Search Results (60)

With the increasing integration of regional energy systems, the dynamic coupling characteristics of cooling, heating, and power flows have become significantly pronounced. However, traditional scheduling models often utilize steady-state assumptions that neglect the thermal transmission delay of the pipeline network, leading to spatiotemporal mismatches between energy supply and load demand. To address this issue, this paper proposes an optimal operation strategy for regional Combined Cooling, Heating, and Power (CCHP) systems that explicitly integrates thermal inertia. First, a Pipeline Fluid Micro-element Discretization Method (PFMDM) is developed based on the Lagrangian specification to accurately characterize the dynamic flow and thermal decay processes without the heavy computational burden of partial differential equations. In addition, the accuracy of PFMDM is directly benchmarked against a high-fidelity transient PDE solver (finite-volume TVD–MUSCL scheme) over a wide range of pipe lengths, flow velocities, and thermal loss coefficients, where the outlet-temperature RMSE remains below 0.2 °C. This model quantitatively reveals the “Virtual Energy Storage” (VES) mechanism of the pipeline network. Second, to overcome the “curse of dimensionality” in dynamic scheduling, a Load-Gradient-Based Adaptive Temporal Discretization (LG-ATD) method is proposed. This method maintains a fine-grained baseline for electrical settlement while dynamically aggregating thermal/cooling steps based on load fluctuations. Simulation results demonstrate that the proposed strategy corrects the significant physical deviations of the traditional steady-state model. The analysis reveals that the steady-state model underestimates the required heating and cooling supply capacities by up to 26.66% and 39.15%, respectively, due to the neglect of transmission losses and delays. By leveraging the VES mechanism, the proposed method enables a fuel-shift in the energy-supply structure, substantially decreasing the electricity purchasing cost (by 75.2% in the tested case). This reduction reflects a reallocation from grid purchases to on-site gas-fired cogeneration to maintain physical feasibility under delay and loss effects, and therefore, it is accompanied by an increase in natural gas consumption and a higher total operating cost. Furthermore, the LG-ATD method significantly alleviates the computational burden by substantially compressing the presolved model size and reducing the overall solving time by more than 80%, thereby effectively mitigating the curse of dimensionality for practical engineering applications. Full article

Global warming has been associated with various adverse effects on human physiology, yet its potential impact on brain structure remains largely unexplored. The present pilot study investigated the relationship between core body temperature and whole-brain gray matter volume (GMV) in healthy adults. Twenty-seven participants (19 males, 8 females; mean age = 38.6 ± 10.3 years) underwent MRI scanning and core temperature assessment. Correlation and partial correlation analyses were performed to examine the association between core body temperature and GMV, controlling for demographic and physiological covariates summarized by the first principal component. Core body temperature showed a significant negative correlation with whole-brain GMV (r = −0.496, p = 0.009; 95% CI = −0.737 to −0.143) and a trend-level significant partial correlation after covariate adjustment (r = −0.373, p = 0.060; 95% CI = −0.660 to 0.008). These trends remained after correction for multiple comparisons using the Benjamini–Hochberg false discovery rate. Exploratory analyses across 116 AAL regions identified the left Fusiform gyrus as showing a significant negative correlation with core body temperature (r = −0.643, p < 0.001). Given the modest sample size, these findings should be interpreted cautiously as preliminary, hypothesis-generating evidence. They suggest that even subtle variations in body temperature within the normal physiological range may relate to differences in global brain structure. Possible mechanisms include heat-induced inflammation, oxidative stress, and increased metabolic load on neural tissue. Understanding how individual differences in body temperature relate to brain morphology may provide insights into the neural health consequences of rising environmental temperatures. Full article

Adeno-associated viruses (AAVs) are a leading vector for gene therapy, yet their clinical utility is limited by the lack of robust quality control methods to distinguish between empty (AAV_empty), partially loaded (AAV_partial), and fully DNA loaded (AAV_full) capsids. Current analytical techniques provide partial insights but remain limited in sensitivity, throughput, or resolution. Here we present a multimodal plasmonic nanopore sensor that integrates optical trapping with electrical resistive-pulse sensing to characterize AAV9 capsids at the single-particle level in tens of μL sample volumes and fM range concentrations. As a model system, we employed AAV9 capsids not loaded with DNA, capsids loaded with a self-complementary 4.7 kbp DNA (AAV_scDNA), and ones loaded with single-stranded 4.7 kbp DNA (AAV_ssDNA). Ground-truth validation was performed with analytical ultracentrifugation (AUC). Nanosensor data were acquired concurrently for optical step changes (occurring at AAV trapping and un-trapping) both in transmittance and reflectance geometries, and electrical nanopore resistive pulse signatures, making for a total of five data dimensions. The acquired data was then filtered and clustered by Gaussian mixture models (GMMs), accompanied by spectral clustering stability analysis, to successfully separate between AAV species based on their DNA load status (AAV_empty, AAV_partial, AAV_full) and DNA load type (AAV_scDNA versus AAV_ssDNA). The motivation for quantifying the AAV_empty and AAV_partial population fractions is that they reduce treatment efficacy and increase immunogenicity. Likewise, the motivation to identify AAV_scDNA population fractions is that these have much higher transfection rates. Importantly, the results showed that the nanosensor could differentiate between AAV_scDNA and AAV_ssDNA despite their identical masses. In contrast, AUC could not differentiate between AAV_scDNA and AAV_ssDNA. An equimolar mixture of AAV_scDNA, AAV_ssDNA and AAV_empty was also measured with the sensor, and the results showed the expected population fractions, supporting the capacity of the method to differentiate AAV load status in heterogeneous solutions. In addition, less common optical and electrical signal signatures were identified in the acquired data, which were attributed to debris, rapid entry re-entry to the optical trap, or weak optical trap exits, representing critical artifacts to recognize for correct interpretation of the data. Together, these findings establish plasmonic nanopore sensing as a promising platform for quantifying AAV DNA loading status and genome type with the potential to extend ultra-sensitive single-particle characterization beyond the capabilities of existing methods. Full article

The Lunar Soil Volatile Measuring Instrument, a key payload of the Chang’e-7 mission, employs a quadrupole mass spectrometer (QMS) to directly analyze gases released from lunar regolith at different temperatures, aiming to determine the types and abundances of volatiles. However, the evolved gases are often complex mixtures, and their direct introduction into the mass spectrometer may compromise the measurement accuracy due to interactions among different species. To investigate the interference from gases on volatile quantification, systematic experiments were performed with one or two gases out of H₂, He, N₂, Ar, CO₂, and CO on a flight-like laboratory unit of the payload. Results show that the QMS exhibited excellent reproducibility and linear response (R² > 0.99) for all pure gases tested. Furthermore, the sensitivity of gases varied in mixtures and was jointly influenced by gas composition and volume fraction. For instance, compared with the sensitivity values obtained in pure gas measurements, the sensitivity of CO was slightly enhanced when mixed with Ar but was reduced when mixed with H₂ or He. A significant sensitivity enhancement of up to 4.6 folds was observed for H₂ when mixed with He. However, as its fraction increased, the sensitivity of a component in a binary mixture exhibited a decreasing deviation and was almost constant when its fraction was above 60%. Based on these findings, we developed a sensitivity correction method which employs an iterative algorithm to obtain more accurate partial pressures calculated from the gas measurement signals. Applications of the method on H₂–He mixtures and a pre-mixed CO–N₂ standard gas demonstrated that the relative errors of calculated pressures can be reduced to within ±10%. This method would significantly improve the accuracy of gas pressure calculated from in situ volatile measurement data and also provides a valuable reference for similar QMSs. Full article

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, yet diagnosis still relies primarily on invasive bone-marrow procedures and advanced laboratory assays. Non-invasive, rapid, and cost-effective tools remain an unmet need. Fourier-transform infrared (FTIR) spectroscopy has shown promise for detecting cancer-associated biochemical changes in biofluids and cells. Methods: Serum from pediatric ALL patients and controls (n = 103; ALL = 45, controls = 58: healthy = 14, hematology controls = 44 with anemia, thrombocytopenia, leukopenia, and pancytopenia) was analyzed using FTIR. Spectra (800–1800, 2800–3500 cm⁻¹) were preprocessed with baseline correction, derivative filtering, and normalization. Group differences were assessed statistically, and logistic regression with stratified 10-fold cross-validation was applied; Receiver operating characteristic (ROC)\precision–recall (PR) analyses were based on out-of-fold predictions. Results: Distinct spectral alterations were observed between ALL and controls. Leukemia samples showed higher amide I (~1640 cm⁻¹) and amide II (~1545 cm⁻¹) absorbance, lower lipid-related bands (~1450, ~2920 cm⁻¹), and increased nucleic-acid–associated signals (~1080 cm⁻¹). Differences were significant (q < 0.05) with moderate effect sizes. Logistic regression achieved area under the curve (AUC) ≈ 0.80 with sensitivity ~0.73–0.84 across practical decision thresholds (0.50 → 0.30) and higher recall attainable at the expense of specificity. Principal component analysis (PCA)\hierarchical cluster analysis (HCA) indicated partial but consistent group separation, aligning with supervised performance. Conclusions: Serum FTIR spectroscopy shows promise for distinguishing pediatric ALL from controls by reflecting disease-related metabolic changes. The technique is rapid, label-free, and requires only small serum volumes. Our findings represent proof-of-concept, and validation in larger, multi-center studies is needed before clinical implementation can be considered. Full article

The ability to predict the vapor pressure and vapor-phase composition of hydrocarbon mixtures (such as jet fuel, sustainable aviation fuel or its un-refined precursors) and partially vaporized hydrocarbon mixtures is important to simulations of processes that involve vaporization such as distillations, flash points, combustion properties of partially vaporized fuels, etc. Raoult’s Law provides a simple algebraic formula relating liquid composition and temperature to vapor composition and pressure. However, Raoult’s Law is not accurate at low mole fractions, which is typical for complex mixtures such as fuels. A common approach to correcting Raoult’s Law is to apply a scale factor, a so-called activity coefficient. Numerous models exist for predicting activity coefficients. Here we benchmark against the UNIFAC model, which predicts activity coefficients based on mole fractions, group fractions, Van der Waals volume and surface area and temperature-dependent interaction terms between groups. While this approach is truly predictive, its accuracy at very low mole fractions has not been validated, and it is computationally intensive, particularly for simulations (especially optimizations) that require vapor composition or pressure within the inner-most loop. Here we present an alternative correction to Raoult’s law, where the vapor pressure of the i^th component is represented by a modified form of the Clausius–Clapeyron equation. The reference temperature ($T_{ref}$) is replaced by a simple algebraic function that converges to $T_{ref}$ as $x_i$ approaches 1 while smoothly increasing from this value as $x_i$ decreases. Simultaneously, the heat of vaporization ($\mathsf{\Delta }{H}_{vap,i}\left(T\right)$) term is replaced by another simple algebraic expression that converges to $\mathsf{\Delta }{H}_{vap,i}\left(T\right)$ as $x_i$ approaches 1 while smoothly decreasing as $x_i$ decreases. In this model, the temperature-dependent heat of vaporization is tuned at each temperature such that the Clausius–Clapeyron equation reproduces the correct vapor pressure of the neat material, while the parameterized algebraic corrections are tuned to vapor pressure data of mixtures involving n-pentane, toluene, and dodecane, where the mole fractions of n-pentane and toluene are maintained below 10%_mol. Validation of the resulting model is accomplished by comparing modeled vapor–liquid equilibrium systems with experimental measurements. This approach improves the accuracy and computational efficiency of volatility predictions, thereby supporting the development, certification, and adoption of sustainable aviation fuel. Full article

Introduction. Post-traumatic stress disorder (PTSD) remains one of the best-described yet also one of the most heterogeneous psychiatric disorders. Existing neuroimaging studies point to key changes in the hippocampus, amygdala, and prefrontal cortex, but the role of PTSD duration in modulating these changes has not been fully explained. Objectives. The aim of the study was to assess the impact of PTSD duration (≤5 years vs. >5 years) on volumetric and microstructural brain parameters, using multiple Magnetic Resonance Imaging (MRI) sequences (3D Ax BRAVO, Cube T2 FLAIR, Diffusion Tensor Imaging—DTI) and a set of macroscopic morphometric measurements. Methods. The study included 92 participants: 33 with PTSD of ≤5 years duration, 31 with PTSD > 5 years, and 28 healthy controls. Volume and diffusion parameters of six Regions of Interest (ROIs) (hippocampus, amygdala, prefrontal cortex—right and left) were evaluated, along with their associations with nine brain measurements (including width of the third ventricle, corpus callosum, and lateral fissures). Statistical analyses included the Kruskal–Wallis test with Compact Letter Display (CLD) correction and Spearman correlations. Results. (1) The volume of the right hippocampus was significantly greater in the PTSD > 5 years group compared to controls (p = 0.006), with intermediate values in the PTSD ≤ 5 years group. (2) In the left amygdala, an increase in Fractional Anisotropy (FA) and related anisotropy measures was observed in PTSD > 5 years (p ≈ 0.02), without volumetric changes. (3) In the left prefrontal cortex, diffusivity was reduced in PTSD ≤ 5 years (p = 0.035), partially normalizing after >5 years. (4) Correlation analysis revealed that chronic PTSD strengthens the negative associations between hippocampal microstructure and both the width of the amygdala and the interhemispheric fissure, indicating a progressive reorganization of fronto-limbic networks. Conclusions. PTSD induces region- and time-dependent brain changes: (a) adaptive/hypertrophic protection of the right hippocampus after many years of illness, (b) cumulative microstructural reorganization of the left amygdala, and (c) transient impairment of diffusion in the left prefrontal cortex in early PTSD. These findings highlight the necessity of considering the temporal dimension in planning therapeutic interventions and in the search for biomarkers of PTSD progression. Full article

Background: Caesarean section (CS) is a critical intervention, yet stark inequities in access persist across low- and middle-income countries (LMICs). Over the last decade, governments have introduced policies to eliminate or subsidize user fees; however, the collective impact of these initiatives on utilization, equity, and financial protection has not been fully synthesized. Methods: We conducted a systematic review in line with PRISMA 2020 guidelines. Searches were conducted in PubMed, Dimensions, Google Scholar, Scopus, Web of Science, and government portals for studies published between 1 January 2009 and 30 May 2025. Eligible studies evaluated government-initiated financing reforms, including full user-fee exemptions, partial subsidies, vouchers, insurance schemes, and provider-payment restructuring. Two reviewers independently applied the PICOS criteria, extracted data using a 15-item template, and assessed the study quality. Given heterogeneity, results were synthesized narratively. Results: Thirty-seven studies from 28 LMICs were included. Most (70%) evaluated fee exemptions. Mixed-methods and cross-sectional designs predominated, while only six studies employed interrupted time series designs. Twenty-two evaluations (59%) reported increased CS uptake, ranging from a 1.4-fold rise in Senegal to a threefold increase in Kano State, Nigeria. Similar surges were also observed in non-African contexts such as Iran and Georgia, where reforms included incentives for vaginal delivery or punitive tariffs to curb overuse. Fourteen of 26 fee-exemption studies documented pro-rich or pro-urban drift, while catastrophic expenditure persisted for 12–43% of households, despite the implementation of “free” policies. Median out-of-pocket costs ranged from USD 14 in Burkina Faso to nearly USD 300 in Dakar’s slums. Only one study linked reforms to a reduction in neonatal mortality (a 30% decrease in Mali/Benin), while none demonstrated an impact on maternal mortality. Qualitative evidence highlighted hidden costs, delayed reimbursements, and weak accountability. At the same time, China and Bangladesh demonstrated how demographic reforms or voucher schemes could inadvertently lead to CS overuse or expose gaps in service readiness. Conclusions: Government-led financing reforms consistently increased CS volumes but fell short of ensuring equity, financial protection, or sustained quality. Effective initiatives combined fee removal with investments in surgical capacity, timely reimbursement, and transparent accountability. Future CS policies must integrate real-time monitoring of equity and quality and adopt robust quasi-experimental designs to enable mid-course correction. Full article

Background: Regional cerebral blood flow (rCBF) is a putative biomarker for neuropsychiatric disorders, including major depressive disorder (MDD). Methods: Here, we show that rCBF can be predicted from resting-state functional MRI (rsfMRI) at the voxel level while correcting for partial volume averaging (PVA) artifacts. Cortical patterns of MDD-related CBF differences decoded from rsfMRI using a PVA-corrected approach showed excellent agreement with CBF measured using single-photon emission computed tomography (SPECT) and arterial spin labeling (ASL). A support vector machine algorithm was trained to decode cortical voxel-wise CBF from temporal and power-spectral features of voxel-level rsfMRI time series while accounting for PVA. Three datasets, Amish Connectome Project (N = 300; 179 M/121 F, both rsfMRI and ASL data), UK Biobank (N = 8396; 3097 M/5319 F, rsfMRI data), and Amen Clinics Inc. datasets (N = 372: N = 183 M/189 F, SPECT data), were used. Results: PVA-corrected CBF values predicted from rsfMRI showed significant correlation with the whole-brain (r = 0.54, p = 2 × 10⁻⁵) and 31 out of 34 regional (r = 0.33 to 0.59, p < 1.1 × 10⁻³) rCBF measures from 3D ASL. PVA-corrected rCBF values showed significant regional deficits in the UKBB MDD group (Cohen’s d = −0.30 to −0.56, p < 10⁻²⁸), with the strongest effect sizes observed in the frontal and cingulate areas. The regional deficit pattern of MDD-related hypoperfusion showed excellent agreement with CBF deficits observed in the SPECT data (r = 0.74, p = 4.9 × 10⁻⁷). Consistent with previous findings, this new method suggests that perfusion signals can be predicted using voxel-wise rsfMRI signals. Conclusions: CBF values computed from widely available rsfMRI can be used to study the impact of neuropsychiatric disorders such as MDD on cerebral neurophysiology. Full article

Background: Prostate-specific membrane antigen (PSMA)-PET imaging has significantly improved prostate cancer (PCa) staging, yet its interpretation remains challenging, even for experienced specialists. No prior study has assessed urologists’ ability to interpret PSMA-PET. Methods: We conducted a multicenter prospective study involving 63 urologists from eight Italian institutions. Participants evaluated 20 PSMA-PET scans of high-risk PCa cases, with no clinical information provided. Proficiency was defined as correctly identifying at least two of three staging components (T, N, M) in ≥75% of cases. Associations between performance and factors such as hierarchy (resident vs. consultant), institution type, surgical volume, and multidisciplinary team (MDT) presence were analyzed using univariable and multivariable logistic regression. Results: Only one participant achieved full staging proficiency, while 44% reached the ≥75% threshold for partial (almost correct) staging. Urologists from centers with ≥300 PCa diagnoses per year demonstrated better T and M stage identification. Institutions with ≥150 robot-assisted radical prostatectomies (RARPs) per year and those with MDTs showed higher accuracy in M staging. No significant predictors of proficiency emerged in the multivariable analysis, although hierarchy and surgical volume approached significance for nodal metastasis detection. Conclusion: PSMA-PET interpretation is complex for urologists, with particular challenges in T and M staging. High institutional case volumes and MDT involvement may enhance interpretation skills. Structured training programs and increased exposure to multidisciplinary imaging discussions are essential to optimize urologists’ diagnostic proficiency and ultimately improve patient care. Full article

Objectives: Low tidal volume ventilation (LTVV) is a ventilatory strategy with the advantages of minimizing diaphragm movements and reducing hypercapnia and barotrauma risks. This preliminary study aims to report on the safety and effectiveness of LTVV applied during percutaneous US-guided liver ablations of focal malignancies. Methods: Patients affected by focal liver malignancies treated with percutaneous microwaves ablation were retrospectively included in this single-center analysis. Arterial gas analysis was performed immediately before and after ablation to evaluate the arterial pH, partial pressure of carbon dioxide (pCO₂), partial pressure of oxygen (pO₂), and plasma lactate levels. The primary endpoint of this study was to evaluate the safety and efficacy of LTVV during percutaneous liver cancer ablation. The secondary endpoint was to assess the procedural technical success in terms of correct needle probe targeting without the need for repositioning. Results: Ten patients affected by a single liver lesion had been analyzed. The ASA score was three in all patients, with three patients also suffering from COPD. The procedural technical success was 100%: ablations were performed with a single liver puncture without the need for changing access or repositioning the needle. No variations in post-ablation arterial gas analysis requiring anesthesiological management remodulation occurred. Lactate levels remained stable and hemodynamic balance was preserved during all procedures. No switch to standard volume ventilation was required. Conclusions: In this preliminary study, LTVV was a safe and effective anesthesiological protocol in patients treated with percutaneous ablations of liver malignancies, offering an ideal balance between patient safety and percutaneous needle probe positioning precision. Larger prospective studies are needed to confirm these findings. Full article

Background and Clinical Significance: Scimitar Syndrome is a rare congenital cardiopulmonary anomaly characterized by partial anomalous pulmonary venous return, often requiring early surgical correction. It may coexist with other congenital or acquired cardiovascular anomalies, including valvular diseases such as mitral regurgitation. When surgical correction of Scimitar Syndrome is combined with mitral valve annuloplasty, the proximity to the atrioventricular node may potentially predispose patients to late-onset conduction disturbances, although causality remains speculative. Case Presentation: We describe the case of a 53-year-old male who developed paroxysmal atrial fibrillation, atrial flutter, and intermittent second-degree AV block decades after undergoing surgical correction of Scimitar Syndrome with concomitant mitral annuloplasty. Multimodal echocardiographic evaluation revealed preserved left atrial volume, normal intra-atrial conduction time, mildly reduced strain, and maintained atrial synchrony. The patient was treated with direct oral anticoagulants and beta-blockers and underwent the implantation of a ventricular leadless pacemaker. Conclusions: This case highlights the supportive role of atrial function imaging in assessing atrial health and informing rhythm management and procedural choices in surgically corrected congenital heart disease. Full article

Background/Objectives: The complex pathophysiology of painful cervical radiculopathy is only partially understood. Neuroimmune activation in the dorsal root ganglion and spinal cord is assumed to underlie the genesis of radicular pain. Molecular positron emission tomography (PET) using the radiotracer [¹¹C]DPA713, which targets the 18-kDa translocator protein (TSPO), offers the ability to quantify neuroinflammation in humans in vivo. The primary objectives of this study were to (1) assess whether uptake of [¹¹C]DPA713, a metric of neuroinflammation, is higher in the neuroforamina and spinal cord of patients with painful cervical radiculopathy compared with that in pain-free participants and (2) assess whether [¹¹C]DPA713 uptake is associated with clinical parameters, such as pain intensity. Methods: Dynamic 60 min [¹¹C]DPA713 PET/CT scans were acquired, and regions of interest were defined for neuroforamina and spinal cord. Resulting time-activity curves were fitted to a single-tissue compartment model using an image-derived input function, corrected for plasma-to-whole blood ratios and parent fractions, to obtain the volume of distribution (V_T) as the primary outcome measure. Secondary neuroinflammation metrics included 1T2k V_T without metabolite correction (1T2k_WB) and Logan V_T. Results: The results indicated elevated levels of 1T2k V_T at the neuroforamina (p < 0.04) but not at the spinal cord (p = 0.16). Neuroforamina and spinal cord 1T2k V_T lack associations with clinical parameters. Secondary neuroinflammatory metrics show associations with clinical parameters such as the likelihood of neuropathic pain. Conclusions: These findings enhance our understanding of painful cervical radiculopathy’s pathophysiology, emphasizing the neuroforamina levels of neuroinflammation as a potential therapeutic target. Full article

Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts’ accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLAIR, T2, and T1ce) and their volume. Full article