Differential local ordering of mixed crowders determines effective size and stability of ss-DNA capped gold nanoparticle.

Understanding the influence of a crowded intracellular environment on the structure and solvation of DNA functionalized gold nanoparticles (ss-DNA AuNP) is necessary for designing applications in nanomedicine. In this study, the effect of single (Gly, Ser, Lys) and mixture of amino acids (Gly+Ser, Gly+Lys, Ser+Lys) at crowded concentrations is examined on the structure of the ss-DNA AuNP using molecular dynamics simulations. Using the structural estimators such as pair correlation functions and ligand shell positional fluctuations, the solvation entropy is estimated. Combining the AuNP-solvent interaction energy with the solvation entropy estimates, the free energy of solvation of the AuNP in crowded solutions is computed. The solvation entropy favours the solvation free energy which becomes more favourable for larger effective size of AuNP in crowded solutions relative to that in water. The effective size of AuNP depends on the different propensity of the crowders to adsorb on Au surface, with the smallest crowder (Gly) having the highest propensity inducing the least effective AuNP size as compared to other single crowder solutions. In mixed crowded solutions of amino acids of variable size and chemistry, distinctive local adsorption of the crowders on the gold surface is observed that controls the additive or non-additive crowding effects which govern an increase (in Gly+Ser) or decrease (in Gly+Lys) in nanoparticle effective size respectively. The results shed light into the fundamental understanding of the influence of intracellular crowding on structure of ss-DNA AuNP and plausible employability of crowding as a tool to design programmable self-assembly of functionalized nanoparticles.

Incidence, Predictors, and Outcomes of Venous and Arterial Thrombosis in COVID-19: A Nationwide Inpatient Analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is known to increase the risk of venous thromboembolism (VTE) and arterial thromboembolism (ATE). However, the incidence, predictors, and outcomes of clinical thrombosis for inpatients with COVID-19 are not well known. This study aimed to enhance our understanding of clinical thrombosis in COVID-19, its associated factors, and mortality outcomes. METHOD: Hospitalised adult (≥18 years of age) patients with COVID-19 in 2020 were retrospectively identified from the US National Inpatient Sample database. Clinical characteristics, incident VTE, ATE, and in-hospital mortality outcomes were recorded. Multivariable logistic regression was performed to identify clinical factors associated with thrombosis and in-hospital mortality in COVID-19 inpatients. RESULTS: A total of 1,583,135 adult patients with COVID-19 in the year 2020 were identified from the National Inpatient Sample database; patients with thrombosis were 41% females with a mean age of 65.4 (65.1-65.6) years. The incidence of thrombosis was 6.1% (97,185), including VTE at 4.8% (76,125), ATE at 3.0% (47,790), and the in-hospital mortality rate was 13.4% (212,785). Patients with thrombosis were more likely to have respiratory symptoms of COVID-19 (76.7% vs 75%, p<0.001) compared with patients without thrombosis. The main factors associated with overall thrombosis, VTE, and ATE were paralysis, ventilation, solid tumours without metastasis, metastatic cancer, and acute liver failure. Although all thrombosis categories were associated with higher in-hospital mortality for COVID-19 inpatients in univariable analyses (p<0.001), they were not in multivariable analyses-thrombosis (odds ratio [OR] 1.24; 95% confidence interval [CI] 0.90-1.70; p=0.19), VTE (OR 0.70; 95% CI 0.52-1.00; p=0.05), and ATE (OR 1.07; 95% CI 0.92-1.25; p=0.36). CONCLUSIONS: The association of COVID-19 with thrombosis and VTE increases with increasing severity of the COVID-19 disease. Risk stratification of thrombosis is crucial in COVID-19 patients to determine the necessity of thromboprophylaxis.

Ethylene glycol energetically disfavours oligomerization of pseudoisocyanine dyestuffs at crowded concentrations.

The intriguing role of the intracellular crowded environment in regulating protein aggregation remains elusive. The convolution of several factors such as the protein sequence-dependence, crowder's shape and size and diverse intermolecular interactions makes it complex to identify systematic trends. One of the ways to simplify the problem is to study a synthetic model for self-assembling proteins. In this study, we examine the aggregation behaviour of the cationic pseudoisocyanine chloride (PIC) dyestuff which is known to self-assemble and form fibril-like J-aggregates in aqueous solutions, similar to those formed by amyloid-forming proteins. Prior experimental studies have shown that polyethylene glycol impedes and Ficoll-400 promotes the self-assembly of PIC dyes. To achieve molecular insights, we examine the effect of crowding by ethylene glycol on the solvation thermodynamics of oligomerization of dyes into H-type and J-type oligomers using extensive molecular dynamics simulations. The binding free energy calculations show that the formation of J-oligomers is more favourable than that of H-oligomers in water. The stability of H- and J- tetramers and pentamers decreases in crowded solutions. The formation of oligomers is supported by the favourable change in dye-solvent interaction energy in both pure water and aqueous ethylene glycol solution although it is opposed by the reduced dye-solvent entropy. Ethylene glycol, as a molecular crowder, disfavours the H- as well as J-oligomerization via preferential binding to the dye oligomers. An unfavourable change in dye-crowder and dye-dye interaction energy on dye association makes the H-oligomer formation less favourable in crowded solution than in pure water solution. In the case of J-oligomers, however, the unfavourable change in dye-crowder interaction energy primarily contributes to making total dye-solvent energy unfavourable. The results are supported by isothermal titration calorimetry measurements where the binding of ethylene glycol to PIC molecules is found to be endothermic. The results provide an emerging view that a crowded environment can disfavour self-assembly of PIC dyes by interactions with the oligomeric states. The findings have implications in understanding the role of a crowded environment in shaping the free energy landscapes of proteins.

Structure, energetics and dynamics in crowded amino acid solutions: a molecular dynamics study.

A comprehensive understanding of crowding effects on biomolecular processes necessitates investigating the bulk thermodynamic and kinetic properties of the solutions with an accurate molecular representation of the crowded milieu. Recent studies have reparameterized the non-bonded dispersion interaction of solutes to precisely model intermolecular interactions, which would circumvent artificial aggregation as shown by the original force-fields. However, the performance of this reparameterization is yet to be assessed for concentrated crowded solutions in terms of investigating the hydration shell structure, energetics and dynamics. In this study, we perform molecular dynamics simulations of crowded aqueous solutions of five zwitterionic neutral amino acids (Gly, Ala, Thr, Pro, and Ser), mimicking the molecular crowding environment, using a modified AMBER ff99SB-ILDN force-field. We systematically examine and show that the reproducibility of the osmotic coefficients, density, viscosity and self-diffusivity of amino acids improves using the modified force-field in crowded concentrations. The modified force-field also improves the structuring of the solute solvation shells, solute interaction energy and convergence of tails of radial distribution functions, indicating reduction in the artificial aggregation. Our results also indicate that the hydrogen bonding network of water weakens and water molecules anomalously diffuse at small time scales in the crowded solutions. These results underscore the significance of examining the solution properties and anomalous hydration behaviour of water in crowded solutions, which have implications in shaping the structure and dynamics of biomolecules. The findings also illustrate the improvement in predicting bulk solution properties using the modified force-field, thereby providing an approach towards accurate modeling of crowded molecular solutions.

Crowding effects on water-mediated hydrophobic interactions.

Understanding the fundamental forces such as hydrophobic interactions in a crowded intracellular environment is necessary to comprehensively decipher the mechanisms of protein folding and biomolecular self-assemblies. The widely accepted entropic depletion view of crowding effects primarily attributes biomolecular compaction to the solvent excluded volume effects exerted by the "inert" crowders, neglecting their soft interactions with the biomolecule. In this study, we examine the effects of chemical nature and soft attractive energy of crowders on the water-mediated hydrophobic interaction between two non-polar neopentane solutes using molecular dynamics simulations. The crowded environment is modeled using dipeptides composed of polar and non-polar amino acids of varying sizes. The results show that amongst the non-polar crowders, Leu2 strengthens the hydrophobic interactions significantly, whereas the polar and small-sized non-polar crowders do not show significant strengthening. Distinct underlying thermodynamic driving forces are illustrated where the small-sized crowders drive hydrophobic interaction via a classic entropic depletion effect and the bulky crowders strengthen it by preferential interaction with the solute. A crossover from energy-stabilized solvent-separated pair to entropy-stabilized contact pair state is observed in the case of bulky non-polar (Leu2) and polar (Lys2) crowders. The influence of solute-crowder energy in affecting the dehydration energy penalty is found to be crucial for determining the neopentane association. The findings demonstrate that along with the entropic (size) effects, the energetic effects also play a crucial role in determining hydrophobic association. The results can be extended and have implications in understanding the impact of protein crowding with varying chemistry in modulating the protein free energy landscapes.

An interplay of excluded-volume and polymer-(co)solvent attractive interactions regulates polymer collapse in mixed solvents.

Cosolvent effects on the coil-globule transitions in aqueous polymer solutions are not well understood, especially in the case of amphiphilic cosolvents that preferentially adsorb on the polymer and lead to both polymer swelling and collapse. Although a predominant focus in the literature has been placed on the role of polymer-cosolvent attractive interactions, our recent work has shown that excluded-volume interactions (repulsive interactions) can drive both preferential adsorption of the cosolvent and polymer collapse via a surfactant-like mechanism. Here, we further study the role of polymer-(co)solvent attractive interactions in two kinds of polymer solutions, namely, good solvent (water)-good cosolvent (alcohol) (GSGC) and poor solvent-good cosolvent (PSGC) solutions, both of which exhibit preferential adsorption of the cosolvent and a non-monotonic change in the polymer radius of gyration with the addition of the cosolvent. Interestingly, at low concentrations, the polymer-(co)solvent energetic interactions oppose polymer collapse in the GSGC solutions and contrarily support polymer collapse in the PSGC solutions, indicating the importance of the underlying polymer chemistry. Even though the alcohol molecules are preferentially adsorbed on the polymer, the trends of the energetic interactions at low cosolvent concentrations are dominated by the polymer-water energetic interactions in both the cases. Therefore, polymer-(co)solvent energetic interactions can either reinforce or compensate the surfactant-like mechanism, and it is this interplay that drives coil-to-globule transitions in polymer solutions. These results have implications for rationalizing the cononsolvency transitions in real systems such as polyacrylamides in aqueous alcohol solutions where the understanding of microscopic driving forces is still debatable.

Small crowder interactions can drive hydrophobic polymer collapse as well as unfolding.

Biomolecules evolve and function in the intracellular crowded environment that is densely packed with macromolecules. Yet, a microscopic understanding of the effects of such an environment on the conformational preferences of biomolecules remains elusive. While prior investigations have attributed crowding effects mainly to the excluded volume (size) effects of the crowders, very little is known about the effects exerted due to their chemical interactions. In this study, crowding effects of tri-alanine peptides on the collapse equilibria of generic hydrophobic polymer are investigated using molecular dynamics simulations. The role of weak, non-specific, attractive polymer-crowder interactions in modulating the polymer collapse equilibria is examined. The results highlight that crowding effects can lead to polymer compaction as well as unfolding depending on the strength of polymer-crowder interaction energy. Strongly interacting crowders weaken hydrophobic collapse (or unfold the polymer) at high volume fractions and induce polymer collapse only under dilute conditions. Weakly interacting crowders induce polymer collapse at all crowder concentrations. Interestingly, the thermodynamic driving forces for polymer collapse are remarkably different in the two cases. Strongly and weakly interacting crowders induce collapse by preferential adsorption and preferential depletion respectively. The findings provide new insights into the possible effects of interplay of intermolecular interactions in a crowded environment. The results have implications in understanding the impact of crowding in altering free energy landscapes of proteins.

Molecular origin of urea driven hydrophobic polymer collapse and unfolding depending on side chain chemistry.

Osmolytes affect hydrophobic collapse and protein folding equilibria. The underlying mechanisms are, however, not well understood. We report large-scale conformational sampling of two hydrophobic polymers with secondary and tertiary amide side chains using extensive molecular dynamics simulations. The calculated free energy of unfolding increases with urea for the secondary amide, yet decreases for the tertiary amide, in agreement with experiment. The underlying mechanism is rooted in opposing entropic driving forces: while urea screens the hydrophobic macromolecular interface and drives unfolding of the tertiary amide, urea's concomitant loss in configurational entropy drives collapse of the secondary amide. Only at sufficiently high urea concentrations bivalent urea hydrogen bonding interactions with the secondary amide lead to further stabilisation of its collapsed state. The observations provide a new angle on the interplay between side chain chemistry, urea hydrogen bonding, and the role of urea in attenuating or strengthening the hydrophobic effect.

Comparison of hydration behavior and conformational preferences of the Trp-cage mini-protein in different rigid-body water models.

The secondary structure conformational properties and hydration shell metrics of the Trp-cage mini-protein are examined in the folded and unfolded ensembles in mTIP3P, TIP4P, and TIP4P-Ew water models with the CHARMM22 force-field using molecular dynamics simulations at 250 K. Upon changing the water model, the conformational order metrics of the peptide show significant differences in the unfolded rather than in the folded ensemble. The unfolding temperatures for Trp-cage are observed to be around 460, 470, and 430 K in mTIP3P, TIP4P, and TIP4P-Ew, respectively. Upon comparing the results with a previous study on a 16-residue ß-hairpin fragment of the 2GB1 protein, the same set of conformational order metrics are found to be insufficient in describing the free energy landscape of peptides having a distinct native secondary structure. However, the hydration shell properties of the peptide have been found to be independent of the sequence of the peptide and it changes in conformation upon unfolding. Our calculations reveal that for a particular water model, the secondary structure preferences in the unfolded ensembles of the two peptides are qualitatively different. The unfolded structures of Trp-cage prefer extended and compact structures in TIP4P-Ew and mTIP3P water, respectively, whereas the ß-hairpin peptide prefers extended unfolded structures in mTIP3P. The conformational preferences of the unfolded peptide in a given water model have been found to depend on the peptide sequence, where the binding energies of the water molecules around the polar residues in the unfolded conformations show sensitivity to the multipole moments of the water models. The significance of an accurate description of peptide-solvent interactions in the parametrization of biomolecular force-fields, to obtain an accurate description of conformational preferences, in particular in the unfolded ensembles of proteins, is highlighted.

Sensitivity of local hydration behaviour and conformational preferences of peptides to choice of water model.

Hydration of the 16-residue ß-hairpin fragment of the protein in the folded and unfolded ensembles is studied with mTIP3P and TIP4P solvent models using the CHARMM22 protein force-field. mTIP3P is a three-site water model which is used for parameterization of the CHARMM force-field and is known to exhibit liquid-state anomalies of water at temperatures about 80 K lower than the experimental temperature. TIP4P is a four-site water model which gives a better description of the experimental phase diagram and liquid-state anomalies of water. At a temperature of 250 K, where the folded ensemble of the peptide is stable and the unfolded ensemble is metastable, secondary structure metrics are much more sensitive to the choice of solvent model in the unfolded, rather than folded, ensemble. In particular, mean values as well as variation in the positional root mean square displacements (RMSD) and configurational entropy are greater in mTIP3P compared to the TIP4P solvent. The peptide structure is relatively more compact in the TIP4P solvent, which supports unfolded as well as hydrophobic core states. In terms of average local order and binding energy of the water surrounding the peptide, strong deviations from bulk behaviour are restricted to the first hydration shell and differences between the folded and unfolded ensembles in the two solvents are small. The strong coupling between the solvent and the peptide is demonstrated, however, by the dependence of the unfolding temperature on the water model (400 K in mTIP3P and 465 K in TIP4P) and the qualitatively different temperature dependence of the hydration layer occupancy signalling the unfolding transition in the two solvents. A residue-wise decomposition of different contributions to the configurational energy indicates that the TIP4P solvent shows far greater variation in the interaction with charged sidegroups of amino acid residues than the mTIP3P solvent. The implications of sequence-dependent sensitivity of peptide secondary structures to the choice of water models for simulating folding-unfolding equilibria and free energy landscapes are discussed.

Potential Applications and Impact of ChatGPT in Radiology.

Radiology has always gone hand-in-hand with technology and artificial intelligence (AI) is not new to the field. While various AI devices and algorithms have already been integrated in the daily clinical practice of radiology, with applications ranging from scheduling patient appointments to detecting and diagnosing certain clinical conditions on imaging, the use of natural language processing and large language model based software have been in discussion for a long time. Algorithms like ChatGPT can help in improving patient outcomes, increasing the efficiency of radiology interpretation, and aiding in the overall workflow of radiologists and here we discuss some of its potential applications.

Cardiovascular Complications With Delivery Hospitalizations in Patients With Pulmonary Hypertension: A Nationwide Study From 2011 to 2020.

BACKGROUND: Pregnancy in patients with pulmonary hypertension (PH) is associated with a heightened risk of medical complications including right heart failure, pulmonary edema, and arrhythmias. Our study investigated the association between PH and these complications during delivery. METHODS AND RESULTS: The National Inpatient Sample was used to identify delivery hospitalizations from 2011 to 2020. Multivariable logistic regression was performed to study the association of PH with the primary outcomes of in-hospital medical and obstetric complications. A total of 37 482 207 delivery hospitalizations in women ≥18 years of age were identified, of which 9593 patients had PH. Pregnant patients with PH had higher incidence of complications during delivery including preeclampsia/eclampsia, arrhythmias, and pulmonary edema among others, compared with those without PH. Pregnant patients with PH also had a higher incidence of in-hospital mortality compared with those without PH (0.51% versus 0.007%). In propensity-matched analyses, PH was still significantly associated with a higher risk of in-hospital mortality (odds ratio [OR], 5.02 [95% CI, 1.82-13.90]; P=0.001), pulmonary edema (OR, 9.11 [95% CI, 6.34-13.10]; P<0.001), peripartum cardiomyopathy (OR, 1.85 [95% CI, 1.37-2.50]; P<0.001), venous thromboembolism (OR, 12.60 [95% CI, 6.04-26.10]; P<0.001), cardiac arrhythmias (OR, 6.11 [95% CI, 4.97-7.53]; P<0.001), acute kidney injury (OR, 3.72 [95% CI, 2.86-4.84]; P<0.001), preeclampsia/eclampsia (OR, 2.24 [95% CI, 1.95-2.58]; P<0.001), and acute coronary syndrome (OR, 2.01 [95% CI, 1.06-3.80]; P=0.03), compared with pregnant patients without PH. CONCLUSIONS: Delivery hospitalizations in patients with PH are associated with a high risk of mortality, pulmonary edema, peripartum cardiomyopathy, venous thromboembolism, arrhythmias, acute kidney injury, preeclampsia/eclampsia, and acute coronary syndrome.

Intracardiac Thrombus in COVID-19 Inpatients: A Nationwide Study of Incidence, Predictors, and Outcomes.

COronaVIrus Disease-2019 (COVID-19) is associated with a hypercoagulable state. Intracardiac thrombosis is a potentially serious complication but has seldom been evaluated in COVID-19 patients. We assessed the incidence, associated factors, and outcomes of COVID-19 patients with intracardiac thrombosis. In 2020, COVID-19 inpatients were identified from the National Inpatient Sample (NIS) database. Data on clinical characteristics, intracardiac thrombosis, and adverse outcomes were collected. Multivariable logistic regression was used to identify factors associated with intracardiac thrombosis, in-hospital mortality, and morbidities. In 2020, 1,683,785 COVID-19 inpatients (mean age 63.8 years, 32.2% females) were studied. Intracardiac thrombosis occurred in 0.10% (1830) of cases. In-hospital outcomes included 13.2% all-cause mortality, 3.5% cardiovascular mortality, 2.6% cardiac arrest, 4.4% acute coronary syndrome (ACS), 16.1% heart failure, 1.3% stroke, and 28.3% acute kidney injury (AKI). Key factors for intracardiac thrombosis were congestive heart failure history and coagulopathy. Intracardiac thrombosis independently linked to higher risks of all-cause mortality (odds ratio [OR]: 3.32 (2.42-4.54)), cardiovascular mortality (OR: 2.95 (1.96-4.44)), cardiac arrest (OR: 2.04 (1.22-3.43)), ACS (OR: 1.62 (1.17-2.22)), stroke (OR: 3.10 (2.11-4.56)), and AKI (OR: 2.13 (1.68-2.69)), but not heart failure. While rare, intracardiac thrombosis in COVID-19 patients independently raised in-hospital mortality and morbidity risks.

PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Water and water-like liquids: relationships between structure, entropy and mobility.

Liquids with very diverse underlying interactions share the thermodynamic and transport anomalies of water, including metalloids, ionic melts and mesoscopic fluids. The generic feature that characterises such water-like liquids is a density-driven shift in the nature of local order in the condensed phases. The key semiquantitative relationships between structural order, thermodynamics and transport that are necessary in order to map out the consequences of this common qualitative feature for liquid-state properties and phase transformations of such systems are reviewed here. The application of these ideas to understand and model tetrahedral liquids, especially water, is discussed and possible extensions to other complex fluids are considered.

Molecular Crowders Can Induce Collapse in Hydrophilic Polymers via Soft Attractive Interactions.

A comprehensive understanding of protein folding and biomolecular self-assembly in the intracellular environment requires obtaining a microscopic view of the crowding effects. The classical view of crowding explains biomolecular collapse in such an environment in terms of the entropic solvent excluded volume effects subjected to hard-core repulsions exerted by the inert crowders, neglecting their soft chemical interactions. In this study, the effects of nonspecific, soft interactions of molecular crowders in regulating the conformational equilibrium of hydrophilic (charged) polymers are examined. Using advanced molecular dynamics simulations, collapse free energies of an uncharged, a negatively charged, and a charge-neutral 32-mer generic polymer are computed. The strength of the polymer-crowder dispersion energy is modulated to examine its effect on polymer collapse. The results show that the crowders preferentially adsorb and drive the collapse of all three polymers. The uncharged polymer collapse is opposed by the change in solute-solvent interaction energy but is overcompensated by the favorable change in the solute-solvent entropy as observed in hydrophobic collapse. However, the negatively charged polymer collapses with a favorable change in solute-solvent interaction energy due to reduction in the dehydration energy penalty as the crowders partition to the polymer interface and shield the charged beads. The collapse of a charge-neutral polymer is opposed by the solute-solvent interaction energy but is overcompensated by the solute-solvent entropy change. However, for the strongly interacting crowders, the overall energetic penalty decreases since the crowders interact with polymer beads via cohesive bridging attractions to induce polymer collapse. These bridging attractions are found to be sensitive to the binding sites of the polymer, since they are absent in the negatively charged or uncharged polymers. These interesting differences in thermodynamic driving forces highlight the crucial role of the chemical nature of the macromolecule as well as of the crowder in determining the conformational equilibria in a crowded milieu. The results emphasize that the chemical interactions of the crowders should be explicitly considered to account for the crowding effects. The findings have implications in understanding the crowding effects on the protein free energy landscapes.

Recurrent sporadic malignant triton tumor in the carotid sheath in the absence of neurofibromatosis.

Malignant Triton Tumors (MTTs) are a rare and aggressive subtype of malignant peripheral nerve sheath tumors (MPNSTs), often associated with neurofibromatosis type 1. This case report describes a unique instance of recurrent sporadic MTT within the carotid sheath in a 33-year-old male without any personal or familial history of neurofibromatosis. The patient initially presented with a biopsy-confirmed MTT in the right neck, involving the carotid body and brachial plexus, and underwent partial resection, radiation therapy, and chemotherapy. Six months later, the patient presented with recurrent MTT, and subsequently underwent radical tumor resection, segmental right carotid artery resection, and deep femoral vein interposition. Recovery was complicated by hematoma formation, and the patient developed vocal fold paralysis and a left vocal fold cyst, necessitating further surgeries. Yearly follow-ups for 8 years revealed no recurrence. This case emphasizes the importance of comprehensive patient evaluation, including clinical history, imaging, and biopsy findings, for accurate diagnosis and prompt surgical intervention in managing such rare and aggressive tumors. Further research is needed to identify novel therapies and improve survival rates for patients with MTTs.

Von Hippel-Lindau Disease (VHL): Characteristic Lesions with Classic Imaging Findings.

Von Hippel-Lindau disease (VHL) is a multisystem cancer syndrome caused by the inactivation of the VHL tumor suppressor gene and involves various organ systems including the central nervous system (CNS), endocrine system, and the kidneys. Tumors seen in patients with VHL disease can be benign or malignant and are usually multifocal, bilateral, and hypervascular in nature. As most lesions associated with VHL are asymptomatic initially, early diagnosis and the institution of an evidence-based surveillance protocol are of paramount importance. Screening, surveillance, and genetic counseling are key aspects in the management of patients diagnosed with VHL disease and often require a multidisciplinary approach and referral to specialized centers. This article will discuss the characteristic lesions seen with VHL disease, their diagnosis, screening protocols and management strategies, as well as an illustrative case to demonstrate the natural progression of the disease with classic imaging findings.

Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets.

Drug design involves the process of identifying and designing molecules that bind well to a given receptor. A vital computational component of this process is the protein-ligand interaction scoring functions that evaluate the binding ability of various molecules or ligands with a given protein receptor binding pocket reasonably accurately. With the publicly available protein-ligand binding affinity data sets in both sequential and structural forms, machine learning methods have gained traction as a top choice for developing such scoring functions. While the performance shown by these models is optimistic, there are several hidden biases present in these data sets themselves that affect the utility of such models for practical purposes such as virtual screening. In this work, we use published methods to systematically investigate several such factors or biases present in these data sets. In our analysis, we highlight the importance of considering sequence, protein-ligand interaction, and pocket structure similarity while constructing data splits and provide an explanation for good protein-only and ligand-only performances in some data sets. Through this study, we provide to the community several pointers for the design of binding affinity predictors and data sets for reliable applicability.

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications.

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities.