Search Results (51)

Background/Objectives: Maintaining high childhood vaccination coverage is essential to prevent outbreaks of vaccine-preventable diseases. In Italy, Law No. 119/2017 introduced mandatory childhood immunizations, leading to significant improvements. However, the COVID-19 pandemic disrupted routine services, potentially jeopardizing these gains. This study aimed to evaluate national and regional trends in vaccine coverage across three phases: post-mandate (2015–2016 vs. 2017–2019), pandemic (2017–2019 vs. 2020–2021), and post-pandemic recovery (2020–2021 vs. 2022–2023). Methods: National and regional administrative data on vaccination coverage at 24 months of age were obtained from the Italian Ministry of Health. Temporal trends were analyzed using Joinpoint regression to estimate annual percent changes (APCs), and absolute changes in coverage (Δ) were calculated across defined periods. Pearson correlation coefficients were used to assess associations between baseline coverage and subsequent changes. Results: After the 2017 mandate, coverage increased significantly for varicella (APC = +28.6%), MenB (+22.6%), and measles (+3.4%). Regionally, varicella coverage rose by up to +58.4% in Emilia-Romagna and measles by +11.1% in Campania. During the pandemic, coverage declined for polio (−2.4% in the South) and measles (−6.2% in Abruzzo), while MenB increased in regions with lower initial uptake (r = −0.918, p < 0.001). Post-pandemic, coverage rebounded, with varicella improving by +20.1% in central regions and measles by +13.9% in Abruzzo. A strong inverse correlation between baseline coverage and improvement was observed for varicella across all periods (r from −0.877 to −0.915). Conclusions: Mandatory vaccination policies led to substantial coverage improvements, and despite the disruption caused by the pandemic, recovery trends were observed for most vaccines. The consistent association between low baseline coverage and stronger gains highlights the resilience of the system, but also the ongoing need for regionally tailored strategies to reduce geographic disparities and ensure equitable immunization across Italy. Full article

A drawback of fringe projection profilometry (FPP) is that it is still a challenge to perform efficient and accurate high-resolution absolute phase recovery with only a single measurement. This paper proposes a single-model self-recovering fringe projection absolute phase recovery method based on deep learning. The built Fringe Prediction Self-Recovering network converts a single fringe image acquired by a camera into four single mode self-recovering fringe images. A self-recovering algorithm is adopted to obtain wrapped phases and fringe grades, realizing high-resolution absolute phase recovery from only a single shot. Low-cost and efficient dataset preparation is realized by the constructed virtual measurement system. The fringe prediction network showed good robustness and generalization ability in experiments with multiple scenarios using different lighting conditions in both virtual and physical measurement systems. The absolute phase recovered MAE in the real physical measurement system was controlled to be 0.015 rad, and the reconstructed point cloud fitting RMSE was 0.02 mm. It was experimentally verified that the proposed method can achieve efficient and accurate absolute phase recovery under complex ambient lighting conditions. Compared with the existing methods, the method in this paper does not need the assistance of additional modes to process the high-resolution fringe images directly. Combining the deep learning technique with the self-recovering algorithm simplified the complex process of phase retrieval and phase unwrapping, and the proposed method is simpler and more efficient, which provides a reference for the fast, lightweight, and online detection of FPP. Full article

BACKGROUND: Sport practice may elevate the risk of cardiovascular events, including sudden cardiac death, in athletes with undiagnosed heart conditions. In Italy, pre-participation screening includes a resting ECG and either the Harvard Step Test (HST) or maximal exercise testing (MET), but the relative efficacy of the latter two tests for detecting arrhythmias and heart conditions remains unclear. METHODS: This study examined 511 paediatric athletes (8–18 years, 76.3% male) without known cardiovascular, renal, or endocrine diseases. All athletes underwent both HST and MET within 30 days. Absolute data and data relative to theoretical peak heart rates, arrhythmias (supraventricular and ventricular) and cardiovascular diagnoses were collected. RESULTS: HST resulted in a lower peak heart rate than MET (181.1 ± 9.8 vs. 187.5 ± 8.1 bpm, p < 0.001), but led to the detection of more supraventricular (18.6% vs. 13.1%, p < 0.001) and ventricular (30.5% vs. 22.7%, p < 0.001) arrhythmias, clustering during recovery (p = 0.014). This pattern was significant in males but not females. Among athletes diagnosed with cardiovascular diseases (22.3%), HST identified more ventricular arrhythmias (26.3% vs. 18.4%, p = 0.05), recovery-phase arrhythmias (20.2% vs. 14.0%, p = 0.035), and polymorphic arrhythmias (6.1% vs. 1.8%, p = 0.025). CONCLUSIONS: HST detects arrhythmias more effectively than MET in young male athletes, especially during recovery. More ventricular arrhythmias were highlighted even in athletes with cardiovascular conditions. Full article

The growing energy demand and the need for climate mitigation strategies have spurred interest in the application of CO₂–enhanced oil recovery (CO₂–EOR) and carbon capture, utilization, and storage (CCUS). Furthermore, natural hydrogen (H₂) production and underground hydrogen storage (UHS) in geological media have emerged as promising technologies for cleaner energy and achieving net–zero emissions. However, selecting a suitable geological storage medium is complex, as it depends on the physicochemical and petrophysical characteristics of the host rock. Solubility is a key factor affecting the above–mentioned processes, and it is critical to understand phase distribution and estimating trapping capacities. This paper conducts a succinct review of predictive techniques and present novel simple and non–iterative predictive models for swift and reliable prediction of solubility behaviors in CO₂–brine and H₂–brine systems under varying conditions of pressure, temperature, and salinity (T–P–m salts), which are crucial for many geological and energy–related applications. The proposed models predict CO₂ solubility in CO₂ + H₂O and CO₂ + brine systems containing mixed salts and various single salt systems (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻) under typical geological conditions (273.15–523.15 K, 0–71 MPa), as well as H₂ solubility in H₂ + H₂O and H₂ + brine systems containing NaCl (273.15–630 K, 0–101 MPa). The proposed models are validated against experimental data, with average absolute errors for CO₂ solubility in pure water and brine ranging between 8.19 and 8.80% and for H₂ solubility in pure water and brine between 4.03 and 9.91%, respectively. These results demonstrate that the models can accurately predict solubility over a wide range of conditions while remaining computationally efficient compared to traditional models. Importantly, the proposed models can reproduce abrupt variations in phase composition during phase transitions and account for the influence of different ions on CO₂ solubility. The solubility models accurately capture the salting–out (SO) characteristics of CO₂ and H₂ gas in various types of salt systems which are consistent with previous studies. The simplified solubility models for CO₂ and H₂ presented in this study offer significant advantages over conventional approaches, including computational efficiency and accuracy across a wide range of geological conditions. The explicit, derivative–continuous nature of these models eliminates the need for iterative algorithms, making them suitable for integration into large–scale multiphase flow simulations. This work contributes to the field by offering reliable tools for modeling solubility in various subsurface energy and environmental–related applications, facilitating their application in energy transition strategies aimed at reducing carbon emissions. Full article

Background/Objectives: Targeted metabolomics is often criticized for the limited metabolite coverage that it offers. Indeed, most targeted assays developed or used by researchers measure fewer than 200 metabolites. In an effort to both expand the coverage and improve the accuracy of metabolite quantification in targeted metabolomics, we decided to develop a comprehensive liquid chromatography–tandem mass spectrometry (LC–MS/MS) assay that could quantitatively measure more than 700 metabolites in serum or plasma. Methods: The developed assay makes use of chemical derivatization followed by reverse phase LC–MS/MS and/or direct flow injection MS (DFI–MS) in both positive and negative ionization modes to separate metabolites. Multiple reaction monitoring (MRM), in combination with isotopic standards and multi-point calibration curves, is used to detect and absolutely quantify the targeted metabolites. The assay has been adapted to a 96-well plate format to enable automated, high-throughput sample analysis. Results: The assay (called MEGA) is able to detect and quantify 721 metabolites in serum/plasma, covering 20 metabolite classes and many commonly used clinical biomarkers. The limits of detection were determined to range from 1.4 nM to 10 mM, recovery rates were from 80% to 120%, and quantitative precision was within 20%. LC–MS/MS metabolite concentrations of the NIST^® SRM^®1950 plasma standard were found to be within 15% of NMR quantified levels. The MEGA assay was further validated in a large dietary intervention study. Conclusions: The MEGA assay should make comprehensive quantitative metabolomics much more affordable, accessible, automatable, and applicable to large-scale clinical studies. Full article

COVID-19, caused by SARS-CoV-2, triggers a complex immune response, with T regulatory cells (Tregs) playing a crucial role in maintaining immune homeostasis and preventing excessive inflammation. The current study investigates the function of T regulatory cells during COVID-19 infection and the subsequent recovery period, emphasizing their impact on immune regulation and inflammation control. We conducted a comprehensive analysis of Treg subpopulations in peripheral blood samples from COVID-19 patients at different stages: acute infection, early convalescence, and long-term recovery. Flow cytometry was employed to quantify Tregs including “naïve”, central memory (CM), effector memory (EM), and terminally differentiated CD45RA⁺ effector cells (TEMRA). Additionally, the functional state of the Tregs was assessed by the expression of purinergic signaling molecules (CD39, CD73). Cytokine profiles were assessed through multiplex analysis. Our findings indicate a significant decrease in the number of Tregs during the acute phase of COVID-19, which correlates with heightened inflammatory markers and increased disease severity. Specifically, we found a decrease in the relative numbers of “naïve” and an increase in EM Tregs, as well as a decrease in the absolute numbers of “naïve” and CM Tregs. During the early convalescent period, the absolute counts of all Treg populations tended to increase, accompanied by a reduction in pro-inflammatory cytokines. Despite this, one year after recovery, the decreased subpopulations of regulatory T cells had not yet reached the levels observed in healthy donors. Finally, we observed the re-establishment of CD39 expression in all Treg subsets; however, there was no change in CD73 expression among Tregs. Understanding these immunological changes across different T regulatory subsets and adenosine signaling pathways offers important insights into the disease’s pathogenesis and provides a broader view of immune system dynamics during recovery. Full article

In carbonate reservoirs, nanoparticles can adhere to rock surfaces, potentially altering the rock wettability and modifying the absolute permeability. In the water-alternating-gas (WAG) process, the introduction of nanoparticles into the water phase, termed nano-water-alternating gas (NWAG), is a promising approach for enhancing oil recovery and CO₂ storage. The NWAG process can alter rock wettability and absolute permeability through the adsorption of nanoparticles on the rock surface. This study investigated the efficiency of the NWAG method, which utilizes nanofluids in CO₂-enhanced oil recovery (EOR) processes to simultaneously recover oil and store CO₂ using 1D core and 3D heterogeneous reservoir models. The simulation results of the 1D core model showed that applying the NWAG method enhanced both oil recovery and CO₂ storage efficiency by increasing to 3%. In a 3D reservoir model, a Dykstra–Parsons coefficient of 0.4 was selected to represent reservoir heterogeneity. Additionally, the capillary trapping of CO₂ during WAG injection was computed using Larsen and Skauge’s three-phase relative permeability hysteresis model. A sensitivity analysis was performed using the NWAG ratio, slug size, injection period, injection cycle, and nanofluid concentration. The results confirmed an increase of 0.8% in oil recovery and 15.2% in CO₂ storage compared with the conventional WAG process. This mechanism suggests that nanofluids can enhance oil recovery and expand CO₂ storage, improving the efficiency of both the oil production rate and CO₂ storage compared to conventional WAG methods. Full article

The infusion autograft absolute number of inhibitory killer immunoglobulin-like receptor (KIR) 2DL2 and activating natural killer (NK)p30 cells are predictors of clinical outcomes in lymphoma patients undergoing autologous peripheral blood hematopoietic stem cell transplantation (APBHSCT). To assess if the long-term recovery of these NK cell subsets still holds clinical relevance, we set up to investigate their prognostic ability at day 100 post-APBHSCT. This was a retrospective single-institution study including 107 patients from our prior phase III trial who had a clinical assessment at day 100 post-APBHSCT. The median follow-up from day 100 was 168.19 months (interquartile range: 156.85–181.28 months). Patients with day 100 inhibitory KIR2DL2 < 0.08 cells/µL and activating NKp30 ≥ 0.19 cells/µL experienced superior overall survival (OS) and progression-free survival (PFS). A multivariate analysis revealed both the day 100 inhibitory KIR2DL2 [OS: HR = 1.449, 95%CI, 1.231–1.895, p < 0.013; and PFS: HR = 2.069, 95%CI, 1.134–3.775, p < 0.021] and activating NKp30 [OS: HR = 4.985, 95%CI, 2.614–9.506, p < 0.0001; and PFS: HR = 4.661, 95%CI, 2.598–8.393, p < 0.0001] were independent predictors for OS and PFS. Inhibitory KIR2DL2 and activating NKp30 NK cells at day 100 are prognostic immune biomarkers in lymphoma patients treated with APBHSCT. Full article

Background: Repeated sprint ability (RSA) is defined as the ability to recover and maintain maximal effort during repeated sprints, recognised as a crucial performance component in team sports. The exercise mode used to test RSA may influence performance and the contributions of different energy systems. The primary aim of this study is to address the critical gap between traditional cycling-based anaerobic tests, such as the Wingate test, and the practical, sport-specific demands of running in field-based team sports. Methods: This study involved 32 professional soccer players (age: 21.2 ± 1.3 years; height: 177.8 ± 4.3 cm; and mass: 71.3 ± 6.4 kg). They performed cycling- and running-based repeated sprint tests, with similar total sprint numbers, durations, and recovery times, on different days. Contributions from adenosine triphosphate-phosphocreatine (ATP-PCr), glycolytic, and oxidative systems were estimated through body weight, oxygen uptake (VO₂), blood lactate (BLa), and the fast component of excess post-exercise oxygen consumption (EPOC). The VO₂ levels and heart rate (HR) were monitored during the rest (10 min), exercise, and recovery (15 min) phases in a breath-by-breath mode using a portable gas exchange system. BLa was measured before (at rest) and 1, 3, 5, 7, and 10 min after the running and cycling tests using a handheld portable analyser. A mono-exponential model estimated the ATP-PCr system contribution, calculated using the fast component of EPOC following the final sprint and the sum of the VO₂-time integral during rest intervals. Results: The cycling tests demonstrated significantly higher values for the peak power (PP), mean power (MP), and rate of perceived exertion (RPE) (p < 0.05), while the heart rate peak and blood lactate responses were similar across all modalities. The fatigue index was notably higher in the running tests (p < 0.05). Furthermore, the running tests showed greater contributions in both the percentage and absolute terms from the adenosine triphosphate-phosphocreatine (ATP-PCr) system (p < 0.01), total energy demand (p < 0.05), and total energy expenditure (TEE) (p < 0.01). Notably, the running tests resulted in an increased phosphocreatine breakdown (p < 0.05) and rapid phosphocreatine replenishment (p < 0.01). A simple linear regression analysis highlighted a significant determination coefficient between these performance variables and the contributions of the energy systems, affirming the robustness of the results. The correlation heatmaps further illustrated these relationships, with higher correlations for the PP and MP across modalities (0.41), emphasising the moderate association between cycling and running tests in these metrics. Conclusions: This study elucidated the similarities and differences in energy system contributions and performance outcomes between a cycling and a running repeated sprint protocol, with a comparable total sprint time and work–rest ratio. The findings reveal that a running repeated sprint test elicits a higher energy demand and a higher contribution from the PCr energy system compared to cycling. Performance variables were not associated between running and cycling tests, suggesting those tests cannot be used interchangeably. Full article

Background/Objectives: The aim of this study was to evaluate brain metabolism using MR spectroscopy (MRS) after recovery from Coronavirus disease (COVID-19) and to test the impact of disease severity on brain metabolites. Methods: We performed MRS on 81 individuals (45 males, 36 females, aged 40–60), who had normal MRI findings and had recovered from COVID-19, classifying them into mild (17), moderate (36), and severe (28) groups based on disease severity during the acute phase. The study employed two-dimensional spectroscopic imaging above the corpus callosum, focusing on choline (Cho), creatine (Cr), and N-acetylaspartate (NAA). We analyzed Cho/Cr and NAA/Cr ratios as well as absolute concentrations using water as an internal reference. Results: Results indicated that the Cho/Cr ratio was higher with increasing disease severity, while absolute Cho and NAA/Cr ratios showed no significant differences across the groups. Notably, absolute Cr and NAA levels were significantly lower in patients with severe disease. Conclusions: These findings suggest that the severity of COVID-19 during the acute phase is associated with significant changes in brain metabolism, marked by an increase in Cho/Cr ratios and a reduction in Cr and NAA levels, reflecting substantial metabolic alterations post-recovery. Full article

Background: Obesity is linked to higher rates of complications; lower absolute recovery of mobility, pain, and function; and increased costs of care following total knee or hip arthroplasty (TKA, THA). The aim of this prospective cohort study was to evaluate the effectiveness of a 12-week partial meal replacement (PMR) weight loss program for people awaiting TKA or THA and living with obesity (body mass index (BMI) ≥ 30 kg/m²). Methods: The intervention was delivered remotely and included a 12-week PMR plan of 1200 calories/day, incorporating two meal replacement shakes/soups and a third suitable simple meal option. The intervention support was provided through online group education sessions, one-to-one teleconsultation with a dietitian, and access to a structured PMR App with functions for goal setting and providing educational content on diet, physical activity, and behaviour changes. Results: Of the 182 patients approached, 29 provided consent to participate, 26 participants commenced the program, and 22 participants completed the 12-week PMR plan. Completers exhibited statistically significant weight loss from baseline to 12 weeks, with a paired difference of 6.3 kg (95% CI: 4.8, 7.7; p < 0.001), with 15 out of 22 (68.2%) participants achieving at least 5% weight loss. Statistically significant reductions in HbA1c and low density lipoprotein (LDL) were observed at 12 weeks compared to baseline. Moreover, a significant increase in the proportion of participants in the action and maintenance phases of the readiness to change diet, physical activity, and weight were observed at 12 weeks. The majority of program completers (18 out of 22) expressed willingness to pay for the service if offered on a long-term basis following the arthroplasty. Conclusions: This study’s findings demonstrated that significant weight loss is achievable for people living with obesity awaiting arthroplasty following a 12-week PMR weight loss program. The remote delivery of the intervention was feasible and well accepted by people awaiting TKA or THA. Full article

An ever-increasing amount of research is being performed on the stability and recovery of soil methane-oxidizing bacteria since this is one of the fundamental processes controlling the amount of methane in the atmosphere. Mineral fertilizers may alter the methane oxidation processes in agricultural soils when they are introduced. Although ammonium (NH₄⁺) is believed to have a significant impact on aerobic methane oxidation activity in soils, there is still little data on how it reacts with lanthanum (La). The recent identification of a novel class of lanthanum-containing enzymes in methanotrophic bacteria may be the foundation for controlling the function of the soil “methane filter” and related microbiota. In the current study, microcosms with agricultural sod-podzolic soils were created and incubated in air or 20% CH₄ in the gas phase with the addition of NH₄⁺ (100 µg/g) and La (5 µg/g) to the soil. Using GC analysis and high-performance 16S rRNA sequencing, the methane oxidation potential and composition of soil bacterial communities were studied over the month of incubation. A negative impact of NH₄⁺ on the oxidation of methane was observed, whereas La had a somewhat beneficial effect. Ammonium had an impact on the composition of methanotrophs, and a significant shift was observed upon La addition. Proteobacteria made up a larger share of the soil microbial community, and Gammaproteobacteria dominated the methanotrophic populations. Methylobacter, a methanotroph, and Methylotenera, an obligatory methylotroph, were the two absolute dominants in the La-amended variants. These findings could help evaluate how lanthanum regulates methanotrophic communities in agricultural soils and lead to the creation of new strategies for controlling the “methane filter” in soil. Full article

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization. Full article

Effective process development towards intensified processing for gene delivery applications using Hepatitis B core Antigen (HBcAg) virus-like particles (VLPs) relies on analytical methods for the absolute quantification of HBcAg VLP proteins and bound nucleic acids. We investigated a silica spin column (SC)-based extraction procedure, including proteinase K lysis and silica chromatography, for the absolute quantification of different species of nucleic acids bound to HBcAg VLPs analyzed by dye-based fluorescence assays. This revealed load-dependent nucleic acid recoveries of the silica-SC-based extraction. We also developed a reversed-phase high-performance liquid chromatography (RP-HPLC) method to separate and quantify the HBcAg proteins and the bound nucleic acids simultaneously without prior sample treatment by dissociation reagents. The method demonstrated sufficient linearity, accuracy, and precision coefficients and is suited for determining absolute protein and nucleic acid concentrations and HBcAg protein purities at various purification stages. Both the silica-SC-based extraction and the RP-based extraction presented overcome the limitations of analytical techniques, which are restricted to relative or qualitative analyses for HBcAg VLPs with bound nucleic acids. In combination with existing analytics, the methods for an absolute quantification of HBcAg VLPs and bound nucleic acids presented here are required to evaluate downstream purification steps, such as the removal of host cell-derived nucleic acids, concurrent protein loss, and efficient loading with therapeutic nucleic acids. Hence, the methods are key for effective process development when using HBcAg VLP as potential gene delivery vehicles. Full article

Hydraulic fracturing of horizontal wells is a common method for enhancing production in low-permeability and unconventional oil reservoirs. However, due to the interference between fractures, issues such as decreased production and water channeling often occur in hydraulic fracturing of horizontal wells. Therefore, studying how to mitigate the effects of fracture interference is of great significance for optimizing hydraulic fracturing design and improving oil and gas recovery rates. In this paper, an oil–water two-phase discrete fracture model was established, and the grid dissection was carried out by using the optimization method to obtain a triangular grid that can finely characterize the fracture in geometry. Then, typical discrete fracture models were designed, and the influences of the fracture permeability ratio, absolute fracture scale, oil–water viscosity ratio, and fracture length on the fracture flow interference were investigated separately. The degree of fracture interference was evaluated using the fracture fractional flow rate ratio, remaining oil saturation, and sweep efficiency. This study verified fracture interaction and identified that the threshold value of the fracture permeability ratio is 9 to classify the degree of interference. Sensitivity analysis shows that the absolute size of the fracture has a significant impact on fracture interference, while the impact of the oil–water viscosity ratio and fracture length on fracture interference is relatively small. Full article