Research Applications Research Articles

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BackgroundThe Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.ResultsPerformance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.ConclusionsResults show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Development of a head and neck lymphoedema specific quality of life tool: The Comprehensive Assessment of Lymphoedema Impact in the Head and Neck.

To develop a head and neck lymphoedema (HNL) specific quality of life (QoL) instrument to assess physical, functional, and social/emotional impacts of HNL. Instrument candidate items were reviewed by patients with HNL and clinicians and rated for importance, clarity, and invasiveness. The Content Validity Ratio was applied for item reduction. Three-step cognitive interviews were conducted with HNL patients to validate the items, survey format, and instructions. Initially, 130 candidate questions were developed. Following item reduction, 52 items progressed to three-step cognitive interviews. Following cognitive interviews, the Comprehensive Assessment of Lymphoedema Impact in Head and Neck (CALI-HaN) included 33 items; 1 global, 10 physical, 7 functional, and 15 emotional. Physical, functional, and socioemotional effects need to be considered when measuring QoL in patients with HNL. This study describes initial development of the CALI-HaN, an instrument that shows promise for clinical and research applications following future validation.

Novel Bayesian distributed adaptive neural structure for titanium and aluminium alloy nanofluidic model with gyrotactic microorganisms

The purpose of this research study is to evaluate the solutions of Titanium and Aluminum alloy nanofluidic model with gyrotactic microorganisms (TAA-NFMGM) by applying stupendous knacks of Bayesian distributed adaptive neural structure (BDANS). The use of nanofluids containing gyrotactic microorganisms in combination with titanium and aluminum alloys have potential applications in diverse fields, including engineering, bioremediation, biomedical devices, biotechnology, and research depending on different factors like design of the system and use of certain microorganism. A dataset for BDANS is generated for the eight different events with Adam numerical technique for TAA-NFMGM by varying Prandtl number (Pr), mixed convection parameter (λ), microorganisms concentration parameter (Ω), bioconvection peclet number (Pe), and bioconvection Lewis number (Lb). The reference data set generated with Adam numerical technique is utilized for numerical calculation of different parameters on TAA-NFMGM by employing the artificial intelligence based BDANS. The accuracy and justification of the performance of BDANS is efficaciously substantiated through negligible level of MSE around 10–10 to 10–13, calculation of regression metrics and distribution of instances of error on histograms with values 10–04 to 10–09 near to reference line.

CRISPR/Cas9: an overview of recent developments and applications in cancer research.

CRISPR-Cas9 has risen as a potent gene editing method with vast potential across numerous domains, including its application in cancer research and therapy. This review article provides an extensive overview of the research that has been done so far on CRISPR-Cas9 with an emphasis on how it could be utilized in the treatment of cancer. We go into the underlying ideas behind CRISPR-Cas9, its mechanisms of action, and its application for the study of cancer biology. Furthermore, we investigate the various uses of CRISPR-Cas9 in cancer research, spanning from the discovery of genes and the disease to the creation of novel therapeutic approaches. We additionally discuss the challenges and limitations posed by CRISPR-Cas9 technology and offer insights into the potential applications and future directions of this cutting-edge field of research. The article intends to consolidate the present understanding and stimulate more research into CRISPR-Cas9's promise as a game-changing tool for cancer research and therapy.

Human stem cell transplantation models of Alzheimer's disease.

Alzheimer's disease (AD) is the most frequent form of dementia. It is characterized by pronounced neuronal degeneration with formation of neurofibrillary tangles and deposition of amyloid β throughout the central nervous system. Animal models have provided important insights into the pathogenesis of AD and they have shown that different brain cell types including neurons, astrocytes and microglia have important functions in the pathogenesis of AD. However, there are difficulties in translating promising therapeutic observations in mice into clinical application in patients. Alternative models using human cells such as human induced pluripotent stem cells (iPSCs) may provide significant advantages, since they have successfully been used to model disease mechanisms in neurons and in glial cells in neurodegenerative diseases in vitro and in vivo. In this review, we summarize recent studies that describe the transplantation of human iPSC-derived neurons, astrocytes and microglial cells into the forebrain of mice to generate chimeric transplantation models of AD. We also discuss opportunities, challenges and limitations in using differentiated human iPSCs for in vivo disease modeling and their application for biomedical research.

Large language models streamline automated machine learning for clinical studies

A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study’s training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice.

AI, Machine Learning, and ChatGPT in Hypertension.

Hypertension, a leading cause of cardiovascular disease and premature death, remains incompletely understood despite extensive research. Indeed, even though numerous drugs are available, achieving adequate blood pressure control remains a challenge, prompting recent interest in artificial intelligence. To promote the use of machine learning in cardiovascular medicine, this review provides a brief introduction to machine learning and reviews its notable applications in hypertension management and research, such as disease diagnosis and prognosis, treatment decisions, and omics data analysis. The challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the hypertension research community to consider machine learning as a key component in developing innovative diagnostic and therapeutic tools for hypertension. By integrating traditional cardiovascular risk factors with genomics, socioeconomic, behavioral, and environmental factors, machine learning may aid in the development of precise risk prediction models and personalized treatment approaches for patients with hypertension.

Simulation of the Multi-Wake Evolution of Two Sandia National Labs/National Rotor Testbed Turbines Operating in a Tandem Layout

The future of wind power systems deployment is in the form of wind farms comprised of scores of such large turbines, most likely at offshore locations. Individual turbines have grown in span from a few tens of meters to today’s large turbines with rotor diameters that dwarf even the largest commercial aircraft. These massive dynamical systems present unique challenges at scales unparalleled in prior applications of wind science research. Fundamental to this effort is the understanding of the wind turbine wake and its evolution. Furthermore, the optimization of the entire wind farm depends on the evolution of the wakes of different turbines and their interactions within the wind farm. In this article, we use the capabilities of the Common ODE Framework (CODEF) model for the analysis of the effects of wake–rotor and wake-to-wake interactions between two turbines situated in a tandem layout fully and partially aligned with the incoming wind. These experiments were conducted in the context of a research project supported by the National Rotor Testbed (NRT) program of Sandia National Labs (SNL). Results are presented for a layout which emulates the turbine interspace and relative turbine emplacement found at SNL’s Scaled Wind Technologies Facility (SWiFT), located in Lubbock, Texas. The evolution of the twin-wake interaction generates a very rich series of secondary transitions in the vortex structure of the combined wake. These ultimately affect the wake’s axial velocity patterns, altering the position, number, intensity, and shape of localized velocity-deficit zones in the wake’s cross-section. This complex distribution of axial velocity patterns has the capacity to substantially affect the power output, peak loads, fatigue damage, and aeroelastic stability of turbines located in subsequent rows downstream on the farm.

Space-time reconstruction of a moving acoustic loading on a membrane by coupling the force analysis technique and full-field non-contact vibration measurements

Identifying acoustical and mechanical loadings on structures is a common problem in acoustics and vibration analysis and stationary loadings are mostly considered on plate-like structures. This work describes a proof of concept for reconstructing the trajectory of an acoustic source moving in front of a membrane. Compared with works focusing on precisely identifying a loading’s amplitude at a given location, the objective is to reconstruct the loading’s trajectory–Qualitative loading identification is sought rather than quantitative. The force analysis technique is used to recover a space-time varying loading on a structure, starting from time-resolved full-field non-contact vibration measurements conducted on a circular membrane. At the same time, a compact and tonal sound source is used to draw freehand shapes in front of the membrane. The loading trajectory, therefore, contains information that was “acoustically written”. Simple hand gestures that correspond to the drawing of a Greek letter (Σ), a capital letter (P), two shapes (♡, ⋆), and a 3-letter word (net) are recovered using the proposed procedure. The effect of various parameters on the reconstructed information is studied. Perspectives in terms of possible research areas and applications are finally discussed. These perspectives include, for example, the use of membranes to help reconstruct complex and space-time-varying loadings or even applications in musical acoustics on membranophones.

Paternal genetic structure analysis of the modern Han populations based on Y-SNP and Y-STR.

The Han populations represent the largest ethnic group in China. Previous studies have primarily focused on investigating their genetic origins, migration and integration, as well as paternal genetic relationships within specific regional Han populations. However, a comprehensive analysis of the global paternal genetic structure of Han populations is lacking. In this study, we performed Y-chromosome sequencing on 362 unrelated male samples from Chinese Han individuals collected from Qinghai, Sichuan and Liaoning provinces. We then integrated relevant data from reported studies. Our final dataset comprised 1830 samples from 16 Han populations across 15 provinces in China, encompassing information on 89 Y-SNPs and 16 Y-STRs. Statistical analyses were conducted to assess Y-STR haplotype diversity (HD) and Y-SNP haplogroup frequencies. Additionally, we employed principal component analysis (PCA), phylogenetic tree and haplotype network to explore genetic differentiation within Han populations and the genetic relationships between Han populations and ethnic minorities surrounding them. Our results demonstrated that the O-M175 haplogroup represents the predominant paternal lineage in Han populations, with frequencies ranging from 60.53% (Qinghai Han) to 92.7% (Guangdong Han). Moreover, the subclades downstream of O-M175 showed distinct regional variations in their distribution patterns. The O2-M122 haplogroup was prevalent in all Han populations and demonstrated a gradual decline in frequency from north to south. Conversely, the distribution frequency of the O1b-M268 haplogroup decreased from south to north, particularly showed significant presence among Han populations in the Lingnan region. Haplogroup O1a-M119 distributed more frequently in the central Han populations. Our findings revealed that Chinese Han populations can be categorized into three subgroups: northern, central, and southern. Notably, there were significant differences among Han in Qinghai and other regions. Regarding the genetic relationships between Han populations and surrounding ethnic minorities, we observed a closer genetic affinity between different Han populations, but northern Han demonstrated a stronger relationship with the Hui ethnic group, while southern Han exhibited a closer connection with the Gelao and Li ethnic groups. In summary, this study presented a systematic analysis of haplogroup distribution, genetic substructure of Han populations and genetic relationships between Han populations and surrounding ethnic minorities based on 89 Y-SNPs and 16 Y-STRs systematically. Our research supplemented valuable insights into population genetics and forensic genetics, and provided data support for the forensic application of Y chromosome. The integration of Y-SNP haplogroups with Y-STR haplotypes offers enhanced understanding of the genetic substructure within Han populations, which holds significance for both population genetics research and forensic science applications.

AIEgens-enhanced rapid sensitive immunofluorescent assay for SARS-CoV-2 with digital microfluidics

BackgroundSensitive and rapid antigen detection is critical for the diagnosis and treatment of infectious diseases, but conventional ELISAs including chemiluminescence-based assays are limited in sensitivity and require many operation steps. Fluorescence immunoassays are fast and convenient but often show limited sensitivity and dynamic range. ResultsTo address the need, an aggregation-induced emission fluorgens (AIEgens) enhanced immunofluorescent assay with beads-based quantification on the digital microfluidic (DMF) platform was developed. Portable DMF devices and chips with small electrodes were fabricated, capable of manipulating droplets within 100 nL and boosting the reaction efficiency. AIEgen nanoparticles (NPs) with high fluorescence and photostability were synthesized to enhance the test sensitivity and detection range. The integration of AIEgen probes, transparent DMF chip design, and the large magnetic beads (10 μm) as capture agents enabled rapid and direct image-taking and signal calculation of the test result. The performance of this platform was demonstrated by point-of-care quantification of SARS-CoV-2 nucleocapsid (N) protein. Within 25 min, a limit of detection of 5.08 pg mL−1 and a limit of quantification of 8.91 pg mL−1 can be achieved using <1 μL sample. The system showed high reproducibility across the wide dynamic range (10–105 pg mL−1), with the coefficient of variance ranging from 2.6% to 9.8%. SignificanceThis rapid, sensitive AIEgens-enhanced immunofluorescent assay on the DMF platform showed simplified reaction steps and improved performance, providing insight into the small-volume point-of-care testing of different biomarkers in research and clinical applications.

Investigating the Effect of GNP Addition on Self-healing of ECC through Mechanical and Nondestructive Testing

Inclusion of nanomaterials is an effective method for enhancing the mechanical and durability characteristics of cement-based materials. Among various graphene-based nanomaterials, graphene nanoplatelets (GNPs) are cost-effective nanoparticles that possess graphene's key properties. Since limited information is available regarding their influence on the fresh, mechanical, non-destructive, and self-healing behavior of Engineered Cementitious Composites (ECC), this research focuses on GNP inclusion on these properties of ECC in a comprehensive way. In this research, the changes in material properties of ECC were monitored with several mechanical and nondestructive test methods. According to the results, GNP inclusion significantly enhanced flexural behavior and also promoted self-healing of ECC, with preloaded specimens exhibiting substantial or complete crack closure and similar performance to virgin specimens. Non-destructive tests and microstructural analyses also confirmed the promoting effects of GNP. A noticeable increase was observed in compressive strength when GNP was included. In conclusion, this study provides compelling evidence about the positive impact of GNP inclusion on improving the properties of ECC. The findings demonstrate the potential of GNPs to enhance the performance of cementitious materials and offer valuable insights for future research and practical applications.

Evolving many-objective dispatching rule pairs for unrelated parallel machine scheduling with sequence-dependent setup times

The real-world problem of unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSPSST) is investigated and focuses on two key aspects: fast solution and many-objective optimization. While dispatching rules (DRs) are effective in meeting demand, the manual design of many-objective DRs (MaODRs) is challenging. An alternative approach, based on the automatic evolution of MaODRs using hyper-heuristics, shows promise. However, limited literature exists on the automatic evolution of MaODRs specifically for UPMSPSST. In this study, a many-objective genetic programming (MaOGP) algorithm is formed by combining many-objective evolutionary algorithms (MaOEAs) with genetic programming (GP) algorithms. This enables the automatic evolution of MaODRs, leveraging the respective advantages of each approach. The exploration results demonstrate that the evolved MaODRs outperform manually designed DRs reported in prior literature. Furthermore, when considering combinations of objectives with different correlations, the MaODRs exhibit outstanding performance across multiple objectives simultaneously, surpassing even the performance of DRs trained separately for individual objectives. These findings highlight the potential of the proposed MaOGP approach in efficiently and effectively solving the UPMSPSST problem, opening avenues for further research and practical applications in related industries.

Lecithin Organogel: A Promising Carrier for the Treatment of Skin Diseases.

Skin is the largest organ of the human body, as it protects the body from the external environment. Nowadays, skin diseases and skin problems are more common, and millions of people are affected daily. Skin diseases are due to numerous infectious pathogens or inflammatory conditions. The increasing demand for theoretical research and practical applications has led to the rising prominence of gel as a semisolid material. To this end, organogels has been widely explored due to their unique composition, which includes organic solvents and mineral or vegetable oils, among others. Organogels can be described as semisolid systems wherein an organic liquid phase is confined within a three-dimensional framework consisting of self-assembled, cross-linked, or entangled gelator fibers. These gels have the ability to undergo significant expansion and retain substantial amounts of the liquid phase, reaching up to 99% swelling capacity. Furthermore, they respond to a range of physical and chemical stimuli, including temperature, light, pH, and mechanical deformation. Notably, due to their distinctive properties, they have aroused significant interest in a variety of practical applications. Organogels favor the significant encapsulation and enhanced permeation of hydrophobic molecules when compared with hydrogels. Accordingly, organogels are characterized into lecithin organogels, pluronic lecithin organogels, sorbitan monostearate-based organogels, and eudragit organogels, among others, based on the nature of their network and the solvent system. Lecithin organogels contain lecithin (natural and safe as a living cell component) as an organogelator. It acts as a good penetration enhancer. In this review, first we have summarized the fundamental concepts related to the elemental structure of organogels, including their various forms, distinctive features, methods of manufacture, and diverse applications. Nonetheless, this review also sheds light on the delivery of therapeutic molecules entrapped in the lecithin organogel system into deep tissue for the management of skin diseases and provides a synopsis of their clinical applications.

A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information

Satellite-derived Chlorophyll-a concentration (Chla) time series products are essential for large-scale marine environmental monitoring. However, the plenty of missing pixels in current satellite Chla products severely hinder their applications for marine research, due to cloud contamination, solar glint, and unfavorable observation conditions. This study proposed a Chla time series gap-filling method for MODIS 8-day composite Chla product by integrating spatiotemporal information (STGF). This method employed spatially neighboring pixels with similar temporal variation to fill the missing values in time series, without involving training or auxiliary data. The performance of the STGF is assessed quantitatively and qualitatively. The correlation coefficients (CC) between gap-filled data and actual observations for years of 2004, 2010, 2016, and 2022 across the entire study area are all greater than 0.97. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are less than 16.1 % and 0.233 mg/m3, respectively. The proposed STGF outperformed linear interpolation and the DINEOF algorithm from both spatial and temporal perspectives, suggesting the effectiveness of STGF in handling continuous data gaps and capturing detailed Chla variation patterns, especially in regions with significant variability. The findings suggest that the proposed STGF method offers a viable alternative for filling missing values in Chla time series data. This supports the demand for long-term, large-scale, and high-coverage ocean color remote sensing data in marine environmental studies.

Hall current and Dufour effect on the flow of a viscous fluid in the presence of suction, chemical reaction, and heat source: Laplace transform procedure

AbstractThe present investigation explores the transient magnetohydrodynamics gravity‐driven flow of a viscous, incompressible, electrically conducting fluid past a permeable exponentially accelerated vertical plate in the presence of thermal radiation and suction. Due to several applications in different areas, the influence of Hall current, Dufour number, heat source parameter, and chemically reactive species diffusion are vital and are incorporated in the study, which is the novelty of the present work. The model equations are derived and resolved by utilizing an analytical technique called the Laplace transform procedure. Various properties of the flow due to the influence of different vital parameters have been depicted with the help of graphs. The computed gradients of velocity, temperature and concentration are presented in tabular form. The Hall parameter is observed to strengthen the secondary flow while diminishing the primary flow. Dufour number and heat source parameter enhance the fluid temperature and velocity profiles while the heat sink declines them. Enhancement of the acceleration parameter significantly amplifies the primary skin friction coefficient. Suction boosts the heat transport as well as the mass transport rates at the surface of the plate, whereas injection declines them. Further, the Dufour number causes a significant reduction in the Nusselt number and magnitude of the secondary skin friction coefficient. The practical applications of the current research are the design of aircraft, cooling of microchips, Hall accelerators, separation of isotopes and many more.

Feasibility studies for imaging [formula omitted] annihilation with modular multi-strip detectors

Studies based on imaging the annihilation of the electron (e−) and its antiparticle positron (e+) open up several interesting applications in nuclear medicine and fundamental research. The annihilation process involves both the direct conversion of e+e− into photons and the formation of their atomically bound state, the positronium atom (Ps), which can be used as a probe for fundamental studies. With the ability to produce large quantities of Ps, manipulate them in a long-lived Ps states, and image their annihilations after a free fall or after passing through atomic interferometers, this purely leptonic antimatter system can be used to perform inertial sensing studies in view of a direct test of Einstein’s equivalence principle. It is envisioned that modular multi-strip detectors can be exploited as potential detection units for this kind of studies. In this work, we report the results of the first feasibility study performed on a e+ beamline using two detection modules to evaluate their reconstruction performance and spatial resolution for imaging e+e− annihilations and thus their applicability for gravitational studies of Ps.

Perspectives of Certified Nurse-Midwives and Physicians on the Structural and Institutional Barriers that Contribute to the Reproductive Inequities of Black Birthing People in the San Francisco Bay Area.

Black birthing people in the United States disproportionately endure inequitable experiences and outcomes during pregnancy and childbirth via structural, interpersonal, and obstetric racism. In this study, the researchers explore provider perspectives of how racism is perpetuated in institutional perinatal and reproductive health care. Critical Race Theory, Reproductive Justice, and midwifery theory were operationalized through secondary thematic analysis of existing qualitative data from the Community Racial Equity and Training Interventions and Evaluation of Current and Future Healthcare Clinicians Study. Twenty-four perinatal providers (certified nurse-midwives [n = 7] and physicians [n = 17]) voluntarily participated in interviews. A comparative approach was used to determine how professional identity and model of care influence physicians' and certified nurse-midwives' perceptions of equity. Thematic analysis produced 5 themes: racism as a comorbidity, health care systems' inability to address the needs of Black birthing people, health care systems prioritizing providers over patients are failed systems, patients are the experts in the optimal health care model, and benefits of interprofessional teams grounded in Reproductive Justice. Additionally, both physicians and midwives expressed a need for a new care model. With these findings, our team proposes a modification of the midwifery model for application by all provider types that could radically shift the experience and outcomes of perinatal and reproductive health care and reduce mortality. Using a human rights approach to care, a Reproductive Justice-Public Health Critical Race praxis-informed midwifery model may be operationalized by all perinatal and reproductive health care providers. This novel model reflects an iterative process that may offer institutions and providers methods to build on past research supporting midwifery-centered care for improving outcomes for all patients by specifically focusing on improving care of Black birthing people. The implications of this work offer broad application in current clinical practice, quality improvement, research, technology, and patient resources.

Current progress in semi-batch reverse osmosis for brackish water desalination

Semi-batch reverse osmosis (SBRO) is an innovative membrane process renowned for its compact system and high recovery in desalination and water reuse. While academic research provides valuable insights, aligning it with real-world implementation is crucial. The study aims to understand the current progress in SBRO for brackish water desalination by identifying performance disparities between academic research and practical applications, evaluating practical performances under realistic parameters, and proposing future research directions. Through gathering and comparing data from SBRO research and practices, performance disparities between theoretical projections and real-world applications were identified. The analysis revealed that specific energy consumption (SEC) in academic research had been underestimated by applying impractical pump efficiencies, and permeate quality was often neglected. To assess the practical SEC and permeate quality of SBRO, engineering design calculations were performed with realistic parameters. Based on the evaluation, the importance of maintaining suitable recovery and incorporating a permeate tank was emphasized to avoid a significant increase in SEC and to stabilize water quality, respectively. Given the challenges of the SBRO system, the discussion further outlined research directions addressing issues in energy efficiency, permeate quality, membrane fouling/scaling, and practical design/operation. It recommended considering these practical aspects for the broader applications of SBRO.

Tensor denoising of quantitative multi-parameter mapping.

Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA). tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions. Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity. tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.