Autologous platelet-rich plasma (PRP) is commonly used to enhance tissue repair. Its effectiveness may be limited by the patient’s individual condition. Given that aging impacts tissue regeneration, it is important to analyze the composition of the PRP obtained from elderly individuals. This study aims to characterize the proteomic profile of PRP to better understand its potential for regenerative applications in this population. PRP and control plasma samples from 32 elderly donors were obtained by sequential centrifugation of blood samples. PRP platelets were activated with calcium gluconate and heparin, followed by centrifugation to remove residual platelets. Proteomic analysis was performed following depletion of high-abundance proteins, protein digestion, liquid chromatography-tandem mass spectrometry, and bioinformatic analysis. A total of 1,378 proteins were identified in the PRP and control plasma samples, with 324 proteins detected only in PRP. Inter-individual variability in protein detection was observed among PRP samples. Functional analysis of the proteins in PRP, whether only found in PRP or shared with control plasma, showed they were linked to vesicle transport, immune and coagulation processes, cytoskeleton organization, and wound healing. Proteins showing inter-individual variability across samples were linked to tissue regeneration processes, highlighting potential variability in regenerative capacity. PRP from elderly individuals contains a wealth of proteins that play a crucial role in tissue regeneration. These findings support the potential clinical use of autologous PRP for regenerative medicine in elderly individuals. Moreover, the observed inter-individual variability in the PRP proteome highlights opportunities for personalized therapeutic applications.
Mass spectrometry enables highly specific and multiplexed typing of amyloid plaques, and is widely used for this purpose in clinical reference laboratories. However, this technique relies on nanoflow liquid chromatography (LC), which reduces penetrance of this methodology due to high up-front and operational costs associated with nanoflow. This study introduces a tandem mass spectrometry (MS/MS) method with high-flow LC, which shortens the sample preparation workflow to < 8 hours. Suspension trapping was utilized to process laser capture microdissected amyloid plaques from 47 patient samples (10 from heart, 37 from kidney). The samples were then evaluated by LC-MS/MS with data-independent acquisition (DIA). The employment of suspension trapping and DIA allow for the use of high-flow LC, which is most commonly coupled with MS in clinical laboratories. A custom selection heuristic was developed to identify the most likely amyloidogenic protein from each plaque. A novel high-flow LC-DIA-MS/MS method was able to identify the amyloidogenic protein in 96
The culture of STEM (Science, Technology, Engineering, and Math) is often cited as being competitive. This creates a driving force for attrition from these fields, especially for minoritized students. But what-according to students-defines competition in the STEM classroom environment? This study seeks to answer that question by examining the factors that students report as contributing to competition in STEM classrooms. To do this, we conducted semistructured interviews with 25 first-generation and racially minoritized students enrolled in an introductory biology course, and with experience in other university-level STEM courses. Using thematic analysis, we identified four broad categories shaping students' sense of competition: student factors (e.g., grade comparisons), instructor factors (e.g., instructor messaging), course factors (e.g., grading), and out-of-class factors (e.g., intended career goals). These findings suggest that competition is not a singular classroom characteristic but rather a multidimensional experience shaped by interpersonal, environmental, and structural factors. We contextualize our findings in a conceptual framework of competition, which identifies how trait-competitiveness, perceived environmental competitiveness, and structural competition all shape competition. Our findings help operationalize these dimensions of competition in STEM classrooms and provide actionable context for mitigating the disproportionate impact of competition on minoritized students.
Incorporating counterstereotypical scientist role models into undergraduate biology courses is a powerful, evidence-based way to support student persistence in STEM fields. However, educational resources featuring counterstereotypical scientists could unintentionally have adverse impacts on student attitudes towards science. Here, we leveraged a multi-institution experiment that manipulated whether educational materials included information on the obstacles faced by counterstereotypical scientists in their careers. After students received these variable materials, we documented the obstacles they perceived they may face as scientists. Through the lens of attribution theory, we found that students are aware of a variety of obstacles they would face as scientists and that student perceptions of those obstacles are not changed by sharing the obstacles that scientists had to overcome. We encourage instructors to share scientists' stories of the obstacles they faced in their careers without concern that this will alter students' perceptions of careers in science.
Social metacognition occurs when students monitor and evaluate their own and others' thinking out loud during group work. Social metacognition, such as directly correcting peers, may be perceived as risky by students. However, this has not yet been systematically investigated. To address this gap in knowledge, we used the sociolinguistic framework of politeness theory to explore student perceptions of risk associated with social metacognition use during group work in undergraduate life science courses. We recorded four small groups as they worked together in introductory biology labs and biochemistry courses and then conducted stimulated recall interviews with 12 students from those groups. Transcripts were analyzed qualitatively and iteratively using a narrative and holistic group profile approach. Our findings support the prediction that social metacognition that involves the skill of evaluation tends to involve more risk. Students employ politeness strategies like hedging and questioning when the use of social metacognition involves greater risk to either the hearer's or speaker's face. Additionally, students' views of group member expertise and identity influenced their perceptions. This is the first study to explore student perceptions of risk associated with social metacognition, providing essential information for developing effective social metacognition interventions.
Stereotypes about scientists narrow how students relate to them and influence how students see themselves in science. However, sharing identities and interests of contemporary scientists enables students to see aspects of their possible selves in that role. Previous work has demonstrated positive impacts of scientist role models on students who share marginalized identities in society and STEM, though less work has addressed whether the same positive effect would occur if the shared identity is more common in a broader societal context. We address this question by exploring the impact of an instructor revealing that she is Christian as a counter-stereotypical scientist identity in a large-enrollment undergraduate biology course. Scientists are less religious than the general population and are often stereotyped as non-religious. We found that briefly revealing the instructor's religious identity had a neutral impact on most students and that Christian students were most likely to report that the information was appropriate, positively impacted their course experience, and positively impacted their sense of inclusion. We contribute to the role model literature with an example of how shared identities positively impact students who most relate, even when the identity is not underrepresented in the educational context or across the United States.
Build Your Research Community (BYRC) is an asynchronous, free online course that guides research students to build effective relationships with their primary research advisors as well as build mentoring networks that provide holistic support as they navigate their research training experiences and careers. Preliminary evaluation evidence of the effectiveness of BYRC was collected from undergraduate, postbaccalaureate, and graduate students in 22 biomedical research training programs, as well as students who enrolled in the course independently from across the country. Students valued the course components, module topics, and structure, and the majority indicated they would recommend it to others. Students self-reported learning gains for all course modules, identified several specific course elements that positively impacted their learning and development, and indicated that the course positively impacted their current and future training experiences and mentoring relationships. Students at earlier training stages reported the greatest impacts. In addition, training program directors reported satisfaction with the course and their ability to integrate the course into their existing programs. Overall, these results indicate that the BYRC course may be an effective addition to research training programs. All course materials are freely available online (https://doi.org/10.17605/OSF.IO/WR6KP), and the modules may be implemented independently.
Metacognition can support undergraduates in challenging science courses by helping them to monitor their understanding of concepts, evaluate their approaches for learning, and change their plans for studying as needed. Fostering metacognitive development can help students succeed in science, yet there is limited understanding of how students develop metacognition in college. We conducted one of the first longitudinal studies to investigate how, when, and why life science students use metacognition throughout their undergraduate career. We used yearly semi-structured interviews to capture students' use of metacognition across four years of college. By conducting longitudinal qualitative data analysis, we outline a framework of metacognitive change that details the stages that are evident for each metacognitive skill. We synthesize our findings into milestones students reach as their metacognition develops. For example, students initially use planning and evaluating separately and reach a milestone when they first connect these skills by using their evaluations to inform their study plans. Later, some, but not all, students reach the milestone of integrating all three skills by connecting monitoring to their planning and evaluating. We use our results to build theory on metacognitive development, and we offer suggestions for instructors who aim to foster their students' metacognition.
While extensive research explores the difficulties undergraduate students face when learning chemistry or biology, less is known about how students learn across disciplines, especially when constructing mechanistic explanations. This study investigates (1) how undergraduate students explain differences in protein function, focusing on their use of conceptual resources and mechanistic reasoning, and (2) how task design influences their explanations. Using three different tasks with the same underlying mechanistic explanation, we analyzed responses from students who had completed a transformed cell and molecular biology course to determine what conceptual resources they use and how they connect those resources. Our findings show that, given a task with appropriate scaffolding, half of the students constructed mechanistic explanations. We also found that students rarely integrated ideas from both biology and chemistry, but when they did, they consistently constructed mechanistic explanations. Task design significantly influenced the conceptual resources students used and the frequency of mechanistic explanations. These findings highlight the need for careful instructional and curricular design to support cross-disciplinary learning and mechanistic reasoning in STEM education.
Allergen-specific immunotherapy (AIT) is the only disease-modifying treatment for allergic rhinitis and asthma, but objective biomarkers for monitoring immune tolerance and predicting adverse reactions remain lacking. ImmunoglobulinG (IgG) glycosylation has been validated as an effective biomarker in numerous inflammatory diseases. This study aimed to explore plasma IgG N-glycan dynamics as predictive markers for AIT tolerance and adverse events. We enrolled 37 patients with allergic asthma or rhinitis undergoing subcutaneous AIT, 26 patients were longitudinally sampled at three time points: pre-treatment, maintenance initiation, and after more than 6 months of maintenance therapy. IgG N-glycans were analyzed by ultra-performance liquid chromatography (UPLC). Longitudinal analysis showed total IgG galactosylation and sialylation gradually increased, while bisecting glycans decreased during AIT. Among 37 patients, those with adverse reactions exhibited significantly elevated GP6, reduced GP18, GP23, lower galactosylation and sialylation, and increased bisecting glycans at baseline compared with tolerant patients. Receiver operating characteristic (ROC) curve analysis revealed that the combined glycan model achieved an area under the curve (AUC) of 0.863 for adverse reaction prediction. These findings demonstrate that IgG N-glycosylation remodeling correlates with AIT progression and can serve as a potential pretreatment biomarker for risk stratification. Plasma IgG N-glycan profiling provides a minimally invasive, mechanism-based tool to improve AIT safety and efficacy, which deserves further multicenter validation.
While assertions are common about what fosters inclusive undergraduate STEM classrooms, few studies have systematically investigated student perspectives. Additionally, it is unclear how students broadly perceive active learning practices in relation to inclusion. As such, we purposefully investigated student perceptions of classroom inclusion across courses in a biology department enriched with evidence-based teaching and situated in an urban, public institution. Student perceptions were investigated using open-ended prompts on a department-wide, end-of-term assessment. Results revealed that students, across social identities, do indeed report active learning strategies-as well as instructor interactions and collaborative classroom culture-as fostering inclusion. Student reports about instructor practices associated with their inclusion or exclusion clustered into four mirrored categories: Building/Dismantling the Instructor/Student Relationship, Making/Compromising Student-Centered Pedagogical Choices, Establishing Collaborative/Non-Collaborative Classroom Culture, and Portraying Science as Inclusive/Exclusive. These categories comprise a new analytical framework, the Student Perceptions of Instructor Practices-Inclusion/Exclusion (SPIP-I/E) Framework, for investigating student perspectives on inclusion. Intriguingly, student reports about inclusion consistently alluded to instructor language, and the emergent SPIP-I/E Framework aligns with existing Instructor Talk frameworks. We hypothesize that modest differences in instructor language-surrounding any teaching strategy-may mediate and be a predictive, explanatory variable for student perceptions of inclusion.
Preeclampsia is a pregnancy-specific hypertensive disorder associated with maternal and perinatal morbidity and mortality and can increase the risk of vascular diseases after pregnancy. However, a timely and unequivocal diagnosis in the first weeks allows access to appropriate medical follow-up. In this exploratory approach, we aim to evaluate the analytical potential of MALDI-TOF spectral fingerprints combined with machine learning algorithms for discriminating serum samples from patients with preeclampsia from those with normotensive pregnancies. The dataset comprised 164 spectra of serum samples from 67 women with preeclampsia and 97 negative controls, which were processed using the Filter-Assisted Sample Preparation (FASP) protocol and analyzed by mass spectrometry. Spectral data analysis was subjected to a machine learning algorithm that demonstrated high performance in classifying cases and controls, with an overall accuracy of 88
Sepsis, a life-threatening syndrome driven by a dysregulated host response to infection, leads to systemic inflammation and multi-organ dysfunction. Despite advancements in supportive care, sepsis remains a significant global health challenge, necessitating innovative approaches to improve outcomes. Proteomic profiling has emerged as a transformative tool, providing detailed insights into the molecular mechanisms underlying sepsis and septic shock. Mass spectrometry-based analyses have identified key proteins such as CD14, lysozyme (LYZ), C1QC, C8A, APOB, ORM1, ApoA1, and ApoE, which are involved in inflammation, immune response, complement activation, and lipid metabolism. These biomarkers may complement traditional diagnostic tools by improving molecular characterization, early risk stratification, and the identification of organ dysfunction trajectories, although their clinical superiority over established diagnostic approaches remains to be validated in larger, multicenter cohorts. While ApoA1 and ApoE have been associated with mortality risk and ApoC3 with potentially protective profiles, these findings remain primarily investigational and require validation before being used to guide individualized therapeutic decisions. Additionally, therapeutic targets such as PRDM16 and AMPK show promise in modulating oxidative stress, inflammation, and metabolic dysregulation, while inflammatory mediators like S100 proteins present actionable pathways for intervention. However, translating these proteomic insights into routine clinical practice remains challenging, requiring further research to validate biomarkers and develop effective delivery systems. These findings support a gradual shift toward precision-oriented sepsis research, although further validation is required before proteomic tools can transform routine clinical management.
Cardiogenic shock secondary to acute myocardial infarction (AMI-CS) prohibitively impacts survival. This prospective study aimed to discover and internally verify candidate serum protein biomarkers and evaluate their potential prognostic value for 30-day mortality in AMI-CS patients. AMI-CS patients were consecutively enrolled into discovery (n = 30) and verification (n = 60) cohorts. Candidate biomarkers were screened using Data-Independent Acquisition (DIA) mass spectrometry, analyzed via differential abundance and weighted gene co-expression network analysis (WGCNA), and verified via targeted Parallel Reaction Monitoring (PRM). Boruta feature selection for five machine learning algorithms were embedded within a rigorous nested cross-validation scheme. Incremental prognostic value over clinical predictors was evaluated using Cox regression and metrics including the integrated discrimination improvement (IDI). DIA proteomics identified 216 proteins differentially abundant between 30-day survivors and non-survivors, and WGCNA defined an outcome-associated module linked to shock severity and enriched for oxidative stress and energy metabolism. During PRM verification, leakage-free nested cross-validation random forest model selected a seven-protein panel (YWHAZ, QDPR, MDH2, FAH, PSMA1, FABP5, and AHCY), which achieved a mean area under the ROC curve of 0.82 (95
Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by progressive joint inflammation and damage. Early clinical diagnosis is crucial for effective intervention but presents significant challenges due to an initial asymptomatic inflammatory phase. Anti-citrullinated-protein antibodies (ACPA) and rheumatoid factor (RF) detection have been useful in early RA diagnosis, but their reliability is debatable as they are often non-specific. This has prompted a quest for alternative, more robust biomarkers. Researchers have turned to advanced proteomic and glycosylation analyses of biological fluids (e.g., synovial fluid, plasma and serum) and tissues using techniques such as MALDI-MS, Q-TOF, and SELDI-TOF. These approaches may offer a comprehensive approach to scrutinize a range of biomolecules as potential RA biomarkers, from citrullinated-proteins and peptides to novel potential protein biomarkers such as thymosin, macrophage-capping protein, calgranulins, and serum amyloid-A. Further, proteomics-based approaches have the ability to specifically monitor changes in the RA proteome (e.g., glycosylated VCAM1/SEMA4D proteins and GFAP/A1BG auto-antibodies) to unearth potential diagnostic and prognostic candidates. Disease monitoring in response to anti-rheumatic drugs using treatment-responsive markers, such as S100A8/A9 heterocomplex and leucine-rich alpha-2 glycoprotein, is another application of proteomics-based technologies. In view of the significance of prediction, early diagnosis and monitoring of RA symptomatology, this review discusses the potential utilities of proteinaceous species as proteomics-based biomarkers that may provide insights into disease mechanisms and offer potential avenues for personalized therapeutic interventions to revolutionize RA management.
Gastric cancer is characterized by substantial molecular heterogeneity, and RNA-mediated regulatory mechanisms may contribute to its biological complexity. PIWI-family proteins are central components of the PIWI-interacting RNA pathway, but their circulating patterns in gastric adenocarcinoma remain insufficiently characterized. This study aimed to describe circulating PIWIL1, PIWIL2, and PIWIL4 protein concentrations and explore intra-cohort co-variation patterns in patients with gastric adenocarcinoma without evaluating disease specificity, diagnostic performance, or clinical utility. In this cross-sectional, targeted ELISA-based exploratory study, circulating PIWIL1, PIWIL2, and PIWIL4 concentrations were quantified in 93 de-identified gastric adenocarcinoma samples. PIWIL1 and PIWIL2 measurements were available for 88 samples, and PIWIL4 measurements were available for 72 samples. Commercial ELISA kits were used according to the manufacturer’s instructions, but independent in-house serum validation, including dilution linearity, serum parallelism, matrix-interference testing, intra- and inter-assay precision, and orthogonal protein confirmation, was not performed. Spearman’s rank correlation analysis with false discovery rate correction was used to assess exploratory intra-cohort relationships among PIWI-family proteins. PIWIL1 and PIWIL2 showed relatively stable circulating distributions, whereas PIWIL4 demonstrated pronounced heterogeneity and right-skewness. The most prominent rank-based relationship was an inverse correlation between PIWIL2 and PIWIL4 (Spearman’s ρ ≈ −0.53, q < 0.001), based on 72 complete cases. This correlation remained statistically significant in sensitivity analyses using log-transformed data and exclusion of extreme values. No statistically significant correlations were observed between PIWI-family proteins and CEA or CA 19 − 9 after false discovery rate correction. Exploratory TP53 analyses were not central to the primary objective and are reported descriptively; PIWIL2 showed a modest positive correlation with TP53 (Spearman’s ρ = 0.33), while PIWIL4 showed a weak positive correlation (ρ = 0.19). This study identified an inverse rank-based co-variation between circulating PIWIL2 and PIWIL4 concentrations within a de-identified gastric adenocarcinoma cohort. However, this observation should be interpreted strictly as a pre-validation, exploratory ELISA-derived intra-cohort signal. Because the study lacked comparator groups, clinical annotation, tissue-level confirmation, functional assays, orthogonal protein quantification, and independent serum validation of the ELISA measurements, the observed correlation cannot be considered an analytically validated proteomic association. Further studies with confirmed assay linearity, serum parallelism, matrix-interference assessment, between-plate reproducibility, re-assay of high-value samples at appropriate dilutions, and independent analytical confirmation are required before any biological or clinical significance can be attributed to this finding.
Prostate cancer (PCa) is the most commonly diagnosed cancer in men worldwide, and prostate-specific antigen (PSA) test is currently the standard of choice for PCa diagnosis. Globally, several methods are employed for diagnosis of PCa, and each holds a unique role and importance. Serum PSA test and Magnetic Resonance Imaging (MRI) are widely used for diagnosis of PCa; however, MRI has certain limitations and disadvantages compared to PSA test. Moreover, growing evidences suggest that solely simple testing of serum PSA levels to diagnose PCa leads to widespread overdiagnosis and overtreatment. This is because simple PSA test has certain limitations, including lack of specificity, elevation in Benign Prostatic Hyperplasia (BPH) and inability to detect a significant number of PSA-negative tumors. Additionally, serum PSA levels do not directly correlate with higher grades and stages of PCa. The purpose of this manuscript is to provide comprehensive and up-to-date knowledge, critical perspectives, and practical applications of derivatives of PSA and glycosylation-specific changes in PSA, with the aim of minimizing unnecessary and excessive biopsy in patients suspected of having PCa. Therefore, the use of various derivatives of PSA was focused here to increase the sensitivity and specificity of serum PSA for diagnosis of PCa. In addition to PSA levels, glycan structure of PSA changes with progression of PCa, which suggest that aberrant PSA glycosylation increases as PCa progresses. Differences in the glycan structure of PSA enable to distinguish normal PSA from cancerous origin, suggesting an important biochemical application of glycan structure of PSA in the diagnosis of PCa. Thus, present study highlights the role of derivatives of PSA and glycosylation-specific changes in PSA in the diagnosis and management of PCa. Additionally, this article emphasizes integration of glycan-based assays with PSA derivatives into multi-analyte panels, paving the way for more sensitive and specific diagnosis of PCa and aiding in the management of PCa. The manuscript will provide extensive information to minimize superfluous biopsies, identify patients truly needing biopsy, differentiate BPH from PCa, distinguish indolent from aggressive PCa, enable early diagnosis and treatment at early-stage, monitor treatment response and early detection of recurrence.
Most advanced ovarian cancer patients develop malignant ascites, which describes a buildup of fluid in the peritoneal cavity caused by increased vascular permeability and obstructed lymphatic drainage. Malignant ascites contains cancer cells, which can aggregate as spheroids, as well as stromal cells, cancer-associated fibroblasts, and blood cells that create a complex tumor microenvironment. This study explores the proteome of ascites and how this environment affects the viability, phenotypes, proteomes, and treatment responses of ovarian cancer cells. Using label-free proteomics, we compared the proteomes of cell-free malignant ascites from ovarian cancer patients with those of serum. Additionally, we examined the ex vivo chemotherapy responses of cancer spheroids cultured in ascites. Through detailed proteomic analysis of cells grown as 2D or 3D in tissue culture medium or ascites, we identified biological pathways and specific proteins induced by ascites. Finally, we performed orthogonal validation of a candidate marker, TGM2, using immunofluorescent staining. Proteomics of cell-free ascites identified increased levels of extracellular, secreted, and membrane proteins when compared to serum. Ascites enhanced the baseline cell viability and spheroid formation of immortalized ovarian cancer cell lines compared to standard cell culture medium. However, chemotherapy-induced cell death of spheroids grown in standard cell culture medium remained proportional to changes observed in ascites-cultured spheroids. Ascites-driven phenotypic changes were not recapitulated by adding selected chemokines nor periostin to the cell culture medium, suggesting that additional factors are required. Notably, ascites induced similar ECM, secreted, and membrane proteins across 2D and 3D models, including TGM2, which was validated in spheroids through immunofluorescent staining. This study contributes to our understanding of the role of ascites in the regulation of the proteome and viability of cancer cells. It provides evidence for the induction of TGM2 expression by ascites. Results from this pilot study warrant further study in a larger cohort.
Earlier detection is strongly associated with increased survival for women with ovarian cancer. Unfortunately, current screening strategies, employing serial serum or ultrasound assessments, lack adequate sensitivity and specificity for use in the low-prevalence general population. In contrast, screening for cervical cancer by Pap tests has been routinely performed for over 50 years. Since ovarian cancer cells have been observed in Pap tests, ovarian cancer protein biomarkers may also be present; yet Pap samples have not been rigorously examined for diagnostic proteins. Assessment of cervical effluent, as can be collected in a Pap test, has the potential to differentiate ovarian cancer cases from healthy controls, and thereby demonstrate that intra-abdominal pathology may be detected using this commonly acquired specimen. We hypothesize that proteins shed by ovarian cancer cells can be detected in the SurePath™ liquid-based Pap test fixative using mass spectrometry (MS)-based proteomics, making it possible to distinguish women with ovarian cancer from healthy women. Candidate ovarian cancer biomarkers were successfully identified in liquid-based Pap test samples from 20 cases of high grade serous ovarian cancer, 10 benign ovarian conditions, and 10 healthy control samples, by performing Tandem Mass Tag™ isobaric labeling, 2D liquid chromatography-MS/MS, and bioinformatics integration. Selected reaction monitoring (SRM) MS-based targeted proteomics was then performed using a panel of candidate biomarkers to quantify their abundance in an expanded patient cohort of 90 liquid-based Pap tests. A multi-protein classifier was developed using the SRM-MS data comprised of 6 proteins and achieving an AUC of 0.880 (95
