Multiparametric Approach Research Articles

Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate

In this study, we analysed lithospheric, atmospheric, and top-side ionospheric magnetic field data six months before the three earthquake doublets occurred in the last ten years around the Arabian tectonic plate. They occurred in 2014, close to Dehloran (Iran), in 2018, offshore Kilmia (Yemen) and in 2022, close to Bandar-e Lengeh (Iran). For all the cases, we considered the equivalent event in terms of total released energy and mean epicentral coordinates. The lithosphere was investigated by calculating the cumulative Benioff strain with the USGS earthquake catalogue. Several atmospheric parameters (aerosol, SO2, CO, surface air temperature, surface latent heat flux humidity, and dimethyl sulphide) have been monitored using the homogeneous data from the MERRA-2 climatological archive. We used the three-satellite Swarm constellation for magnetic data, analysing the residuals after removing a geomagnetic model. The analysis of the three geo-layers depicted an interesting chain of lithosphere, atmosphere, and ionosphere anomalies, suggesting a geophysical coupling before the Dehloran (Iran) 2014 earthquake. In addition, we identified interesting seismic accelerations that preceded the last 20 days, the Kilmia (Yemen) 2018 and Bandar-e Lengeh (Iran) 2022 earthquake doublets. Other possible interactions between the geolayers have been observed, and this underlines the importance of a multiparametric approach to properly understand a geophysical complex topic as the preparation phase of an earthquake.

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

The diagnostic accuracy of aortic valve calcium score in patients with low gradient and high gradient aortic stenosis

Abstract Introduction The standard imaging modality for diagnosing aortic stenosis (AS) is echocardiography (echo), by which the definition of severe AS is based on the transvalvular mean gradient (AMG) (>40 mmHg) and aortic valve area (AVA) (<1 cm2). However, in around one quarter of the cases, echo provides discordant findings (AVA<1 cm2, despite of an AMG <40 mmHg), complicating clinical decision making. Computed tomography aortic valve calcium scoring (CT- AVCS) offers an alternative diagnostic method. In cases with discordant echo measurements current guidelines recommend the use CT-AVCS to decide on severity. Purpose Our aim was to validate the sensitivity of the recommended AVCS thresholds within our patient population, as well as to identify any factors associated with below cut-off AVCS values despite confirmed severe AS (low AVCS). Methods Our retrospective study included 332 consecutive patients with severe AS (AVA ≤1 cm2), who underwent CT angiography (CTA) for transcatheter aortic valve implantation (TAVI) planning. Based on the AMG the patients were classified into high gradient (HG: AMG ≥ 40 mmHg) and low gradient (LG: AMG <40 mmHg) groups. In the LG group AS severity was assessed by a multiparametric algorithm. To specify the predictors of low AVCS among patients with HG-AS, logistic regression analysis was used. The patients were followed for 50 months. Cox proportional hazard model was used to assess the risk of death from any cause. Results The HG-AS group consisted of 244 and the LG-AS cohort of 88 patients. In the HG-AS population the sensitivity of AVCS to diagnose severe AS was 89%. Within the HG severe AS group, logistic regression analysis revealed an association between low AVCS and the AMG (OR 0.89, CI 0.83 – 0.95, p <0,001), whereas none of the other tested factors (age, sex, hypertension, diabetes, left ventricle function, renal function) showed any association. In the LG-AS group, contrary to the HG- AS patients, the sensitivity of AVCS to define severe LG AS was limited (53% in males and 67% in females). Over a median follow-up of 50 months, the prognosis of HG AS patients with low AVCS was more favourable than that of the high AVCS patient population (HR 0.41; CI 017 – 097; p=0.04), however, in the LG AS group, the prognosis was not different when stratifying the patients according to high or low AVCS (p=0.21). Conclusions Our results confirmed that among patients with HG AS, the recommended AVCS thresholds provide a good diagnostic sensitivity for severe AS. Nonetheless, the number of severe AS patients with low AVCS is not negligible, especially within the LG-AS population, where the diagnosis is the most challenging. Our findings underline that in patients with discordant echocardiographic findings, a personalized, multiparametric diagnostic approach is needed, instead basing the final diagnosis exclusively on AVCS.

Morphologic differences in atrial and ventricular secondary tricuspid regurgitation by computed tomography

Abstract Background Differences in the mechanism behind atrial (ASTR) and ventricular (VSTR) secondary tricuspid regurgitation cause distinct remodeling in the tricuspid valve annulus (TA), right atrium (RA) and right ventricle (RV). Although these morphological changes have been described by echocardiography, computed tomography (CT) can provide better spatial resolution and detailed anatomical information particularly when planning valvular interventions. Purpose To compare the morphologic characteristics of the TA, RA and RV between ASTR and VSTR as measured by CT. Methods Patients with at least moderate secondary TR and who underwent CT were selected as follows: for the ASTR group (n=32), patients referred for atrial fibrillation (AF) ablation; for the VSTR group, patients referred for transcatheter aortic valve implantation, and which were further subdivided according to the presence of AF (with AF [n=51] and without AF [n=68]). Multiparametric echocardiographic approach was used to define TR severity. CT dataset were analyzed in diastole using a dedicated software for the following parameters: TA area, perimeter, antero-posterior (AP) and septal-lateral (SL) diameters, eccentricity index, annulus depth (to assess TA saddle shape), and RA and RV dimensions and volumes (Fig. 1). Results TR was more severe in VSTR, as 55% and 43% of patients had severe to massive TR in the VSTR group with and without AF, respectively, as compared to 12% in the ASTR group (p<0.001). The TA area was significantly larger in the ASTR and VSTR with AF patients (Fig.2), with a trend towards a more circular annulus by eccentricity index. Annulus depth was lower (flatter) in ASTR compared to both VSTR groups. For the RA, annulus to roof distance was larger in VSTR patients with AF, but the diameter width was wider for both ASTR and VSTR with AF. Indexed RA volume was larger in the VSTR with AF compared to both ASTR and VFTR without AF. The RV, on the other hand, showed a wide base and narrow mid in the ASTR group, both a wide base and mid in the VSTR group with AF, and slightly narrower base and wider mid in the VSTR without AF. RV base-to-mid ratio was lower for both VSTR groups compared to ASTR, but volumes did not significantly differ. RA-to-RV-volume ratio, however, was higher for the ASTR and VSTR with AF. Conclusion Remodeling of the TA and right chambers differ significantly between TR etiologies. ASTR is characterized by a larger and flatter TA, a widened RA and RV base, narrow RV mid, and almost proportional RA-to-RV volumes. VSTR remodeling differs with the presence of AF, as those without AF have a smaller and deep TA, smaller RA, a proportional dilatation of RV diameters, and a larger RV compared to RA. Differentiating these patterns may be useful to guide future interventions.

Multiparametric Approach to the Colorectal Cancer Phenotypes Integrating Morphofunctional Assessment and Computer Tomography.

(1) Background: Accurate body composition assessment in CCR patients is crucial due to the high prevalence of malnutrition, sarcopenia, and cachexia affecting survival. This study evaluates the correlation between body composition assessed by CT imaging as a reference technique, BIVA, nutritional ultrasound, and handgrip strength in CCR patients. (2) Methods: This retrospective study included CCR patients assessed by the Endocrinology and Nutrition Services of Virgen de la Victoria in Malaga and Vall d'Hebron in Barcelona from October 2018 to July 2023. Assessments included anthropometry, BIVA, NU, HGS, and AI-assisted CT analysis at the L3 level for body composition. Pearson's analysis determined the correlation of CT-derived variables with BIVA, NU, and HGS. (3) Results: A total of 267 CCR patients (mean age 68.2 ± 10.9 years, 61.8% men) were studied. Significant gender differences were found in body composition and strength. CT-SMI showed strong correlations with body cell mass (r = 0.65), rectus femoris cross-sectional area (r = 0.56), and handgrip strength (r = 0.55), with a Cronbach's alpha of 0.789. CT-based adipose tissue measurements showed significant correlations with fat mass (r = 0.56), BMI (r = 0.78), A-SAT (r = 0.49), and L-SAT (r = 0.66). Regression analysis indicated a high predictive power for CT-SMI, explaining approximately 80% of its variance (R2 = 0.796). (4) Conclusions: Comprehensive screening of colorectal cancer patients through BIVA, NU, HGS, and CT optimizes the results of the evaluation. These methods complement each other in assessing muscle mass, fat distribution, and nutritional status in CCR. When CT is unavailable or bedside assessment is needed, HGS, BIVA, and NU provide an accurate assessment of body composition.

RUL forecasting for wind turbine predictive maintenance based on deep learning

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future-RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models—ForeNet-2d and ForeNet-3d—are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM.

Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review.

Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler's Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.

Exploring the interplay between phytoplankton taxonomy, pigments, and optical properties in Ratnagiri coastal waters using a multi-parameter approach

This study presents a comprehensive analysis of phytoplankton diversity and optical properties in the coastal waters of Ratnagiri, India. Ratnagiri, located on the southern coast of Maharashtra, is one of the major centre for fishing activities in India as well as a developing hub for industrial activities. Given its ecological significance and the pressures it faces, the coastal waters of Ratnagiri present a valuable case for studying phytoplankton dynamics and their light absorption properties. We integrated parameters like phytoplankton taxonomy, pigment composition, Coloured Dissolved Organic Matter (CDOM), and phytoplankton absorption characteristics to give a comprehensive view of the phytoplankton community and its optical environment. Phytoplankton samples from Ratnagiri were analyzed with microscopy and high-performance liquid chromatography (HPLC) for taxonomy and pigment profiling, while CDOM and absorption properties were measured using spectrophotometry. Our findings reveal more or less spatial uniformity in phytoplankton composition (Pielou's Evenness Index (J’) ranged between 0.84 and 0.9), with significant diversity (Shannon Diversity Index (H’) ranged from 3.7 to 4.19) within the phytoplankton community. Pigment analysis revealed key marker pigments associated with various phytoplankton classes including bacillariophytes (diatoms), chlorophytes, cyanophytes, haptophytes and cryptophytes, providing insights into community structure. Concurrently, CDOM absorption coefficient (aCDOM (440)) that ranged between 0.08 and 0.3 m−1 indicated varying levels of dissolved organic matter, influencing light absorption and availability within the water column. The phytoplankton absorption data highlighted the specific wavelengths most affected by phytoplankton presence, notably those contributed by diatoms and cyanophytes, providing insights into the optical properties of Ratnagiri's coastal waters. This integrative approach underscores the importance of considering multiple parameters to accurately assess phytoplankton dynamics and their impact on the aquatic environment. The results contribute to a better understanding of the ecological processes governing coastal waters in Ratnagiri and offer valuable information for future monitoring and management efforts.

Substrate stiffness modulates extracellular vesicles’ release in a triple-negative breast cancer model

Aim: The microenvironment effect on the tumoral-derived Extracellular Vesicle release, which is of significant interest for biomedical applications, still represents a rather unexplored field. The aim of the present work is to investigate the interrelation between extracellular matrix (ECM) stiffness and the release of small EVs from cancer cells. Here, we focus on the interrelation between the ECM and small extracellular vesicles (sEVs), specifically investigating the unexplored aspect of the influence of ECM stiffness on the release of sEVs. Methods: We used a well-studied metastatic Triple-Negative Breast Cancer (TNBC) cell line, MDA-MB-231, as a model to study the release of sEVs by cells cultured on substrates of different stiffness. We have grown MDA-MB-231 cells on two collagen-coated polydimethylsiloxane (PDMS) substrates at different stiffness (0.2 and 3.6 MPa), comparing them with a hard glass substrate as control, and then we isolated the respective sEVs by differential ultracentrifugation. After checking the cell growth conditions [vitality, morphology by immunofluorescence microscopy, stiffness by atomic force microscopy (AFM)], we took advantage of a multi-parametric approach based on complementary techniques (AFM, Nanoparticle Tracking Analysis, and asymmetric flow field flow fractionation with a multi-angle light scattering detector) to characterize the TNBC-derived sEV obtained in the different substrate conditions. Results: We observe that soft substrates induce TNBC cell softening and rounding. This effect promotes the release of a high number of larger sEVs. Conclusion: Here, we show the role of ECM physical properties in the regulation of sEV release in a TNBC model. While the molecular mechanisms regulating this effect need further investigation, our report represents a step toward an improved understanding of ECM-cell-sEVs crosstalk.

Ecological index analysis in the coastal and estuarine waters of the tropical Amazon Region

The Brazilian Amazon Coastal Zone holds the largest estuarine area in the world, located in the Amazon Continental Shelf (ACS). Additionally, it encompasses numerous estuaries and channels, such as Furo do Muriá (FM) and Furo da Laura (FL), which make up the local landscape and favor the primary productivity of water. The objective of this study was using the trophic index (TRIX) and O'Boyle index to determine the trophic state in the ACS area, FL, and FM, compare the results, and relate them to seasonality and river discharge in the sites. In the three estuaries, the applied methodology was the same: temperature, salinity, pH, dissolved oxygen, and saturation rate of dissolved oxygen data were obtained in situ, and the samples were filtered for the analysis of nitrite, nitrate, ammonia, phosphate, and chlorophyll a. The highest TRIX values were found in FL (3.89–3.12) and ACS (4.43–4.44), while for the O'Boyle index, the highest values were found in FL (16.73–100) and FM (43.3–100). Comparing the indices, it was observed that their classification was similar only in the ACS during the period of maximum discharge of the Amazon River (eutrophic), varying slightly in the minimum discharge period (potentially eutrophic for O'Boyle and eutrophicated for TRIX). This difference can be explained by the additional variables considered by the TRIX, such as nutrients and chlorophyll a. The scenario was completely different for FM and FL, where the TRIX showed greater variation owing to the presence of dissolved inorganic nutrients. Both indices can be used to simply and directly reflect the eutrophication conditions of estuarine areas: the joint and multiparametric approach of the indices provides complementary information regarding the dynamics of studied environments through their trophic status.

2023 ESC guidelines for management of cardiomyopathies: short review with implications for the clinical practice in Bulgaria

The 2023 European Society of Cardiology guidelines for the management of cardiomyopathies (CM) provide practical recommendations for diagnosis and treatment. They emphasize on the need for a systematic clinical evaluation beginning with clinical suspicion of CM and using a multiparametric approach that leads to classification of CM into one of five distinct phenotypes. А cardiomyopathy mindset, together with a multidisciplinary approach are crucial. Further investigations, including advanced imaging and genetic testing, are needed to make a phenotype-based etiological diagnosis. Detection of pathogenic genetic variants allows risk stratification of sudden cardiac death, where new recommendations are available regarding primary prevention. Therapeutic options are expanded with the approval of a new class of drugs for the treatment of symptomatic hypertrophic cardiomyopathy with left ventricular outflow tract obstruction. Specific exercise recommendations are given and reproductive issues in patients with CM are addressed. The lack of reimbursement by the National Health Insurance Fund (NHIF) of genetic testing and the severely limited reimbursement of some imaging methods are a significant problem in Bulgaria, which leads to difficulties in implementing the new guidelines.

A hierarchical multi-parametric programming approach for dynamic risk-based model predictive quality control

In this work, we present a hierarchical batch quality control strategy with real-time process safety management. It features a multi-time-scale decision-making framework augmenting: (i) Risk-aware model predictive controller for short-term set point tracking and dynamic risk control under disturbances; (ii) Control-aware optimizer for long-term quality and safety optimization over the entire batch operation; (iii) Intermediate surrogate model to bridge the timescale gap by readjusting the optimizer operating decisions for the controller. All of the above problems are solved via multi-parametric mixed-integer quadratic programming with a key advantage to generate offline explicit control/optimization laws as affine functions of process and risk variables. This allows for the design of a fit-for-purpose risk management plan prior to real-time implementation, while reducing the need for repetitive online dynamic optimization. A unified process model is used to underpin the consistency of hierarchical operational optimization. The proposed approach offers a flexible strategy to integrate distinct decision-making time scales which can be selected separately tailored to the process-specific need of control, fault prognosis, and end-batch quality control. A T2 batch reactor case study is presented to showcase this approach to systematically address the interactions and trade-offs of multiple decision layers toward improving process efficiency and safety.

MRI Evaluation and Characterization of Ovarian Lesions Based on Ovarian-Adnexal Reporting and Data System MRI.

Background Managing ovarian lesions requires differentiating between benign and malignant cases. The development of a multiparametric MRI approach combining anatomical and functional criteria has led to the creation of the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI scoring system, which enhances diagnostic accuracy. Objectives To study ovarian lesions and their characteristics, along with their risk stratification based on MRI O-RADS. Methods A prospective study used the O-RADS MRI criteria to categorize ovarian lesions. Clinical findings and MRI results were compared with histopathological outcomes to assess diagnostic accuracy. Results We identifiedabdominal pain as the most prevalent clinical finding among our cases (64, 91.43%), followed by a lump in the abdomen (33, 47.5%), dysmenorrhea (33, 47.5%), bleeding per vaginal (15, 21.43%), and weight loss (11, 15.71%). A total of 80 ovarian lesions were examined and characterized on the basis of the O-RADSMRI risk stratification system.Among the 80 ovarian lesions, 54 were histopathologically confirmed ovarian lesions (39 (72.22%) were benign, and 15 (27.77%) were malignant). The most common benign lesions were ovarian serous cystadenoma (28.20%) and ovarian mucinous cystadenoma (20.51%), while the most common malignant lesions were serous carcinoma (33.33%) and mucinous carcinoma (20%). Using the O-RADS MRI scoring system, we categorized six lesions (7.5%) as O-RADS 1 (all benign), 34 lesions (42.50%) as O-RADS 2 (32 benign and 2 malignant), 24 lesions (30%) as O-RADS 3 (23 benign and 1 malignant), seven lesions (8.75%) as O-RADS 4 (four benign and three malignant), and nine lesions (11.25%) as O-RADS 5 (all malignant). Our findings revealed significant differences in the size of lesions, the presence of thick septa, high T2-weighted signal intensity within solid tissue, and patterns of solid component enhancement and wall irregularity between malignant and benign lesions. The MRI cut-off score of ≥4 for malignancy demonstrated a sensitivity of 94.59%, a specificity of 97.5%, an accuracy of 97.62%, a positive predictive value of 94.5%, and a negative predictive value of 97.5%. The positive likelihood ratio was 32.7, while the negative likelihood ratio was 0.025. These results affirm the high diagnostic accuracy of the O-RADS MRI scoring system in distinguishing benign from malignant ovarian lesions. Conclusion The O-RADS MRI score is a highly accurate tool for differentiating between benign and malignant ovarian lesions. Its application can significantly enhance the management and treatment outcomes for patients with adnexal masses. The study confirms the scoring system's high sensitivity, specificity, and overall diagnostic accuracy.

Integration and capacity optimization of molten-salt heat storage in coal-fired power plant with carbon capture system

Developing a new generation of economical, low-carbon, and flexible coal-fired power plants (CFPP) is crucial for achieving the low-carbon transformation of the energy industry. Deploying molten salt heat storage (MSHS) in the CFPP can effectively enhance its peak shaving capability and reduce the limitations of carbon capture on wide range operation of the plant. To this end, this paper proposes four integration schemes for MSHS into the power plant-carbon capture system. The thermal efficiencies, exergy efficiencies, and peak shaving capabilities of the entire plant under different integration schemes are evaluated and compared. The results indicate that the optimal integration scheme involves utilizing hot reheat steam to heat the cold molten salt and further provide heat for the carbon capture system during the heat charging process and utilizing hot molten salt to heat the outlet water of the feed water pump during the heat discharging process, which achieves the highest thermal efficiency of 38.00 % and exergy efficiency of 37.09 % across the entire cycle. A novel integrated design, scheduling, and control optimization (IDSCO) method is proposed to find the optimal capacity configuration of MSHS, in which the investment, maintenance, fuel, carbon emission, and long-timescale and short-timescale load tracking deviation penalty costs are fully considered. A design parameter-aware control system is developed based on a multi-parametric programming model predictive control approach to ensure a fair evaluation of closed-loop dynamic performance under different configuration capacities. The simulation results demonstrate that the deployment of MSHS can significantly enhance the flexibility of the power plant-carbon capture system and the proposed IDSCO approach can reduce 17.05 % of load tracking deviation penalty cost and 1.38 % of totalized cost compared to the conventional configuration approach. This paper effectively leverages the potential of MSHS in enhancing the operational flexibility of CFPP-PCC system, thereby providing useful guidance for future deployment of MSHS within CFPP-PCC system.

Integrated Analysis of Pollution Characteristic and Ecotoxicological Effect Reveals the Fate of Lithium in Soil-Plant Systems: A Challenge to Global Sustainability.

Lithium, as an emerging contaminant, lacks sufficient information regarding its environmental and ecotoxicological implications within soil-plant systems. Employing maize, wheat, pea, and water spinach, we conducted a thorough investigation utilizing a multispecies, multiparameter, and multitechnique approach to assess the pollution characteristics and ecotoxicological effects of lithium. The findings suggested that lithium might persist in an amorphous state, altering surface functional groups and chemical bonds, although semiquantitative analysis was unattainable. Notably, lithium demonstrated high mobility, with a mild acid-soluble fraction accounting for 29.66-97.02% of the total, while a minor quantity of exogenous lithium tended to be a residual fraction. Plant analysis revealed that in 10-80 mg Li/kg soils lithium significantly enhanced certain growth parameters of maize and pea, and the calculated LC50 values for aerial part length across the four plant species varied from 173.58 to 315.63 mg Li/kg. Lithium accumulation in the leaves was up to 1127.61-4719.22 mg/kg, with its inorganic form accounting for 18.60-94.59%, and the cytoplasm fraction (38.24-89.70%) predominantly harbored lithium. Furthermore, the model displayed that growth stimulation might be attributed to the influence of lithium on phytohormone levels. Water spinach exhibited superior accumulation capacity and tolerance to lithium stress and was a promising candidate for phytoremediation strategies. Our findings contribute to a more comprehensive understanding of lithium's environmental behavior within soil-plant systems, particularly within the context of global initiatives toward carbon neutrality.

3D MR fingerprinting for dynamic contrast-enhanced imaging of whole mouse brain.

Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n = 3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) in the whole mouse brain was demonstrated (n = 5). Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor of up to 48. Dynamic contrast-enhancedMRF scans achieved a spatial resolution of 192 × 192 × 500 μm3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 × 192 × 250 μm3) in 30 min. We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.

Unravelling the evolution of a continental rift by a multi-proxy provenance study (Albertine Rift, Uganda)

Sedimentary provenance analysis has rarely been applied to continental rifts, because source-rock terrains are assumed to be uniform and supply systems stable and closely connected to the formation of graben shoulders. Here, we test the suitability of multi-proxy sedimentary provenance analysis to unravel the evolution of a segment of the East African Rift System from its sedimentary record, where an extreme relief has been created and a continent-wide drainage system has been affected. Multi-proxy techniques include petrographic-mineralogical analysis (framework, heavy mineral spectra, zircon morphology, and colour), as well as single-grain garnet geochemistry, Zr-in-rutile thermometry, and geochronological methods (detrital zircon U–Pb ages). Published data are compiled and complemented by new U–Pb detrital zircon data of rift sediments. Because a comprehensive database of basement source-rock compositions from East Africa is missing, published modern river sand compositions have been added to provide endmember source fingerprints. The selected Albertine Rift is surrounded by four main source terrains within the East African Basement, which cannot be separated clearly by individual provenance indicators. Most useful were heavy mineral spectra, garnet composition, and with limitation Zr-in-rutile thermometry showing different metamorphic overprint. U–Pb detrital zircon spectra assisted well in detecting juvenile magmatism of different orogenic events, but repeated inheritance in younger units prevented a strict allocation. Petrographic maturity turned out to be more dependent on multi-cycle detritus as on climate shift. Applying endmember composition to rift sediments, three evolutionary stages of the Albertine Rift could be identified which coincide with basin-scale unconformities. Moreover, we could prove a large, southwest trending drainage network in East Africa during the Miocene, which was destructed step-wise by ongoing rifting. Extreme uplift of the Rwenzori Mountains up to 5109 m could be documented by a specific set of provenance indicators justified by modern river sand. Three main lessons can be drawn from this study: (1) basement units can be only reliably characterized by a combination of provenance-sensitive parameters, and here, the frequently used U–Pb detrital zircon ages are not sufficient; (2) it is possible to define subunits, e.g., individual fault-blocks within the rift; and (3) chemical weathering is unproblematic when applying a multi-parameter approach even in a tropical setting.Graphical abstract

Identifying Malnutrition Risk in the Elderly: A Single- and Multi-Parameter Approach.

Malnutrition is a significant concern affecting the elderly, necessitating a complex assessment. This study aims to deepen the understanding of factors associated with the assessment of malnutrition in the elderly by comparing single- and multi-parameter approaches. In this cross-sectional study, 154 individuals underwent a comprehensive geriatric assessment (CGA). Malnutrition risk was determined using the mini nutritional assessment (MNA). Additional factors assessed included sarcopenia, polypharmacy, depression, appetite, handgrip strength, and gait speed. Phase angle (PA) and body composition were measured using bioelectrical impedance analysis (BIA). The MNA identified a malnutrition risk in 36.8% of individuals. The geriatric depression scale (GDS) and PA demonstrated moderate effectiveness in assessing malnutrition risk, with AUC values of 0.69 (95% CI: 0.60-0.78) and 0.62 (95% CI: 0.54-0.72), respectively. A logistic regression model incorporating handgrip strength, skeletal muscle mass, sarcopenia, osteoporosis, depression, specific antidepressant use, mobility, appetite, and smoking achieved superior performance in predicting malnutrition risk, with an AUC of 0.84 (95% CI: 0.77-0.91). In conclusion, this study demonstrates that integrating multiple parameters into a composite model provides a more accurate and comprehensive assessment of malnutrition risk in elderly adults.

Monitoring imatinib decreasing pericyte coverage and HIF-1α level in a colorectal cancer model by an ultrahigh-field multiparametric MRI approach

BackgroundExcessive pericyte coverage promotes tumor growth, and a downregulation may solve this dilemma. Due to the double-edged sword role of vascular pericytes in tumor microenvironment (TME), indiscriminately decreasing pericyte coverage by imatinib causes poor treatment outcomes. Here, we optimized the use of imatinib in a colorectal cancer (CRC) model in high pericyte-coverage status, and revealed the value of multiparametric magnetic resonance imaging (mpMRI) at 9.4T in monitoring treatment-related changes in pericyte coverage and the TME.MethodsCRC xenograft models were evaluated by histological vascular characterizations and mpMRI. Mice with the highest pericyte coverage were treated with imatinib or saline; then, vascular characterizations, tumor apoptosis and HIF-1α level were analyzed histologically, and alterations in the expression of Bcl-2/bax pathway were assessed through qPCR. The effects of imatinib were monitored by dynamic contrast-enhanced (DCE)-, diffusion-weighted imaging (DWI)- and amide proton transfer chemical exchange saturation transfer (APT CEST)-MRI at 9.4T.ResultsThe DCE- parameters provided a good histologic match the tumor vascular characterizations. In the high pericyte coverage status, imatinib exhibited significant tumor growth inhibition, necrosis increase and pericyte coverage downregulation, and these changes were accompanied by increased vessel permeability, decreased microvessel density (MVD), increased tumor apoptosis and altered gene expression of apoptosis-related Bcl-2/bax pathway. Strategically, a 4-day imatinib effectively decreased pericyte coverage and HIF-1α level, and continuous treatment led to a less marked decrease in pericyte coverage and re-elevated HIF-1α level. Correlation analysis confirmed the feasibility of using mpMRI parameters to monitor imatinib treatment, with DCE-derived Ve and Ktrans being most correlated with pericyte coverage, Ve with vessel permeability, AUC with microvessel density (MVD), DWI-derived ADC with tumor apoptosis, and APT CEST-derived MTRasym at 1 µT with HIF-1α.ConclusionsThese results provided an optimized imatinib regimen to achieve decreasing pericyte coverage and HIF-1α level in the high pericyte-coverage CRC model, and offered an ultrahigh-field multiparametric MRI approach for monitoring pericyte coverage and dynamics response of the TME to treatment.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis

Background and objectivesIn multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes.MethodsWe analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue.Results and conclusionsStable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.