Multiple Damage Research Articles

A novel structural damage detection method using a hybrid IDE–BP model

Recently, artificial intelligence has been widely applied in the field of structural damage detection. In this study, a back-propagation neural network (BPNN) hybrid with an improved differential evolution algorithm (IDE) is proposed to advance the development of structural damage detection. Generally, most engineering structures’ dynamic characteristics can be directly obtained by modal analysis, and the corresponding vibration features can provide information on structural damage. Thus, based on the results of modal analysis, artificial intelligent methods such as BP neural network find their way to locate the damage and quantify the damage severity. However, the existing research shows that the original BP has insufficient accuracy in structural damage detection, especially cannot effectively deal with complex structural damage. In this study, an improved differential evolution algorithm is proposed, introducing a group sampling strategy and an adaptive mutation strategy. Then, the proposed IDE and BP neural networks are hybridized into IDE–BP to promote the neural network’s data classification and regression ability. Subsequently, based on the IDE–BP model, a method for detecting the location and severity of multi-damage scenarios is established. The decision tree algorithm (DT) is utilized to identify the number of damaged elements. To verify the effectiveness of the proposed damage detection method, the IDE–BP model is applied to the damage detection for a steel pipe and a cantilever plate. To study the anti-noise ability and robustness of the IDE–BP model, the noise and incomplete modes are introduced. Compared with the original BP, SVM, KNN, PSO–BP,​ and DE–BP, the numerical results show that the damage detection method based on the IDE–BP model performs better for all single and multiple damage scenarios.

Change over time of lipid profile relates to steroid treatment but not to an inflammatory state in Granulomatosis with poliangioitis polyangiitis (GPA)

Aim: Granulomatosis with polyangiitis (GPA) is a small vessel vasculitis. Inflammation of the vessel wall may induce multiple vascular damages. Atherosclerosis is accelerated during vasa inflammation. Metabolic profile and cardiovascular risk are far to be determined in these patients. Thus, Cardiovascular atherosclerotic disease (ASCVD) may represent a risk for patients' outcomes. The purpose is to evaluate ASCVD risk in GPA over time during disease follow-up. Methods: We retrospectively evaluated 37 patients (22 Females, aged 51.45±17.15) who received a diagnosis of GPA (T0). Patients were evaluated at 1 (T1) and 2 (T2) year follow-up. All patients were treated with high steroid dose followed by a one-year tapering, associated to another immunosuppressant. Lipid profile included total cholesterol, HDL, LDL and Triacylglycerol. To evaluate inflammatory activity, we evaluate erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR) at the same time points. ANOVA for repeated values was used to evaluate the trend over time and Tukey's multiple comparisons test was a second step evaluation. Results: At T1 there was an increase in total cholesterol compared to baseline (T1vsT0, p<0.05) and T2 (T1vs T2, p<0.05). Similarly, LDL (T1vsT0, p<0.05) presents the same trend, while Triacylglycerol increased in T1 compared to baseline (T1vsT0, p<0.05), but no difference there was in T2 compared to T1 or T0. No difference was found in HDL between the different time points. CRP was no different, despite a reduction being noticed. On the contrary, we found a reduction at T2 but not in T1 in ESR (T1vsT0, p<0.05) and NLR (T1vsT0, p<0.05). Conclusion: Our data suggest that a change in lipid profile may not relate to better control of inflammation. On the contrary, the increase in the first year of follow-up should be a consequence of steroid treatment needed to spread disease control. These data may be helpful in the evaluation of both cardiovascular disease and lipid metabolism due to the connection between the two parameters with vessel inflammation. Further studies are needed to better evaluate the cardiovascular effect of vasculitis and consequent treatment.

AQUADA PLUS: Automated damage inspection of cyclic-loaded large-scale composite structures using thermal imagery and computer vision

This study develops and demonstrates a new method to locate and track multiple fatigue damages in large-scale composite structures subject to cyclic loads using thermography and computer vision. Progressive damages generate thermal features due to material friction, allowing efficient detection using passive thermography. Automated damage localization and evaluation are done by thermal image processing incorporated with thermodynamics principles. The proposed new method, AQUADA PLUS, is demonstrated on two composite wind turbine blades in which artificial defects are introduced to trigger damage growth when subject to cyclic loading. Multiple damage sites are located, tracked, and evaluated automatically despite the complex thermal background that changes considerably when the thermal videos are taken. A pioneering drone-based field test has been conducted to demonstrate the possibility of field application where complex environmental conditions and varying thermal backgrounds are present. The associated challenges are identified and the possible solutions are discussed for the further development of the proposed method toward real-world application. All original thermal videos presented in this study are shared with the public for future study.

Vibration data‐driven machine learning architecture for structural health monitoring of steel frame structures

AbstractA vibration data‐based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag‐of‐Features algorithm that extracts the speeded‐up robust features (SURF) from the time‐frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K‐means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k‐nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.

Theoretical and Numerical Investigation of Damage Sensitivity of Steel–Concrete Composite Beam Bridges

To investigate the sensitivity of the overall mechanical performance of steel–concrete composite beam bridges (SCCBBs) to different types of damage, this paper proposes a method of analyzing the sensitivity of SCCBBs to damage based on the extremely randomized trees (ET) algorithm in machine learning. A steel–concrete composite continuous beam bridge was used as the engineering basis, and the finite element method was used to analyze the changes in the static and dynamic response of the bridge caused by seven types of damage. The proposed SCCBB damage sensitivity analysis theory was used to explore the sensitivity factors of the seven types of damage. The results show that microcracks in steel beams have the most significant impact on the mechanical performance sensitivity of SCCBBs, followed by the concrete slab stiffness degradation and bridge deck breakage. The sensitivity of the damage caused by transverse diaphragms and bridge pier stiffness degradation is relatively low, while the sensitivity of stud fractures and bearing damage is minimal. The impact factors of damage sensitivity were 0.51, 0.19, 0.13, 0.08, 0.05, 0.03 and 0.01. This research can provide a reference for the damage classification of SCCBBs with multiple damage interlacing.

De novo variants implicate chromatin modification, transcriptional regulation, and retinoic acid signaling in syndromic craniosynostosis

Craniosynostosis (CS) is the most common congenital cranial anomaly. Several Mendelian forms of syndromic CS are well described, but a genetic etiology remains elusive in a substantial fraction of probands. Analysis of exome sequence data from 526 proband-parent trios with syndromic CS identified a marked excess (observed 98, expected 33, p= 4.83×10-20) of damaging de novo variants (DNVs) in genes highly intolerant to loss-of-function variation (probability of LoF intolerance>0.9). 30 probands harbored damaging DNVs in 21 genes that were not previously implicated in CS but are involved in chromatin modification and remodeling (4.7-fold enrichment, p= 1.1×10-11). 17 genes had multiple damaging DNVs, and 13 genes (CDK13, NFIX, ADNP, KMT5B, SON, ARID1B, CASK, CHD7, MED13L, PSMD12, POLR2A, CHD3, and SETBP1) surpassed thresholds for genome-wide significance. A recurrent gain-of-function DNV in the retinoic acid receptor alpha (RARA; c.865G>A [p.Gly289Arg]) was identified in two probands with similar CS phenotypes. CS risk genes overlap with those identified for autism and other neurodevelopmental disorders, are highly expressed in cranial neural crest cells, and converge in networks that regulate chromatin modification, gene transcription, and osteoblast differentiation. Our results identify several CS loci and have major implications for genetic testing and counseling.

Features of the course of pneumonia in children with coronavirus infection

BACKGROUND:The most frequent complication of coronavirus infection (COVID-19) in young children is severe viral pneumonia.
 AIM:To reveal the features of the course of coronavirus pneumonia in children.
 MATERIALS AND METHODS:A retrospective analysis of 54 case histories of children with coronavirus pneumonia, aged 1 month to 15 years, who received inpatient treatment in the Department of Pulmonology was performed.
 RESULTS:The disease was most often registered in children of the first three years of life (66,7%). 49 (90,7%) children turned out to be from family foci of coronavirus infection. The majority of children (4481.5%) had signs of respiratory failure of I-II degree. Polymerase chain reaction (PCR) for the presence of coronavirus was positive in 37 (68.5%) children. Radiologically, bilateral infiltrates were detected predominantly in the middle and lower parts of the lungs. The multisystem inflammatory syndrome associated with SARS-CoV-2 was characterized by persistent fever, multiple organ damage and clinical and laboratory shifts.
 The vast majority of cases involved transmission from adult patients. The disease most often developed in children under 3 years of age. The course of the disease is accompanied by the development of grade III respiratory failure and intoxication. PCR for the presence of coronavirus may be negative due to late admission of patients to the hospital. Radiological changes were characterized by extensive bilateral lesions mainly in the lower and middle lobes of the lungs. Persistent fever, multiorgan lesions, clinical and laboratory shifts in coronavirus pneumonia in children indicate the development of multisystem inflammatory syndrome associated with SARS-CoV-2.
 CONCLUSIONS:Pneumonia in most cases proceeded with the development of respiratory failure, symptoms of gastrointestinal tract damage, a pronounced intoxication syndrome. At the same time, there was revealed inconsistency of radiologically extensive lung lesions with more scanty auscultatory data.

Multiple damage prediction in tubular rectangular beam model using frequency response-based mode shape curvature with back-propagation neural network

In the civil infrastructure, the structures made up of tubular sections played an important role due to an economic point of view, lower self-weight, and stability against functional loads. The periodical maintenance or inspection of the structure is mandatory nowadays to achieve the potential utilization of the structural system. In this study, an experimental, numerical, and analytical study is carried out to investigate the structural fault and its severity in a tubular rectangular beam made up of structural steel. Modal parameters are extracted with the help of a dynamic data logger (B&K) by exciting an impact hammer on model structure and extracting a data with a set of unidirectional accelerometers. First, two displacement mode shapes are extracted using obtained modal parameters. The modal parameters are expected to contain environmental noise during experimentation, so, de-noising is must to obtain noise-free data. Artificial neural network training is utilized to reduce the noise from experimental modal parameters. Using the modified modal parameters, the mode shape curvature is obtained, and so-called modified mode shape curvature (MMSC) is used to calculate the curvature damage index. The curvature damage index is appropriate to investigate multiple fault locations with different fault levels in tubular rectangular beam structures.

Abstract LB031: SHR-A1811, a novel anti-HER2 ADC with superior bystander effect, optimal DAR and favorable safety profiles

Abstract Current clinical HER2-targeting ADCs, especially T-DXd, have shown strong efficacy in HER2-expressing/mutant cancers, however, with adverse events such as hematologic toxicities and interstitial lung disease (ILD)/pneumonitis. Here we presented a potential best-in-class HER2-directed ADC, SHR-A1811, which was composed of trastuzumab, a stable and cleavable linker, and a novel topoisomerase I inhibitor payload (SHR9265). SHR9265 was a delicately selected exatecan derivative with a better liposolubility and cellular permeability: SHR9265 had a higher AlogP value than SHR7971, synthesized using published DXd structure (3.67 vs. 2.72). Consistently, SHR9265 showed ~5 times higher membrane permeability (PAMPA model) at pH5 and pH7.4. Cell killing activity of SHR9265 was ~3 times more potent than SHR7971. SHR-A1811 had a drug-to-antibody ratio (DAR) of 5.7, and showed HER2-dependent growth inhibition against various breast cancer and gastric cancer cell lines. In the bystander killing system, SHR-A1811 was able to kill both SK-BR-3 (HER2+) and MDA-MB-468 (HER2-) cells when co-cultured, with the IC50 on MDA-MB-468 of 0.28 nM. The in vitro efficacy of SHR-A1811 was comparable with anti-Her2-SHR9265 (DAR 7.5) and ADC1 (synthesized using T-DXd structure), and stronger than anti-Her2-SHR9265 (DAR 3.5). In SK-BR-3 (HER2 high), JIMT-1 (HER2 moderate) and capan-1 (HER2 low) xenograft models, SHR-A1811 treatment resulted in a dramatic and sustained inhibition of tumor growth. A significantly stronger antitumor activity was observed for SHR-A1811 than ADC1 under the same dosages. Moreover, SHR-A1811 showed good stability and improved safety profiles, presumably due to the proper steric hindrance which was purposely designed on the payload (SHR9265). Less than 2% of payload release was observed in human plasma after a 21-day incubation. The HNSTD of SHR-A1811 in cynomolgus monkeys was 40mpk with thymus as the main target organ. No death and lung lesions were observed at dose levels up to 70mpk for 42 days, while ADC1 led to one male cynomolgus monkey death on day 10 at 70mpk with multiple lung damages. These findings were consistent with safety profiles observed in a multi-center, dose-escalation phase I clinical trial (NCT0444620). Patients with different solid tumor types received initial intravenous doses of SHR-A1811 from 1 to 8.0 mg/kg. Only trace amount of free toxin was detected. The Cmax of payload was 3.85 ng/ml at 8mg/kg of SHR-A1811. The incidence of G2 ILD was < 2.5% and the treatment discontinuation rate was 5.1% across doses. In summary, with a highly permeable payload, optimized DAR, great potency and better clinical safety profiles, SHR-A1811 has demonstrated the best-in-class potential. Currently SHR-A1811 has entered phase II and phase III clinical studies for breast cancer, gastric cancer, colorectal cancer, and NSCLC (NCT05424835, NCT05482568, NCT04818333, NCT05349409). Citation Format: Ting Zhang, Lingfeng You, Jianyan Xu, Junzhao Yin, Bolei Qu, Yuchang Mao, Beibei Fu, Dan Cao, Linda Zhao, Jun Feng, Min Hu, Feng He. SHR-A1811, a novel anti-HER2 ADC with superior bystander effect, optimal DAR and favorable safety profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB031.

A full-scale wind turbine blade monitoring campaign: detection of damage initiation and progression using medium-frequency active vibrations

This work is concerned with a structural health monitoring campaign of a 52-m wind turbine blade. Multiple artificial damages are introduced in the blade sequentially, and fatigue testing is conducted with each damage in sequence. Progressive fatigue-driven damage propagation is achieved, enabling investigations concerning detection of initiation and propagation of damage in the blade. Using distributed accelerometers, operational modal analysis is performed to extract the lower-order natural vibration modes of the blade, which are shown to not be sensitive to small damages in the blade. To enable monitoring of small damages, an active vibration monitoring system is used, comprised of an electrodynamic vibration shaker and distributed accelerometers. From the accelerometer data, frequency domain methods are used to extract features. Using the extracted features, outlier detection is performed to investigate changes in the measurements resulting from the introduced damages. Capabilities of using features based on the active vibration data for detection of initiation and progression of damage in a wind turbine blade during fatigue testing are investigated, showing good correlation between the observed damage progression and the calculated changes in the damage index.

Autophagy regulates multiple organ injury in obstructive sleep apnea and its related mechanisms

Obstructive sleep apnea is a sleep-related hypoxia/reoxygenation syndrome that can lead to cardiovascular and cerebrovascular diseases, glucose and lipid metabolism, nervous system and even multiple organ damage, and is a serious threat to human health. Autophagy is a process by which eukaryotic cells rely on the lysosome pathway to degrade abnormal proteins and organelles, maintain homeostasis of intracellular environment and achieve self-renewal. Many studies have found that obstructive sleep apnea causes damage to myocardial, hippocampus, kidney and other organs, and its mechanism may be related to autophagy.

Quantitative detection of multiple damages in wind turbine blade based on the operating deflection shape and natural frequencies

Structural damage detection based on vibration signals has gained significant attention from researchers due to its ease of implementation. However, while the detection of damage using natural frequencies and mode shapes is effective for static structures, it is not directly applicable to structures in operation. To address this limitation, a two-stage multi-damage detection method has been developed to quantitatively detect damage in wind turbine (WT) blades during operation. This method utilizes the operating deflection shape (ODS) and random forest (RF) techniques. In the first stage, a two-dimensional (2D) wavelet transform is employed to decompose the ODS of the WT blade and reveal the multi-damage locations represented by the singularity points in the third detailed map. In the second stage, the finite element method (FEM) is applied to establish relationships between the natural frequencies and damage severities at fixed damage locations of the blade. This relationships form a natural frequency database, which is then used in conjunction with the RF algorithm to search for the damage severity when the natural frequency of a damaged blade is known. To verify the performance of the proposed two-stage multi-damage detection method, simulations of WT blades with two or three damages were performed. In addition, experimental tests were conducted using a laser scanning vibrometer to measure the ODS and natural frequencies of small WT blades in the two damage conditions. Both the numerical simulations and experimental investigations demonstrate that the proposed method can achieve acceptable damage-detection precision.

Comparison of the application of high-flow nasal oxygen with two different oxygen concentrations in infant and child laryngotracheal surgery.

This study aimed to compare the use of the STRIVE Hi technique with 70 and 100% oxygen concentrations in children with 1st or 2nd degree laryngeal obstruction undergoing suspension laryngoscopic surgery. Children aged 1 month to 6 years scheduled for suspension laryngoscopic surgery with spontaneous respiration were randomly divided into the 70% oxygen concentration group (HFNO70% group) and the 100% oxygen concentration group (HFNO100% group). The data recorded for all the patients included age and sex, comorbidities, preoperative physiological status, methods of induction and maintenance of anesthesia, course of the disease and surgical options, and duration of operation. The primary endpoint was the lowest oxygen saturations during the surgery. The secondary endpoints included the partial pressure of oxygen PaO2, the arterial pressure of carbon dioxide PaCO2, the peak transcutaneous carbon dioxide PtcCO2, and the incidence of desaturation (defined as SpO2 < 90%) or hypercarbia (PtcCO2 > 65 mmHg). A total of 80 children with 1st or 2nd degree laryngeal obstruction were included in the analysis. The median [IQR (range)] duration of spontaneous ventilation using STRIVE Hi was 52.5 [40-60 (30-170)]min and 62.5 [45-81 (20-200)]min in the HFNO 70% and HFNO 100% groups, respectively (p = 0.99); the lowest oxygen saturation recorded during the operation was 97.8 ± 2.1% and 96.8 ± 2.5%, respectively (p = 0.053); the mean PaO2 at the end of surgery was 184.6 ± 56.3 mmHg and 315.2 ± 101.3 mmHg, respectively (p < 0.001); and the peak transcutaneous CO2 was 58.0 ± 13.0 mmHg and 60.4 ± 10.9 mmHg, respectively (p = 0.373), despite a long operation time. STRIVE Hi had a positive effect on children undergoing tubeless laryngeal surgery with spontaneous ventilation, and for children with 1st or 2nd degree laryngeal obstruction, there was no significant difference in maintaining the intraoperative oxygenation between the 70 and 100% oxygen concentration groups. The 100% oxygen concentration group showed significant hyperoxia, which has been proven to be associated with multiple organ damage. Using a relatively lower oxygen concentration of 70% can effectively reduce the hazards associated with hyperoxia compared to 100% oxygen concentration. [www.chictr.org.cn], identifier [CHICTR2200064500].

Prevalence Rate of Rheumatoid Arthritis among Patients Attending Rheumatology Consultation Clinic at Baquba Teaching Hospital

Background: Rheumatoid arthritis (RA) is an inflammatory autoimmune disease that causes progressive and painful of multiple joints damage. Objective: To explore the prevalence of Rheumatoid arthritis (RA) among middle-aged and older of Iraqi patients attended the outpatients’ clinics. Patients and Methods: A prospective cross-sectional study was conducted from 1st February to 31st July 2022 at the rheumatology outpatient clinic at the Baquba teaching hospitals, Diyala province, Iraq. The eligible patients were interviewed using semi-structured questionnaires. Diagnosis of RA is based on clinical and serological parameters in accordance with criteria issued by American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) guideline. Descriptive, bivariate, and logistic regression analysis was performed. All statistical analyses were performed using SPSS 16.0 software. P-value &lt; 0.05 was considered statistically significant. Results: A total of 417 patients with an average age of 55.05 (+10.31) (ranged 40-79 years) have been included in the study. More than half were females (57.8%), married (74.3%), unemployed (67.9%), and 41.0% doctor-diagnosed arthritis. The prevalence of RA in adults was 0.96% compared to 2.34% in doctor-diagnosed arthritis group (females 1.75% vs. males 0.59%, P= 0.011). Results of the multiple logistic regressions showed that older age (odds ratio (OR) = 1.100, 95% CI: 1.070 to 1.130) was associated significantly with "doctor-diagnosed arthritis" (p &lt; 0.001). The female patients (OR = 4.928, 95% CI: 2.826 to 8.593), single or had no spouse (unmarried, widow, divorce) (OR = 5.076, 95% CI: 2.742 to 9.396) and had diabetes mellitus (DM) (OR = 7.251, 95% CI: 4.247 to 12.378) were associated significantly with "doctor-diagnosed arthritis" (p &lt; 0.001). Conclusion: With aging, the Iraqi females who have no spouse and suffer from diabetes mellitus become more vulnerable to symptomatic arthritis.

Prevalence Rate of Rheumatoid Arthritis among Patients Attending Rheumatology Consultation Clinic at Baquba Teaching Hospital

Background: Rheumatoid arthritis (RA) is an inflammatory autoimmune disease that causes progressive and painful of multiple joints damage. Objective: To explore the prevalence of Rheumatoid arthritis (RA) among middle-aged and older of Iraqi patients attended the outpatients’ clinics.&#x0D; Patients and Methods: A prospective cross-sectional study was conducted from 1st February to 31st July 2022 at the rheumatology outpatient clinic at the Baquba teaching hospitals, Diyala province, Iraq. The eligible patients were interviewed using semi-structured questionnaires. Diagnosis of RA is based on clinical and serological parameters in accordance with criteria issued by American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) guideline. Descriptive, bivariate, and logistic regression analysis was performed. All statistical analyses were performed using SPSS 16.0 software. P-value &lt; 0.05 was considered statistically significant.&#x0D; Results: A total of 417 patients with an average age of 55.05 (+10.31) (ranged 40-79 years) have been included in the study. More than half were females (57.8%), married (74.3%), unemployed (67.9%), and 41.0% doctor-diagnosed arthritis. The prevalence of RA in adults was 0.96% compared to 2.34% in doctor-diagnosed arthritis group (females 1.75% vs. males 0.59%, P= 0.011). Results of the multiple logistic regressions showed that older age (odds ratio (OR) = 1.100, 95% CI: 1.070 to 1.130) was associated significantly with "doctor-diagnosed arthritis" (p &lt; 0.001). The female patients (OR = 4.928, 95% CI: 2.826 to 8.593), single or had no spouse (unmarried, widow, divorce) (OR = 5.076, 95% CI: 2.742 to 9.396) and had diabetes mellitus (DM) (OR = 7.251, 95% CI: 4.247 to 12.378) were associated significantly with "doctor-diagnosed arthritis" (p &lt; 0.001). Conclusion: With aging, the Iraqi females who have no spouse and suffer from diabetes mellitus become more vulnerable to symptomatic arthritis.

Abstract 5736: AZD9592: An EGFR-cMET bispecific antibody-drug conjugate (ADC) targeting key oncogenic drivers in non-small-cell lung cancer (NSCLC) and beyond

Abstract De novo and acquired drug resistance can limit the long-term efficacy of targeted cancer therapies such as tyrosine kinase inhibitors targeting key oncogenic drivers like EGFR and cMET. Mechanisms of resistance include secondary mutations of EGFR and cMET and other downstream oncogenic pathways such as KRAS and amplification of alternate growth factor receptors. MET amplification or protein overexpression has been established as the most common mechanism of clinical resistance to EGFR inhibitors such as osimertinib. AZD9592 is a first-in-class bispecific ADC designed to target EGFR and cMET, while overcoming pathway-mediated resistance mechanisms that limit other targeted agents. Here we describe the generation, characterization and preclinical evaluation of AZD9592. The ADC was constructed on the backbone of the clinically validated DuetMab monovalent bispecific IgG platform and was engineered with higher affinity for cMET compared to EGFR (&amp;gt;15 fold), with the aim of reducing EGFR-driven toxicity in normal tissues. The antibody is conjugated via a cleavable linker to a proprietary topoisomerase 1 inhibitor (TOP1i) payload (AZ14170132). The internalization and in vitro cytotoxicity (IC50 in the low nM range) of AZD9592 were found to be optimal when both EGFR and cMET were engaged. When EGFR alone was engaged, cytotoxicity was significantly reduced, consistent with the lower affinity for EGFR. Treatment of cells with AZD9592 induced multiple DNA damage response pathway markers (like ATM, ATR, γΗ2ΑX), consistent with the proposed primary mechanism of action (MOA) of direct tumor-cell killing caused by double strand DNA breaks. AZD9592 monotherapy showed activity in vivo in patient-derived xenograft (PDX) models representing multiple EGFR and cMET expressing tumor types, including both EGFR mutant (m) and wild-type NSCLC and head and neck squamous cell carcinoma. Responses (≥30% regression from baseline tumor volume) were observed across a wide range of clinically relevant dose levels, including a 41% response rate in EGFRm NSCLC tumors treated at the lowest tested dose of 2 mg/kg. AZD9592 combined with osimertinib also showed benefit in PDX models derived from patients who progressed on osimertinib alone, as well as models representing primary resistance (EGFR ex20ins). AZD9592 was well tolerated in cynomolgus monkeys over a 6-week period (dosing every 3 weeks). The key safety findings were limited hematological effects, consistent with the MOA of the TOP1i payload. Plasma pharmacokinetics in cynomolgus monkeys showed an acceptable profile at tolerated doses, in line with other EGFR and cMET directed antibodies. These results demonstrate that AZD9592 has a promising efficacy and safety profile in preclinical models representing diverse opportunities in multiple clinical settings. Citation Format: Frank Comer, Yariv Mazor, Elaine Hurt, Chunning Yang, Ryan Fleming, Harini Shandilya, Balakumar Vijayakrishnan, Meghan Sterba, Ruoyan Chen, Edward Rosfjord, Nicolas Floch, Anton I. Rosenbaum, Yue Huang, Jiaqi Yuan, Kevin Beaumont, Lisa Godfrey, Lara McGrath, Fernanda Arnaldez, Puja Sapra. AZD9592: An EGFR-cMET bispecific antibody-drug conjugate (ADC) targeting key oncogenic drivers in non-small-cell lung cancer (NSCLC) and beyond. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5736.

Immune-related pulmonary toxicities of checkpoint inhibitors in non-small cell lung cancer: Diagnosis, mechanism, and treatment strategies.

Immune checkpoint inhibitors (ICI) therapy based on programmed cell death-1 (PD-1) and programmed cell death ligand 1 (PD-L1) has changed the treatment paradigm of advanced non-small cell lung cancer (NSCLC) and improved the survival expectancy of patients. However, it also leads to immune-related adverse events (iRAEs), which result in multiple organ damage. Among them, the most common one with the highest mortality in NSCLC patients treated with ICI is checkpoint inhibitor pneumonitis (CIP). The respiratory signs of CIP are highly coincident and overlap with those in primary lung cancer, which causes difficulties in detecting, diagnosing, managing, and treating. In clinical management, patients with serious CIP should receive immunosuppressive treatment and even discontinue immunotherapy, which impairs the clinical benefits of ICIs and potentially results in tumor recrudesce. Therefore, accurate diagnosis, detailedly dissecting the pathogenesis, and developing reasonable treatment strategies for CIP are essential to prolong patient survival and expand the application of ICI. Herein, we first summarized the diagnosis strategies of CIP in NSCLC, including the classical radiology examination and the rising serological test, pathology test, and artificial intelligence aids. Then, we dissected the potential pathogenic mechanisms of CIP, including disordered T cell subsets, the increase of autoantibodies, cross-antigens reactivity, and the potential role of other immune cells. Moreover, we explored therapeutic approaches beyond first-line steroid therapy and future direction based on targeted signaling pathways. Finally, we discussed the current impediments, future trends, and challenges in fighting ICI-related pneumonitis.

Abstract 6102: Targeting BARD1 slows PDAC growth in vitro and in vivo and sensitizes cells to DNA damaging agents

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent malignant neoplasm of the pancreas, responsible for over 90% of pancreatic cancer cases. Despite recent therapeutic advances, the five-year overall survival rate for PDAC is only 11% and therapies remain limited. It is now known that deficiency in DNA damage repair (DDR) renders PDAC cells vulnerable to certain DNA damage agents, for example, poly (ADP-ribose) polymerase inhibitors (PARPi). Most of the DDR genes mutated in PDAC are crucial for the proper functioning of the homologous recombination repair (HRR) pathway, and loss-of-function alterations in DDR genes sensitize cells to DNA-damaging agents. Since this treatment strategy is currently limited to 5-10% of PDAC cases with known mutations in the DDR genes, it underscores a dire need to understand new therapeutic avenues and expand the PDAC patient population that could benefit from this therapeutic approach. We have recently published that BARD1 (BRCA1- Associated- Ring- Domain- 1), an obligate binding partner of BRCA1, is upregulated in PDAC cells exposed to PARPi/platinums. We found that transient inhibition of BARD1 rendered PDAC cells extremely sensitive to olaparib (PARPi)/oxaliplatin (platinum), and enhanced DNA damage. Based on these results, we created two genetic models of BARD1 inhibition and utilized these model systems to investigate if loss of BARD1 chemo-sensitizes PDAC cells to other PARPis and DNA damage agents. Doxycycline inducible shRNA and BARD1 CRISPR KO models were created to inhibit gene expression of BARD1 in PDAC cells. We first characterized these model systems to confirm the loss of BARD1 protein and mRNA expression by western blot and qRT-PCR analyses. Using colony formation and Pico green cell survival assays, we found that inhibiting BARD1 resulted in slower growth of pancreatic cancer cells in vitro. Moreover, expression of several cell cycle genes from G2-M phase was significantly decreased (p=0.05). These studies were recapitulated in a mouse xenograft subcutaneous model, where BARD1 KO and WT PDAC cells were injected subcutaneously and tumor growth was analyzed over 25-30 days. Kaplan-Meier curves were calculated and plotted using Log-rank test. We found that inhibiting BARD1 significantly (p=0.026) delayed formation of PDAC tumors in vivo. Next, we analyzed the efficacies of various DNA damaging agents when BARD1 was inhibited. Silencing or CRISPR KO of BARD1 enhanced efficacy of mitomycin C, irinotecan, as well as other PARPis, which are currently in clinical trials (rucaparib, veliparib) in vitro and lowered their IC50s. These results indicate that targeting BARD1 has the potential to benefit patients in terms of novel or improved treatments. Our ongoing and future studies will expand on these findings to understand BARD1-mediated pathway and evaluate sensitivity of multiple DNA damage agents by screening a library of &amp;gt; 250 compounds. Citation Format: Sohum Patel, Anoohya Arkala, Avinoam Nevler, Carolyn Coats, Charles J. Yeo, Aditi Jain. Targeting BARD1 slows PDAC growth in vitro and in vivo and sensitizes cells to DNA damaging agents. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6102.

A Retrospective Study of Clinical and Immunological Features of a Pediatric Population with Talaromyces marneffei Infection.

Talaromyces marneffei (T. Marneffei) infection is considered as an indicator of immunosuppression in immunocompromised individuals, leading to multiple organ damage. Our study aimed to evaluate both the clinical characteristics and immunological features of pediatric patients infected with T. marneffei from our institute, providing novel insights into diagnosis and treatment for this life-threatening disease. Thirteen pediatric patients with T. marneffei infection were enrolled in Guangzhou Women and Children's Medical Center during 2012 to 2020. Clinical data and laboratory findings were collected and further analyzed. Pearson correlation coefficient was calculated to determine the relationship between serum immunoglobulins (Igs) levels and white blood cell count, or the absolute lymphocyte count. Patients were diagnosed as having T. Marneffei infection mainly based on the results of fungal culture and Gram stain of specimens. The most common presentations were fever (69%), pneumonia (38%) and immunodeficiency (38%). The total levels of Igs (IgE, IgA, and IgM) were positively correlated with both white blood cell count and absolute lymphocyte count. Serum Ig expression Pattern in patients diagnosed with T. marneffei infection might serve as an effective prognostic marker which would help with the development of early interventions for children with this fatal disease.

Damage identification in steel frames using dual-criteria vibration-based damage detection method and artificial neural network

Vibration-based damage identification methods have proven to be effective in identifying damage in various structures by employing modal parameters. This paper presents and develops a two-stage multi-criteria damage detection method based on modified damage indices (DIs), specifically modal flexibility and modal strain energy-based damage indices, and artificial neural network (ANN) to locate and quantify damage in steel frames. Only information from the first three bending modes was used to calculate the DIs. The location of damages is detected using DIs in the first stage, and two separate artificial neural networks are trained based on the DIs as input parameters in the second stage. The feasibility and performance of the proposed method were assessed by applying different single and multiple damage scenarios to a 3D finite element model of a simply supported steel beam and a steel frame of an industrial shed. The results demonstrate the efficiency and competency of the proposed non-destructive method for damage detection in such steel frames.