Autism in patients with eosinophilic gastrointestinal disease: A systematic review with meta-analysis.

PURPOSE: Neurodevelopmental disorders, such as autism spectrum disorder (ASD), have been increasingly associated with eosinophilic gastrointestinal disorders (EGID). However, the relationship between these diseases remains unclear. We performed a systematic review with meta-analysis to address this issue. METHODS: The search was performed according to the PRISMA guidelines using descriptors for ASD and EGIDs from the MEDLINE, Embase, PsycInfo, LILACS, and Web of Science databases. Observational studies with the prevalence of ASD in any EGID were included. The study protocol was registered on the PROSPERO platform under the number CRD42023455177. RESULTS: The total dataset comprised 766,082 participants. The result of the single-arm meta-analysis showed an overall prevalence of ASD in the population with EGID of 21.59% (95% CI: 10.73-38.67). There was an association between EGID and ASD (OR: 3.44; 95% CI: 1.25-2.21), also significant when restricted only to EoE (OR: 3.70; 95% CI: 2.71-5.70). DISCUSSION: Recent studies have implicated the influence of an inadequate epithelial barrier integrity in the pathogenesis of several diseases. The role of this mechanism can be extended to situations beyond allergic reactions, including other conditions with underlying immunological mechanisms. Several diseases are potentially related to the systemic effect of bacterial translocation in tissues with defective epithelial barriers. CONCLUSION: Our meta-analysis provides evidence that supports the consideration of EGID in patients with ASD and ASD in patients with EGID. Despite its limitations, the results should also be validated by future studies, preferably using multicenter prospective designs in populations with low referral bias.

The protective effect of breastfeeding on febrile seizures: a systematic review with meta-analysis.

Several potential risk factors have been identified in the etiopathogenesis of febrile seizures (FS), including the type and extent of breastfeeding (BF). Given the lack of conclusive data, this study aims to systematically evaluate the evidence on the association between BF and FS. We conducted a systematic review and meta-analysis according to PRISMA guidelines. The search was conducted using descriptors for FS, BF, and formula feeding in MEDLINE, Embase, and Web of Science databases. We included observational studies that compared the incidence of FS between those who had ever breastfed and those who were formula fed. The study protocol was registered on the PROSPERO platform under the number CRD42023474906. A total of 1,893,079 participants from 8 datasets were included. Our main analysis showed no significant association of any type of BF on the incidence of FS compared with formula-fed children (OR: 0.84; CI: 0.67-1.04; I2 = 78%; Cochran's Q = 0.0001), although meta-regression showed that BF was associated with a lower incidence of FS in preterm infants. Our secondary outcome showed a significantly reduced incidence of FS in children who received BF exclusively (OR: 0.80; CI: 0.65-0.99; I2 = 70%; Cochran's Q = 0.02).    Conclusion: There was no significant reduction in the incidence of FS in those who were breastfed compared to formula feeding. However, our meta-regression analysis indicated an association between BF and a lower incidence of FS in preterm infants. Additionally, children who exclusively received BF had a significantly reduced incidence of FS. These findings should be further investigated in prospective cohorts. What is Known: • Breastfeeding can modify risk factors for febrile seizures, such as susceptibility to viral and bacterial infections, micronutrient deficiencies, and low birth weight. • However, studies have shown conflicting results regarding the impact of breastfeeding on febrile seizures. What is New: • When comparing any breastfeeding pattern with no breastfeeding, there is no significant difference in the incidence of febrile seizures. • When comparing exclusive breastfeeding with no breastfeeding, there may be a decrease in the occurrence of febrile seizures.

Congenital herpes simplex with ophthalmic and multisystem features: a case report.

BACKGROUND: Neonatal herpes simplex virus (HSV) infection is rare and has significant morbimortality rates. Approximately 85% of newborns are infected intrapartum, and risk factors for mother-to-child transmission include vaginal delivery, primary maternal infection, and prolonged rupture of membranes. Neonatal HSV can manifest with isolated mucocutaneous lesions, neurological involvement, or disseminated disease. In general, herpetic infection can cause blepharoconjunctivitis or keratitis. We report a rare case of congenital herpes with ophthalmologic manifestations and multisystemic involvement. CASE PRESENTATION: A preterm infant, born at 32 weeks and 2 days, with presumed neonatal infection developed intestinal and respiratory complications, as well as hyperemic lesions on the left nostril and oral mucosa. An ophthalmological assessment was requested and brought up the suspicion of HSV infection, indicating empirical treatment with endovenous acyclovir. Later, a new ocular examination was suggestive of panuveitis. Afterward, serum IgM antibodies to HSV-1 and HSV-2 were positive. Proper antiviral therapy led to an improvement in the condition. DISCUSSION: Neonatal herpes is associated with a high risk of persistent skin lesions, long-term neurological disability and other lasting sequelae. It is essential to consider HSV infection in cases of neonatal conjunctivitis, especially in patients with an epithelial defect and no improvement after initial treatment with topical or systemic antibiotics. CONCLUSIONS: In the management of neonatal HSV, early diagnosis is essential for the timely initiation of antiviral therapy. Our report highlights that ocular assessment can be crucial in the correct diagnostic investigation of this condition.

Neural Network Water Model Based on the MB-Pol Many-Body Potential.

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor-liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. In order to address this limitation, we developed a "deep potential" neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor-liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO2.

In this work, we construct distinct first-principles-based machine-learning models of CO2, reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density functional theory. We employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initio molecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid-phase configurations, they are able to simulate a stable interfacial system and predict vapor-liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for the liquid phase and vapor-liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water.

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology-a machine learning approach-to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure, and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K and evaluate the Deep Potential model performance against experimental results and the semiempirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation.

A deep potential model with long-range electrostatic interactions.

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal.

Vapor-liquid equilibrium of water with the MB-pol many-body potential.

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor-liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor-liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor-liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. These results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram.

When do short-range atomistic machine-learning models fall short?

We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.

Pronounced changes in atomistic mechanisms for the Cl- + CH3I SN2 reaction with increasing collision energy.

In a previous direct dynamics simulation of the Cl- + CH3I → ClCH3 + I- SN2 reaction, predominantly indirect and direct reaction was found at collision energies Erel of 0.20 and 0.39 eV, respectively. For the work presented here, these simulations were extended by studying the reaction dynamics from Erel of 0.15 to 0.40 eV in 0.05 eV intervals. A transition from a predominantly indirect to direct reaction is found for Erel of 0.27-0.28 eV, a finding consistent with experiment. The simulation results corroborate the understanding that in experiments indirect reaction is characterized by small product translational energies and isotropic scattering, while direct reaction has higher translational energies and anisotropic scattering. The traditional statistical theoretical model for the Cl- + CH3I SN2 reaction assumes the Cl--CH3I pre-reaction complex (A) is formed, followed by barrier crossing, and then formation of the ClCH3-I- post-reaction complex (B). This mechanism is seen in the dynamics, but the complete atomistic dynamics are much more complex. Atomistic SN2 mechanisms contain A and B, but other dynamical events consisting of barrier recrossing (br) and the roundabout (Ra), in which the CH3-moiety rotates around the heavy I-atom, are also observed. The two most important mechanisms are only formation of A and Ra + A. The simulation results are compared with simulations and experiments for Cl- + CH3Cl, Cl- + CH3Br, F- + CH3I, and OH- + CH3I.

Chemical dynamics simulations of CID of peptide ions: comparisons between TIK(H+)2 and TLK(H+)2 fragmentation dynamics, and with thermal simulations.

Gas phase unimolecular fragmentation of the two model doubly protonated tripeptides threonine-isoleucine-lysine (TIK) and threonine-leucine-lysine (TLK) is studied using chemical dynamics simulations. Attention is focused on different aspects of collision induced dissociation (CID): fragmentation pathways, energy transfer, theoretical mass spectra, fragmentation mechanisms, and the possibility of distinguishing isoleucine (I) and leucine (L). Furthermore, discussion is given regarding the differences between single collision CID activation, which results from a localized impact between the ions and a colliding molecule N2, and previous thermal activation simulation results; Z. Homayoon, S. Pratihar, E. Dratz, R. Snider, R. Spezia, G. L. Barnes, V. Macaluso, A. Martin-Somer and W. L. Hase, J. Phys. Chem. A, 2016, 120, 8211-8227. Upon thermal activation unimolecular fragmentation is statistical and in accord with RRKM unimolecular rate theory. Simulations show that in collisional activation some non-statistical fragmentation occurs, including shattering, which is not present when the ions dissociate statistically. Products formed by non-statistical shattering mechanisms may be related to characteristic mass spectrometry peaks which distinguish the two isomers I and L.