Ovarian endometriomas and IVF: a retrospective case-control study

We performed this retrospective case-control study analyzing 428 first-attempt in vitro fertilization (IVF) cycles, among which 254 involved women with a previous or present diagnosis of ovarian endometriosis. First, the results of these 254 cycles were compared with 174 cycles involving patients with proven non-endometriotic tubal infertility having similar age and body mass index. Women with ovarian endometriosis had a significantly higher cancellation rate, but similar pregnancy, implantation and delivery rates as patients with tubal infertility. Second, among the women with ovarian endometriosis, the women with a history of laparoscopic surgery for ovarian endometriomas prior to IVF and no visual endometriosis at ovum pick-up (n = 112) were compared with the non-operated women and visual endometriomas at ovum pick-up (n = 142). Patients who underwent ovarian surgery before IVF had significantly shorter period, lower antral follicle count and required higher gonadotropin doses than patients with non-operated endometriomas. The two groups of women with a previous or present ovarian endometriosis did, however, have similar pregnancy, implantation and live birth rates. In conclusion, ovarian endometriosis does not reduce IVF outcome compared with tubal factor. Furthermore, laparoscopic removal of endometriomas does not improve IVF results, but may cause a decrease of ovarian responsiveness to gonadotropins

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig (∼1/3 of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of “statistical identity” for metal surfaces, which is key to understanding their behaviors and properties

A retrospective study on IVF outcome in euthyroid patients with anti-thyroid antibodies: effects of levothyroxine, acetyl-salicylic acid and prednisolone adjuvant treatments

<p>Abstract</p> <p>Background</p> <p>Anti-thyroid antibodies (ATA), even if not associated with thyroid dysfunction, are suspected to cause poorer outcome of in vitro fertilization (IVF).</p> <p>Methods</p> <p>We retrospectively analyzed: (a) the prevalence of ATA in euthyroid infertile women, (b) IVF outcome in euthyroid, ATA+ patients, and (c) the effect of adjuvant treatments (levothyroxine alone or associated with acetylsalicylic acid and prednisolone) on IVF results in ATA+ patients. One hundred twenty-nine euthyroid, ATA+ women undergoing IVF were compared with 200 matched, ATA-controls. During IVF cycle, 38 ATA+ patients did not take any adjuvant treatment, 55 received levothyroxin (LT), and 38 received LT +acetylsalicylic acid (ASA)+prednisolone (P).</p> <p>Results</p> <p>The prevalence of ATA among euthyroid, infertile patients was 10.5%, similar to the one reported in euthyroid women between 18 and 45 years. ATA+ patients who did not receive any adjuvant treatment showed significantly poorer ovarian responsiveness to stimulation and IVF results than controls. ATA+ patients receiving LT responded better to ovarian stimulation, but had IVF results as poor as untreated ATA+ women. Patients receiving LT+ASA+P had significantly higher pregnancy and implantation rates than untreated ATA+ patients (PR/ET 25.6% and IR 17.7% vs. PR/ET 7.5% and IR 4.7%, respectively), and overall IVF results comparable to patients without ATA (PR/ET 32.8% and IR 19%).</p> <p>Conclusion</p> <p>These observations suggest that euthyroid ATA+ patients undergoing IVF could have better outcome if given LT+ASA+P as adjuvant treatment. This hypothesis must be verified in further randomized, prospective studies.</p

Nonlinear optical response of a polycarbazolyldiacetylene film through femtosecond two-photon spectroscopy

We report the two-photon absorption (TPA) spectrum of a polycarbazolyldiacetylene film in the interval 900–1550 nm. In this range, the TPA coefficient exhibits a broad peak with a maximum of 33 cm/GW around 1125 nm, which indicates a two-photon state positioned at 2.2 eV, slightly below the one-photon state at 2.36 eV. The magnitude of the nonlinear refraction in the infrared is predicted on the base of a Three Level Model, which accounts fairly well for the nonlinear absorption in the explored range

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles

Abstract It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties

Machine Learning of Atomic Dynamics and Statistical Surface Identities in Gold Nanoparticles

It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. Here we show a machine learning approach that allows us to reconstruct the complex atomic dynamics of metal NPs from high-dimensional data extracted from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. Tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs based on the intrinsic atomic dynamics present within them

Sampling real-time atomic dynamics in metal nanoparticles by combining experiments, simulations, and machine learning

The atomic dynamics of metal nanoparticles (NPs), prominent already at low temperatures, is crucial for their properties but also challenging to elucidate. Recent advances in experimental approaches may provide atomically resolved snapshots of the structure of NPs in relevant regimes, but limitations in experimental data acquisition hinder the reconstruction of the atomic dynamics present within them. Molecular simulations -- typically starting from ideal/perfect NP structures -- allow tracking the motion of atoms over time, but suffer from limited sampling and provide results that, being dependent on the initial (putative) structure, are often only indicative. Here, combining state-of-the-art experimental and computational approaches, we demonstrate how it is possible to tackle the inherent limitations of both methods and resolve the atomistic dynamics present in metal NPs under realistic conditions. Annular dark-field scanning transmission electron microscopy (ADF-STEM) enables the acquisition of a time series of ten high-resolution images of an Au NP. Each image is taken at intervals of 0.6 seconds, providing data on a second timescale during the experimental sampling. These are used to reconstruct atomistic 3D structures of the real NP that are then used as starting configurations for ten independent molecular dynamics (MD) simulations. Unsupervised machine learning analysis of the data extracted from the MD trajectories using advanced structural and dynamical descriptors allows tracking and resolving the real-time atomic dynamics present within the NP under relevant conditions. This provides new perspectives into the realistic atomic dynamics within such NPs. We expect that such integrated experimental/computational approaches will become fundamental in various fields where the dynamics of NPs plays a key role, from catalysis to, e.g., nanoelectronics and biomedicine

SICOB-endorsed national Delphi consensus on obesity treatment optimization: focus on diagnosis, pre-operative management, and weight regain/insufficient weight loss approach

PurposeOverweight and obesity affects 60% of adults causing more than 1.2 million deaths across world every year. Fight against involved different specialist figures and multiple are the approved weapons. Aim of the present survey endorsed by the Italian Society of Bariatric Surgery (SICOB) is to reach a national consensus on obesity treatment optimization through a Delphi process.MethodsEleven key opinion leaders (KOLs) identified 22 statements with a major need of clarification and debate. The explored pathways were: (1) Management of patient candidate to bariatric/metabolic surgery (BMS); (2) Management of patient not eligible for BMS; (3) Management of patient with short-term (2 years) weight regain (WR) or insufficient weight loss (IWL); (4) Management of the patient with medium-term (5 years) WR; and (5) Association between drugs and BMS as WR prevention. The questionnaire was distributed to 65 national experts via an online platform with anonymized results.Results54 out of 65 invited panelists (83%) respond. Positive consensus was reached for 18/22 statements (82%); while, negative consensus (s20.4; s21.5) and no consensus (s11.5, s17) were reached for 2 statements, respectively (9%).ConclusionThe Delphi results underline the importance of first-line interdisciplinary management, with large pre-treatment examination, and establish a common opinion on how to properly manage post-operative IWL/WR