Response Of Model Research Articles

Prediction model for treatment response of primary membranous nephropathy with nephrotic syndrome.

To investigate the predictors and establish a nomogram model for the prediction of the response to treatment in primary membranous nephropathy (PMN) with nephrotic syndrome (NS). The clinical, laboratory, pathological and follow-up data ofpatients withbiopsy-proven membranous nephropathy at the Affiliated Hospital of QingdaoUniversity were collected. A total of 373 patients were randomly assigned into development group (n = 262) and validation group (n = 111). Logistic regression analysis was performed in the development group to determine the predictors of treatment response. A nomogram model was established based on the multivariate logistic regression analysis and validated in the validation group. The C-index and calibration plots were used for the evaluation of the discrimination and calibration performance, respectively. Serum albumin levels (OR = 1.151, 95% CI 1.078-1.229, P < 0.001) and glomerular C3 deposition (OR = 0.407, 95% CI 0.213-0.775, P = 0.004) were identified as independent predictive factors for treatment response in PMN with NS, then a nomogram was established combining the above indicators and treatment regimen. The C-indices of this model were 0.718 (95% CI 0.654-0.782) and 0.789 (95% CI 0.705-0.873) in the development and validation groups, respectively. The calibration plots showed that the predicted probabilities of the model were consistent with the actual probabilities (P > 0.05), which indicated favorable performance of this model in predicting the treatment response probability. Serum albumin levels and glomerular C3 deposition were predictors for treatment response of PMN with NS. A novel nomogram model with good discrimination and calibration was constructed to predict treatment response probability at an early stage.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Modelling the Impact of NETosis During the Initial Stage of Systemic Lupus Erythematosus

The development of autoimmune diseases often takes years before clinical symptoms become detectable. We propose a mathematical model for the immune response during the initial stage of Systemic Lupus Erythematosus which models the process of aberrant apoptosis and activation of macrophages and neutrophils. NETosis is a type of cell death characterised by the release of neutrophil extracellular traps, or NETs, containing material from the neutrophil’s nucleus, in response to a pathogenic stimulus. This process is hypothesised to contribute to the development of autoimmunogenicity in SLE. The aim of this work is to study how NETosis contributes to the establishment of persistent autoantigen production by analysing the steady states and the asymptotic dynamics of the model by numerical experiment.

Improving prediction accuracy of CSM-CERES-Wheat model for water and nitrogen response using a modified Penman-Monteith equation in a semi-arid region

Context or problemThe implementation of crop models for water and nitrogen management are important in preserving water resources and reducing groundwater pollution. The Decision Support System for Agrotechnology Transfer (DSSAT) is a collection of field-scale process-based crop models that simulate crop growth and yield in response to water and nitrogen management as well as many other factors. Objective or research questionThe objective of this study was to improve the simulation of irrigation and nitrogen fertilizer effects on growth and yield of winter wheat using locally modified Penman-Monteith (PM) equation. Specifically, we focused on improving the estimation of ETo using a locally modified Penman-Monteith (PM) equation in the CSM-CERES-Wheat model. MethodsField measured data from a two-year experiment (2009–2010 and 2010–2011) in a semi-arid region were used to calibrate and evaluate the CSM-CERES-Wheat model in a region with strong advective condition. In this experiment, the irrigation treatments were 1.2, 1.0, 0.8, 0.5 times by full irrigation requirements and rain-fed, and nitrogen treatments were 0, 46, 92, and 136 kg N ha−1. ResultsUsing the CSM-CERES-Wheat model with the unmodified PM equation resulted in some growth variables such as: aboveground dry matter, grain yield, and grain nitrogen uptake with high normalized root mean square error (NRMSE) of 0.51, 0.35, and 0.32, respectively. However, using the modified PM equation for ETo module under semi-arid condition improved the soil water content estimation and the model outputs, such as the above ground dry matter, grain yield and grain nitrogen uptake with NRMSE of 0.2, 0.15 and 0.16, respectively, that are accurate. This modification provided more accurate estimations, particularly in our experimental site in semi-aid region with strong advective conditions. ConclusionsThe findings of this study indicated that the locally modified PM equation in the CSM-CERES-Wheat model enhanced the accuracy of estimating irrigation and nitrogen fertilizer on the growth and yield of winter wheat. By improving the estimation of ETo value, the model's performance in simulating crop responses to water and nitrogen management was significantly enhanced. Implications or significanceThe utilization of the accurately estimated ETo value of the modified PM equation has significant importance in the practical application of the CSM-CERES-Wheat model. Furthermore, this modification greatly enhances the accuracy of model outputs, particularly at our experimental site located in a semi-arid region with robust advective conditions.

Impact of motivation and participative leadership style on employee performance: Mediating the role of job satisfaction

African higher educational institutions are characterized by limited resources hindering progress.  Employees in such institutions are inclined to maintain dedication to their roles, address challenges and pursue excellence in their tasks. Motivation and leadership style play a crucial role in overcoming these challenges. Therefore, this study focuses on the relationship between motivation, participative leadership style, job satisfaction and employee performance at public universities in Ghana. It explored the impact of motivation and participative leadership style on employee performance as well as how job satisfaction plays a moderating role in achieving a goal. This study determined how work satisfaction mediated motivation, participatory leadership style and employee performance in six Ghanaian public universities. A quantitative approach was adopted and a structured questionnaire was administered to 306 university employees who were selected by using both cluster and simple random sampling techniques. The acquired data were analyzed quantitatively by using the Structural Equation Model (SEM) in response to the given hypothesis. The findings revealed that motivation and a participatory leadership style had a considerable positive impact on staff performance. Furthermore, the study revealed that job satisfaction served as a mediator in the relationship between motivation, a participative leadership style and employee performance. Based on the findings, it is recommended that employers at public universities enhance employee motivation and engagement. This can be done by fostering a work environment characterized by supportive leadership, opportunities for growth and development and cultivating a culture that emphasizes appreciation and acknowledgment of performance.

Single-beam three-axis SERF atomic magnetometer based on coordinate system rotation

We propose what we believe to be a new single-beam three-axis spin exchange relaxation free (SERF) vector atomic magnetometer scheme based on coordinate system deflection. A theoretical model for the system response under arbitrary angle deflection was established for the first time, and the system response at different angles was simulated and analyzed. The simulation results show that the system response increases in the direction of the non-sensitive axis and decreases in the direction of the sensitive axis as the deflection angle increases, and the two responses tend to be the same when the angle is deflected to 45-degrees. Experimental measurements were carried out at a deflection angle of 45-degrees and the results showed that the sensitivity of the magnetometer was 55fT/Hz1/2 in the x1-axis, 38fT/Hz1/2 in the y1-axis and 60fT/Hz1/2 in the z1-axis. This single-beam magnetometer can be used to construct a miniaturized and low-cost weak magnetic sensor, which is expected to be used for vector measurement of biomagnetism.

ChatGPT's adherence to otolaryngology clinical practice guidelines.

Large language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines. We presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses. The study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests. Using a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use.

Defining predictors of responsiveness to advanced therapies in Crohn’s disease and ulcerative colitis: protocol for the IBD-RESPONSE and nested CD-metaRESPONSE prospective, multicentre, observational cohort study in precision medicine

IntroductionCharacterised by chronic inflammation of the gastrointestinal tract, inflammatory bowel disease (IBD) symptoms including diarrhoea, abdominal pain and fatigue can significantly impact patient’s quality of life. Therapeutic developments in the...

Experimental lap-shear tests on friction dampers with single and double slotted holes

Several experimental lap shear tests have been performed to assess the initial values of the slip resistance and their evolution during a cyclic loading history. Both friction dampers with parallel slotted holes and double slotted holes have been investigated. Friction devices with shims made of a previously tested material, called M4, have been tested and the tribological behaviour, the degradation of the friction coefficient, the influence of the bolts’ preload and the effect of Belleville washers have also been investigated. Moreover, a phenomenological model for the force-displacement response has been calibrated for the modelling of the cyclic response of the friction device. This is particularly important for the development of non-linear dynamic analyses of seismic resistant frames equipped with friction joints aiming at an accurate prediction of the structural response during a seismic event. Finally, a quality control of the friction properties of different batches of friction shims has been performed by means of the Fisher-Berens and Snedecor tests.

An overview of the E3SM version 2 large ensemble and comparison to other E3SM and CESM large ensembles

Abstract. This work assesses a recently produced 21-member climate model large ensemble (LE) based on the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 2 (E3SM2). The ensemble spans the historical era (1850 to 2014) and 21st century (2015 to 2100), using the SSP370 pathway, allowing for an evaluation of the model's forced response. A companion 500-year preindustrial control simulation is used to initialize the ensemble and estimate drift. Characteristics of the LE are documented and compared against other recently produced ensembles using the E3SM version 1 (E3SM1) and Community Earth System Model (CESM) versions 1 and 2. Simulation drift is found to be smaller, and model agreement with observations is higher in versions 2 of E3SM and CESM versus their version 1 counterparts. Shortcomings in E3SM2 include a lack of warming from the mid to late 20th century, likely due to excessive cooling influence of anthropogenic sulfate aerosols, an issue also evident in E3SM1. Associated impacts on the water cycle and energy budgets are also identified. Considerable model dependence in the response to both aerosols and greenhouse gases is documented and E3SM2's sensitivity to variable prescribed biomass burning emissions is demonstrated. Various E3SM2 and CESM2 model benchmarks are found to be on par with the highest-performing recent generation of climate models, establishing the E3SM2 LE as an important resource for estimating climate variability and responses, though with various caveats as discussed herein. As an illustration of the usefulness of LEs in estimating the potential influence of internal variability, the observed CERES-era trend in net top-of-atmosphere flux is compared to simulated trends and found to be much larger than the forced response in all LEs, with only a few members exhibiting trends as large as observed, thus motivating further study.

Genital arousal and responsive desire among women with and without sexual interest/arousal disorder symptoms.

Models depicting sexual desire as responsive to sexual arousal may be particularly apt for women experiencing arousal or desire difficulties, and the degree to which arousal triggers desire may depend on the relationship context and desire target and timing-yet, these associations have not been directly tested among women with and without sexual interest/arousal disorder (SIAD). To assess the role of SIAD status and relationship satisfaction in the associations between genital arousal and 4 types of responsive desire. One hundred women (n = 27 meeting diagnostic criteria for SIAD) in romantic relationships with men viewed a sexual film (pleasurable intimate depiction of oral sex and penile-vaginal intercourse) while their genital arousal was recorded via vaginal photoplethysmography (n = 63) or thermal imaging of the labia (n = 37). Partner and solitary desire was assessed immediately before and after the film (immediate desire) and 3days later (delayed desire). Outcomes consisted of genital response (z scored by method) and associations between genital response and responsive sexual desire. The key difference between women with and without SIAD was not in their ability to experience genital arousal but in how their genital responses translated to responsive sexual desire. Women with SIAD actually exhibited greater genital arousal than unaffected women. Associations between genital arousal and desire were significant only for women with SIAD and depended on relationship satisfaction and desire type. For women with SIAD with low relationship satisfaction, higher arousal predicted lower immediate desire for a partner; for those with high relationship satisfaction, arousal was either positively related (vaginal photoplethysmography) or unrelated (thermal imaging of the labia) to immediate desire for a partner. Associations with other desire types were not significant. Patterns of genital arousal and partner-specific responsive desire among women affected with SIAD were indicative of an avoidance model in response to heightened genital arousal, unless relationship satisfaction was high; attending to genital arousal sensations could be a means of triggering sexual desire for women with SIAD who are satisfied in their relationships. This is one of the first sexual psychophysiologic studies to connect relationship factors to patterns of sexual response. The differing arousal assessment procedures and lack of official diagnosis may have attenuated results. The homogeneous sample and in-person session requirement limit generalizability. When compared with unaffected women, women affected by SIAD may exhibit stronger arousal responses with sufficiently incentivized sexual stimuli, and the connection between their genital arousal and responsive desire for their partners may be stronger and more dependent on relationship context.

A mathematical model for temporal cerebral blood flow response to acetazolamide evaluated in patients with Moyamoya disease

Background: Paired cerebral blood flow (CBF) measurement is usually acquired before and after vasoactive stimulus to estimate cerebrovascular reserve (CVR). However, CVR may be confounded because of variations in time-to-maximum CBF response (tmax) following acetazolamide injection. With a mathematical model, CVR can be calculated insensitive to variations in tmax, and a model offers the possibility to calculate additional model-derived parameters. A model that describes the temporal CBF response following a vasodilating acetazolamide injection is proposed and evaluated. Methods: A bi-exponential model was adopted and fitted to four CBF measurements acquired using arterial spin labelling before and initialised at 5, 15 and 25 min after acetazolamide injection in a total of fifteen patients with Moyamoya disease. Curve fitting was performed using a non-linear least squares method with a priori constraints based on simulations. Results: Goodness of fit (mean absolute error) varied between 0.30 and 0.62 ml·100 g- 1·min-1. Model-derived CVR was significantly higher compared to static CVR measures. Maximum CBF increase occurred earlier in healthy- compared to diseased vascular regions. Conclusions: The proposed mathematical model offers the possibility to calculate CVR insensitive to variations in time to maximum CBF response which gives a more detailed characterisation of CVR compared to static CVR measures. Although the mathematical model adapts generally well to this dataset of patients with MMD it should be considered as experimental; hence, further studies in healthy populations and other patient cohorts are warranted.

New findings on the risk of hypertension from organophosphorus exposure under different glycemic statuses: The key role of lipids?

Background and objectiveConsidering the widespread use of organophosphorus pesticides (OPs) and the global prevalence of hypertension (HTN), as well as studies indicating that different glycemic statuses may respond differently to the biological effects of OPs. Therefore, this study, based on the Henan rural cohort, aims to investigate the association between OPs exposure and HTN, and further explores whether lipids mediate these associations. MethodsWe measured the plasma levels of OPs in 2730 participants under different glycemic statuses using gas chromatography-triple quadrupole mass spectrometry (GC–MS/MS). A generalized linear model, Quantile g-computation (QGC), adaptive elastic net (AENET), and Bayesian kernel machine regression (BKMR) models were used to assess the impact of OPs exposure on HTN, with least absolute shrinkage and selection operator (LASSO) penalty regression identifying main OPs. Mediation models were used to evaluate the intermediary role of blood lipids in the OPs-HTN relationship. ResultsThe detection rates for all OPs were high, ranging from 76.35 % to 99.17 %. In the normal glucose tolerance (NGT) population, single exposure models indicated that malathion and phenthoate were associated with an increased incidence of HTN (P-FDR < 0.05), with corresponding odds ratios (ORs) and 95 % confidence intervals (CIs) of 1.624 (1.167,2.260) and 1.290 (1.072,1.553), respectively. QGC demonstrated a positive association between OP mixtures and HTN, with malathion and phenthoate being the primary contributors. Additionally, the AENET model's Exposure Response Score (ERS) suggested that the risk of HTN increases with higher ERS (P < 0.001). Furthermore, BKMR revealed that co-exposure to OPs increases HTN risk, with phenthoate having a significant impact. Furthermore, triglycerides (TG) mediated 6.55 % of the association between phenthoate and HTN. However, no association was observed in the impaired fasting glucose (IFG) and type 2 diabetes mellitus (T2DM) populations. ConclusionsOur findings suggest that in the NGT population, OPs may significantly contribute to the development of HTN, proposing TG as a potential novel target for HTN prevention.

Non‐Linear Climate Change Impacts on Crop Yields May Mislead Stakeholders

AbstractWe utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non‐linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so‐called “hot” models and can skew multi‐model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact‐drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways.

Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery.

Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT's 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT's GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model's performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model's responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Research on the centre-of-mass adjustment method for the head of an anthropometric test device based on a head impact biomechanics model

Head injury is the primary cause of serious lesions or death in automobile accidents. During crash tests, the mass and centre-of-mass position of the head of anthropometric test devices are crucial factors determining the accuracy of head injury test data. However, owing to the irregular shape as well as complex structure and material composition of the head of dummies, problems such as uneven mass distribution easily occur, resulting in deviations between the actual and designed positions of the centre-of-mass of the head. This study conducted research on head impact biomechanics and the centre-of-mass adjustment method for the head of dummies. Specifically, the biomimetic mechanical equivalent of the head was investigated, and head impact mechanics were analysed. Then, a predictive model was established for head drop mechanical response; the accuracy of this model was verified through head drop experiments. Based on the model, the law of centre-of-mass influence on the mechanical response of the dummy head was investigated, yielding an evaluation method for the different mechanical responses of the head. Using the rotational centre-of-mass measurement method, the mass and centre-of-mass position of the head were obtained. The distributed centre-of-mass adjustment method based on the principle of torque equilibrium was utilized to solve the deviations in the centre-of-mass position, the geometric dimensions of the counterweights and the centre-of-mass itself. Two counterweights were distributed on both sides of the internal cavity wall of the dummy head, resolving the positioning issue of the counterweights in the head cavity. The rationality of the centre-of-mass adjustment results was analysed using the predictive model for head impact mechanical response. The analysis results indicate that the centre-of-mass-adjusted dummy head better aligned with the mechanical response of the head in real collision scenarios, providing a new approach for the adjustment of the centre-of-mass of the head of dummies.

Programming mechanics in knitted materials, stitch by stitch

Knitting turns yarn, a 1D material, into a 2D fabric that is flexible, durable, and can be patterned to adopt a wide range of 3D geometries. Like other mechanical metamaterials, the elasticity of knitted fabrics is an emergent property of the local stitch topology and pattern that cannot solely be attributed to the yarn itself. Thus, knitting can be viewed as an additive manufacturing technique that allows for stitch-by-stitch programming of elastic properties and has applications in many fields ranging from soft robotics and wearable electronics to engineered tissue and architected materials. However, predicting these mechanical properties based on the stitch type remains elusive. Here we untangle the relationship between changes in stitch topology and emergent elasticity in several types of knitted fabrics. We combine experiment and simulation to construct a constitutive model for the nonlinear bulk response of these fabrics. This model serves as a basis for composite fabrics with bespoke mechanical properties, which crucially do not depend on the constituent yarn.

Abstract 4106: A novel synthetic long peptide vaccine composition targeting multiple PD-L1 T-cell epitopes exhibits anti-tumor efficacy in a syngeneic mouse colorectal cancer model

Abstract Background Cancer immunotherapy targeting immune checkpoints has shown promising clinical anti-tumor efficacy over the past decades. Monoclonal antibodies against PD-1 or PD-L1 have demonstrated clinical efficacy in a wide spectrum of cancer types. Despite the clinical success of therapies using immune checkpoint inhibitors (ICI), only a subset of patients shows long-lasting tumor remission, while resistance and toxicities to these agents remain to be addressed. This supports the need to explore alternative immunotherapeutic approaches or additional therapies for synergistic combinations to augment the anti-tumor efficacy for cancer patients receiving ICI therapies. Methods As an active immunotherapy approach, we developed a blend of four novel synthetic peptide vaccines each containing one PD-L1 T-cell epitope with individual covalent conjugation of the carrier protein, keyhole limpet haemocyanin (KLH), which is to break immune tolerance towards self-molecules. The four PD-L1 epitopes range from 30 to 40 amino acids in length, which target different domains of mouse PD-L1 protein. The PD-L1 peptides were synthesized using the technique of solid-phase peptide synthesis and purified by high performance liquid chromatography. All naked peptides were conjugated to KLH protein by thiol-maleimide reaction, followed by dialysis for purification. The peptide vaccines were formulated with a Toll-like receptor 9 agonist CpG ODN 1826 and aluminum hydroxide gel for animal immunization. Results These synthetic long PD-L1 peptide vaccines are highly immunogenic in a syngeneic BALB/c mouse model for a cell-mediated immune response when admixed and formulated with adjuvants. The vaccine composition is well tolerated by the mice with no evidence of toxicity and autoimmunity. In vivo administration of the vaccine formulation can elicit a positive PD-L1-specific T cell response in splenocytes with an IFN-γ ELISPOT assay. Moreover, in vivo vaccination of these PD-L1 peptide vaccines in a CT26 syngeneic mouse preventive colorectal cancer model exhibits a potent anti-tumor activity, with a 54% tumor growth inhibition (p&amp;lt;0.001) in tumor volume compared to that of the adjuvant-control group at the end of the study. Conclusions Taken together, our data demonstrate a proof-of-concept that T cell-based PD-L1 peptide vaccines are a promising therapeutic strategy for inducing anti-tumor immunity to combat cancers. Citation Format: Kenneth Nansheng Lin, Melvin Toh. A novel synthetic long peptide vaccine composition targeting multiple PD-L1 T-cell epitopes exhibits anti-tumor efficacy in a syngeneic mouse colorectal cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4106.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

Coupled Climate Models Systematically Underestimate Radiation Response to Surface Warming

AbstractA realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future.