Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (~30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (~10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

In-depth coverage of proteomic analysis could enhance our understanding to the mechanism of the protein functions. Unfortunately, many highly hydrophobic proteins and low-abundance proteins, which play critical roles in signaling networks, are easily lost during sample preparation, mainly attributed to the fact that very few extractants can simultaneously satisfy the requirements on strong solubilizing ability to membrane proteins and good enzyme compatibility. Thus, it is urgent to screen out ideal extractant from the huge compound libraries in a fast and effective way. Herein, by investigating the interior mechanism of extractants on the membrane proteins solubilization and trypsin compatibility, a molecular dynamics simulation system was established as complement to the experimental procedure to narrow down the scope of candidates for proteomics analysis. The simulation data shows that the van der Waals interaction between cation group of ionic liquid and membrane protein is the dominant factor in determining protein solubilization. In combination with the experimental data, 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) is on the shortlist for the suitable candidates from comprehensive aspects. Inspired by the advantages of C12Im-Cl, an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, over 3,300 proteins were confidently identified from 10³ HeLa cells (~100 ng proteins) in a single run, an improvement of 53% over the conventional FASP method. Then the i-FASP method was further successfully applied to the label-free relative quantitation of human liver cancer and para-carcinoma tissues with obviously improved accuracy, reproducibility and coverage than the commonly used urea-based FASP method. The above results demonstrated that the i-FASP method could be performed as a versatile tool for the in-depth coverage proteomic analysis of biological samples.

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Using a simple, environment friendly proteome extraction (TOP), we were able to optimize the analysis of clinical samples. Using our TOP method we analyzed a clinical cohort of microsatellite stable (MSS) and unstable (MSI-H) colorectal carcinoma (CRC). We identified a tumor cell specific, STAT1-centered, immune signature expressed by the MSI-H tumor cells. We then showed that long, but not short, exposure to Interferon- induces a similar signature in vitro. We identified 10 different temporal protein expression patterns, classifying the Interferon- protein temporal regulation in CRC. Our data sheds light on the changes that tumor cells undergo under long-term immunological pressure in vivo, the importance of STAT proteins in specific biological scenarios. The data generated could help find novel clinical biomarkers and therapeutic approaches.

Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.

Trypsin is the protease of choice in bottom-up proteomics. However, its application can be limited by the amino acid composition of target proteins and the pH of the digestion solution. In this study we characterize ProAlanase, a protease from the fungus Aspergillus niger that cleaves primarily on the C-terminal side of proline and alanine residues. ProAlanase achieves high proteolytic activity and specificity when digestion is carried out at acidic pH (1.5) for relatively short (2 h) time periods. To elucidate the potential of ProAlanase in proteomics applications, we conducted a series of investigations comprising comparative multi-enzymatic profiling of a human cell line proteome, histone PTM analysis, ancient bone protein identification, phosphosite mapping and de novo sequencing of a proline-rich protein and disulfide bond mapping in mAb. The results demonstrate that ProAlanase is highly suitable for proteomics analysis of the arginine- and lysine-rich histones, enabling high sequence coverage of multiple histone family members. It also facilitates an efficient digestion of bone collagen thanks to the cleavage at the C terminus of hydroxyproline which is highly prevalent in collagen. This allows to identify complementary proteins in ProAlanase- and trypsin-digested ancient bone samples, as well as to increase sequence coverage of noncollagenous proteins. Moreover, digestion with ProAlanase improves protein sequence coverage and phosphosite localization for the proline-rich protein Notch3 intracellular domain (N3ICD). Furthermore, we achieve a nearly complete coverage of N3ICD protein by de novo sequencing using the combination of ProAlanase and tryptic peptides. Finally, we demonstrate that ProAlanase is efficient in disulfide bond mapping, showing high coverage of disulfide-containing regions in a nonreduced mAb.

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS²) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS² data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC–MS measurements. Peptide masses are then extracted from MS¹ data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS¹ peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.

Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.

We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

The absence of the dystrophin protein in Duchenne muscular dystrophy (DMD) results in myofiber fragility and a plethora of downstream secondary pathologies. Although a variety of experimental therapies are in development, achieving effective treatments for DMD remains exceptionally challenging, not least because the pathological consequences of dystrophin loss are incompletely understood. Here we have performed proteome profiling in tibialis anterior muscles from two murine DMD models (mdx and mdx52) at three ages (8, 16, and 80 weeks of age), all n = 3. High-resolution isoelectric focusing liquid chromatography-tandem MS (HiRIEF-LC–MS/MS) was used to quantify the expression of 4974 proteins across all 27 samples. The two dystrophic models were found to be highly similar, whereas multiple proteins were differentially expressed relative to WT (C57BL/6) controls at each age. Furthermore, 1795 proteins were differentially expressed when samples were pooled across ages and dystrophic strains. These included numerous proteins associated with the extracellular matrix and muscle function that have not been reported previously. Pathway analysis revealed multiple perturbed pathways and predicted upstream regulators, which together are indicative of cross-talk between inflammatory, metabolic, and muscle growth pathways (e.g. TNF, INF, NF-B, SIRT1, AMPK, PGC-1α, PPARs, ILK, and AKT/PI3K). Upregulation of CAV3, MVP and PAK1 protein expression was validated in dystrophic muscle by Western blot. Furthermore, MVP was upregulated during, but not required for, the differentiation of C2C12 myoblasts suggesting that this protein may affect muscle regeneration. This study provides novel insights into mutation-independent proteomic signatures characteristic of the dystrophic phenotype and its progression with aging.

The increasing consumption of high-fat foods combined with a lack of exercise is a major contributor to the burden of obesity in humans. Aerobic exercise such as running is known to provide metabolic benefits, but how the over-consumption of a high fat diet (HFD) and exercise interact is not well characterized at the molecular level. Here, we examined the plasma proteome in mice for the effects of aerobic exercise as both a treatment and as a preventative regime for animals on either HFD or a healthy control diet. This analysis detected large changes in the plasma proteome induced by the HFD, such as increased abundance of SERPINA7, ALDOB, and down-regulation of SERPINA1E, CFD (adipsin). Some of these changes were significantly reverted using exercise as a preventative measure, but not as a treatment regime. To determine if either the intensity, or duration, of exercise influenced the outcome, we compared high-intensity interval training (HIIT) and endurance running. Endurance running slightly out-performed HIIT exercise, but overall, both provided similar reversion in abundance of plasma proteins modulated by the high-fat diet including SERPINA7, APOE, SERPINA1E, and CFD. Finally, we compared the changes induced by over-consumption of HFD to previous data from mice fed an isocaloric high saturated fat (SFA) or polyunsaturated fat (PUFA) diet. This identified several common changes including increased APOC2 and APOE, but also highlighted changes specific for either over-consumption of HFD (ALDOB, SERPINA7, CFD), SFA-based diets (SERPINA1E), or PUFA-based diets (Haptoglobin - Hp). Together, these data highlight the importance of early intervention with exercise to revert HFD-induced phenotypes and suggest some of the molecular mechanisms leading to the changes in the plasma proteome generated by high fat diet consumption. Web-based interactive visualizations are provided for this dataset (larancelab.com/hfd-exercise), which give insight into diet and exercise phenotypic interactions on the plasma proteome.

We developed a simple and rapid method to enrich protein N-terminal peptides, in which the protease TrypN is first employed to generate protein N-terminal peptides without Lys or Arg and internal peptides with two positive charges at their N-termini, and then the N-terminal peptides with or without N-acetylation are separated from the internal peptides by strong cation exchange chromatography according to a retention model based on the charge/orientation of peptides. This approach was applied to 20 μg of human HEK293T cell lysate proteins to profile the N-terminal proteome. On average, 1,550 acetylated and 200 unmodified protein N-terminal peptides were successfully identified in a single LC/MS/MS run with less than 3% contamination with internal peptides, even when we accepted only canonical protein N-termini registered in the Swiss-Prot database. Since this method involves only two steps, protein digestion and chromatographic separation, without the need for tedious chemical reactions, it should be useful for comprehensive profiling of protein N-termini, including proteoforms with neo-N-termini.

Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of PTM profiles detected in open searches based on attributes such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multi-experiment comparisons for studying changes in modification profiles, e.g. in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed paraffin-embedded samples, detects extreme underalkylation of cysteine in some datasets, discovers an artefactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multi-center proteomics profiling study.

To identify novel autoantibodies of Takayasu arteritis (TAK) using HuProt array-based approach. A two-phase approach was adopted. In Phase I, serum samples collected from 40 TAK patients, 15 autoimmune disease patients, and 20 healthy subjects were screened to identify TAK-specific autoantibodies using human protein (HuProt) arrays. In Phase II, the identified candidate autoantibodies were validated with TAK-focused arrays using an additional cohort comprised of 109 TAK patients, 110 autoimmune disease patients, and 96 healthy subjects. Subsequently, the TAK-specific autoantibodies validated in Phase II were further confirmed using Western blot analysis. We identified and validated eight autoantibodies as potential TAK-specific diagnostic biomarkers, including anti-SPATA7, -QDPR, -SLC25A2, -PRH2, -DIXDC1, -IL17RB, -ZFAND4, and -NOLC1 antibodies, with AUC of 0.803, 0.801, 0.780, 0.696, 0.695, 0.678, 0.635 and 0.613, respectively. SPATA7 could distinguish TAK from healthy and disease controls with 73.4% sensitivity at 85.4% specificity, while QDPR showed 71.6% sensitivity at 86.4% specificity. SLC25A22 showed the highest sensitivity of 80.7%, but at lower specificity of 67.0%. In addition, PRH2, IL17RB and NOLC1 showed good specificities of 88.3%, 85.9% and 86.9%, respectively, but at lower sensitivities (<50%). Finally, DIXDC1 and ZFAND4 showed moderate performance as compared with the other autoantibodies. Using a decision tree model, we could reach a specificity of 94.2% with AUC of 0.843, a significantly improved performance as compared to that by each individual biomarker. The performance of three autoantibodies, namely anti-SPATA7, -QDPR and -PRH2, were successfully confirmed with Western blot analysis. Using this two-phase strategy, we identified and validated eight novel autoantibodies as TAK–specific biomarker candidates, three of which could be readily adopted in a clinical setting.

Methods to shorten [¹⁸F]FDG Patlak PET imaging procedures ranging from 65–90 to 20–30 min after injection, using a population-averaged input function (PIF) scaled to patient-specific image-derived input function (IDIF) values, were recently evaluated. The aim of the present study was to explore the feasibility of ultrashort 10-min [¹⁸F]FDG Patlak imaging at 55–65 min after injection using a PIF combined with direct Patlak reconstructions to provide reliable quantitative accuracy of lung tumor uptake, compared with a full-duration 65-min acquisition using an IDIF. Methods: Patients underwent a 65-min dynamic PET acquisition on a long-axial-field-of-view (LAFOV) Biograph Vision Quadra PET/CT scanner. Subsequently, direct Patlak reconstructions and image-based (with reconstructed dynamic images) Patlak analyses were performed using both the IDIF (time to relative kinetic equilibrium between blood and tissue concentration (t*) = 30 min) and a scaled PIF at 30–60 min after injection. Next, direct Patlak reconstructions were performed on the system console using only the last 10 min of the acquisition, that is, from 55 to 65 min after injection, and a scaled PIF using maximum crystal ring difference settings of both 85 and 322. Tumor lesion and healthy-tissue uptake was quantified and compared between the differently obtained parametric images to assess quantitative accuracy. Results: Good agreement was obtained between direct- and image-based Patlak analyses using the IDIF (t* = 30 min) and scaled PIF at 30–60 min after injection, performed using the different approaches, with no more than 8.8% deviation in tumor influx rate value (K_i) (mean difference ranging from –0.0022 to 0.0018 mL/[min x g]). When direct Patlak reconstruction was performed on the system console, excellent agreement was found between the use of a scaled PIF at 30–60 min after injection versus 55–65 min after injection, with 2.4% deviation in tumor K_i (median difference, –0.0018 mL/[min x g]; range, –0.0047 to 0.0036 mL/[min x g]). For different maximum crystal ring difference settings using the scan time interval of 55–65 min after injection, only a 0.5% difference (median difference, 0.0000 mL/[min x g]; range, –0.0004 to 0.0013 mL/[min x g]) in tumor K_i was found. Conclusion: Ultrashort whole-body [¹⁸F]FDG Patlak imaging is feasible on an LAFOV Biograph Vision Quadra PET/CT system without loss of quantitative accuracy to assess lung tumor uptake compared with a full-duration 65-min acquisition. The ultrashort 10-min direct Patlak reconstruction with PIF allows for its implementation in clinical practice.

Neoadjuvant therapy in patients with locally advanced rectal cancer (LARC) has achieved good pathologic complete response (pCR) rates, potentially eliminating the need for surgical intervention. This study investigated preoperative methods for predicting pCR after neoadjuvant short-course radiotherapy (SCRT) combined with immunochemotherapy. Methods: Treatment-naïve patients with histologically confirmed LARC were enrolled from February 2023 to July 2023. Before surgery, the patients received neoadjuvant SCRT followed by 2 cycles of capecitabine and oxaliplatin plus camrelizumab. ⁶⁸Ga-labeled fibroblast activation protein inhibitor ([⁶⁸Ga]Ga-FAPI-04) PET/MRI, [¹⁸F]FDG PET/CT, and contrast-enhanced MRI were performed before treatment initiation and before surgery in each patient. PET and MRI features and the size and number of lesions were also collected from each scan. Each parameter’s sensitivity, specificity, and diagnostic cutoff were derived via receiver-operating-characteristic curve analysis. Results: Twenty eligible patients (13 men, 7 women; mean age, 60.2 y) were enrolled and completed the entire trial, and all patients had proficient mismatch repair or microsatellite-stable LARC. A postoperative pCR was achieved in 9 patients (45.0%). In the visual evaluation, both [⁶⁸Ga]Ga-FAPI-04 PET/MRI and [¹⁸F]FDG PET/CT were limited to forecasting pCR. Contrast-enhanced MRI had a low sensitivity of 55.56% to predict pCR. In the quantitative evaluation, [⁶⁸Ga]Ga-FAPI-04 change in SUL_peak percentage, where SUL_peak is SUV_peak standardized by lean body mass, had the largest area under the curve (0.929) with high specificity (sensitivity, 77.78%; specificity, 100.0%; cutoff, 63.92%). Conclusion: [⁶⁸Ga]Ga-FAPI-04 PET/MRI is a promising imaging modality for predicting pCR after SCRT combined with immunochemotherapy. The SUL_peak decrease exceeding 63.92% may provide valuable guidance in selecting patients who can forgo surgery after neoadjuvant therapy.

Suboxone is a medicine prescribed to treat opioid use disorder. But in recent times, it has gained a poor reputation for contributing to severe side effects, such as dental damage and decay. Many patients have had to undergo costly dental repair surgery to treat the gum and tooth issues they faced. But the side effects […]

The post How To Cope With the Psychological Symptoms of Suboxone Withdrawal first appeared on What is Psychology?.

Luke Clark
Nov 6, 2013; 33:17617-17623
Symposium and Mini-Symposium

Bradley Voytek
Sep 23, 2015; 35:13257-13265
BehavioralSystemsCognitive

Key event-related potentials (ERPs) of perceptual decision-making such as centroparietal positivity (CPP) elucidate how evidence is accumulated toward a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs different hemifield relative to targets) on perceptual decision-making. We confirmed that responses for perceptual decisions were slower for spatially incongruent versus congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent versus congruent. To evaluate the effects of congruency further, we applied drift diffusion modeling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter nondecision times when considering the effect of congruency. The modeling also suggested that congruency's effect on behavior occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase and then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor.

The CIA burglar who went rogue: http://j.mp/UpJtCJ Using the Enigma and the Sigaba, world powers encrypted their messages in hopes of catching their opponents by surprise.

New DNA analysis adds to growing research indicating the famous Pacific island did not collapse from overuse of resources before the arrival of Europeans

World's #1 mainstream analysis software adds 100-plus new features that put powerful design validation in easy-to-use packages

Lisa Gatzke-Kopp, professor of human development and family studies, recently was named president-elect of the Society for Psychophysiological Research. The presidential role spans three years, over which she will serve as president-elect, president and past-president, each for a one-year period.

Erin Kitt-Lewis, associate director of education for the Ross and Carol Nese College of Nursing’s Tressa Nese and Helen Diskevich Center of Geriatric Nursing Excellence, has been named a 2024 Distinguished Educator in Gerontological Nursing by the National Hartford Center of Gerontological Nursing Excellence. 

hello:

i use the command "net logic" to change/assign the net name of the pins but the command can be used for only one pin at a time.

is there a way to change/assign the same net name on 100 pins all at once?

i have a daisy chain design so i need to assign one net name for 100s of pins.

========

thank you david, i was able to do it.

i am writing this section because i can not reply to your comment.

I'm currently work in coverage analysis. In my design certain register bits remain unused, which could potentially lower toggle coverage. Specifically, I'd like to know how to disable coverage for specific unused register bits within a 32-bit register. For instance, I want to deactivate coverage for bit 17 and bit 20 in a 32-bit register to optimize toggle coverage. Could you please provide guidance on how to accomplish this?

Check here each week to keep up with the latest from John MacArthur's pulpit at Grace Community Church.

A member of the team, a water boy, accidentally stumbled upon a stone that turned out to be the first step of an ancient staircase.

The Battle of al-Qadisiyyah was a crucial victory for Arab Muslim forces of the Rashidun Caliphate over the Sasanian Empire.

Extending across seven provinces and two autonomous regions, the Yellow River Ecological Corridor (YREC) comprises the second largest river basin in the People's Republic of China (PRC) with a population of 120 million. The basin is critical for food security yet faces complex ecological and environmental challenges. The region suffers from severe water scarcity, impacts from climate change and environmentally unsustainable development.

In most vertebrates, a pattern of chemical marks on the genome is a reliable indicator of age, but in axolotls this clock seems to stop after the first four years of life

Title: Kids With Neurological Conditions at Higher Risk of Flu Death: CDC
Category: Health News
Created: 8/29/2012 10:05:00 AM
Last Editorial Review: 8/29/2012 12:00:00 AM

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = a_iHRⁱ was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5^th–95^th percentiles of the simulations, and the estimated peak concentrations (C_max) and area under the curve (AUC) of drugs were also within 0.5–2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters.

Cannabinoid and opioid receptor activities can be modulated by a variety of post-translational mechanisms including the formation of interacting complexes. This study examines the involvement of endogenous and exogenous chaperones in modulating the abundance and activity of cannabinoid CB₁ receptor (CB₁R), opioid receptor (DOR), and CB₁R-DOR interacting complexes. Focusing on endogenous protein chaperones, namely receptor transporter proteins (RTPs), we examined relative mRNA expression in the mouse spinal cord and found RTP4 to be expressed at higher levels compared with other RTPs. Next, we assessed the effect of RTP4 on receptor abundance by manipulating RTP4 expression in cell lines. Overexpression of RTP4 causes an increase and knock-down causes a decrease in the levels of CB₁R, DOR, and CB₁R-DOR interacting complexes; this is accompanied by parallel changes in signaling. The ability of small molecule lipophilic ligands to function as exogenous chaperones was examined using receptor-selective antagonists. Long-term treatment leads to increases in receptor abundance and activity with no changes in mRNA supporting a role as pharmacological chaperones. Finally, the effect of cannabidiol (CBD), a small molecule ligand and a major active component of cannabis, on receptor abundance and activity in mice was examined. We find that CBD administration leads to increases in receptor abundance and activity in mouse spinal cord. Together, these results highlight a role for chaperones (proteins and small molecules) in modulating levels and activity of CB₁R, DOR, and their interacting complexes potentially through mechanisms including receptor maturation and trafficking.

This single-center, single-arm, phase II trial (ChiCTR2100050057) investigated the ability of ¹⁸F-labeled fibroblast activation protein inhibitor ([¹⁸F]AlF-NOTA-FAPI-04, denoted as ¹⁸F-FAPI) PET/CT to predict the response to neoadjuvant camrelizumab plus chemotherapy (nCC) in locally advanced esophageal squamous cell carcinoma (LA-ESCC). Methods: This study included 32 newly diagnosed LA-ESCC participants who underwent ¹⁸F-FAPI PET/CT at baseline, of whom 23 also underwent scanning after 2 cycles of nCC. The participants underwent surgery after 2 cycles of nCC. Recorded PET parameters included maximum, peak, and mean SUVs and tumor-to-background ratios (TBRs), metabolic tumor volume, and total lesion FAP expression. PET parameters were compared between patient groups with good and poor pathologic responses, and the predictive performance for treatment response was analyzed. Results: The good and poor response groups each included 16 participants (16/32, 50.0%). On ¹⁸F-FAPI PET/CT, the posttreatment SUVs were significantly lower in good responders than in poor responders, whereas the changes in SUVs with treatment were significantly higher (all P < 0.05). SUV_max (area under the curve [AUC], 0.87; P = 0.0026), SUV_peak (AUC, 0.89; P = 0.0017), SUV_mean (AUC, 0.88; P = 0.0021), TBR_max (AUC, 0.86; P = 0.0031), and TBR_mean (AUC, 0.88; P = 0.0021) after nCC were significant predictors of pathologic response to nCC, with sensitivities of 63.64%–81.82% and specificities of 83.33%–100%. Changes in SUV_max (AUC, 0.81; P = 0.0116), SUV_peak (AUC, 0.82; P = 0.0097), SUV_mean (AUC, 0.81; P = 0.0116), and TBR_mean (AUC, 0.74; P = 0.0489) also were significant predictors of the pathologic response to nCC, with sensitivities and specificities in similar ranges. Conclusion: ¹⁸F-FAPI PET/CT parameters after treatment and their changes from baseline can predict the pathologic response to nCC in LA-ESCC participants.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care.

Hearing disorders pose significant challenges to individuals experiencing them and their overall quality of life, emphasizing the critical need for advanced pharmacological approaches to address these conditions. Current treatment options often focus on amplification devices, cochlear implants, or other rehabilitative therapies, leaving a substantial gap regarding effective pharmacological interventions. Advancements in our understanding of the molecular and cellular mechanisms involved in hearing disorders induced by noise, aging, and ototoxicity have opened new avenues for drug development, some of which have led to numerous clinical trials, with promising results. The development of optimal drug delivery solutions in animals and humans can also enhance the targeted delivery of medications to the ear. Moreover, large genome studies contributing to a genetic understanding of hearing loss in humans combined with advanced molecular technologies in animal studies have shown a great potential to increase our understanding of the etiologies of hearing loss. The auditory system exhibits circadian rhythms and temporal variations in its physiology, its vulnerability to auditory insults, and its responsiveness to drug treatments. The cochlear clock rhythms are under the control of the glucocorticoid system, and preclinical evidence suggests that the risk/benefit profile of hearing disorder treatments using chronopharmacological approaches would be beneficial. If translatable to the bedside, such approaches may improve the outcome of clinical trials. Ongoing research into the molecular and genetic basis of auditory disorders, coupled with advancements in drug formulation and delivery as well as optimized timing of drug administration, holds great promise of more effective treatments.

Both preclinical and clinical studies implicate functional impairments of several neuroactive metabolites of the kynurenine pathway (KP), the major degradative cascade of the essential amino acid tryptophan in mammals, in the pathophysiology of neurologic and psychiatric diseases. A number of KP enzymes, such as tryptophan 2,3-dioxygenase (TDO2), indoleamine 2,3-dioxygenases (IDO1 and IDO2), kynurenine aminotransferases (KATs), kynurenine 3-monooxygenase (KMO), 3-hydroxyanthranilic acid oxygenase (3-HAO), and quinolinic acid phosphoribosyltransferase (QPRT), control brain KP metabolism in health and disease and are therefore increasingly considered to be promising targets for the treatment of disorders of the nervous system. Understanding the distribution, cellular expression, and regulation of KP enzymes and KP metabolites in the brain is therefore critical for the conceptualization and implementation of successful therapeutic strategies.

BACKGROUND AND PURPOSE:

Overuse of CT-based cerebrovascular imaging in the emergency department and inpatient settings, notably CTA of the head and neck for minor and nonfocal neurologic presentations, stresses imaging services and exposes patients to radiation and contrast. Furthermore, such CT-based imaging is often insufficient for definitive diagnosis, necessitating additional MR imaging. Recent advances in fast MRI may allow timely assessment and a reduced need for head and neck CTA in select populations.

BACKGROUND AND PURPOSE:

Alterations of the basilar artery (BA) anatomy have been suggested as a possible MRA feature of Fabry disease (FD). Nonetheless, no information about their clinical or pathophysiologic correlates is available, limiting our comprehension of the real impact of vessel remodeling in FD.

SUMMARY:

The 2021 World Health Organization Classification of Tumors of the Central Nervous System (CNS5), introduced significant changes, impacting tumors ranging from glial to ependymal neoplasms. Ependymal tumors were previously classified and graded based on histopathology, which had limited clinical and prognostic utility. The updated CNS5 classification now divides ependymomas into 10 subgroups based on anatomic location (supratentorial, posterior fossa, and spinal compartment) and genomic markers. Supratentorial tumors are defined by zinc finger translocation associated (ZFTA) (formerly v-rel avian reticuloendotheliosis viral oncogene [RELA]), or yes-associated protein 1 (YAP1) fusion; posterior fossa tumors are classified into groups A (PFA) and B (PFB), spinal ependymomas are defined by MYCN amplification. Subependymomas are present across all these anatomic compartments. The new classification kept an open category of "not elsewhere classified" or "not otherwise specified" if no pathogenic gene fusion is identified or if the molecular diagnosis is not feasible. Although there is significant overlap in the imaging findings of these tumors, a neuroradiologist needs to be familiar with updated CNS5 classification to understand tumor behavior, for example, the higher tendency for tumor recurrence along the dural flap for ZFTA fusion-positive ependymomas. On imaging, supratentorial ZFTA-fused ependymomas are preferentially located in the cerebral cortex, carrying predominant cystic components. YAP1-MAMLD1-fused ependymomas are intra- or periventricular with prominent multinodular solid components and have significantly better prognosis than ZFTA-fused counterparts. PFA ependymomas are aggressive paramedian masses with frequent calcification, seen in young children, originating from the lateral part of the fourth ventricular roof. PFB ependymomas are usually midline, noncalcified solid-cystic masses seen in adolescents and young adults arising from the fourth ventricular floor. PFA has a poorer prognosis, higher recurrence, and higher metastatic rate than PFB. Myxopapillary spinal ependymomas are now considered grade II due to high recurrence rates. Spinal-MYCN ependymomas are aggressive tumors with frequent leptomeningeal spread, relapse, and poor prognosis. Subependymomas are noninvasive, intraventricular, slow-growing benign tumors with an excellent prognosis. Currently, the molecular classification does not enhance the clinicopathologic understanding of subependymoma and myxopapillary categories. However, given the molecular advancements, this will likely change in the future. This review provides an updated molecular classification of ependymoma, discusses the individual imaging characteristics, and briefly outlines the latest targeted molecular therapies.