Browse Articles

Comparing the accuracy of Pipelle versus hysteroscopy and curettage in the diagnosis of chronic endometritis in women with recurrent implantation failure: A prospective cross-sectional study

Objectives Chronic endometritis (CE) is defined as chronic inflammation in the endometrium; when treated, implantations significantly improve. The standard test for CE confirmation is an endometrial biopsy, but the appropriate sampling method needs to be clarified. We conducted this study to compare pipelle biopsy and hysteroscopy with curettage. Study design This is a prospective cross-sectional study with all (40 patients) RIF patients under 40 referred to our tertiary center between December 2021, and December 2022 who underwent pipelle biopsy and hysteroscopy with curettage between days twelve to fifteen of their menstruation cycle. We then compared the diagnostic accuracy, demographics, and previous IVF history between the CE and non-CE groups. Results Patients had a mean age of 34 ( ± 5.4) years and BMI of 25.8 ( ± 3.6). Thirteen patients (32.5%) were diagnosed with CE. There was no significant difference between CE and non-CE groups regarding maternal or paternal age, BMI, number of IVFs and embryos, and interval from the last IVF. Pipelle biopsy had 100% accuracy for CE diagnosis, while hysteroscopy with curettage had a sensitivity of 92.3% (95% CI: 77.8% - 100%) and specificity of 100%. Based on McNemar’s test, the two sampling methods had no significant difference (P = 1.0 and 0.317, respectively). Conclusion There is no significant difference between the two methods in the diagnosis accuracy of CE in RIF patients. Since pipelle is more cost-effective and has fewer complications than hysteroscopy with curettage, pipelle biopsy may replace curettage for CE diagnosis.

An attention enhanced dilated bottleneck network for kidney disease classification

Abstract Computer-Aided Design (CAD) techniques have been developed to assist nephrologists by optimising clinical workflows, ensuring accurate results and effectively handling extensive datasets. The proposed work introduces a Dilated Bottleneck Attention-based Renal Network (DBAR-Net) to automate the diagnosis and classification of kidney diseases like cysts, stones, and tumour. To overcome the challenges caused by complex and overlapping features, the DBAR_Net model implements a multi-feature fusion technique. Two fold convolved layer normalization blocks $$\:({\text{C}\text{L}\text{N}}_{\text{b}1}$$ & $$\:{\text{C}\text{L}\text{N}}_{\text{b}2})$$ capture fine-grained detail and abstract patterns to achieve faster convergence and improved robustness. Spatially focused features and channel-wise refined features are generated through dual bottleneck attention modules $$\:{(\text{A}}_{\text{b}\text{a}\text{m}1})\:\&\:{(\text{A}}_{\text{b}\text{a}\text{m}2})$$ to improve the representation of convolved features by highlighting channel and spatial regions resulting enhanced interpretability and feature generalisation. Additionally, adaptive contextual features are obtained from a dilated convolved layer normalisation block $$\:\left({\text{D}\text{C}\text{L}\text{N}}_{\text{b}}\right)$$ , which effectively captures contextual insights from semantic feature interpretation. The resulting features are fused additively and processed through a linear layer with global average pooling and layer normalization. This combination effectively reduces spatial dimensions, internal covariate shifts and improved generalization along with essential features. The proposed approach was evaluated using the CT KIDNEY DATASET that includes 8750 CT images classified into four categories: Normal, Cyst, Tumour, and Stone. Experimental results showed that $$\:\text{t}\text{h}\text{e}$$ improved feature detection ability enhanced the performance of DBAR_Net model attaining a F1 score as 0.98 with minimal computational complexity and optimum classification accuracy of 98.86%. The integration of these blocks resulted in precise multi-class kidney disease detection, thereby leading to the superior performance of DBAR_Net compared to other transfer learning models like VGG16, VGG19, ResNet50, EfficientNetB0, Inception V3, MobileNetV2, and Xception.

High-temperature effects on oscillatory conductivity in disordered quantum dot arrays: Role of Coulomb interactions and localization length

Using a theoretical model that incorporates the full energy landscape of the electronic density of states of disordered arrays of quantum dots (DAQDs) with a realistic size distribution and non-uniform doping, we have examined the oscillatory behavior of electrical conductivity σ as a function of the average doping concentration ND. We find that Coulomb interactions between charged quantum dots suppress the magnitude of these oscillations, whereas increasing the temperature increases the magnitude of these oscillations. Furthermore, the magnitude of the oscillations increases with an increase of the localization length ξ. The observed oscillatory behavior of σ suggests the potential for optimizing the conductivity of DAQDs for application-specific purposes through controlled doping.

Time-frequency analysis of femtosecond CARS spectroscopy of N2 and O2 using the superlet transform

Molecular dynamics plays a crucial role in understanding molecular interactions, rovibrational coupling mechanisms, and energy transfer processes. Femtosecond time-resolved coherent anti-Stokes Raman scattering spectroscopy was employed to study the molecular dynamics of N2 and O2 in air at room temperature. To reveal hidden spectral features, we have for the first time applied an analytical method that balances time resolution and frequency resolution, namely, the superlet transform (SLT), to perform time-frequency resolved spectral analysis of the complex molecular dynamics of N2 and O2 in air. A distinct evolution of the partial rotational modes of N2 and O2 outside the selective excitation region was observed, which is related to energy transfer collisions between N2 and O2 molecules during the rotational energy relaxation process in air. The SLT results accord well with the S-branch rotational spectra of N2 and O2 obtained from theoretical calculations, confirming the validity of SLT analysis. This method provides a valuable experimental analysis technique to deepen the understanding of the microscopic dynamic processes in molecular dynamics.

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening

Deciphering the complex relationships between cellular morphology and phenotypic manifestations is crucial for understanding cell behavior, particularly in the context of neuropathological states. Despite its importance, the application of advanced image analysis methodologies to central nervous system (CNS) cells, including neuronal and glial cells, has been limited. Furthermore, cutting-edge techniques in the field of cell image analysis, such as deep learning (DL), still face challenges, including the requirement for large amounts of labeled data, difficulty in detecting subtle cellular changes, and the presence of batch effects. Our study addresses these shortcomings in the context of neuroinflammation. Using our in-house data and a DL-based approach, we have effectively analyzed the morphological phenotypes of neuronal and microglial cells, both in pathological conditions and following pharmaceutical interventions. This innovative method enhances our understanding of neuroinflammation and streamlines the process for screening potential therapeutic compounds, bridging a gap in neuropathological research and pharmaceutical development.

The relationship between age related changes in strength and fitness with body size, shape and composition

Abstract Handgrip strength (HGS), cardiorespiratory fitness (CRF) and body size, shape, and composition are all related to cardiometabolic health and are associated in cross-sectional settings. Their longitudinal relationship is less clear. We used observational data from the Study of Health in Pomerania at baseline (SHIP-TREND-0; 2008–2012) and follow-up (SHIP-TREND-1; 2016–2019) with 1,214 men and 1,293 women. HGS was measured with a hand dynamometer. CRF was assessed using cardiopulmonary exercise testing. Linear regression models were adjusted appropriately. Several sensitivity analyses were performed. From baseline to follow-up (7 years) HGS decreased in men (3.5 kg) and women (0.8 kg). VO2peak lessened in men (36 ml/min) and increased in women (53 ml/min). We only found significant relations in men where a 1 l decline in VO2peak was associated with a 0.87 kg larger decrease in fat free mass and with a 1.15 kg stronger decline in body weight. All other analysis revealed non-significant findings. This longitudinal analysis suggests that age related changes in strength and CRF are not related to body size and shape but only composition (in men). A novelty of our findings are the sex-specific aspects given that strength decreased much stronger in men compared to women.

Phonon local non-equilibrium at Al/Si interface from machine learning molecular dynamics

All electronics are equipped with metal/semiconductor junctions, resulting in resistance to thermal transport. The nanoscale phononic complexities, such as phonon local non-equilibrium and inelastic scattering, add to the computational or experimental characterization difficulty. Here, we use a neural network potential (NNP) trained by ab initio data, demonstrating near-first-principles precision more accurate than classical potentials used in molecular dynamics (MD) simulations to predict thermal transport at the Al/Si interface. The interfacial thermal conductance of 380±33MW/m2K from our NNP-MD simulations is in good agreement with the previous experimental consensus while considering the crucial physics of interfacial bonding nature, phonon local non-equilibrium, and inelastic scattering. Furthermore, we extract phonon mode insights from the NNP-MD simulations to reveal the decrease in local non-equilibrium of the longitudinal acoustic modes at the Al/Si interface. Our work demonstrates the utility of a machine learning MD to predict and extract accurate insights about interfacial thermal transport.

Reaction-limited evaporation for the color-gradient lattice Boltzmann model

We propose a reaction-limited evaporation model within the color-gradient lattice Boltzmann (LB) multicomponent framework to address the lack of intrinsic evaporation mechanisms. Unlike diffusion-driven approaches, our method directly enforces mass removal at the fluid interface in a reaction-limited manner while maintaining numerical stability. Using the inherent color-gradient magnitude and a single adjustable parameter, evaporation sites are chosen in a computationally efficient way with seamless mass exchange between the components, with no change to the core algorithm. Extensive validation across diverse interface geometries and evaporation flux magnitudes demonstrates high accuracy, with errors below 5% for unit density ratios. For density contrasts, the method remains robust in the limit of smaller evaporation flux magnitudes and density ratios. Our approach extends the applicability of the color-gradient LB model to scenarios involving reaction-limited evaporation, such as droplet evaporation on heated substrates, vacuum evaporation of molten metals, and drying processes in porous media.

What is the cross-sectional association of geospatially derived walkability with walking for leisure and transport?

Background Built environments have been shown to shape active living behaviours, including walking. However, this literature is drawn predominantly from Europe and North America. This study aimed to create a geospatially derived city-wide walkability index and further investigate the association with walking in Belo Horizonte, Brazil. Methodology A cross-sectional analysis was conducted using data from participants in the 2014-15 MOVE-SE study in Belo Horizonte. A walkability index was created at the census tract level, which included net residential density, land use mix, and street connectivity, using ArcGIS software. Walking for leisure and transportation was self-reported via the International Physical Activity Questionnaire. Covariates such as sociodemographic characteristics, health indicators, and neighbourhood context were measured. A multilevel negative binomial regression was employed, incorporating confounders across five combined models with sequential addition of covariate groups. All statistical analyses were conducted in R software with a significance threshold of 5%. Results The study included 1,372 adults aged 18 years and older, with a female majority of 60.5%, a median age of 41, and 45.9% completed at most primary schooling. The family income for 63.7% ranged between one to three times the minimum wage. Self-rated health was considered good by 64.7% of participants, and the median Body Mass Index (BMI) was 26.2 kg/m2. Regarding neighbourhood context, the median length of residence was 15 years, per capita monthly income was US$175, and the average land slope was 8.2%. Participants reported a median of 180 minutes per week (interquartile range: 120 – 250) for walking for leisure and transportation. The median walkability index was -0.51 (interquartile range: -1.40 – 1.21). After adjusting for confounders, the final model indicated a positive association between the walkability index and walking for leisure (IRR: 1.33; CI95%:1.32-1.35; p < 0.001) and transportation (IRR: 1.22; CI95%:1.20-1.24; p < 0.001). Discussion The findings demonstrate a positive association between higher levels of walkability and increased walking behaviours in various contexts. It underscores the importance of urban planning, design, and policy interventions tailored to local environments to promote walkability, reduce car dependency, and facilitate healthier lifestyles as part of everyday living.

Cerebrovascular health impacts processing speed through anterior white matter alterations: a UK biobank study

Abstract Cerebrovascular disease is associated with an increased likelihood of developing dementia. Cerebrovascular risk factors are modifiable and may reduce the risk of later-life cognitive dysfunction, however, the relationship between cerebrovascular risk factors, brain integrity and cognition remains poorly characterised. Using a UK Biobank sample of mid-to-old aged adults, without neurological disease, our structural equation mediation models showed that poor cerebrovascular health, indicated by the presence of cerebrovascular risk factors, was associated with slowed processing speed. This effect was best explained by anterior white matter microstructure (e.g. genu, anterior corona radiata), rather than posterior (e.g. splenium, posterior corona radiata)—the mediatory effect of anterior white matter strengthened further with age. Effects were also significantly reduced when considering other forms of cognition, demonstrating both regional- and cognitive-specificity. Our findings also illustrate that cerebrovascular risk factors cross-sectionally predict cognitive processing speed performance, which can be further strengthened by accounting for risk factor duration, particularly hypertensive duration. In summary, our study highlights the vulnerability of anterior regions and sensitivity of processing speed performance to cerebrovascular burden, and show this effect is amplified with age. We also highlight an improved method of cerebrovascular burden quantification, which accounts for factor duration, as well as risk factor presence and degree. Future work will aim to establish the role of medication and effective risk factor control in alleviating or preventing white matter pathology and cognitive dysfunction.

Thermal effects on metalenses

Metalenses, which are crucial for advancing miniaturization and enhancing performance in optical devices, have attracted considerable attention recently. However, the impact of temperature variations on metalenses (thermal effects) remains under-explored. In this study, we developed a theoretical framework to analyze these thermal effects using a linear thermal expansion model. To validate our framework, we performed finite element method simulations. In conventional lenses, thermal effects are considerably influenced by changes in refractive indices and/or surface curvatures. In contrast, our findings indicate that, for metalenses, thermal effects are mainly driven by the expansion of the substrate, with changes in index or deformation of the meta-atom being negligible. We believe that these insights will help guide the development of athermal hybrid lens systems, enabling robust performance across diverse temperature environments by effectively combining metalenses and conventional lenses.

On the applicability of CCSD(T) for dispersion interactions in large conjugated systems

In light of the recent discrepancies reported between fixed node diffusion Monte Carlo and local natural orbital coupled cluster with single, double, and perturbative triples [CCSD(T)] methodologies for non-covalent interactions in large molecular systems [Al-Hamdani et al., Nat. Commun. 12, 3927 (2021)], the applicability of CCSD(T) is assessed using a model framework. The use of the semi-empirical π-space only Pariser–Parr–Pople (PPP) model for studying large molecules is critically examined and is shown to recover both bandgap closure as system size increases and long range dispersive behavior of r−6 with increasing separation between monomers. Since bandgap closure in systems with long-range Coulomb interactions is problematic for perturbative methods, such as CCSD(T), this model, therefore, serves as a testing ground for such methods, enabling them to be benchmarked with high-order CC methods, which are not possible with ab initio Hamiltonians. Using the PPP model, coupled cluster methodologies, CCSDTQ and CCSDT(Q), are then used to benchmark CCSDT and CCSD(T) methodologies for non-covalent interactions in large one- and two-dimensional molecular systems up to the dibenzocoronene dimer. We show that CCSD(T) demonstrates no signs of overestimating the interaction energy for these systems. Furthermore, by examining the Hartree–Fock HOMO–LUMO gap of these large molecules, the perturbative treatment of the triples contribution in CCSD(T) is not expected to cause problems for accurately capturing the interaction energy for system sizes up to at least circumcoronene.

Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method

Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for early detection. Typically, AI-based glaucoma screening relies on fundus imaging. To tackle the resource and time challenges in glaucoma screening with convolutional neural network (CNN), we chose the Data-efficient image Transformers (DeiT), a vision transformer, known for its reduced computational demands, with preprocessing time decreased by a factor of 10. Our approach utilized the meticulously annotated GlauCUTU-DATA dataset, curated by ophthalmologists through consensus, encompassing both unanimous agreement (3/3) and majority agreement (2/3) data. However, DeiT’s performance was initially lower than CNN. Therefore, we introduced the “pie method," an augmentation method aligned with the ISNT rule. Along with employing polar transformation to improved cup region visibility and alignment with the vision transformer’s input to elevated performance levels. The classification results demonstrated improvements comparable to CNN. Using the 3/3 data, excluding the superior and nasal regions, especially in glaucoma suspects, sensitivity increased by 40.18% from 47.06% to 88.24%. The average area under the curve (AUC) ± standard deviation (SD) for glaucoma, glaucoma suspects, and no glaucoma were 92.63 ± 4.39%, 92.35 ± 4.39%, and 92.32 ± 1.45%, respectively. With the 2/3 data, excluding the superior and temporal regions, sensitivity for diagnosing glaucoma increased by 11.36% from 47.73% to 59.09%. The average AUC ± SD for glaucoma, glaucoma suspects, and no glaucoma were 68.22 ± 4.45%, 68.23 ± 4.39%, and 73.09 ± 3.05%, respectively. For both datasets, the AUC values for glaucoma, glaucoma suspects, and no glaucoma were 84.53%, 84.54%, and 91.05%, respectively, which approach the performance of a CNN model that achieved 84.70%, 84.69%, and 93.19%, respectively. Moreover, the incorporation of attention maps from DeiT facilitated the precise localization of clinically significant areas, such as the disc rim and notching, thereby enhancing the overall effectiveness of glaucoma screening.

Classification method based on surf and sift features for alzheimer diagnosis using diffusion tensor magnetic resonance imaging

Abstract Alzheimer’s disease (AD), the most common dementia in the elderly, poses a challenge for early diagnosis due to its progressive nature and hidden microstructural changes. While traditional T1 and T2 weighted MRI can assess macro-structural brain atrophy, diffusion tensor imaging (DTI) unveils these hidden microstructural alterations. This study explores the use of DTI data, specifically visual patterns in Fractional Anisotropy (FA), Mean Diffusivity (MD), and Radial Diffusivity (RD) maps, to characterize AD progression. This paper proposes a computer-aided diagnosis (CAD) framework employing SIFT and SURF descriptors and a bag-of-words approach to build AD-specific signatures for the hippocampus region, known to be heavily affected by the disease. These signatures are extracted from MD, FA, and RD maps and used to differentiate between AD, mild cognitive impairment (MCI), and normal controls (NC) in both multiclass and binary classification scenarios. Additionally, we investigate late fusion of visual map features for enhanced decision-making. The experiments were accomplished with a subset of participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n = 6), Late Mild Cognitive Impairment (LMCI) (n = 24) and cognitively healthy elderly Normal Controls (NC) (n = 31). Promising preliminary results demonstrate the potential of the proposed system as a useful tool to capture the AD leanness with achieving accuracies of 87.5%, 87.4%, 89%, and 95.2% for MD, FA, RD, and fusion of features respectively for the multiclass system using SIFT features. Using FA features for binary discrimination achieves 97.5%. Moreover, the fusion based on the decision level model reached an accuracy of 93.3% AD/MCI, 95.7% AD/NC, and 93.3% MCI/NC (96.2 ± 3.6 MCI vs. NC, 97.5 ± 5 AD vs. NC). Furthermore, fusion of features led to a noteworthy precision boost of 96%. These findings suggest that our DTI-based CAD framework holds promise as a reliable and accurate tool for capturing AD progression, paving the way for earlier diagnosis and potentially improved patient outcomes.

Ultrasonically induced microscopic refractive index gradient and the relationship with high-frequency ultrasonic cavitation

The refractive index of a medium can be modulated by external stimuli such as pressure, temperature, or electromagnetic forces. This principle enables fast, precise, and reversible optical control and has catalyzed the development of optical devices and optical measurement technology. Here, we report the relationship between the refractive index change induced by high-frequency ultrasonic irradiation and ultrasonic cavitation. The cavitation nanobubbles generated by ultrasonic irradiation were measured using dynamic light scattering to be approximately 100 nm in diameter. The apparent volume fraction of the nanobubbles induced near the surface of the ultrasonic transducer was calculated using an effective medium approximation. The apparent volume fraction was 0.12 at the position where the refractive index change was maximal (Δn = 0.04: value from the fitting function). The technique to control light propagation in a local (microscale) region with ultrasonic irradiation has a wide range of applications from optofluidic devices for lab-on-chip devices to variable-focus lenses for industrial metrology.

Erratum: “Alignment of ND3 molecules in dc-electric fields” [J. Chem. Phys. 160, 204305 (2024)]

Epistemic Trust, Mistrust and Credulity Questionnaire (ETMCQ) validation in French language: Exploring links to loneliness

The concept of epistemic trust is gaining traction in the mental health field. Epistemic trust is thought to play a foundational role as a resilience factor against the development and maintenance of psychopathology by fostering social learning. The primary aim of this study was to validate the French-language version of the Epistemic Trust, Mistrust and Credulity Questionnaire (ETMCQ). We further sought to replicate previously reported associations with key developmental and psychological factors (childhood trauma, mentalizing and attachment) and test for epistemic trust’s potential mediating roles between childhood traumatic experiences and psychopathology, and between loneliness and psychopathology. A total of 302 participants were recruited via the online survey platform Prolific. Confirmatory factor analysis and generalized linear models of mediation were conducted. Our findings suggest that the ETMCQ is a valid instrument to assess epistemic trust in the French language. Satisfactory psychometric properties were found to replicate the original three-factor solution in a Francophone population with a 12-item version of the questionnaire, with criterion-related validity similar to that previously published in validations of the ETMCQ in other languages. We also replicate previous findings reporting differential associations between epistemic stances (trust, mistrust and credulity) and attachment dimensions and styles, while also replicating mediation analyses showing the role of epistemic stances in the relationship between childhood traumatic experiences and psychopathology. Finally, we report preliminary evidence suggesting that epistemic trust mediates the well-described association between loneliness and psychopathology. Future research should investigate the ETMCQ in clinical populations in which psychopathological expressions are severe, enduring and co-occurring, where identifying potential mediators could help target and personalize psychosocial interventions.

Multisensory training enhances anticipation skills in badminton novices

Shock wave mitigation using periodically discrete material layers of variable orientation: Experiments and simulations

This paper reports the shock response of layered composites subjected to flyer-plate impact. The composites comprised of Oxygen-Free Copper (Cu) and polymethyl methacrylate layers angled between 0° and 90°. Multi-layered samples were bonded at various orientations, with 0° indicating target layers aligned parallel to the impact direction. At lower angles of orientation, a twofold wave structure consisting of a low-amplitude elastic precursor and a high-amplitude stress wave was observed. The elastic precursor was characterized and influenced by the longitudinal sound speed of Cu and diminished with an increase in sample orientation. As the orientation of the sample increased, an increase in the rise time and a decrease in the wave velocity were recorded. Numerical simulations highlighted the role of the impedance mismatch, as well as geometric dispersion, and oblique interference scattering in layered composites with varying orientations. It is shown that these three factors play a crucial role in shock wave dissipation and dispersion.

Phase behavior of x-shaped liquid crystalline macromolecules

X-shaped liquid crystalline macromolecules (XLCMs) are obtained by tethering two flexible end A-blocks and two flexible side B-blocks to a semiflexible R-block. A rich array of ordered structures can be formed from XLCMs, driven by the competition between the interactions between the chemically distinct blocks and the molecular connectivity. Here, we report a theoretical study on the phase behavior of XLCMs with symmetric and asymmetric side blocks by using the self-consistent field theory (SCFT). A large number of ordered structures, including smectic phases, simple and giant polygons, are obtained as solutions of the SCFT equations. Phase diagrams of XLCMs as a function of the total length and asymmetric ratio of the side chains are constructed. For XLCMs with symmetric side blocks, the theoretically predicted phase transition sequence is in good agreement with experiments. For XLCMs with a fixed total side chain length, transitions between layered structure to polygonal phases, as well as between different polygonal phases, could be induced by varying the asymmetry of the side chains. The free energy density, domain size, side chain stretching, and molecular orientation are analyzed to elucidate mechanisms stabilizing the different ordered phases.