Combined acquisition of diffusion and T2*-weighted measurements using simultaneous multi-contrast magnetic resonance imaging

Object In this work, we present a technique called simultaneous multi-contrast imaging (SMC) to acquire multiple contrasts within a single measurement. Simultaneous multi-slice imaging (SMS) shortens scan time by allowing the repetition time (TR) to be reduced for a given number of slices. SMC imaging preserves TR, while combining different scan types into a single acquisition. This technique offers new opportunities in clinical protocols where examination time is a critical factor and multiple image contrasts must be acquired. Materials and methods High-resolution, navigator-corrected, diffusion-weighted imaging was performed simultaneously with T2*-weighted acquisition at 3 T in a phantom and in five healthy subjects using an adapted readout-segmented EPI sequence (rs-EPI). Results The results demonstrated that simultaneous acquisition of two contrasts (here diffusion-weighted imaging and T2*-weighting) with SMC imaging is feasible with robust separation of contrasts and minimal effect on image quality. Discussion The simultaneous acquisition of multiple contrasts reduces the overall examination time and there is an inherent registration between contrasts. By using the results of this study to control saturation effects in SMC, the method enables rapid acquisition of distortion-matched and well-registered diffusion-weighted and T2*-weighted imaging, which could support rapid diagnosis and treatment of acute stroke.

Validation of enantioseparation and quantitation of an active metabolite of abrocitinib in human plasma

Aims: A chiral HPLC–MS/MS method for quantitation of an active metabolite (M2) of abrocitinib was validated in human plasma. Methods: Protein precipitation extraction and normal phase LC with baseline separation of five analytes (abrocitinib; isomeric metabolites M1, M2, M3 and M4) were achieved followed by mass spectrometric quantitation of M2 using positive-mode APCI. Results: With a 5–5000 ng/ml assay range using 100 μl K2EDTA aliquot, the assay provided short (17-min) runtime and robust separation up to approximately 330 injections on one column. Interday and intraday accuracy ranged from ‐6.80% to 13.4%; between-day and within-day precision was ≤10.4%. Conclusion: The method was used in multiple clinical studies, with excellent run passing rate and incurred sample reproducibility.

HPLC Method Development for the Fast Separation of a Complex Explosive Mixture

The growing threat of terrorism in many parts of the world has called for the urgent need to find rapid and reliable means of analyzing explosives. This is in view to help forensic scientists to identify different swabs from post-blast debris. The present study aims to achieve an efficient separation and identification of a mixture of sixteen explosive compounds (including nitroaromatics, nitramines, and nitrate esters) by high performance liquid chromatography using a diode array detection (HPLC/DAD) and an Agilent Poroshell 120 EC-120 C18 column at two wavelengths (235 and 214 nm). Relevant chromatographic parameters such as capacity factors, resolution, selectivity and number of theoretical plates have been optimized in order to achieve the best separation of the different components. In this respect, the effects of various parameters such as gradient time, column temperature, flow rate of mobile phase and initial percentage organic mobile phase on the separation of these compounds were investigated. It was revealed that the method allowed a fairly acceptable separation of all the compounds in less than 15 minutes except for two isomers, namely 4-A-2,6-DNT, 2-A-4,6-DNT and 2,6- DNT which could not be resolved by the used C18 column. This shortcoming notwithstanding, the developed method produced satisfactory results and demonstrated sensitive and robust separation, furthermore indicating that the HPLC developed method can be both fast and efficient for the analysis of complex mixtures of explosive compounds.

Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection

Patients hospitalized with COVID-19 infection are at a high general risk for in-hospital mortality. A simple and easy-to-use model for predicting mortality based on data readily available to clinicians in the first 24 hours of hospital admission might be useful in directing scarce medical and personnel resources toward those patients at greater risk of dying. With this goal in mind, we evaluated factors predictive of in-hospital mortality in a random sample of 100 patients (derivation cohort) hospitalized for COVID-19 at our institution in April and May, 2020 and created potential models to test in a second random sample of 148 patients (validation cohort) hospitalized for the same disease over the same time period in the same institution. Two models (Model A: two variables, presence of pneumonia and ischemia); (Model B: three variables, age > 65 years, supplemental oxygen ≥ 4 L/min, and C-reactive protein (CRP) > 10 mg/L) were selected and tested in the validation cohort. Model B appeared the better of the two, with an AUC in receiver operating characteristic curve analysis of 0.74 versus 0.65 in Model A, but the AUC differences were not significant (p = 0.24. Model B also appeared to have a more robust separation of mortality between the lowest (none of the three variables present) and highest (all three variables present) scores at 0% and 71%, respectively. These brief scoring systems may prove to be useful to clinicians in assigning mortality risk in hospitalized patients.

Survival in Patients With Brain Metastases: Summary Report on the Updated Diagnosis-Specific Graded Prognostic Assessment and Definition of the Eligibility Quotient

PURPOSE Conventional wisdom has rendered patients with brain metastases ineligible for clinical trials for fear that poor survival could mask the benefit of otherwise promising treatments. Our group previously published the diagnosis-specific Graded Prognostic Assessment (GPA). Updates with larger contemporary cohorts using molecular markers and newly identified prognostic factors have been published. The purposes of this work are to present all the updated indices in a single report to guide treatment choice, stratify research, and define an eligibility quotient to expand eligibility. METHODS A multi-institutional database of 6,984 patients with newly diagnosed brain metastases underwent multivariable analyses of prognostic factors and treatments associated with survival for each primary site. Significant factors were used to define the updated GPA. GPAs of 4.0 and 0.0 correlate with the best and worst prognoses, respectively. RESULTS Significant prognostic factors varied by diagnosis and new prognostic factors were identified. Those factors were incorporated into the updated GPA with robust separation ( P < .01) between subgroups. Survival has improved, but varies widely by GPA for patients with non–small-cell lung, breast, melanoma, GI, and renal cancer with brain metastases from 7-47 months, 3-36 months, 5-34 months, 3-17 months, and 4-35 months, respectively. CONCLUSION Median survival varies widely and our ability to estimate survival for patients with brain metastases has improved. The updated GPA (available free at brainmetgpa.com) provides an accurate tool with which to estimate survival, individualize treatment, and stratify clinical trials. Instead of excluding patients with brain metastases, enrollment should be encouraged and those trials should be stratified by the GPA to ensure those trials make appropriate comparisons. Furthermore, we recommend the expansion of eligibility to allow for the enrollment of patients with previously treated brain metastases who have a 50% or greater probability of an additional year of survival (eligibility quotient > 0.50).

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

&lt;p&gt;Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, we revisit the framework from Hawkins and Sutton (2009) for uncertainty partitioning for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We also investigate forced changes in variability itself, something that is newly possible with SMILEs. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.&lt;/p&gt;

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error

Monitoring of immunoglobulin N- and O-glycosylation in health and disease

Protein N- and O-glycosylation are well known co- and post-translational modifications of immunoglobulins. Antibody glycosylation on the Fab and Fc portion is known to influence antigen binding and effector functions, respectively. To study associations between antibody glycosylation profiles and (patho) physiological states as well as antibody functionality, advanced technologies and methods are required. In-depth structural characterization of antibody glycosylation usually relies on the separation and tandem mass spectrometric (MS) analysis of released glycans. Protein- and site-specific information, on the other hand, may be obtained by the MS analysis of glycopeptides. With the development of high-resolution mass spectrometers, antibody glycosylation analysis at the intact or middle-up level has gained more interest, providing an integrated view of different post-translational modifications (including glycosylation). Alongside the in-depth methods, there is also great interest in robust, high-throughput techniques for routine glycosylation profiling in biopharma and clinical laboratories. With an emphasis on IgG Fc glycosylation, several highly robust separation-based techniques are employed for this purpose. In this review, we describe recent advances in MS methods, separation techniques and orthogonal approaches for the characterization of immunoglobulin glycosylation in different settings. We put emphasis on the current status and expected developments of antibody glycosylation analysis in biomedical, biopharmaceutical and clinical research.