and healthcare workers (HCWs) in hospitals has been the primary focus of every healthcare organization during the COVID 19 pandemic. This article discusses the NIH Clinical Center's interdisciplinary approach to deploying an organizational Asymptomatic Staff Testing System.

Partitioning of Benzoic Acid into 1,2-Dimyristoyl-sn-glycero-3-phosphocholine and Blood-Brain Barrier Mimetic Bilayers.

Using an all-atom explicit water model and replica exchange umbrella sampling simulations, we investigated the molecular mechanisms of benzoic acid partitioning into two model lipid bilayers. The first was formed of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids, whereas the second was composed of an equimolar mixture of DMPC, 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine, palmitoylsphingomyelin, and cholesterol to constitute a blood-brain barrier (BBB) mimetic bilayer. Comparative analysis of benzoic acid partitioning into the two bilayers has revealed qualitative similarities. Partitioning into the DMPC and BBB bilayers is thermodynamically favorable although insertion into the former lowers the free energy of benzoic acid by approximately an additional 1 kcal mol-1. The partitioning energetics for the two bilayers is also largely similar based on the balance of benzoic acid interactions with apolar fatty acid tails, polar lipid headgroups, and water. In both bilayers, benzoic acid retains a considerable number of residual water molecules until reaching the bilayer midplane where it experiences nearly complete dehydration. Upon insertion into the bilayers, benzoic acid undergoes several rotations primarily determined by the interactions with the lipid headgroups. Nonetheless, in addition to the depth of the free energy minimum, the BBB bilayer differs from the DMPC counterpart by a much deeper location of the free energy minimum and the appearance of a high free energy barrier and positioning of benzoic acid near the midplane. Furthermore, DMPC and BBB bilayers exhibit different structural responses to benzoic acid insertion. Taken together, the BBB mimetic bilayer is preferable for an accurate description of benzoic acid partitioning.

Is the Conformational Ensemble of Alzheimer's Aß10-40 Peptide Force Field Dependent?

By applying REMD simulations we have performed comparative analysis of the conformational ensembles of amino-truncated Aß10-40 peptide produced with five force fields, which combine four protein parameterizations (CHARMM36, CHARMM22*, CHARMM22/cmap, and OPLS-AA) and two water models (standard and modified TIP3P). Aß10-40 conformations were analyzed by computing secondary structure, backbone fluctuations, tertiary interactions, and radius of gyration. We have also calculated Aß10-40 3JHNHα-coupling and RDC constants and compared them with their experimental counterparts obtained for the full-length Aß1-40 peptide. Our study led us to several conclusions. First, all force fields predict that Aß adopts unfolded structure dominated by turn and random coil conformations. Second, specific TIP3P water model does not dramatically affect secondary or tertiary Aß10-40 structure, albeit standard TIP3P model favors slightly more compact states. Third, although the secondary structures observed in CHARMM36 and CHARMM22/cmap simulations are qualitatively similar, their tertiary interactions show little consistency. Fourth, two force fields, OPLS-AA and CHARMM22* have unique features setting them apart from CHARMM36 or CHARMM22/cmap. OPLS-AA reveals moderate ß-structure propensity coupled with extensive, but weak long-range tertiary interactions leading to Aß collapsed conformations. CHARMM22* exhibits moderate helix propensity and generates multiple exceptionally stable long- and short-range interactions. Our investigation suggests that among all force fields CHARMM22* differs the most from CHARMM36. Fifth, the analysis of 3JHNHα-coupling and RDC constants based on CHARMM36 force field with standard TIP3P model led us to an unexpected finding that in silico Aß10-40 and experimental Aß1-40 constants are generally in better agreement than these quantities computed and measured for identical peptides, such as Aß1-40 or Aß1-42. This observation suggests that the differences in the conformational ensembles of Aß10-40 and Aß1-40 are small and the former can be used as proxy of the full-length peptide. Based on this argument, we concluded that CHARMM36 force field with standard TIP3P model produces the most accurate representation of Aß10-40 conformational ensemble.