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Proteolysis-targeting chimera (PROTAC) nanomedicines toward cancer treatment: From synthesis to therapeutic delivery
No full textProteolysis-targeting chimera (PROTAC) has emerged as a groundbreaking therapeutic strategy by hijacking the endogenous ubiquitin proteasome system (UPS) for targeted protein degradation. These heterobifunctional molecules recruit E3 ligases to recognize the protein of interest (POI) and facilitate its ubiquitination, leading to subsequent proteasomal degradation. Compared to conventional protein inhibitors, PROTACs offer a broader range of target degradation and remain effective even against proteins with drug-resistant mutations. Moreover, PROTACs function in a catalytic manner to degrade POIs, allowing for significantly lower administration dosages. In recent years, PROTACs have shown great promise in cancer therapy due to their high efficiency and broad applicability. However, their clinical applications remain challenging due to low bioavailability, limited tumor-targeting ability, and potential side effects. Utilizing nanomedicine for the delivery of PROTACs offers a promising strategy to enhance bioavailability, improve tumor selectivity, and minimize toxicity, thereby advancing their applications in cancer treatment. In this review, we outline the fundamental design principles of PROTACs, summarize the latest progress of nanomedicines from molecular design to drug delivery for improved tumor treatment, introduce PROTAC-based combination therapies and emerging design strategies, and discuss current challenges and future prospects of PROTAC nanomedicines toward clinical translation.
Proteolysis-targeting chimeras (PROTACs) have garnered increasing attention in cancer treatment due to their exceptional protein degradation efficacy. However, their clinical applications are hindered by challenges such as low bioavailability, limited tumor-targeting capability, and potential off-target effects. The rational design of PROTAC-based nanomedicines holds great promise for overcoming these limitations and advancing their clinical translation. [Display omitted]
•This review illustrates the design principles and delivery strategies of PROTACs.•This review highlights the latest advancement of PROAC-based synergistic therapies and novel PROTAC design strategies.•This review summarizes the key challenges and discusses future research direction of PROTAC nanomedicines
Exchange of Dual Clumped Isotopes of CO2 With the Phosphoric Acid-Water System
No full textRationale The reaction of carbonate materials with phosphoric acid is a standard method for extracting CO2, not only for the subsequent analysis of the ratios of 13C/12C and 18O/16O but also for the ratios of 13C18O16O/12C16O16O and 12C18O18O/12C16O16O and the calculation of the Delta 47 and Delta 48 values. For the determination of Delta 47 and Delta 48 values, possible exchange between the CO2 and H2O in the acid is of particular importance as this influences the precision and accuracy of the measurement.Methods Stochastic and non-stochastic CO2 was exposed to "fresh" and "used" phosphoric acid at 90 degrees C. Fresh acid is defined as acid that had not previously reacted with calcium carbonate, whereas "used" acid had previously dissolved a significant amount of carbonate material. The delta 13C, delta 18O, Delta 47, and Delta 48 values of the CO2 were then measured using a Thermo-253 plus using conventional methods, and the rate and the amount of exchange were determined.Results When exposed to "fresh" phosphoric acid, 13C18O16O and 12C18O18O readily exchanged with the H2O in the acid, attaining Delta 47 and Delta 48 values close to those expected as a result of equilibration with H2O at 90 degrees C. In contrast with "used" acid, the rate of exchange of 13C18O16O with H2O in the acid was negligible, whereas the exchange of 12C18O18O, although reduced compared with "fresh" acid, was still significant.Conclusions Such data have implications on the precision and accuracy of Delta 47 and Delta 48 values in systems in which the acid has been previously used to react in multiple samples, as well as in those in which fresh acid is used for every sample. The use of a sacrificial amount of carbonate prior to the reaction of "real" samples will reduce some of the changes observed in this study
Cognitive mapping variants and their training algorithms
No full textCognitive modeling has been crucial in understanding and simulating complex systems, especially for decision-making processes. From Tolman’s cognitive maps of rats to Kosko’s fuzzy cognitive maps and modern quantum cognitive models, researchers have sought representations that combine human expertise with computational rigor. This survey offers a comprehensive, historical, and mathematical treatment of concept maps, cognitive maps, and a rich family of fuzzy and hybrid cognitive map models. We discuss rule-based, crisp, gray, rough, neutrosophic, and quantum cognitive maps, and we review machine learning algorithms used to train them, including Hebbian and differential Hebbian learning, genetic and evolutionary algorithms, particle swarm optimization, reinforcement learning, and other hybrid techniques. The aim is to present a unified theoretical framework that traces the development of cognitive mapping from its origins to the latest advances, providing clear pathways for future research
Body Talk: Correlates of Gut-Immune Dysregulation Phenotypes in People Living with HIV Who Use Methamphetamine
No full textMicrobial translocation, immune activation, inflammation, and dysregulated metabolism of neurotransmitter precursors are interacting pathophysiologic processes linked to neuropsychiatric comorbidities and faster HIV disease progression. We examined correlates of distinct phenotypes of gut-immune dysregulation in people living with HIV (PWH) who use methamphetamine.
Participants were 122 PWH who had biochemically confirmed recent methamphetamine use, including non-injection use. Peripheral plasma markers reflected: intestinal permeability, microbial translocation, immune activation, inflammation, and dysregulated metabolism of neurotransmitter precursors. Using latent profile analysis (i.e., clustering) of these markers, we identified gut-immune phenotypes and their clinical, demographic, and stigma-related correlates.
Three immune profiles emerged: (1) low gut-immune dysregulation with lower microbial translocation, macrophage activation, inflammation, and tryptophan catabolism; (2) moderate gut-immune dysregulation with all markers within average range; and (3) high gut-immune dysregulation with higher microbial translocation, immune activation, inflammation, and tryptophan catabolism. In adjusted analyses, higher viral load (one log10 copy/ml; AOR=1.97, 95% CI=1.02-3.82), injection of methamphetamine (AOR=3.60, 95% CI=1.23-10.50), and internalized stigma (AOR=1.78, 95% CI=1.01-3.15) were associated with having a moderate gut-immune dysregulation profile. Additionally, higher viral load (AOR=2.98, 95% CI=1.53-5.24) and injecting methamphetamine (AOR=5.45, 95% CI=1.34-17.78) were associated with having a high gut-immune dysregulation profile.
Distinct patterns of microbial translocation, immune activation, inflammation, and metabolism of amino acid precursors distinguished gut-immune phenotypes of PWH reporting injection methamphetamine use and greater internalized stigma. Interventions tailored to PWH who inject methamphetamine or struggle with internalized stigma could optimize HIV-related health outcomes
Potential and pitfalls: accuracy versus adequacy of ChatGPT’s performance on surgery shelf examination
No full textPurpose
The recent surge in artificial intelligence (AI)-related technologies presents an opportunity for revolutionary advances in traditional methods of medical education. ChatGPT is one such application of AI that can take free-form text as input and generate a human-like response. This study sought to evaluate ChatGPT’s performance on a simulated surgery shelf exam and assess its potential as a learning tool for medical students.
Methods
Two 50-question tests were randomly selected from the National Board of Medical Examiners (NBME) practice surgery shelf exams. ChatGPT (Generative Pretrained Transformer-4o, September 2024) evaluated each question sequentially. Questions with images were excluded. Responses were recorded, and a board-certified general surgeon evaluated each justification. Each justification was graded as either having no errors, minor errors that do not significantly impact understanding of the topic, or major errors that significantly impact understanding of the topic.
Results
ChatGPT answered 96.6% of questions correctly. 9.2% of all responses contained minor errors, and 9.2% contained major errors. Among correctly answered questions, 9.5% contained minor errors, while 6.0% contained major errors. All major errors were due to incorrect information that was presented as correct.
Conclusion
ChatGPT demonstrates high accuracy when assessed on its ability to correctly answer multiple-choice questions that medical students could encounter on a surgery shelf exam. However, caution must be used when using ChatGPT as an adjunct to traditional education methods. With 15.5% of ChatGPT’s correct responses containing errors, often confidently asserting false information, students risk learning incorrect information if unaware of this limitation
From followers to leaders: A four-stage process model of EMNE capability development
No full textWe advance the springboard perspective by investigating how emerging-market multinational enterprises (EMNEs) evolve into global leaders, uncovering the specific mechanisms for capability development in the post-springboarding phase. We propose a four-stage process model that delineates the transformation of EMNEs, in response to technological paradigm shifts and business opportunities, from followers to leaders: (1) capability acquisition via springboarding from international value-chain partners, (2) capability scaling via co-exploitation with domestic value-chain partners, (3) capability creation via co-exploration with domestic value-chain partners, and (4) capability diffusion via reverse knowledge transfer to international value-chain partners. Using the transition of the automotive supply sector in China from internal combustion engines (ICE) to electric vehicles (EV) as an exemplary case, we illustrate how EMNEs can evolve from learning extant knowledge from, and to contributing new knowledge to, advanced-market multinational enterprises (AMNEs). Our model extends the springboard perspective by detailing the rarely-explored post-springboarding phase, particularly in the context of contemporary geopolitical tensions. More broadly, we contribute to a deeper understanding of the action-based view of dynamic capabilities in international business by specifying the entrepreneurial actions that make EMNE capability development and transformation possible
Treatment-related Outcomes and Patterns of Relapse in Secondary CNS Involvement by Large B-cell Lymphoma
No full textSecondary central nervous system (CNS) large B-cell lymphoma (SCNSL) occurs in the de novo setting, as a CNS-isolated relapse, or synchronous (concomitant CNS and systemic) relapse. SCNSL is a devastating event without therapeutic consensus. Thus, we aimed to evaluate treatment outcomes in an international cohort. Progression-free survival (PFS), overall survival (OS) and cumulative incidence of relapse (CIR, estimated using competing-risk models) were reported. Prognostic factors were identified in a 6-month landmark multivariate analysis. Outcomes following thiotepa autologous stem cell transplant (ASCT) and chimeric antigen receptor T-cell therapy (CAR-T) delivered at relapse were compared following propensity score matching (PSM). A total of 1139 patients were included in the analysis (de novo: 537; relapsed SCNSL: 602). 2-year PFS estimates were 40.4%, 43.9% and 16.2% for de novo SCNSL, CNS-isolated relapse, and synchronous relapse respectively. Patients with CNS-isolated relapse demonstrated low rates of systemic recurrence (24-month CIR 6%). Thiotepa-ASCT correlated with longer survival in de novo SCNSL (PFS: HR=0.57; P=0.005; and OS: HR=0.62; P=0.023) and CNS-isolated relapses (PFS: HR=0.55; P=0.002; and OS: HR=0.39; P<.0001) in 6-month multivariable landmark analysis. ASCT (thiotepa or non-thiotepa) also associated with improved survival in synchronous relapses (PFS: HR=0.57; P=0.023; and OS: HR=0.48; P=0.019). Higher survival with thiotepa-ASCT compared to CAR-T was observed in survival analyses following PSM (PFS: HR=0.45; P=0.005 and OS: HR=0.41; P=0.014). These data support thiotepa-ASCT in eligible patients, particularly de novo disease and CNS-isolated relapses. CNS-isolated relapse was infrequently associated with systemic recurrence, supporting treatment regimens adopted from primary CNS lymphoma
Use of Allowable Blood Loss in Cesarean Timeouts to Improve Transfusion Stewardship and Reduce Costs
No full textBlood transfusions occur in ∼2% of U.S. cesarean deliveries. Despite established guidelines, obstetric patients are often over-transfused, leading to unnecessary risk and inefficient use of limited blood products. Allowable blood loss (ABL) offers an individualized transfusion threshold that may reduce avoidable intraoperative transfusions and associated costs. This study explores the clinical and economic impact of ABL reporting during cesarean timeouts at a quaternary care safety-net hospital.
A retrospective cohort study compared transfusion practices before (Sept 2022–Jan 2023) and after (Aug 2024–Dec 2024) ABL reporting was introduced. Patients with preoperative hemoglobin (Hgb) <7 g/dL or only autologous cell-salvaged blood were excluded. Descriptive and nonparametric tests were employed, with significance set at p < .05. Holm-Bonferroni corrections were applied for correlated variables.
87 patients met inclusion criteria (pre n=76; post n=11). Post-intervention patients had lower preoperative Hb (p= .027) and ABL (p = .020), but similar blood loss, PPH risk, and postoperative outcomes. Data showed reductions in avoidable intraoperative transfusions and unused units of pRBCs per patient with no statistically reliable trend. (p = 1.000; p = 1.000).
The modest trend towards improved stewardship following ABL reporting during cesarean timeouts may yield economic benefits through reduced crossmatching, fewer unused units, and monthly reductions in total and avoidable transfusions. This simple intervention may enhance transfusion stewardship, reduce inefficient use of scarce resources, and decrease costs. Larger studies are needed to confirm these preliminary findings and assess long-term outcomes
Seawater-mixed concrete – A review with focus on durability properties
No full textFreshwater scarcity remains a major global concern, particularly in coastal regions. Utilizing seawater for concrete production offers a sustainable approach to conserve freshwater resources while supporting construction in marine environments. This study conducts a systematic and data-driven review of seawater-mixed concrete research using the Scopus database, focusing on durability aspects. The analysis reveals that seawater accelerates the early hydration of ordinary Portland cement (OPC) due to the ionic activity of Cl⁻, Na⁺, and Mg²⁺, leading to a denser early-age microstructure and enhanced initial strength. However, long-term strength and durability outcomes remain inconsistent across studies, influenced by curing conditions, ionic composition, and mixture design. Seawater-mixed concrete generally exhibits improved sulfate resistance but shows variable carbonation behavior and increased susceptibility to alkali–silica reaction (ASR), shrinkage, and freeze–thaw damage. The incorporation of supplementary cementitious materials (SCMs) such as metakaolin (MK), fly ash (FA), and ground granulated blast-furnace slag (GGBS) enhances chloride binding and refines the microstructure, mitigating some adverse effects. Overall, this review identifies key research gaps in long-term durability and emphasizes the need for standardized methodologies and data-driven investigations to optimize seawater-mixed concrete for sustainable coastal and marine infrastructure
A Semi-automatic Approach for Minimizing Expert Annotations in Medical Image Segmentation
No full textDeep learning excels in medical imaging segmentation but requires extensive annotated data. To reduce reliance on expert labeling, we propose a semi-automatic method, where a small subset of the available data is annotated to train a model. The model then generates pseudo-annotations for the remaining data, refining the model iteratively until no further improvement is observed. We demonstrate the method’s effectiveness by training models on the Kvasir-Seg and Breast Ultrasound Images Dataset using SegFormer and U-Net architectures. With only 30% of the annotated data, our method achieves at least 90% of the accuracy of models trained on the full dataset. We also propose a method to estimate optimal data annotation using the model’s entropy measure