Symmetric Electron Transfer Coordinates are Intrinsic to Bridged Systems: An ab Initio Treatment of the Creutz-Taube Ion.

A long-standing question in electron transfer research concerns the number and identity of collective nuclear motions that drive electron transfer or localisation. It is well established that these nuclear motions are commonly gathered into a so-called electron transfer coordinate. In this theoretical study, we demonstrate that both anti-symmetric and symmetric vibrational motions are intrinsic to bridged systems, and that both are required to explain the characteristic shape of their intervalence charge transfer bands. Using the properties of a two-state Marcus-Hush model, we identify and quantify these two coordinates as linear combinations of normal modes from ab initio calculations. This quantification gives access to the potential coupling, reorganization energy and curvature of the potential energy surfaces involved in electron transfer, independent of any prior assumptions about the system of interest. We showcase these claims with the Creutz-Taube ion, a prototypical Class III mixed valence complex. We find that the symmetric dimension is responsible for the asymmetric band shape, and trace this back to the offset of the ground and excited state potentials in this dimension. The significance of the symmetric dimension originates from geometry dependent coupling, which in turn is a natural consequence of the well-established superexchange mechanism. The conceptual connection between the symmetric and anti-symmetric motions and the superexchange mechanism appears as a general result for bridged systems.

Monitoring Live Mycobacteria in Real-Time Using a Microfluidic Acoustic-Raman Platform.

Tuberculosis (TB) is the most common cause of death from an infectious disease. Although treatment has been available for more than 70 years, it still takes too long and many patients default risking relapse and the emergence of resistance. It is known that lipid-rich, phenotypically antibiotic-tolerant, bacteria are more resistant to antibiotics and may be responsible for relapse necessitating extended therapy. Using a microfluidic system that acoustically traps live mycobacteria, M. smegmatis, a model organism for M. tuberculosis we can perform optical analysis in the form of wavelength-modulated Raman spectroscopy (WMRS) on the trapped organisms. This system can allow observations of the mycobacteria for up to 8 h. By adding antibiotics, it is possible to study the effect of antibiotics in real-time by comparing the Raman fingerprints in comparison to the unstressed condition. This microfluidic platform may be used to study any microorganism and to dynamically monitor its response to many conditions including antibiotic stress, and changes in the growth media. This opens the possibility of understanding better the stimuli that trigger the lipid-rich downregulated and phenotypically antibiotic-resistant cell state.

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning.

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.

BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS: The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION: Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Physical analysis of aspirin in different phases and states using density functional theory.

This study analyzed the aspirin molecule (C9H8O4) using Density Functional Theory (DFT) on Gaussian 09W software. First, the structure of aspirin was optimized using the DFT method with the B3LYP functional and the 6-311+G (d,p) basis set. A global reactivity study was employed to understand the reactivity of aspirin in gas and solvent water for both anion and neutral states. To understand the involvement of orbitals in chemical stability and electron conductivity, we calculated the HOMO-LUMO. The thermodynamic function of a molecule was understood using thermochemistry. Molecular Electrostatic Potential (MEP) was employed to understand the physiochemical properties of aspirin. We observed the Mulliken atomic charge to calculate the atomic charge of aspirin. Finally, the title molecule's UV-Vis, FTIR, and Raman spectra are analyzed and compared with the experimental data.

Effect of near-infrared spectroscopy on postoperative delirium in cardiac surgery with cardiopulmonary bypass: a systematic review and meta-analysis.

Background: Postoperative delirium (POD) is a common anesthetic side effect in cardiac surgery. However, the role of oxygen saturation monitoring in reducing postoperative delirium has been controversial. Therefore, this meta-analysis aimed to analyze whether NIRS monitoring during cardiac surgery under cardiopulmonary bypass could reduce the incidence of postoperative delirium. Methods: PubMed, Web of Science, Cochrane Library, Embase and China National Knowledge Infrastructure (CNKI) databases were systematically searched using the related keywords for randomized-controlled trials (RCTs) published from their inception to March 16, 2024. This review was conducted by the Preferred Reporting Project and Meta-Analysis Statement (PRISMA) guidelines for systematic review. The primary outcome was postoperative delirium, and the second outcomes included the length of ICU stay, the incidence of kidney-related adverse outcomes, and the incidence of cardiac-related adverse outcomes. Results: The incidence of postoperative delirium could be reduced under the guidance of near-infrared spectroscopy monitoring (OR, 0.657; 95% CI, 0.447-0.965; P = 0.032; I2 = 0%). However, there were no significant differences in the length of ICU stay (SMD, 0.005 days; 95% CI, -0.135-0.146; P = 0.940; I2 = 39.3%), the incidence of kidney-related adverse outcomes (OR, 0.761; 95% CI, 0.386-1.500; P = 0.430; I2 = 0%), and the incidence of the cardiac-related adverse outcomes (OR, 1.165; 95% CI, 0.556-2.442; P = 0.686; I2 = 0%) between the two groups. Conclusion: Near-infrared spectroscopy monitoring in cardiac surgery with cardiopulmonary bypass helps reduce postoperative delirium in patients. Systematic Review Registration: PROSPERO, identifier, CRD42023482675.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Synthesis of 2,4-dihydroxyacetophenone derivatives as potent PDE-1 and -3 inhibitors: in vitro and in silico insights.

Aim: Synthesis of novel bis-Schiff bases having potent inhibitory activity against phosphodiesterase (PDE-1 and -3) enzymes, potentially offering therapeutic implications for various conditions. Methods: Bis-Schiff bases were synthesized by refluxing 2,4-dihydroxyacetophenone with hydrazine hydrate, followed by treatment of substituted aldehydes with the resulting hydrazone to obtain the product compounds. After structural confirmation, the compounds were screened for their in vitro PDE-1 and -3 inhibitory activities. Results: The prepared compounds exhibited noteworthy inhibitory efficacy against PDE-1 and -3 enzymes by comparing with suramin standard. To clarify the binding interactions between the drugs, PDE-1 and -3 active sites, molecular docking studies were carried out. Conclusion: The potent compounds discovered in this study may be good candidates for drug development.

[Box: see text].

A rapid method to monitor structural perturbations of high-concentrated therapeutic antibody solutions using Intrinsic Tryptophan Fluorescence Emission spectroscopy.

Drug product development of therapeutic antibody formulations is still dictated by the risk of protein particle formation during processing or storage, which can lead to loss of potency and potential immunogenic reactions. Since structural perturbations are the main driver for irreversible protein aggregation, the conformational integrity of antibodies should be closely monitored. The present study evaluated the applicability of a plate reader-based high throughput method for Intrinsic Tryptophan Fluorescence Emission (ITFE) spectroscopy to detect protein aggregation due to protein unfolding in high-concentrated therapeutic antibody samples. The impact of fluorophore concentration on the ITFE signal in microplate readers was investigated by analysis of dilution series of two therapeutic antibodies and pure tryptophan. At low antibody concentrations (< 5 mg/mL, equivalent to 0.8 mM tryptophan), the low inner filter effect suggests a quasi-linear relationship between antibody concentration and ITFE intensity. In contrast, the constant ITFE intensity at high protein concentrations (> 40 mg/mL, equivalent to 6.1 mM tryptophan) indicate that ITFE spectroscopy measurements of IgG1 antibodies are feasible in therapeutically relevant concentrations (up to 223 mg/mL). Furthermore, the capability of the method to detect low levels of unfolding (around 1 %) was confirmed by limit of detection (LOD) determination with temperature-stressed antibody samples as degradation standards. Change of fluorescence intensity at the maximum (ΔIaM) was identified as sensitive descriptor for protein degradation, providing the lowest LOD values. The results demonstrate that ITFE spectroscopy performed in a microplate reader is a valuable tool for high-throughput monitoring of protein degradation in therapeutic antibody formulations.

Jahn-Teller effect and features of divalent copper ion behavior in multicomponent borate crystals.

Crystals of YGa3(BO3)4, YAl3(BO3)4, EuGa3(BO3)4 and EuAl3(BO3)4 with copper alloy were studied by electron paramagnetic resonance and X-ray diffraction analysis. The lattice parameters and coordinates of copper-doped boron atoms were determined. The study of EPR spectra showed that copper is in the divalent state and replaces aluminum ions with C2 node symmetry. In YAl3(BO3)4:Cu crystals, a ligand structure exists due to the interaction of copper electrons with yttrium nuclei. The parameters of the spin Hamiltonian describing the behavior of the Cu2+ spectrum have been determined. The deviation of the Z-axis spectra from the C3 axis by 54(1)° is due to Jahn-Teller vibronic interaction and monoclinic distortion. In the EuGa3(BO3)4 crystal, a new spectrum 2 was found, which also belongs to divalent copper but is observed at an excited state 31 cm-1 away from the ground state. Above 70 K, an isotropic EPR line with a width of 450 Gs, g = 2.1, appears and exists up to room temperature.

Integrating fNIRS and machine learning: shedding light on Parkinson's disease detection.

The purpose of this research is to introduce an approach to assist the diagnosis of Parkinson's disease (PD) by classifying functional near-infrared spectroscopy (fNIRS) studies as PD positive or negative. fNIRS is a non-invasive optical signal modality that conveys the brain's hemodynamic response, specifically changes in blood oxygenation in the cerebral cortex; and its potential as a tool to assist PD detection deserves to be explored since it is non-invasive and cost-effective as opposed to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance. All the implementations used a logistic regression model for classification. A set of 792 temporal and spectral features were extracted from each participant's fNIRS study. In the two best performing implementations, an ensemble of feature-ranking techniques was used to select a reduced feature subset, which was subsequently reduced with a genetic algorithm. Achieving optimal detection performance, our approach reached 100 % accuracy, precision, and recall, with an F1 score and area under the curve (AUC) of 1, using 14 features. This significantly advances PD diagnosis, highlighting the potential of integrating fNIRS and machine learning for non-invasive PD detection.

Correlation between cerebral neurotransmitters levels by proton magnetic resonance spectroscopy and HbA1c in patients with type 2 diabetes.

BACKGROUND: Cognitive dysfunction is the main manifestation of central neuropathy. Although cognitive impairments tend to be overlooked in patients with diabetes mellitus (DM), there is a growing body of evidence linking DM to cognitive dysfunction. Hyperglycemia is closely related to neurological abnormalities, while often disregarded in clinical practice. Changes in cerebral neurotransmitter levels are associated with a variety of neurological abnormalities and may be closely related to blood glucose control in patients with type 2 DM (T2DM). AIM: To evaluate the concentrations of cerebral neurotransmitters in T2DM patients exhibiting different hemoglobin A1c (HbA1c) levels. METHODS: A total of 130 T2DM patients were enrolled at the Department of Endocrinology of Shanghai East Hospital. The participants were divided into four groups according to their HbA1c levels using the interquartile method, namely Q1 (< 7.875%), Q2 (7.875%-9.050%), Q3 (9.050%-11.200%) and Q4 (≥ 11.200%). Clinical data were collected and measured, including age, height, weight, neck/waist/hip circumferences, blood pressure, comorbidities, duration of DM, and biochemical indicators. Meanwhile, neurotransmitters in the left hippocampus and left brainstem area were detected by proton magnetic resonance spectroscopy. RESULTS: The HbA1c level was significantly associated with urinary microalbumin (mALB), triglyceride, low-density lipoprotein cholesterol (LDL-C), homeostasis model assessment of insulin resistance (HOMA-IR), and beta cell function (HOMA-ß), N-acetylaspartate/creatine (NAA/Cr), and NAA/choline (NAA/Cho). Spearman correlation analysis showed that mALB, LDL-C, HOMA-IR and NAA/Cr in the left brainstem area were positively correlated with the level of HbA1c (P < 0.05), whereas HOMA-ß was negatively correlated with the HbA1c level (P < 0.05). Ordered multiple logistic regression analysis showed that NAA/Cho [Odds ratio (OR): 1.608, 95% confidence interval (95%CI): 1.004-2.578, P < 0.05], LDL-C (OR: 1.627, 95%CI: 1.119-2.370, P < 0.05), and HOMA-IR (OR: 1.107, 95%CI: 1.031-1.188, P < 0.01) were independent predictors of poor glycemic control. CONCLUSION: The cerebral neurotransmitter concentrations in the left brainstem area in patients with T2DM are closely related to glycemic control, which may be the basis for the changes in cognitive function in diabetic patients.

Electroluminescence Rectification and High Harmonic Generation in Molecular Junctions.

The field of molecular electronics has emerged from efforts to understand electron propagation through single molecules and to use them in electronic circuits. Serving as a testbed for advanced theoretical methods, it reveals a significant discrepancy between the operational time scales of experiments (static to GHz frequencies) and theoretical models (femtoseconds). Utilizing a recently developed time-linear nonequilibrium Green function formalism, we model molecular junctions on experimentally accessible time scales. Our study focuses on the quantum pump effect in a benzenedithiol molecule connected to two copper electrodes and coupled with cavity photons. By calculating both electric and photonic current responses to an ac bias voltage, we observe pronounced electroluminescence and high harmonic generation in this setup. The mechanism of the latter effect is more analogous to that from solids than from isolated molecules, with even harmonics being suppressed or enhanced depending on the symmetry of the driving field.

Effect of Inspiratory Muscle-Loaded Exercise Training on Ventilatory Response and Intercostal Muscle Deoxygenation During Incremental Cycling Exercise.

Purpose: This study evaluated the effects of exercise training (ET) and inspiratory muscle-loaded exercise training (IMLET) on ventilatory response and intercostal muscle deoxygenation levels during incremental cycling exercise. Methods: Twenty-one male participants were randomly divided into IMLET (n = 10) or ET (n = 11) groups. All participants underwent a 4-week cycling exercise training at 60% peak oxygen uptake. IMLET loaded 50% of maximal inspiratory pressure (PImax). Respiratory muscle strength test, respiratory muscle endurance test (RMET), resting hypoxic ventilatory responsiveness (HVR) test, and incremental cycling test were performed pre- and post-training. Results: The extent of improvement in the PImax was significantly greater in the IMLET group (24%) than in the ET group (8%) (p = .018), and an extended RMET time was observed in the IMLET group (p < .001). Minute ventilation (V˙E) during exercise was unchanged in both groups before and after training, but tidal volume during exercise increased in the IMLET group. The increase in the exercise intensity threshold for muscle deoxygenation was similar in both groups (p < .001). HVR remained unchanged in both groups post-training. The exercise duration for the incremental exercise until reaching fatigue increased by 7.9% after ET and 6.9% after IMLET (p < .001). Conclusion: The 4-week IMLET improved respiratory muscle strength and endurance but did not alter HVR. Respiratory muscle deoxygenation was alleviated by exercise training, with a limited impact of inspiratory load training.

Application of signal processing techniques for the spectroscopic analysis of dolutegravir and lamivudine: a comparative assessment and greenness appraisal.

HIV treatment has greatly improved over the years, with the introduction of antiretroviral drugs that target the virus and suppress its replication. Dolutegravir and lamivudine are two such antiretroviral drugs that are commonly used in HIV treatment regimens. Herein, three spectrophotometric methods manipulating ratio spectra were developed for the simultaneous analysis of dolutegravir and lamivudine in their binary mixtures. These methods include mathematical processing stages like ratio difference method or signal processing approaches such as the first derivative of the ratio spectra, and continuous wavelet transform. The developed spectrophotometric methods exploit the characteristic spectral differences between dolutegravir and lamivudine in order to quantify them simultaneously. These methods have shown promising results in terms of sensitivity and selectivity as validated per the ICH guidelines. Moreover, these methods offer a straightforward and economical alternative to more intricate analytical methodologies like high-performance liquid chromatography. By incorporating the analytical eco-scale and AGREE for greenness evaluation of the proposed methods, we can further ensure that these techniques are effective and environmentally friendly, aligning with the principles of green chemistry. This evaluation will provide a comprehensive understanding of the environmental friendliness of these spectrophotometric methods in pharmaceutical analysis.

A Strep-tag Imprinted Polymer Platform for Heterogenous Bio(electro)catalysis.

Molecularly imprinted polymers (MIPs) are artificial receptors equipped with selective recognition sites for target molecules. One of the most promising-strategies for protein MIPs relies on the exploitation of short surface-exposed protein fragments, termed epitopes, as templates to imprint binding sites in a polymer scaffold for a desired protein. However, the lack of high-resolution structural data of flexible surface-exposed regions challenges the selection of suitable epitopes. Here, we addressed this drawback by developing a polyscopoletin-based MIP that recognizes recombinant proteins via the widely used Strep-tag II affinity peptide. Electrochemistry, surface-sensitive spectroscopy, and molecular dynamics simulations were employed to ensure an utmost control of the Strep-MIP electrosynthesis. The functionality of this novel platform was verified with two Strep-tag labeled enzymes: an O2-tolerant [NiFe]-hydrogenase, and an alkaline phosphatase. The enzymes preserved their biocatalytic activities after multiple utilization confirming the efficiency of Strep-MIP as a general biocompatible platform to confine recombinant proteins for exploitation in biotechnology.

From Ultrafast Photoinduced Small Polarons to Cooperative and Macroscopic Charge-Transfer Phase Transition.

We study by femtosecond infrared spectroscopy the ultrafast and persistent photoinduced phase transition of the Rb0.94Mn0.94Co0.06[Fe(CN)6]0.98∙0.2H2O material, induced at room temperature by a single laser shot. This system exhibits a charge-transfer based phase transition with a 75 K wide thermal hysteresis, centred at room temperature, from the low temperature Mn3+-N-C-Fe2+ tetragonal phase to the high temperature Mn2+-N-C-Fe3+ cubic phase. At room temperature, the photoinduced phase transition is persistent. However, the out-of-equilibrium dynamics leading to this phase is multi-scale. Femtosecond infrared spectroscopy, particularly sensitive to local reorganizations through the evolution of the frequency of the N-C vibration modes with the different characteristic electronic states, reveals that at low laser fluence and on short time scale, the photoexcitation of the Mn3+-N-C-Fe2+ phase creates small charge-transfer polarons [Mn2+-N-C-Fe3+]* within ≃ 250 fs. The local trapping of photoinduced intermetallic charge-transfer is characterized by the appearance of a polaronic infrared band, due to the surrounding Mn2+-N-C-Fe2+ species. Above a threshold fluence, when a critical fraction of small CT-polarons is reached, the macroscopic phase transition to the persistent Mn2+-N-C-Fe3+ cubic phase occurs within ≃ 100 ps. This non-linear photo-response results from elastic cooperativity, intrinsic to a switchable lattice and reminiscent of a feedback mechanism.

Brain Metabolite Differences in Fetuses With Cytomegalovirus Infection: A Magnetic Resonance Spectroscopy Study.

BACKGROUND: Cytomegalovirus (CMV) is the most common intrauterine infection and may be associated with unfavorable outcomes. While some CMV-infected fetuses may show gross or subtle brain abnormalities on MRI, their clinical significance may be unclear. Conversely, normal development cannot be guaranteed in CMV-infected fetuses with normal MRI. PURPOSE: To assess brain metabolite differences in CMV-infected fetuses using magnetic resonance spectroscopy (MRS). STUDY TYPE: Retrospective. SUBJECTS: Out of a cohort of 149 cases, 44 with maternal CMV infection, amniocentesis results, and good-quality MRS were included. CMV-infected fetuses with positive polymerase chain reaction (PCR) (N = 35) were divided based on MRI results as follows: typical brain abnormalities (gross findings, N = 8), exclusive white matter hyperintense signal (WMHS) on T2-weighted images (subtle findings, N = 7), and normal MRI (N = 20). Uninfected fetuses (negative PCR) with normal MRI were included as controls (N = 9). FIELD STRENGTH: 3 T, T2-weighted half Fourier single-shot turbo spin-echo (HASTE), T2-weighted true fast imaging with steady-state free precession (TrueFISP), T1- and T2*-weighted fast low angle shot (FLASH), and 1H-MRS single-voxel point resolved spectroscopy (PRESS) sequences. ASSESSMENT: MRI findings were assessed by three radiologists, and metabolic ratios within the basal ganglia were calculated using LCModel. STATISTICAL TESTS: Analysis of covariance test with Bonferroni correction for multiple comparisons was used to compare metabolic ratios between groups while accounting for gestational age. A P-value <0.05 was deemed significant. RESULTS: MRS was successfully acquired in 63% of fetuses. Substantial agreement was observed between radiologists (Fleiss' kappa [k] = 0.8). Infected fetuses with gross MRI findings exhibited significantly reduced tNAA/tCr ratios (0.64 ± 0.08) compared with infected fetuses with subtle MRI findings (0.85 ± 0.19), infected fetuses with normal MRI (0.8 ± 0.14) and controls (0.81 ± 0.15). No other significant differences were detected (P ≥ 0.261). CONCLUSION: Reduced tNAA/tCr within the apparently normal brain tissue was detected in CMV-infected fetuses with gross brain abnormalities, suggesting extensive brain damage. In CMV-infected fetuses with isolated WMHS, no damage was detected by MRS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Interfacial Electron Beam Lithography Converts an Insulating Organic Monolayer to a Patterned Single-Layer Conductor with Puzzling Charge Transport Performance.

The direct generation of conducting paths within an insulating surface represents a conceptually unexplored approach to single-layer electrical conduction that opens vistas for exciting research and applications fundamentally different from those based on specific layered materials. Herein we report surface channels with single-layer -COOH functionality patterned on insulating n-octadecyltrichlorosilane monolayers on silicon that exhibit unusual ionic-electronic conduction when equipped with ion-releasing silver electrodes. The strong dependence of charge transport in such channels on their lateral dimensions (nanosize, macro-size), the type (p, n) and resistivity (doping level) of the underlying silicon substrate, the nature of the insulating spacer layer between the conducting channel and the silicon surface, and the postpatterning chemical manipulation of channel's -COOH functionality allows designing channels with variable resistivities, ranging from that of a practical insulator to some unexpectedly low values. The unusually low resistivities displayed by channels with nanometric widths and micrometer-millimeter lengths are attributed primarily to enhanced electronic transport within ultrathin nanowire-like silver metal films formed along their conductive paths. Function-structure correlations derived from a comprehensive analysis of electrical, atomic force microscopy, and Fourier transform infrared spectral data suggest an unconventional mode of conduction in these channels, which has yet to be elucidated, apparently involving coupled ionic-electronic transport mediated and enhanced by interfacial electrical interactions with charge carriers located outside the conducting channel and separated from those carrying the measured current. These intriguing findings hint at effects akin to Coulomb pairing in the proposed mechanisms of excitonic superconductivity in interfacial nanosystems structurally related to the present metalized surface channels.